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

Abstract

The embodiment of the invention provides a multi-channel-oriented adaptive signal recovery method, device and equipment, which are applied to the technical field of communication, and the method comprises the following steps: 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; performing quality evaluation and threshold comparison on 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 a quality evaluation and threshold comparison result; merging according to a data frame synchronization result and the matched soft information merging mode to obtain an optimal channel receiving soft value; demodulating the channel receiving soft value, and calculating a log-likelihood ratio; and performing iterative decoding based on the log-likelihood ratio to recover an original signal. In this way, the compatible processing capacity of a communication system on signals with different qualities 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

Technical Field

[0001] The present disclosure relates to the field of communication technology, and further to the field of multi-channel wireless communication technology, and in particular to a multi-channel oriented adaptive signal recovery method, device and equipment. Background Art

[0002] Multi-channel transmission in a complex electromagnetic environment has complex and changeable channel conditions, unstable channel quality, prone to multiple interferences, high bit error rate, different noise and delay on each channel, etc., making it difficult for multi-channel signals to effectively coordinate, so that the receiving end is difficult to accurately restore the original signal when merging and processing the received signals; therefore, there is an urgent need for a method that can reduce the bit error rate, improve the receiving performance, and enhance the reliability of the communication system in a complex electromagnetic environment. Summary of the invention

[0004] The present disclosure provides a multi-channel oriented adaptive signal recovery method, device and equipment.

[0005] According to a first aspect of the present disclosure, a multi-channel oriented adaptive signal recovery method is provided, which is applied to a communication system, and the method includes: 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, the quality evaluation and threshold comparison are performed on the valid information of each channel, and the soft information merging method 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 the log-likelihood ratio is calculated; iterative decoding is performed based on the log-likelihood ratio to restore the original signal.

[0006] In some implementations of the first aspect, receiving 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 include: 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.

[0007] In some implementations of the first aspect, the method further includes: The frame header adopts a flexible framing frame header, which includes one or more functional sequence blocks.

[0008] In some implementations of the first aspect, the function sequence block includes: 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.

[0009] In some implementable embodiments of the first aspect, quality evaluation and threshold comparison are performed on valid information of each channel according to key information guiding soft information merging in frame header information, and a soft information merging mode is adaptively matched according to the quality evaluation 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, bit error rate; the preset signal quality indicator thresholds include a first threshold and a second threshold, and the first threshold is higher than the second threshold; the merging methods include: selective merging, uniform merging, and maximum ratio merging.

[0010] In some implementations of the first aspect, the signal quality index of each channel valid information is compared with a preset signal quality index threshold, and the adaptive matching and merging mode is performed according to the comparison result, including: 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.

[0011] In some implementable manners of the first aspect, performing soft information merging according to a data frame synchronization result and a matched soft information merging method to obtain an optimal channel reception soft value includes: 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 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; Among them, according to the data frame synchronization result, the effective signals of each channel are weighted and combined according to their respective signal quality indicators, and the weighted combination result is used as the best channel reception soft value, including: 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.

[0012] In some implementable manners of the first aspect, 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 includes: Demodulating the channel received soft value using a demodulation algorithm; Based on the log-likelihood ratio calculation formula in the pre-built channel model, the log-likelihood ratio of the demodulated channel reception soft value is calculated; The log-likelihood ratio is input as the initial soft information into the decoder for iterative decoding until the maximum number of iterations or the preset threshold is reached, the soft information is output, and the original signal is restored.

[0013] According to a second aspect of the present disclosure, there is provided a multi-channel oriented adaptive signal recovery device, which is applied to a communication system, and the device includes: 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 indicator information, data type and format information; 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.

[0014] 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 connected to the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described above.

[0015] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method described above.

[0016] In the present disclosure, soft information merging is performed according to an adaptive matching merging method based on a flexible frame structure, which can quickly and effectively integrate multi-channel resources and obtain the best channel reception soft value; iterative decoding based on the best channel reception soft value can more accurately restore the original signal.

