CN119402142A

CN119402142A - Multi-channel adaptive signal recovery method, device and equipment

Info

Publication number: CN119402142A
Application number: CN202411958096.2A
Authority: CN
Inventors: 罗映; 姚琳; 徐熊; 高洪旭
Original assignee: Shandong Promote Electromechanical Technology Co ltd
Current assignee: Shandong Promote Electromechanical Technology Co ltd
Priority date: 2024-12-30
Filing date: 2024-12-30
Publication date: 2025-02-07

Abstract

The embodiment of the disclosure provides a multi-channel-oriented adaptive signal recovery method, device and equipment, which are applied to the technical field of communication, wherein the method comprises the steps of carrying out frame synchronization on a data frame of each channel according to a unique identification code and a channel identifier in frame header information; the method comprises the steps of carrying out quality evaluation and threshold comparison on effective information of each channel according to key information for guiding soft information combination in frame header information, adaptively matching soft information combination modes according to quality evaluation and threshold comparison results, combining according to data frame synchronization results and the matched soft information combination modes to obtain optimal channel receiving soft values, demodulating the channel receiving soft values, calculating log likelihood ratios, carrying out iterative decoding based on the log likelihood ratios, and recovering original signals. In this way, the compatible processing capacity of the communication system to different quality signals in a multi-channel transmission scene can be improved, the quality of soft information required by iterative decoding is improved, and the reliability of the communication system is improved.

Description

Multi-channel-oriented adaptive signal recovery method, device and equipment

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, to a method, an apparatus, and a device for recovering adaptive signals for multiple channels.

Background

The multi-channel transmission in the complex electromagnetic environment has the problems of complex and variable channel conditions, unstable channel quality, easy occurrence of various interferences, high error rate, different noise and time delay on each channel and the like, so that the multi-channel signals are difficult to effectively cooperate, and the receiving end is difficult to accurately recover the original signals when the receiving signals are combined and processed, and therefore, a method for reducing the error rate, improving the receiving performance and improving the reliability of a communication system in the complex electromagnetic environment is needed.

Disclosure of Invention

The disclosure provides a multi-channel-oriented adaptive signal recovery method, device and equipment.

According to a first aspect of the present disclosure, there is provided a multi-channel oriented adaptive signal recovery method applied to a communication system, the method comprising:

The method comprises the steps of receiving effective information of each channel, capturing frame header information of each data frame in the effective information, and carrying out frame synchronization on the data frame of each channel according to a unique identification code and a channel identifier in the frame header information;

Performing quality evaluation and threshold comparison on effective information of each channel according to key information for guiding soft information combination in frame header information, and adaptively matching a soft information combination mode according to quality evaluation and threshold comparison results;

Soft information combination is carried out according to the data frame synchronization result and the matched soft information combination mode, and an optimal channel receiving soft value is obtained;

demodulating the soft value received by the channel, calculating the log-likelihood ratio, and iteratively decoding based on the log-likelihood ratio to recover the original signal.

In some implementations of the first aspect, receiving the valid information of each channel, capturing frame header information of each data frame in the valid information, and performing frame synchronization on the data frame of each channel according to the unique identification code and the channel identifier in the frame header information, including:

capturing a frame header of each data frame in the effective information of each channel based on a preselected frame synchronization method, extracting a unique identification code and a channel identification in the frame header, and carrying out frame synchronization on the data frames according to the unique identification code and the channel identification so as to enable the data frames of each channel to be synchronous in time and matched in content;

Further comprises:

Judging whether each data frame in the effective information accords with the frame header characteristics, capturing the frame header if so, extracting a unique identification code and a channel identification in the frame header, homing the data frame according to a classification frame and carrying out content verification according to the unique identification code and a preset two-dimensional sequencing standard so as to realize frame synchronization, so that each channel data frame is synchronous in time and matched in content.

In some implementations of the first aspect, the method further includes:

the frame header adopts a flexible framing frame header, which comprises one or more functional sequence blocks.

In some implementations of the first aspect, the functional sequence block includes:

A first sequence block and a second sequence block;

the first sequence block is a guard interval, so that multipath influence under a single channel is reduced, and crosstalk of front and rear data is avoided;

The second sequence block is a lifting block, and utilizes the channel estimation performance and the balanced rapid convergence characteristic of the lifting block to carry out more accurate channel estimation and improve the accuracy of LLR.

In some implementations of the first aspect, performing quality assessment and threshold comparison on effective information of each channel according to key information guiding soft information combining in frame header information, and adaptively matching soft information combining modes according to quality assessment and threshold comparison results, including:

according to the signal quality index information in the key information for guiding soft information combination, carrying out quality assessment on the effective information of each channel, respectively comparing the signal quality index of the effective information of each channel with a preset signal quality index threshold value, and according to the comparison result, adaptively matching the combination mode, wherein:

the signal quality index information comprises a signal to noise ratio and an error rate, the preset signal quality index threshold comprises a first threshold and a second threshold, the first threshold is higher than the second threshold, and the combining mode comprises selective combining, uniform combining and maximum ratio combining.

In some implementations of the first aspect, comparing the signal quality indicator of the effective information of each channel with a preset signal quality indicator threshold, and adaptively matching and combining according to a comparison result, including:

comparing the signal quality index of the effective information of each channel with a preset signal quality index threshold;

if the signal quality index of one or more channels is higher than or equal to a first threshold, matching the selective combining mode;

If the signal quality indexes of the channels are lower than the first threshold and higher than or equal to the second threshold, matching the maximum ratio combining mode;

And if the signal quality indexes of the channels are lower than the second threshold value, matching the uniform combination mode.

In some implementations of the first aspect, performing soft information combining according to a data frame synchronization result and a matched soft information combining manner to obtain an optimal channel receiving soft value includes:

If the signal quality index of one or more channels in the plurality of channels is higher than or equal to a first threshold value and high-quality signals exist, matching the selective combination mode, and selecting a receiving soft value corresponding to the signal with the highest signal quality index as an optimal channel receiving soft value;

If the signal quality indexes of the channels are lower than the first threshold value and higher than or equal to the second threshold value, the signal quality is medium, matching the maximum ratio combining mode, carrying out weighted combination on the effective signals of the channels according to the respective signal quality indexes according to the data frame synchronization result, and taking the weighted combination result as the optimal channel receiving soft value;

if the signal quality indexes of the channels are lower than a second threshold value and the signal quality is poor, matching a uniform combining mode, carrying out mean combining calculation on the effective signals of all the channels according to the data frame synchronization result, and taking a receiving soft value corresponding to the calculation result as an optimal channel receiving soft value;

the method for weighting and combining the effective signals of each channel according to the data frame synchronization result and the respective signal quality index, taking the weighted and combined result as the optimal channel receiving soft value comprises the following steps:

And calling a preset weight distribution table, assigning values to the effective signals of all channels according to the signal quality indexes of the effective signals of all channels, and carrying out weighted combination according to the data frame synchronization result and the weight of the effective signals of all channels, wherein the weighted combination result is used as an optimal channel receiving soft value.