[0017] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which: Figure 1 A schematic diagram showing an exemplary application environment in which embodiments of the present disclosure can be implemented; Figure 2 A schematic diagram of a frame structure provided by an embodiment of the present disclosure is shown; Figure 3 A schematic diagram of a frame header structure provided by an embodiment of the present disclosure is shown; Figure 4 A flowchart of a multi-channel adaptive signal recovery method provided by an embodiment of the present disclosure is shown; Figure 5 A block diagram of an adaptive signal recovery device based on multi-channel transmission according to an embodiment of the present disclosure is shown; Figure 6 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0020] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0021] In view of the problems mentioned in the background technology, the present disclosure provides a multi-channel oriented adaptive signal recovery method, device and equipment, which are applied to a communication system.

[0022] Specifically, valid information of each channel is received, and frame header information of each data frame in the valid information is captured; frame synchronization is performed on the data frame of each channel according to the unique identification code and channel identifier in the frame header information; quality evaluation and threshold comparison are performed on the valid information of each channel according to the key information guiding soft information merging in the frame header information, and the soft information merging method is adaptively matched according to the quality evaluation and threshold comparison results; the key information guiding soft information merging 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 reception soft value is demodulated and the log-likelihood ratio is calculated; iterative decoding is performed based on the log-likelihood ratio to restore the original signal.

[0023] In this way, effective information from each channel can be utilized to enhance the communication system's ability to process signals of different qualities in multi-channel transmission scenarios, improve the quality of soft information required for iterative decoding, reduce the bit error rate, and improve the reliability of the communication system.

[0024] In wireless communication systems, signals can be transmitted to the receiving end through multiple paths. The noise and delay on each transmission path are different. In order to address the complex and changeable channel conditions, uncertain channel quality, intentional or unintentional interference and other issues in complex electromagnetic environments, by establishing a distributed channel transmission model, studying multi-path merging soft information enhancement technology and designing a highly reliable and flexible distributed receiving information frame structure, the receiving end can make maximum use of signals on different channels, reduce bit error rate, improve receiving performance and ensure the reliability of the communication system.

[0025] First, a distributed channel transmission model is established. When a wireless signal reaches the receiving end from the transmitter, it can go through multiple propagation channels. When the signal is transmitted to the receiving end through different channels, due to the influence of factors such as propagation distance, reflection, refraction, and interference, the signal attenuation, transmission delay, and noise in different channels are also different.

[0026] Figure 1 A schematic diagram showing an exemplary application environment in which embodiments of the present disclosure can be implemented; Figure 1 For example, the transmission scenario Figure 1 As shown, the transmitter Tx and the receiver Rx perform point-to-point communication, in which there are three transmission paths, namely radio transmission, satellite communication and microwave scattering, and the channel identifier is denoted by H 1 , H 2 , H 3 , the corresponding noise is n 1 、n 2 、n 3 , the delay is t 1 ,t 2 ,t 3 The transmitter sends the same frame data to different channels at the same time. Each channel can independently obtain the channel soft information required for decoding. By merging these soft information through certain methods, the receiving performance can be improved to the greatest extent. In addition, the corresponding frame structure design is required to achieve the alignment of the same frame data sent through different channels by adding frame header information to ensure the correctness of the combined reception.

[0027] Figure 2 The frame structure diagram provided by the embodiment of the present disclosure is shown. A highly reliable and flexible distributed receiving frame structure can be designed at the transmitting end. In the information merging process at the receiving end, the same frame data from the transmitting end needs to be merged to obtain the correct merged information to ensure the system performance. However, due to the different delays of different propagation paths, the time when the receiving end receives the corresponding data frames from different channels is also different. Therefore, before the transmitting end sends the data, some frame header information (for example, a sequence number ID) can be added to the front of the data frame so that the receiving end can identify the frame order and realize the receiving soft information merging of the corresponding data frame. The specific frame structure diagram can be as follows Figure 2shown.

[0028] Since the ID information contained in the frame header is very important for the merging at the receiving end, a certain frame synchronization method is needed to determine the starting point and end point of a frame of data and obtain effective information that can guide the merging from the frame header information. For example, a PN sequence frame synchronization method can be used, that is, a PN sequence of a certain length is added to the frame header information, and an autocorrelation operation is performed with the received signal through the local sequence at the receiving end. The maximum autocorrelation result is used as the beginning of a frame to achieve frame header capture.