In some implementations of the first aspect, demodulating the channel received soft values and calculating log-likelihood ratios, iteratively decoding based on the log-likelihood ratios to recover the original signal includes:

Demodulating the channel receiving soft value by using a demodulation algorithm;

Calculating the log-likelihood ratio of the soft value received by the demodulated channel based on a log-likelihood ratio calculation formula in a pre-constructed channel model;

and (3) taking the log-likelihood ratio as initial soft information to be input into a decoder for iterative decoding until the maximum iteration number or a preset threshold value is reached, outputting soft information and recovering an original signal.

According to a second aspect of the present disclosure, there is provided a multi-channel oriented adaptive signal recovery apparatus for use in a communication system, the apparatus comprising:

The first processing module is used for receiving the effective information of each channel, capturing the frame header information of each data frame in the effective information, and carrying out frame synchronization on the data frame of each channel according to the unique identification code and the channel identification in the frame header information;

The second processing module is used for carrying out quality evaluation and threshold comparison on the effective information of each channel according to key information for guiding soft information combination in the frame header information and adaptively matching a soft information combination mode according to quality evaluation and threshold comparison results, wherein the key information for guiding soft information combination comprises timestamp information, signal quality index information, data type and format information;

The third processing module is used for carrying out soft information combination according to the data frame synchronization result and the matched soft information combination mode to obtain an optimal channel receiving soft value;

and the fourth processing module is used for demodulating the channel receiving soft value, calculating the log likelihood ratio, and carrying out iterative decoding based on the log likelihood ratio to recover the original signal.

According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes at least one processor and a memory communicatively coupled to the at least one processor, the memory storing instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a method as described above.

In the method, soft information combination is carried out according to a self-adaptive matching combination mode on the basis of adopting a flexible frame structure, multi-channel resources can be integrated rapidly and effectively, an optimal channel receiving soft value is obtained, iterative decoding is carried out on the basis of the optimal channel receiving soft value, and an original signal can be recovered more accurately.

It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. For a better understanding of the present disclosure, and without limiting the disclosure thereto, the same or similar reference numerals denote the same or similar elements, wherein:

FIG. 1 illustrates a schematic diagram of an exemplary application environment in which embodiments of the present disclosure can be implemented;

FIG. 2 shows a schematic diagram of a frame structure provided by an embodiment of the present disclosure;

Fig. 3 illustrates a frame header structure provided by an embodiment of the present disclosure;

fig. 4 shows a flowchart of a multi-channel-oriented adaptive signal recovery method provided by an embodiment of the present disclosure;

Fig. 5 shows a block diagram of an adaptive signal recovery apparatus based on multi-channel transmission according to an embodiment of the present disclosure;

fig. 6 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.

In addition, the term "and/or" is merely an association relation describing the association object, and means that three kinds of relations may exist, for example, a and/or B, and that three kinds of cases where a exists alone, while a and B exist alone, exist alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

In view of the problems mentioned in the background art, the present disclosure provides a method, an apparatus and a device for recovering adaptive signals for multiple channels, which are applied to a communication system.

The method comprises the steps of receiving effective information of each channel, capturing frame header information of each data frame in the effective information, carrying out frame synchronization on the data frames of each channel according to unique identification codes and channel identifications in the frame header information, carrying out quality assessment and threshold comparison on the effective information of each channel according to key information guiding soft information combination in the frame header information, adaptively matching soft information combination modes according to quality assessment and threshold comparison results, guiding the key information combination of the soft information to comprise timestamp information, signal quality index information, data type and format information, carrying out soft information combination according to the data frame synchronization results and the matched soft information combination modes, obtaining optimal channel receiving soft values, demodulating the channel receiving soft values, calculating log likelihood ratios, carrying out iterative decoding based on the log likelihood ratios, and recovering original signals.

In this way, the effective information from each channel can be utilized, the compatible processing capacity of the communication system to different quality signals in a multi-channel transmission scene is improved, the quality of soft information required by iterative decoding is improved, the error rate is reduced, and the reliability of the communication system is improved.

In a wireless communication system, signals can be transmitted to a receiving end through a plurality of paths, noise and time delay on each transmission path are different, and the problems of complex and variable channel conditions, uncertain channel quality, intentional or unintentional interference and the like in a complex electromagnetic environment are solved.

Firstly, a distributed channel transmission model is established, when a wireless signal reaches a receiving end from a transmitting end, a plurality of propagation channels can be experienced, and when the signal propagates to the receiving end through different channels, the signal attenuation, the transmission delay and the noise under different channels are also different due to the influence of factors such as propagation distance, reflection, refraction and interference.

Fig. 1 shows a schematic diagram of an exemplary application environment in which an embodiment of the present disclosure can be implemented, where, taking a transmission scenario in fig. 1 as an example, as shown in fig. 1, a transmitting end Tx and a receiving end Rx perform peer-to-peer communication, where three transmission paths exist, namely, radio station transmission, satellite communication and microwave scattering, a channel identifier is marked as H ₁、H₂、H₃, a corresponding noise is n ₁、n₂、n₃, a time delay is t ₁、t₂、t₃, a transmitting end simultaneously transmits the same frame data to different channels, channel soft information required for decoding can be obtained independently under each channel, and these soft information are combined by a certain method, so that the receiving performance can be improved to the greatest extent.

Fig. 2 shows a schematic diagram of a frame structure provided by the embodiment of the disclosure, a highly reliable and flexible distributed receiving frame structure may be designed at a transmitting end, in the process of combining information at a receiving end, the same frame data from the transmitting end needs to be combined to obtain correct combined information, so as to ensure system performance, but because the time delays of different propagation paths are different, the receiving end receives corresponding data frames from different channels, so that part of frame header information (for example, a serial number ID) may be added before the data frames are sent by the transmitting end, so that the receiving end can distinguish the frame sequence, and the combination of the received soft information of the corresponding data frames is realized, and the schematic diagram of the specific frame structure may be shown in fig. 2.