[0029] The PN sequence can be a Frank-Zadoff sequence and a Chu sequence. These two sequences not only have very good autocorrelation, but also have a wideband and stable frequency response. In addition to being used as a synchronization sequence, they also have a positive effect on channel estimation.

[0030] A PN sequence generator with a length of P is divided into two orthogonal IQ paths, and the generation formulas of the two IQ paths are: ; Where n is any integer in the range of 1 to P. When the Chu sequence is generated, ;When generating a Frank-Zadoff sequence,; .

[0031] and They are generated by the following formulas: ; .

[0032] Among them, the Frank-Zadoff series is only used in synchronization sequences with an even power of 2. Both sequences have constant amplitudes and uniformly distributed angles in both time and frequency domains. They have good autocorrelation and periodicity, and can effectively identify the starting position of the data frame. At the same time, the PN sequence is unique, and different channels can use different PN sequences for frame synchronization to distinguish data from different channels. The length and periodicity of the PN sequence can be modulated according to system requirements and transmission strategies to flexibly meet different transmission scenarios.

[0033] In addition, a more flexible framing method can be designed, with a frame synchronization PN sequence of length P as a sequence block, and the block type or number can be adjusted in the frame header to meet more transmission requirements. For example, by adding a certain number of sequence blocks as protection intervals, the multipath effect under a single channel can be reduced to avoid crosstalk between previous and next data; or by adding a certain number of sequence blocks, using their excellent channel estimation performance and fast convergence characteristics of equalization to perform more accurate channel estimation and improve LLR accuracy to serve decoding performance. Figure 3 A schematic diagram of a frame header structure provided by an embodiment of the present disclosure is shown; the frame header structure may be as follows Figure 3 shown.

[0034] Due to the differences in conditions of various transmission channels in the environment, the signal quality of the transmitted signal after being transmitted through different channels to the receiving end is different. Some signals still maintain high-quality information, but some signals contain too much noise and interference, making it difficult to recover the original information. Therefore, selecting an appropriate merging method at the receiving end and flexibly merging multiple signals can improve the quality of the received signal, obtain more reliable received soft information, and improve system reliability.

[0035] Assume that the data currently sent by the sender is S i , then the received signal can be expressed as , where k=1,2,3 represents the channel identifier.

[0036] The receiving end combines the received information from different channels to obtain the best channel reception soft value, and calculates the log-likelihood ratio (LLR) information after demodulation, and sends it to the decoder for LDPC iterative decoding. The LLR calculation formula is: ; It can be seen When it is positive, the posterior probability of taking 0 is greater than the posterior probability of taking 1. The larger the value, the greater the posterior probability of taking 0; assuming , that is, the prior probability of sending different symbols is equal. According to the Bayesian criterion, we can get: ; According to different principles of information merging, the merging methods can be divided into selective merging (SC, Selection Combining), uniform merging (EGC, Equal Gain Combining) and maximum ratio merging (MRC, Maximum Ratio Combining); among them: In the selective combining (SC) method, the receiving end can receive multiple signals at the same time, 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 bit error rate to a certain extent; therefore, this method requires a signal quality assessment step, usually the signal under the channel with the highest signal-to-noise ratio is considered to have the best quality, and the corresponding LLR calculation formula becomes: .

[0037] In the uniform combining (EGC) method, the receiver calculates the average value of all signals as the combining result. This method is simple and effective. It is suitable for the case where the receiver cannot accurately estimate the signal-to-noise ratio. It does not require a complex signal-to-noise ratio estimation module to provide a certain performance gain. In this case, the corresponding LLR calculation formula becomes: .

[0038] In the maximum ratio combining (MRC) method, the receiving end performs weighted combination of each received signal according to their respective signal-to-noise ratios. The weighting coefficient of the signal with better quality is large, and the weighting coefficient of the signal with poorer quality is small. This method makes the combined output signal have the maximum signal-to-noise ratio and minimizes the influence of noise and interference. Assume that the weighting coefficient is , which is inversely proportional to the signal-to-noise ratio. The higher the signal-to-noise ratio, the larger the corresponding weighting coefficient. At this time, the corresponding LLR calculation formula becomes: .