Since the ID information contained in the frame header is very important for the combination of the receiving end, a certain frame synchronization method is required to determine the start point and the end point of a frame of data, and effective information capable of guiding the combination is obtained from the frame header information.

The PN sequence can be a Frank-Zadoff sequence and a Chu sequence, and the two sequences have good autocorrelation and broadband and stable frequency response, and have positive effects on channel estimation besides being used as a synchronous sequence.

A PN sequence generator with the length of P is divided into two paths of IQ which are orthogonal, and the respective generation formulas of the two paths of IQ are as follows:

;

where n is any integer in the range of 1 to P, when the Chu sequence is generated, When the Frank-Zadoff sequence is generated;。

And Generated by the following formulas respectively:

;

。

The Frank-Zadoff series is only used for synchronizing sequences with the even-numbered power length of 2, the amplitude of the two sequences is constant in the time domain or the frequency domain, the argument is uniformly distributed, the method has good autocorrelation and periodicity, the starting position of a data frame can be effectively identified, meanwhile, PN sequences have uniqueness, different channels can use different PN sequences for frame synchronization so as to distinguish data from different channels, the length and the periodicity of the PN sequences can be modulated according to the system requirements and transmission strategies, and different transmission scenes can be flexibly met.

In addition, a more flexible framing mode can be designed, a frame synchronization PN sequence with the length of P is used as a sequence block, the type or the number of the blocks is adjusted at the frame head part to meet more transmission requirements, for example, a certain number of sequence blocks are added as a protection interval to reduce multipath influence under a certain single channel and avoid crosstalk of front and back data, or a certain number of sequence blocks are added, more accurate channel estimation is performed by utilizing excellent channel estimation performance and balanced rapid convergence characteristics of the sequence blocks, LLR accuracy is improved to serve decoding performance, and a frame head structure schematic diagram provided by the embodiment of the disclosure is shown in fig. 3.

Because of the difference of the conditions of all transmission channels in the environment, the quality of the signals after the transmission signals reach the receiving end through different channels is different, and some signals still keep high-quality information, but some signals contain too much noise and interference and are difficult to recover the original information, so that a proper combination mode is selected at the receiving end, multiple paths of signals are flexibly combined, the quality of the received signals can be improved, more reliable received soft information can be obtained, and the reliability of the system is improved.

Assuming that the current transmission data of the transmitting end is S _i, the received signal can be expressed asWhere k=1, 2,3, represents the channel identity.

The receiving end combines the receiving information from different channels to obtain the optimal channel receiving soft value, calculates the Log Likelihood Ratio (LLR) information after demodulation, and sends the LLR information to the decoder for LDPC iterative decoding, wherein the LLR calculation formula is as follows:

;

It can be seen that When the posterior probability of 0 is a positive value, the posterior probability of 1 is larger than the posterior probability of 0,The larger the posterior probability of 0 is, assuming thatI.e. the prior probabilities of sending different symbols are equal, it can be obtained according to the bayesian criterion:

;

the merging modes can be divided into selective merging (SC, selection Combining), uniform merging (EGC, equal Gain Combining) and maximum ratio merging (MRC, maximal Ratio Combining) according to different information merging principles, wherein:

In the Selective Combining (SC) mode, the receiving end may receive multiple signals simultaneously, and select the received soft value corresponding to the signal with the best signal quality as the combining result, that is, select the best signal for decoding, thereby reducing the error rate to a certain extent, so that in this mode, a signal quality evaluation step is required, usually, the signal with the highest signal-to-noise ratio is considered to be the best signal, and at this time, the corresponding LLR calculation formula becomes:

。

In a uniform combining (EGC) mode, the receiving end calculates an average value of all signals, and the mode is simple and effective as a combining result, and is suitable for the situation that the receiving end cannot accurately estimate the signal to noise ratio, a complex signal to noise ratio estimation module is not needed to provide a certain performance gain, and at the moment, a corresponding LLR calculation formula becomes:

。

In a Maximum Ratio Combining (MRC) mode, a receiving end performs weighted combination on each received signal according to respective signal-to-noise ratio, a signal weighting coefficient with better quality is large, a signal weighting coefficient with poor quality is small, the mode enables the signals which are combined and output to have the maximum signal-to-noise ratio, the influence of noise and interference is reduced to the greatest extent, and the weighting coefficient is assumed to be The weighting coefficient corresponding to the signal with higher signal-to-noise ratio is larger in inverse proportion to the signal-to-noise ratio of the signal, and the corresponding LLR calculation formula becomes:

。

The channel is complex and changeable under a complex electromagnetic environment, the real-time performance is strong, in a multiplexing scene, a single channel can be interfered by intention or not, so that the channel quality is reduced sharply, and at the moment, if the correct data is difficult to recover only according to the low-quality receiving information of the channel, the flexible combining strategy of the self-adaptive matching combining mode is adopted, so that the compatible processing capacity of the communication system on different quality signals under the multi-channel transmission scene is improved, the quality of soft information required by iterative decoding is improved, the error rate is reduced, and the reliability of the communication system is improved.

On the basis of the design, a channel state monitoring module can be additionally arranged at a transmitting end to further improve the transmission efficiency, transmission resources are dynamically allocated according to the quality of channel transmission conditions, data transmission schemes of different channels are adjusted, a transmitting node periodically detects the transmission environment of each channel, including scanning surrounding frequency bands, detecting the interference degree and signal strength on the channels, and the like, knows the current available resources and channel conditions, selects a channel with the best condition as a main transmission channel according to the detection result, and is poor as an auxiliary transmission channel, and even data transmission can be suspended for the channel with the extremely poor current condition until the channel is restored to reliable communication conditions.

A multi-channel-oriented adaptive signal recovery method provided by the present disclosure is further described below with reference to fig. 4 and the specific embodiments.

Fig. 4 shows a flowchart of a multi-channel-oriented adaptive signal recovery method according to an embodiment of the present disclosure, and as shown in fig. 4, the multi-channel-oriented adaptive signal recovery method 400 may include:

s410, receiving the effective information of each channel, capturing the frame header information of each data frame in the effective information, and carrying out frame synchronization on the data frames of each channel according to the unique identification code and the channel identification in the frame header information.

The frame synchronization can adopt a PN sequence frame synchronization method, a Barker code frame synchronization method, an m sequence (longest linear feedback shift register sequence) frame synchronization method or a frame head and frame tail marking method and a byte counting synchronization method.