[0039] In a complex electromagnetic environment, channels are complex and changeable, and have strong real-time characteristics. In a multi-channel transmission scenario, a single channel may be subject to intentional or unintentional interference, resulting in a sharp decline in channel quality. At this time, if only the low-quality received information of the channel is used, it is difficult to recover the correct data. Therefore, the present invention adopts a flexible merging strategy of adaptive matching merging to improve the communication system's compatible processing capabilities for signals of different qualities in multi-channel transmission scenarios, improve the quality of soft information required for iterative decoding, reduce the bit error rate, and improve the reliability of the communication system.

[0040] On the basis of the above design, in order to further improve the transmission efficiency, a channel status monitoring module can be added at the sending end to dynamically allocate transmission resources according to the channel transmission conditions and adjust the data transmission schemes of different channels; the sending node regularly detects the transmission environment of each channel, including scanning the surrounding frequency bands, detecting the interference level and signal strength on the channel, etc., to understand the current available resources and channel conditions; according to the detection results, the channel with the best condition is selected as the main transmission channel, and the poorer one is selected as the auxiliary transmission channel. For channels with extremely poor current conditions, data transmission can even be suspended until they are restored to reliable communication conditions.

[0041] Based on the above contents and attached Figure 4 The specific embodiments further illustrate a multi-channel adaptive signal recovery method provided by the present disclosure.

[0042] Figure 4 A flowchart of a multi-channel adaptive signal recovery method provided by an embodiment of the present disclosure is shown; Figure 4 As shown, the multi-channel oriented adaptive signal recovery method 400 may include: S410, receiving valid information of each channel, capturing frame header information of each data frame in the valid information; performing frame synchronization on the data frame of each channel according to the unique identification code and channel identifier in the frame header information.

[0043] Among them, frame synchronization can adopt PN sequence frame synchronization method, Barker code frame synchronization method, m sequence (longest linear feedback shift register sequence) frame synchronization method or frame head and frame tail mark method, byte count synchronization method.

[0044] Specifically, based on the preselected 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.

[0045] More specifically, based on the pre-selected frame synchronization method, when valid information of each channel arrives successively, autocorrelation operation is performed with the received signal through the local sequence, and the maximum autocorrelation result is taken as the beginning of a frame to realize the capture of the frame header, and the unique identification code is extracted from the frame header information. The data frame is frame synchronized according to the unique identification code, so that the data frames of each channel are synchronized in time and matched in content; wherein, the unique identification code can be a data frame ID; it can also be generated based on the encoding rules pre-set by the sender, for example, a unique character sequence or digital identification is obtained after combining multiple factors such as data frame ID, timestamp, channel identification, data packet number, etc. through encryption algorithm or hash function operation.

[0046] In some embodiments, judgment conditions can also be preset to determine whether each data frame in the valid information meets the frame header characteristics. If it meets the requirements, the frame header is captured, and the unique identification code and channel identifier in the frame header are extracted. According to the unique identification code and the preset two-dimensional sorting benchmark, the data frame is sorted according to the classification framework, and the content is checked to achieve frame synchronization, so that the data frames of each channel are synchronized in time and matched in content.

[0047] Specifically, a judgment condition is preset to judge whether each data frame of valid information of each channel meets the frame header feature. If it meets the feature, the frame header is captured to extract the unique identification code and channel identifier in the frame header; a two-dimensional sorting benchmark is set, the main dimension classifies the data frames of the same batch according to the order of transmission in time, and the secondary dimension arranges the data frames of different channels at the same time according to the channel sequence number from small to large, and constructs a classification framework to put the data frames in place; for each batch and each channel of the data frame, a content check is introduced, and the key information fields in the data frame are checked to be complete and correct according to the data frame format and content specifications pre-defined by the communication protocol. If the check is abnormal, it is combined with the associated information of the surrounding normally synchronized data frames, and the difference algorithm, trend prediction, etc. are used to repair or mark it for subsequent retransmission requests; a dynamic adjustment sorting mechanism is preset, and when a frame is lost, the position is estimated according to the associated information of the surrounding frame identification code, and a vacant space is reserved for waiting for completion or interpolation completion. If a vacant space is reserved, it is inserted and completed according to the identification code check after the delayed frame is received; wherein: The judgment condition may be a special field, character, or logo in the frame header.