Specifically, based on a preselected frame synchronization method, capturing the frame header of each data frame in the effective information of each channel, extracting the unique identification code and the channel identification in the frame header, and carrying out frame synchronization on the data frames according to the unique identification code and the channel identification, so that the data frames of all channels are synchronous in time and matched in content.

More specifically, when the effective information of each channel arrives successively based on a preselected frame synchronization method, the local sequence and the received signal are subjected to autocorrelation operation, the position with the largest autocorrelation result is used as the beginning of a frame, the capture of the frame header is realized, the unique identification code is extracted from the frame header information, the data frames are subjected to frame synchronization according to the unique identification code, so that the data frames of each channel are synchronous in time and matched in content, wherein the unique identification code can be a data frame ID, or can be generated based on a preset coding rule of a transmitting end, for example, a unique character sequence or a digital identification is obtained after the encryption algorithm or hash function operation by combining multiple elements such as the data frame ID, a time stamp, a channel identification, a data packet number and the like.

In some embodiments, a judgment condition may be preset, and whether each data frame in the effective information accords with the frame header feature may be judged, if so, the frame header is captured, the unique identification code and the channel identifier in the frame header are extracted, the data frame is returned according to the unique identification code and the preset two-dimensional sorting reference, and the content verification is performed according to the classification frame, so as to realize frame synchronization, so that each channel data frame is synchronous in time and matched in content.

The method comprises the steps of firstly, setting a two-dimensional sorting standard, setting a main dimension, sorting the same batch of data frames according to time sequence, arranging different channel data frames at the same time according to channel sequence number from small to large by the main dimension, constructing a sorting frame to return the data frames, introducing content verification for each batch of data frames of each channel, checking whether key information fields in the data frames are complete or not according to a predefined data frame format and content specification of a communication protocol, if the key information fields are abnormal, repairing or marking the associated information of the peripheral normal synchronous data frames by utilizing a difference algorithm, trend prediction and the like so as to initiate a retransmission request later, presetting a dynamic adjustment sorting mechanism, estimating positions according to the associated information of the peripheral frame identification codes when the frames are lost, reserving gaps to wait for completion or interpolation to be completed, and inserting completion according to the identification codes after waiting for delayed frame reception if the gaps are reserved, wherein the key information fields are completely inserted according to the identification codes after the identification codes are reserved:

The judgment condition may be a special field, character, identification, etc. in the frame header.

The main dimension of the two-dimension sequencing reference is to take the sending sequence as the leading, and to carry out primary carding on the data frames of each channel according to the time sequence logic, so as to ensure that the data frames sent from different channels at the same moment can be classified into the same batch consideration, the secondary dimension considers the channel sequence number, and the data frames of different channels belonging to the same sending moment are arranged according to the sequence from small to large of the channel number, and can further construct a classifying frame by means of the two-dimension sequencing reference, and return the data frames to the corresponding time slot-channel grids, so that the data frames of each channel can be synchronously in time and matched in content even if the transmission delay of each channel is different in a multi-channel parallel transmission scene.

In addition, considering the complex and changeable network conditions in the multi-channel transmission process, such as intermittent frame loss, burst delay and other abnormal phenomena, the sorting process can adopt a dynamic adjustable mechanism, when the loss of data frames is detected, the system estimates the approximate position of the lost frame according to the identification code association information of the peripheral sorted data frames through a difference algorithm, trend prediction and the like, reserves a gap for subsequent completion or reasonable interpolation substitution, and once the severely delayed data frames are received, the identity is checked rapidly based on the identification code, the data frames are flexibly inserted into corresponding positions according to the data queue grid which is initially sorted at present, so that the whole sorting chain is ensured to be always consistent and orderly.

In some embodiments, the multi-channel oriented adaptive signal recovery method 400 further comprises:

The frame header is set to be a flexible framing frame header at a transmitting end, the frame header comprises one or more functional sequence blocks, the functional sequence blocks comprise a first sequence block and a second sequence block, the first sequence block is a protection interval, multipath influence under a single channel is reduced, crosstalk of front and rear data is avoided, the second sequence block is a lifting block, channel estimation performance and balanced rapid convergence characteristics of the second sequence block can be utilized to carry out more accurate channel estimation, LLR accuracy is improved, and transmission requirements can be met by flexibly adjusting types and numbers of the sequence blocks in the frame header.

Illustratively:

the first sequence block can be PN sequence or cyclic prefix, and the multipath influence under a certain single channel is reduced by adding a certain number of sequence blocks such as PN sequence or cyclic prefix as a protection interval, so as to avoid the crosstalk of front and rear data, that is, the frame synchronous PN sequence or cyclic prefix with the length of P can be used as a sequence block to be added into a frame header, so that the crosstalk of the front and rear data is avoided, and the frame header structure diagram can be shown as figure 3.

The second sequence block can be Zadoff-Chu sequence, gold sequence or Hadamard sequence, chaos sequence and other sequence blocks in PN sequence, and a certain number of sequence blocks are added, so that the excellent channel estimation performance and the balanced fast convergence characteristic are utilized to perform more accurate channel estimation, and LLR accuracy is improved to serve the decoding performance.

Further, when the number of the sequence blocks is 64-256 bits based on the OFDM system, the communication system can selectively add 4-8 sequence blocks when the transmission efficiency is extremely high, the channel environment is relatively stable and the multipath delay spread is small, when the number of the subcarriers is 128-256 scales for most conventional outdoor mobile communication or general indoor complex environments (a certain degree of multipath, shielding and the like exists), 12-20 sequence blocks are added when the number of the subcarriers is 128-256 scales, 32-64 sequence blocks can be added when the number of the subcarriers is 256 in a scene of extremely bad channel environment such as serious scattering of a remote zone signal and the like, when the number of the subcarriers is 100-500 bits based on a single carrier system, 2-4 sequence blocks can be added when the length of the data block is 100-200 bits and relatively stable and noise interference is limited, and when the length of the data block is 100-200 bits based on a single carrier system, and 16-32 sequence blocks can be allocated when the data block is 300-500 bits in a scene of severe damaged and strong noise and strong multipath is high.