[0048] The main dimension of the two-dimensional sorting benchmark is based on the sending order. The data frames of each channel are initially sorted according to the time sequence logic to ensure that the data frames sent from different channels at the same time can be classified into the same batch. The secondary dimension considers the channel number, and arranges the data frames of different channels belonging to the same sending time in order from small to large according to the channel number. The two-dimensional sorting benchmark can be further used to build a classification framework to put the data frames back in place and put them into the corresponding "time slot-channel" grid. This ensures that in the scenario of multi-channel parallel transmission, even if the transmission delays of each channel are different, the data frames of each channel can be synchronized in time and matched in content.

[0049] In addition, taking into account the complex and changeable network conditions during multi-channel transmission, abnormal phenomena such as intermittent frame loss and sudden delays often occur. Therefore, the sorting process can adopt a dynamic and adjustable mechanism. When a data frame is detected to be lost, the system estimates the approximate position of the lost frame based on the identification code association information of the surrounding sorted data frames through difference algorithms, trend predictions, etc., and reserves space for subsequent completion or reasonable interpolation and replacement; for data frames that encounter severe delays, once received, their identities are quickly verified based on their identification codes, and they are flexibly inserted into the corresponding positions according to the current preliminarily sorted data queue grid, to ensure that the entire sorting chain always remains coherent and orderly.

[0050] In some embodiments, the multi-channel oriented adaptive signal recovery method 400 further includes: At the transmitting end, the frame header is set to a flexible framing frame header, which contains one or more functional sequence blocks, including a first sequence block and a second sequence block; the first sequence block is a protection interval, which reduces the multipath effect under a single channel and avoids crosstalk between previous and next data; the second sequence block is a boosting block, which can use its channel estimation performance and fast convergence characteristics of equalization to perform more accurate channel estimation and improve the accuracy of LLR; the type and number of sequence blocks in the frame header can be flexibly adjusted to meet transmission requirements.

[0051] For example: The first sequence block can be a PN sequence or a cyclic prefix. By adding a certain number of PN sequences or cyclic prefixes as a protection interval, the multipath effect under a single channel can be reduced to avoid crosstalk between the previous and next data. In other words, a frame synchronization PN sequence or cyclic prefix with a length of P can be added to the frame header as a sequence block to avoid crosstalk between the previous and next data. The schematic diagram of the frame header structure can be as follows: Figure 3 shown.

[0052] The second sequence block can be a Zadoff-Chu sequence, Gold sequence, Hadamard sequence, chaotic sequence or other type of sequence block in the PN sequence. A certain number of such sequence blocks are added to utilize their excellent channel estimation performance and fast convergence characteristics of equalization to perform more accurate channel estimation and improve LLR accuracy to serve the decoding performance.

[0053] Furthermore, for the above number of sequence blocks, based on the OFDM system and the number of subcarriers is 64-256 bits, when the communication system has extremely high requirements for transmission efficiency and the channel environment is relatively stable and the multipath delay spread is small, 4-8 sequence blocks can be added; for most conventional outdoor mobile communications or general indoor complex environments (there are a certain degree of multipath, obstruction, etc.), when the number of subcarriers is in the scale of 128-256, it is more appropriate to add 12-20 sequence blocks; in extremely harsh channel environments, such as scenes with severe signal scattering in remote areas, the number of subcarriers is 2 56, 32-64 sequence blocks can be added; based on a single carrier system and the data block length is 100-500 bits, for a relatively stable channel, limited noise interference, and a data block length of 100-200 bits, 2-4 sequence blocks can be added; in general complex channels, there are certain noise fluctuations, mild multipath, and the data block length is 200-300 bits, 6-10 sequence blocks can be added; for scenarios with severely damaged channels, strong noise, and deep multipath, 16-32 sequence blocks can be configured when the data block length is 300-500 bits.