The following are some specific examples:

In indoor short-distance Wireless Local Area Network (WLAN) communication, the channel condition is good, the signal propagation path is simple, 4 Zadoff-Chu sequence blocks are added to be placed at the frame head for channel estimation, so that the requirement of acquiring basic channel state information can be met, channel equalization primary adjustment can be rapidly completed, normal development of decoding can be ensured, transmission cost caused by additional sequence blocks can be reduced to the greatest extent, high-efficiency transmission efficiency can be maintained, high-speed data interaction can be realized, the corresponding error rate can be controlled at 10 ^-5-10^-4 orders, and the accuracy of effective boosting decoding can be improved; under the coverage scene of a 4G/5G micro base station like an urban street, the movement of personnel and vehicles frequently causes the dynamic change of a channel, 16 Gold sequence blocks can assist a receiver to accurately track the fluctuation of the channel by virtue of good cross-correlation characteristics, channel estimation is updated every 2-3 OFDM symbol periods, channel equalization parameters are continuously calibrated, decoding is enabled to work based on reliable channel states, the error rate is stabilized in a 10 ^-6-10^-5 interval, decoding performance is greatly improved, a single-carrier link for digitally transmitting short-distance wired analog signals in an industrial factory is provided, the interference sources are few, the channel characteristics are fixed, 2 Zadoff-Chu sequence blocks are enough to outline channel contour to assist channel equalization before decoding, the error rate is ensured to be lower than 10 ^-4, efficient and stable decoding is realized, 24 Zadoff-Chu sequence blocks are utilized to support complex channel analysis, and after fine channel estimation and equalization, the error rate is stabilized to 10 ^-6-10^-5, and bad channel resistance and signal restoration can be resisted.

Further, another example is provided to illustrate adding a certain number of sequence blocks to make more accurate channel estimation, improving the accuracy of LLR.

Assuming a wireless communication system, which adopts an orthogonal frequency division multiplexing technology for data transmission, the system works in an environment with obvious multipath fading and certain interference, the communication frequency range is 2.4GHz-2.4835GHz, and the bandwidth is divided into 64 subcarriers.

A sequence block designed based on Zadoff-Chu (ZC) sequence is selected and added to a frame head, the ZC sequence has good auto-correlation and cross-correlation characteristics, multipath interference and channel noise can be effectively resisted, a ZC sequence with the length of 16 is designed as the sequence block, and the mathematical expression is as follows:

;

Wherein u is a root index, the value 3;N is the sequence length 16, the value of n is from 0 to 15, and the specific sequence values as follows are obtained:

,, Etc.

The ZC array block with the length of 16 is added at the forefront end of each data frame, so that the frame head is changed into 16 sampling points, corresponding to 2 symbol duration, the array block with 8 sampling points carried by each symbol is considered, the original conventional frame head part is added, and the whole frame structure is changed into an array block+a conventional synchronization field+a frame control field+payload data+a frame tail.

After receiving a frame signal containing a sequence block, a receiving end firstly locates to a sequence block starting position of a frame head through a synchronization algorithm, extracts a sequence block signal r (n) (n=0, 1,) corresponding to the 16 sampling points, wherein the signal is deformation of a transmission sequence s (n) after channel fading and noise interference, and the relation can be expressed as r (n) =h (n) ×s (n) +w (n) by using a mathematical model, wherein h (n) is impulse response, and w (n) is additive noise.

Calculating a correlation function of a received sequence block signal r (n) and a locally pre-stored ZC sequence s (n) according to the autocorrelation characteristic of the ZC sequence:

;

By searching the peak value position and amplitude of R (k), the time delay expansion and amplitude fading condition of the channel are estimated, and the estimated value of the channel impulse response h (n) is obtained And finding R (3) as peak value and corresponding amplitude as A through calculation, and preliminarily judging that the channel time delay is corresponding to sampling point interval and sampling periodTime delay ofThe amplitude fading coefficient is A, thereby constructing。

Based on estimated channel impulse responsePerforming channel equalization operation on the received whole OFDM symbol, and for each subcarrier k in the frequency domain, equalizing the signal to be:

;

y (k) is the frequency domain value of the received signal at subcarrier k, Is thatWhen the value of the subcarrier k corresponding to the frequency domain is transformed, when channel decoding is carried out, the LLR value is accurately calculated by utilizing the equalized signal according to a decoding algorithm, and compared with the situation that a sequence block is not used for carrying out accurate channel estimation, the LLR value can reflect the real value possibility of the data bit, and because the influence of channel fading and noise is more accurately compensated in the channel estimation and equalization process.

It can be understood that, because the time delay of different propagation paths in the multi-channel transmission scene is different, the time when the receiving end receives the corresponding data frames from different channels is also different, and in the information combining process, the same frame data from the transmitting end needs to be combined to obtain correct high-quality information, so as to ensure the system performance, therefore, the present disclosure provides a highly reliable and flexible design method of a distributed receiving frame structure, and by adding part of special frame header information before the data frames, the sequence discrimination of the data packets by the receiving end is realized, so as to ensure the accuracy of soft information combination, and meanwhile, the applicability and flexibility of the data frames are improved, and the specific frame structure can be shown as fig. 2.

S420, quality evaluation and threshold comparison are carried out on the effective information of each channel according to key information for guiding soft information combination in the frame header information, and the soft information combination mode is adaptively matched according to the quality evaluation and threshold comparison results.

The key information for guiding the soft information combination comprises time stamp information, signal quality index information, data type and format information.

The method comprises the steps of carrying out quality assessment on effective information of each channel according to signal quality index information in key information for guiding soft information combination, respectively comparing signal quality indexes of the effective information of each channel with preset signal quality index thresholds, and carrying out self-adaptive matching combination modes according to comparison results, wherein the signal quality index information comprises a signal-to-noise ratio and an error rate, the preset signal quality index thresholds comprise a first threshold and a second threshold, the first threshold is higher than the second threshold, and the combination modes comprise selective combination, uniform combination and maximum ratio combination.

Further, comparing the signal quality index of each channel effective information with a preset signal quality index threshold, and adaptively matching and combining according to the comparison result, wherein the method comprises the following steps:

The method comprises the steps of comparing signal quality indexes of effective information of each channel with a preset signal quality index threshold value, matching a selective combining mode if the signal quality index of one or more channels is higher than or equal to the first threshold value, matching a maximum ratio combining mode if the signal quality index of the channels is lower than the first threshold value and higher than or equal to the second threshold value, and matching a uniform combining mode if the signal quality index of the channels is lower than the second threshold value.

According to the embodiment of the disclosure, the applicability of the data frame is improved by adopting the flexible frame structure, and the multi-channel signal frames are properly combined after being synchronized by combining the self-adaptive matching combining mode, so that the accuracy of soft information combination is effectively ensured.

S430, soft information combination is carried out according to the data frame synchronization result and the matched soft information combination mode, and the optimal channel receiving soft value is obtained.