[0054] Here are some specific examples: In indoor short-range wireless local area network (WLAN) communication, the channel condition is good and the signal propagation path is simple. Adding four Zadoff-Chu sequence blocks and placing them in the frame header for channel estimation can not only meet the basic channel state information acquisition requirements, quickly complete the initial channel equalization adjustment, and ensure the normal decoding, but also minimize the transmission overhead caused by the additional sequence blocks, maintain high transmission efficiency, and realize high-speed data interaction. At this time, the corresponding bit error rate can be controlled within 10 -5 -10 -4 In the case of 4G / 5G micro-base station coverage on city streets, the frequent movement of people and vehicles leads to dynamic changes in the channel. The 16 Gold sequence blocks can assist the receiver to accurately track the channel fluctuations with their good cross-correlation characteristics, update the channel estimation every 2-3 OFDM symbol periods, and continuously calibrate the channel equalization parameters, so that the decoding can work based on the reliable channel state, and the bit error rate is stable at 10 -6 -10 -5 interval, greatly improving the decoding performance; within the industrial plant, the single-carrier link of the short-distance wired analog signal digital transmission has few interference sources and fixed channel characteristics. Two Zadoff-Chu sequence blocks are sufficient to outline the channel contour and assist in channel equalization before decoding, ensuring that the bit error rate is less than 10 -4 , achieving efficient and stable decoding; in old buildings, long-distance wiring and single-carrier communication with strong RF interference sources around, 24 Zadoff-Chu sequence blocks are used to support complex channel analysis. After fine channel estimation and equalization, the bit error rate is suppressed to 10 -6 -10 -5 , enabling decoding to resist harsh channels and restore signals.

[0055] Furthermore, another example is provided to illustrate adding a certain number of sequence blocks to perform more accurate channel estimation and improve the accuracy of LLR.

[0056] Assume that a wireless communication system uses 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 2.4 GHz-2.4835 GHz, and the bandwidth is divided into 64 subcarriers.

[0057] A sequence block based on Zadoff-Chu (ZC) sequence is selected and added to the frame header. The ZC sequence has good autocorrelation and cross-correlation characteristics and can effectively resist multipath interference and channel noise. A ZC sequence with a length of 16 is designed as the sequence block. Its mathematical expression is: ; Among them, u is the root index, the value is 3; N is the sequence length 16; n ranges from 0 to 15; the specific sequence value is as follows: , , wait.

[0058] A ZC sequence block with a length of 16 is added to the front end of each data frame, so that the frame header becomes 16 sampling points, corresponding to a 2-symbol duration. Considering that each symbol carries a sequence block of 8 sampling points, plus the original regular frame header part, the overall frame structure becomes a sequence block + regular synchronization field + frame control field + payload data + frame tail.

[0059] After the receiver receives the frame signal containing the sequence block, it first locates the starting position of the sequence block in the frame header through the synchronization algorithm, and extracts the sequence block signal r(n) (n=0, 1,..., 15) corresponding to these 16 sampling points. This signal is the deformation of the transmitted sequence s(n) after channel fading and noise interference. The relationship can be expressed by a mathematical model: r(n)=h(n)*s(n)+w(n), where h(n) is the channel impulse response and w(n) is the additive noise.

[0060] The correlation function between the received sequence block signal r(n) and the locally stored ZC sequence s(n) is calculated based on the autocorrelation characteristics of the ZC sequence: ; By finding the peak position and amplitude of R(k), the delay spread and amplitude fading of the channel are estimated, and the estimated value of the channel impulse response h(n) is obtained. ; After calculation, it is found that R(3) is the peak value, and the corresponding amplitude is A. It is preliminarily judged that the channel delay is the corresponding sampling point interval, and the sampling period , the delay is , the amplitude attenuation coefficient is A, thus constructing .

[0061] Based on the estimated channel impulse response , channel equalization is performed on the entire received OFDM symbol. For each subcarrier k in the frequency domain, the equalized signal is: ; Y(k) is the frequency domain value of the received signal at subcarrier k, yes The value of the corresponding subcarrier k is transformed to the frequency domain. When performing channel decoding, the equalized signal is used to accurately calculate the LLR value according to the decoding algorithm. Compared with the case where the sequence block is not used for accurate channel estimation, the LLR value at this time can better reflect the actual value possibility of the data bit, because the influence of channel fading and noise is more accurately compensated in the channel estimation and equalization process.

[0062] It is understandable that, due to the different delays of different propagation paths in a multi-channel transmission scenario, the time when the receiving end receives the corresponding data frames from different channels is also different. In the information merging process, the same frame data from the sending end needs to be merged to obtain correct high-quality information and ensure system performance. Therefore, the present disclosure provides a highly reliable and flexible distributed receiving frame structure design method. By adding some special frame header information before the data frame, the receiving end can distinguish the order of the data packet, ensure the correctness of the soft information merging, and improve the applicability and flexibility of the data frame. The specific frame structure can be as follows: Figure 2 shown.