Specifically, if one or more channels have signal quality indexes higher than or equal to a first threshold and high-quality signals exist, a selective combining mode is matched, a receiving soft value corresponding to a signal with the highest signal quality index is selected as an optimal channel receiving soft value, if the signal quality indexes of the channels are lower than the first threshold and higher than or equal to a second threshold and the signal quality is medium, a maximum ratio combining mode is matched, the effective signals of the channels are weighted and combined according to respective signal quality indexes according to a data frame synchronization result, the weighted and combined result is used as an optimal channel receiving soft value, if the signal quality indexes of the channels are lower than a second threshold and the signal quality is poor, an average combining calculation is performed on the effective signals of the channels according to the data frame synchronization result, the receiving soft value corresponding to the calculation result is used as an optimal channel receiving soft value, and the effective signals of the channels are weighted and combined according to the respective signal quality indexes according to the data frame synchronization result, the weighted and combined result is used as an optimal channel receiving soft value, and the method comprises the steps of:

The method comprises the steps of acquiring a preset weight distribution table, assigning a value to the effective signals of each channel according to the signal quality index of the effective signals of each channel, assigning a high weight to the signals with high signal quality index and assigning a low weight to the signals with low signal quality index, and carrying out weighted combination according to the data frame synchronization result and the weight of the effective signals of each channel, wherein the weighted combination result is used as the optimal channel receiving soft value.

Illustratively:

Assuming that 4 channels are transmitting data and are respectively marked as channel 1, channel 2, channel 3 and channel 4, the frame synchronization operation of the data frames transmitted by each channel at the same time is completed, and the signal quality index signal to noise ratio (for example, signal to noise ratio) of each channel is obtained through frame header information, a first threshold value is set to be 10dB, and a second threshold value is set to be 5dB.

1. The presence of high quality signals

The signal-to-noise ratio of each channel and the corresponding received soft value are assumed as follows:

The signal-to-noise ratio of the channel 1 is 12dB, and the received soft value is S ₁ = (0.8, -0.3, 0.5);

The signal-to-noise ratio of the channel 2 is 9dB, and the received soft value is S ₂ = (0.6, -0.2, 0.4);

the signal-to-noise ratio of the channel 3 is 15dB, and the received soft value is S ₃ = (1.0, -0.4,0.6);

the signal to noise ratio of channel 4 is 8dB and the received soft value is S ₄ = (0.5, -0.1, 0.3).

Because the signal-to-noise ratio of the channel 1 and the channel 3 is higher than the first threshold, it is determined that there is a high-quality signal, and a selective combining mode is adopted to select the received soft value corresponding to the channel 3 with the highest signal-to-noise ratio as a combining result, so that the combining result (i.e., the best channel received soft value) is the received soft value of the channel 3.

2. Medium signal quality

Assume that the signal quality index (again, taking the signal-to-noise ratio as an example) and the corresponding received soft value of each channel are as follows:

the signal-to-noise ratio of the channel 1 is 8dB, and the received soft value is S ₁ = (0.7, -0.3, 0.4);

The signal-to-noise ratio of the channel 2 is 6dB, and the received soft value is S ₂ = (0.5, -0.2,0.3);

the signal-to-noise ratio of the channel 3 is 7dB, and the received soft value is S ₃ = (0.6, -0.1, 0.5);

The signal to noise ratio of channel 4 is 5.5dB and the received soft value is S ₄ = (0.4, -0.1, 0.2).

At this time, the signal-to-noise ratio of each channel is lower than the first threshold but higher than the second threshold, and the signal quality is determined to be medium, and a maximum ratio combining mode is adopted.

The method comprises the steps of presetting a weight distribution table for distributing weights according to signal quality indexes in a system, wherein the table follows that signals with higher signal quality indexes are endowed with high weights, signals with lower signal quality indexes are endowed with low weights, relatively taking signal to noise ratios as examples, sorting lists can be carried out on the signal to noise ratios of all channels according to a sequence from high to low, preset weights of all channels can be endowed according to the sorting list according to the weights from large to small, and the weights can be determined according to the signal to noise ratio, so that the relative level of the signal quality is reflected. In this example, the specific assignment calculation may be as follows:

total signal to noise ratio = 8+6+7+5.5 = 26.5dB;

weights of channel 1 ;

Weights of channel 2;

Weights of channel 3;

Weights of channel 4。

Because each channel has completed data frame synchronization, the consistency of soft information of each channel in time and content sequence is ensured, the merging operation can be correctly carried out on each soft information position, and each soft information position is respectively weighted and summed according to weight, taking the first soft information position as an example:

First element of combined soft information =

w₁×S₁(1)+w₂×S₂(1)+w₃×S₃(1)+w₄×S₄(1)=0.302×0.7+0.226×0.5+0.264×0.6+0.208×0.4=0.566.

Likewise, a second soft information location is calculated:

Second element of combined soft information =

w₁×S₁(2)+w₂×S₂(2)+w₃×S₃(2)+w₄×S₄(2)=0.302×(0.3)+0.226×(0.2)+0.264×(0.1)+0.208×(0.1)=0.183.

Calculating a third soft information position:

The third element of the combined soft information=

w₁×S₁(3)+w₂×S₂(3)+w₃×S₃(3)+w₄×S₄(3)=0.302×0.4+0.226×0.3+0.264×0.5+0.208×0.2=0.3622.

Then there is a best channel received soft value= (0.566,0.183,0.3622).

3. Poor signal quality

the signal-to-noise ratio of the channel 1 is 4dB, and the received soft value is S ₁ = (0.3,0.2,0.2);

the signal-to-noise ratio of the channel 2 is 3dB, and the received soft value is S ₂ = (0.2,0.1,0.1);

the signal-to-noise ratio of the channel 3 is 2dB, and the received soft value is S ₃ = (0.1,0.1,0.1);

The signal to noise ratio of channel 4 is 1dB and the received soft value is S ₄ = (0.1,0.0,0.0).

At this time, the signal-to-noise ratio of each channel is lower than the second threshold, the signal quality is judged to be poor, and the average value combination calculation is carried out on the effective signals of each channel by adopting a uniform combination mode.

For the first soft information location:

;

for the second soft information position:

;

for the third soft information position:

。

Therefore, the best channel receives soft values= (0.175,0.1,0.1).

By the above three examples of different situations, a specific calculation process for obtaining the best channel receiving soft value according to different signal quality situations and the corresponding matched soft information combining mode is shown in detail.

According to the embodiment of the disclosure, the optimal combination strategy is flexibly decided according to different channel quality conditions, the cooperation of effective information of all channels is considered, the advantage of high-quality information is exerted, the more excellent channel receiving soft value is obtained, the information loss caused by improper combination mode is effectively reduced, the soft information combination effect is enhanced, the adaptability of the system to different channel conditions is improved, and a reliable basis is provided for subsequent decoding.