[0063] S420, performing quality evaluation and threshold comparison on valid information of each channel according to key information guiding soft information merging in the frame header information, and adaptively matching the soft information merging mode according to the quality evaluation and threshold comparison results.

[0064] Among them, the key information guiding the merging of soft information includes: timestamp information, signal quality indicator information, data type and format information.

[0065] Specifically, according to the signal quality indicator information in the key information guiding the merging of soft information, the quality of the valid information of each channel is evaluated, and the signal quality indicator of the valid information of each channel is compared with the preset signal quality indicator threshold, and the merging method is adaptively matched according to the comparison result; wherein: the signal quality indicator information includes: signal-to-noise ratio, 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 methods include: selective merging, uniform merging, and maximum ratio merging.

[0066] Furthermore, the signal quality index of each channel's valid information is compared with a preset signal quality index threshold, and a matching and combining method is adaptively performed according to the comparison result, including: The signal quality index of each channel's valid information is compared 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, the selective merging method is matched; if the signal quality indicators of multiple channels are lower than the first threshold and higher than or equal to a second threshold, the maximum ratio merging method is matched; if the signal quality indicators of multiple channels are lower than the second threshold, the uniform merging method is matched.

[0067] According to the embodiments of the present disclosure, a flexible frame structure is adopted to improve the applicability of data frames, and combined with an adaptive matching merging method, multiple signal frames are synchronized and then appropriately merged, thereby effectively ensuring the accuracy of soft information merging.

[0068] S430, performing soft information merging according to the data frame synchronization result and the matched soft information merging mode to obtain the best channel reception soft value.

[0069] Specifically, if the signal quality index of one or more channels among the multiple channels is higher than or equal to the first threshold, and there is a high-quality signal, the selective merging method is matched, and the receiving soft value corresponding to the signal with the highest signal quality index is selected as the best channel receiving soft value; if the signal quality indicators of multiple channels are lower than the first threshold, higher than or equal to the second threshold, and the signal quality is medium, the maximum ratio merging method is matched, and the effective signals of each channel are weighted and merged according to the data frame synchronization result according to their respective signal quality indicators, and the weighted merging result is used as the best channel receiving soft value; if the signal quality indicators of multiple channels are lower than the second threshold, and the signal quality is poor, the uniform merging method is matched, and the effective signals of each channel are averaged and merged according to the data frame synchronization result, and the receiving soft value corresponding to the calculation result is used as the best channel receiving soft value; wherein, the effective signals of each channel are weighted and merged according to their respective signal quality indicators according to the data frame synchronization result, and the weighted merging result is used as the best channel receiving soft value, including: Recall the preset weight allocation table, assign values ​​to the valid signals of each channel according to their signal quality indicators, assign high weights to signals with high signal quality indicators and low weights to signals with low signal quality indicators; perform weighted combination based on the data frame synchronization results and the weights of the valid signals of each channel, and use the weighted combination result as the best channel reception soft value.

[0070] For example: Assume that there are four channels transmitting data, marked as channel 1, channel 2, channel 3, and channel 4 respectively. The data frames transmitted by each channel at the same time have completed the frame synchronization operation, and we obtain the signal quality indicator signal-to-noise ratio of each channel (taking the signal-to-noise ratio as an example) through the frame header information; set the first threshold to 10dB and the second threshold to 5dB.

[0071] 1. High-quality signal exists Assume that the signal-to-noise ratio of each channel and the corresponding received soft value are as follows: The signal-to-noise ratio of channel 1 is 12dB, and the received soft value is: S 1 =(0.8, -0.3, 0.5); The signal-to-noise ratio of channel 2 is 9dB, and the received soft value is: S 2 =(0.6, -0.2, 0.4); The signal-to-noise ratio of 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).

[0072] Because the signal-to-noise ratio of channel 1 and channel 3 is higher than the first threshold, it is determined that there is a high-quality signal. A selective merging method is adopted to select the received soft value corresponding to channel 3 with the highest signal-to-noise ratio as the merging result. Therefore, the merging result (i.e., the best channel received soft value) is the received soft value of channel 3.