S440, demodulating the channel receiving soft value, calculating the log likelihood ratio, and iteratively decoding based on the log likelihood ratio to recover the original signal.

The method comprises the steps of demodulating a channel receiving soft value by using a demodulation algorithm, calculating the log likelihood ratio of the demodulated channel receiving soft value based on a log likelihood ratio calculation formula in a pre-constructed channel model, inputting the log likelihood ratio as initial soft information into a decoder for iterative decoding until the maximum iterative times or a preset threshold value are reached, outputting soft information, and recovering an original signal.

For example, if binary phase shift keying modulation is used at the transmitting end, the corresponding demodulation algorithm is usually coherent demodulation or noncoherent demodulation. Taking coherent demodulation as an example, firstly recovering carrier signals with the same frequency and phase as the transmitting end, multiplying the received channel receiving soft value with a local carrier by a multiplier, moving the modulated signals to a baseband, and filtering high-frequency components by a low-pass filter to obtain baseband signals. Let the received soft value of the channel be r (t), the locally recovered carrier beWherein, the method comprises the steps of, wherein,For the carrier angular frequency, phi is the phase, r (t) c (t) is obtained after multiplication, and the baseband signal after low-pass filtering can be expressed asThe carrier component is effectively removed, the baseband signal form carrying information is restored, and demodulation is completed.

The calculation of the log likelihood ratio of the demodulated channel received soft value based on the log likelihood ratio calculation formula in the pre-constructed channel model has been described in detail above and listed, and will not be described in detail here.

Then, the log-likelihood ratio is used as initial soft information to be input into a decoder for iterative decoding until reaching the maximum iteration number or a preset threshold value, soft information is output, and the original signal is recovered, which can comprise:

And selecting a proper iterative decoding algorithm, such as the iterative decoding of a Turbo code or the iterative decoding algorithm of a low density parity check code (LDPC), and orderly inputting the calculated log-likelihood ratio serving as initial soft information to a corresponding port of a decoder.

Taking Turbo code decoding as an example, LLR values are distributed to two component decoders, and the LLR values are used as priori information to be combined with observation information from a channel to start a first decoding process, and are processed in each component decoder according to respective decoding algorithm.

In the iterative decoding process, each component decoder works alternately, soft information is updated continuously and transmitted interactively, and each iteration is performed, the prior information input in the next iteration is updated according to the soft information output by the current decoder, and key parameters such as likelihood ratio, posterior probability and the like are recalculated to perform decoding optimization.

For example, in LDPC decoding, by repeatedly transmitting a message between a check node and a variable node, calculating an update message based on a belief propagation algorithm according to a connection relation between nodes and current soft information, gradually approaching a correct decoding result, setting a maximum iteration number as a safety mechanism to prevent infinite iteration, or setting a preset threshold, such as an error rate threshold, once the error rate is reached or the maximum iteration number is completed in the iteration process, stopping iteration immediately, and outputting final soft information. The soft information is hard judged according to the rule of the decoding algorithm, so that the original transmitted signal sequence is recovered, and the original signal is recovered.

According to the embodiment of the disclosure, the following technical effects are achieved:

The adaptive and efficient signal recovery system is constructed through flexible frame header information and structure, quality evaluation and threshold matching, adaptive combining mode matching, calculation, demodulation and decoding, and can cope with a multi-channel complex transmission environment, adapt to multi-signal quality and data types, effectively improve the data synchronization precision of each channel, soft information combining effect and decoding error correction capability, reduce error rate and signal distortion, enhance the stability, reliability and transmission efficiency of a communication system, meet the requirements of modern complex communication services, and ensure high-quality information interaction.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.

The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.

Fig. 5 shows a block diagram of a multi-channel oriented adaptive signal recovery apparatus according to an embodiment of the present disclosure, as shown in fig. 5, the multi-channel oriented adaptive signal recovery apparatus 500 may include:

the first processing module 510 is configured to receive the valid information of each channel, capture frame header information of each data frame in the valid information, and perform frame synchronization on the data frame of each channel according to the unique identification code and the channel identifier in the frame header information.

The second processing module 520 is configured to perform quality evaluation and threshold comparison on the effective information of each channel according to key information for guiding soft information merging in the frame header information, and adaptively match a soft information merging mode according to quality evaluation and threshold comparison results, where the key information for guiding soft information merging includes timestamp information, signal quality index information, data type and format information.

And a third processing module 530, configured to perform soft information combination according to the data frame synchronization result and the matched soft information combination mode, and obtain an optimal channel receiving soft value.

The fourth processing module 540 is configured to demodulate the soft values received by the channel, calculate a log likelihood ratio, and iteratively decode based on the log likelihood ratio to recover the original signal.

It can be understood that each module in the multi-channel-oriented adaptive signal recovery apparatus 500 shown in fig. 5 has a function of implementing each step in the multi-channel-oriented adaptive signal recovery method 400 provided in the embodiments of the present disclosure, and can achieve corresponding technical effects, and the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein for convenience and brevity of description.

According to an embodiment of the disclosure, the disclosure further provides an electronic device, a readable storage medium.

The electronic device 600 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

The electronic device 600 comprises a computing unit 601 that can perform various suitable actions and processes according to a computer program stored in a read only memory ROM602 or a computer program loaded from a storage unit 608 into a random access memory RAM 603. In the RAM603, various programs and data required for the operation of the electronic device 600 can also be stored. The computing unit 601, ROM602, and RAM603 are connected to each other by a bus 604. An I/O interface 605 is also connected to bus 604.

Various components in the electronic device 600 are connected to the I/O interface 605, including an input unit 606, such as a keyboard, mouse, etc., an output unit 607, such as various types of displays, speakers, etc., a storage unit 608, such as a magnetic disk, optical disk, etc., and a communication unit 609, such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices through a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as method 400. For example, in some embodiments, the method 400 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 600 via the ROM602 and/or the communication unit 609. One or more of the steps of method 400 described above may be performed when a computer program is loaded into RAM603 and executed by computing unit 601. Alternatively, in other embodiments, computing unit 601 may be configured to perform method 400 by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be a special or general purpose programmable processor, operable to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user, for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a Local Area Network (LAN), a Wide Area Network (WAN), and the Internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A multi-channel adaptive signal recovery method, applied to a communication system, wherein the communication system includes a transmitting end and a receiving end, characterized in that the method comprises:

Receive valid information of each channel and capture the frame header information of each data frame in the valid information; perform frame synchronization on the data frame of each channel according to the unique identification code and channel identifier in the frame header information;

According to the key information guiding the merging of soft information in the frame header information, quality evaluation and threshold comparison are performed on the valid information of each channel, and the soft information merging mode is adaptively matched according to the quality evaluation and threshold comparison results; the key information guiding the merging of soft information includes: timestamp information, signal quality indicator information, data type and format information;

Soft information is merged according to the data frame synchronization result and the matched soft information merging method to obtain the best channel reception soft value;

The channel received soft value is demodulated and a log-likelihood ratio is calculated; and iterative decoding is performed based on the log-likelihood ratio to restore the original signal.

2. The method according to claim 1, characterized in that the receiving of valid information of each channel, capturing the frame header information of each data frame in the valid information; and performing frame synchronization on the data frame of each channel according to the unique identification code and channel identifier in the frame header information, comprises:

Based on the pre-selected frame synchronization method, the frame header of each data frame in the valid information of each channel is captured, the unique identification code and channel identifier in the frame header are extracted, and the data frame is synchronized according to the unique identification code and channel identifier, so that the data frames of each channel are synchronized in time and matched in content;

Also includes:

Determine whether each data frame in the valid information meets the frame header characteristics. If yes, capture the frame header, extract the unique identification code and channel identifier in the frame header, sort the data frames according to the classification framework based on the unique identification code and the preset two-dimensional sorting benchmark, and perform content verification to achieve frame synchronization, so that the data frames of each channel are synchronized in time and matched in content.

3. The method according to claim 1, characterized in that the method further comprises:

The frame header is a flexible framing frame header, which includes one or more functional sequence blocks.

4. The method according to claim 3, characterized in that the functional sequence block comprises:

a first sequence of blocks and a second sequence of blocks;

The first sequence block is a guard interval, which reduces the multipath effect in a single channel and avoids crosstalk between previous and next data.

The second sequence block is a boosting block, which uses its channel estimation performance and fast convergence characteristics of equalization to perform more accurate channel estimation and improve the accuracy of LLR.

5. The method according to claim 1 is characterized in that the quality assessment and threshold comparison are performed on the valid information of each channel according to the key information guiding the soft information merging in the frame header information, and the soft information merging mode is adaptively matched according to the quality assessment and threshold comparison results, including:

According to the signal quality index information in the key information guiding the soft information merging, the quality of the valid information of each channel is evaluated, and the signal quality index of the valid information of each channel is compared with the preset signal quality index threshold, and the merging mode is adaptively matched according to the comparison result; wherein:

The signal quality indicator information includes: signal-to-noise ratio and bit error rate;

The preset signal quality indicator threshold includes a first threshold and a second threshold, and the first threshold is higher than the second threshold;

The merging modes include: selective merging, uniform merging, and maximum ratio merging.

6. The method according to claim 5, characterized in that the signal quality index of each channel's valid information is compared with a preset signal quality index threshold, and adaptively matching and combining the modes according to the comparison results, comprises:

Compare the signal quality index of each channel's valid information with a preset signal quality index threshold value respectively;

If the signal quality index of one or more channels is higher than or equal to the first threshold, matching the selective combining mode;

If the signal quality indicators of multiple channels are all lower than the first threshold and higher than or equal to the second threshold, the maximum ratio combining mode is matched;

If the signal quality indicators of the multiple channels are all lower than the second threshold, the uniform combining mode is matched.

7. The method according to any one of claims 5 or 6, characterized in that the soft information merging is performed according to the data frame synchronization result and the matched soft information merging mode to obtain the best channel reception soft value, comprising:

If the signal quality index of one or more channels among the multiple channels is higher than or equal to the first threshold value, and there is a high-quality signal, a selective merging mode is matched, and a received soft value corresponding to the signal with the highest signal quality index is selected as the best channel received soft value;

If the signal quality indicators of multiple channels are all lower than the first threshold, higher than or equal to the second threshold, and the signal quality is medium, the maximum ratio combining mode is matched, and the valid signals of each channel are weighted and combined according to their respective signal quality indicators according to the data frame synchronization result, and the weighted combination result is used as the best channel reception soft value;

If the signal quality indicators of multiple channels are all lower than the second threshold value and the signal quality is poor, a uniform merging mode is matched, and the valid signals of each channel are averaged and merged according to the data frame synchronization result, and the received soft value corresponding to the calculation result is used as the best channel received soft value;

The step of weighting and combining the valid signals of each channel according to their respective signal quality indicators based on the data frame synchronization result, and taking the weighted combination result as the best channel reception soft value, comprises:

Recall the preset weight allocation table, assign values to the effective signals of each channel according to their signal quality indicators, perform weighted combination based on the data frame synchronization results and the weights of the effective signals of each channel, and use the weighted combination result as the best channel reception soft value.

8. The method according to claim 1, characterized in that the demodulating the channel received soft value and calculating the log-likelihood ratio; and iteratively decoding based on the log-likelihood ratio to restore the original signal comprises:

Demodulating the channel received soft value using a demodulation algorithm;

Calculating the log-likelihood ratio of the demodulated channel reception soft value based on the log-likelihood ratio calculation formula in the pre-constructed channel model;

The log-likelihood ratio is input as initial soft information into a decoder for iterative decoding until a maximum number of iterations or a preset threshold is reached, and the soft information is output to restore the original signal.

9. A multi-channel adaptive signal recovery device, applied to a communication system, characterized in that the device comprises:

The first processing module is used to receive valid information of each channel, capture the frame header information of each data frame in the valid information; perform frame synchronization on the data frame of each channel according to the unique identification code and channel identifier in the frame header information;

The second processing module is used to perform quality evaluation and threshold comparison on the valid information of each channel according to the key information guiding the merging of soft information in the frame header information, and adaptively match the soft information merging mode according to the quality evaluation and threshold comparison results; the key information guiding the merging of soft information includes: timestamp information, signal quality index information, data type and format information; the third processing module is used to merge the soft information according to the matched merging mode and the data frame synchronization result to obtain the best channel reception soft value;

The third processing module is used to merge the soft information according to the data frame synchronization result and the matched soft information merging mode to obtain the best channel reception soft value;

The fourth processing module is used to demodulate the channel received soft value and calculate the log-likelihood ratio; perform iterative decoding based on the log-likelihood ratio to restore the original signal.

10. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

a memory communicatively connected to the at least one processor; wherein,

The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 8.