[0073] 2. Medium signal quality Assume that the signal quality indicators of each channel (still taking the signal-to-noise ratio as an example) and the corresponding received soft values ​​are as follows: The signal-to-noise ratio of channel 1 is 8dB, and the received soft value is: S 1 =(0.7, -0.3, 0.4); The signal-to-noise ratio of channel 2 is 6dB, and the received soft value is: S 2 =(0.5, -0.2, 0.3); The signal-to-noise ratio of 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).

[0074] 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 the maximum ratio combining method is adopted.

[0075] A weight allocation table is preset in the system to allocate weights according to the signal quality index. The table follows the principle of assigning high weights to signals with higher signal quality indexes and low weights to signals with lower signal quality indexes. Relatively speaking, taking the signal-to-noise ratio as an example, the signal-to-noise ratios of each channel can be sorted from high to low, and the preset weights can be assigned to each channel in order from large to small according to the sorting table. The weights can also be determined according to the proportion of the signal-to-noise ratio to reflect the relative level of its signal quality. In this example, the specific value assignment calculation method can be as follows: Total signal-to-noise ratio = 8 + 6 + 7 + 5.5 = 26.5 dB; Channel 1 weight ; Channel 2 weight ; Weight of channel 3 ; Channel 4 weight .

[0076] Since each channel has completed data frame synchronization, the consistency of the soft information of each channel in time and content order is guaranteed, and the merging operation can be performed correctly corresponding to each soft information position. For each soft information position, weighted summation calculation is performed according to the weight. Taking the first soft information position as an example: The first element of the merged soft information = w 1 ×S 1 (1)+w 2 ×S 2 (1)+w 3 ×S 3 (1)+w 4 ×S 4 (1)=0.302×0.7+0.226×0.5+0.264×0.6+0.208×0.4=0.566.

[0077] Similarly, calculate the second soft information position: The second element of the combined soft information = w 1 ×S 1 (2)+w 2 ×S 2 (2)+w 3 ×S 3 (2)+w 4 ×S 4 (2)=0.302×(0.3)+0.226×(0.2)+0.264×(0.1)+0.208×(0.1)=0.183.

[0078] Calculate the third soft information position: The third element of the combined soft information = w 1 ×S 1 (3)+w 2 ×S 2 (3)+w 3 ×S 3 (3)+w 4 ×S 4 (3)=0.302×0.4+0.226×0.3+0.264×0.5+0.208×0.2=0.3622.

[0079] Then, the best channel received soft value = (0.566, 0.183, 0.3622).

[0080] 3. Poor signal quality Assume that the signal quality indicators of each channel (still taking the signal-to-noise ratio as an example) and the corresponding received soft values ​​are as follows: The signal-to-noise ratio of channel 1 is 4dB, and the received soft value is: S 1 =(0.3, 0.2, 0.2); The signal-to-noise ratio of channel 2 is 3dB, and the received soft value is: S 2 =(0.2, 0.1, 0.1); The signal-to-noise ratio of 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).

[0081] At this time, the signal-to-noise ratio of each channel is lower than the second threshold, and the signal quality is determined to be poor. A uniform merging method is adopted to perform mean merging calculation on the valid signals of each channel.

[0082] For the first soft information position: ; For the second soft information position: ; For the third soft information position: .

[0083] Therefore, the best channel received soft value = (0.175, 0.1, 0.1).

[0084] Through the examples of the above three different situations, the specific calculation process of obtaining the best channel reception soft value according to different signal quality conditions and the corresponding matching soft information merging method is demonstrated in detail.

[0085] According to the embodiments of the present disclosure, the optimal merging strategy is flexibly selected according to different channel quality conditions, taking into account the coordination of effective information of each channel, giving play to the advantages of high-quality information, obtaining better channel reception soft values, effectively reducing information loss caused by improper merging methods, enhancing the soft information merging effect, and improving the system's adaptability to different channel conditions, providing a reliable foundation for subsequent decoding.

[0086] S440, demodulate the channel reception soft value and calculate the log-likelihood ratio; perform iterative decoding based on the log-likelihood ratio to restore the original signal.

[0087] Specifically, a demodulation algorithm is used to demodulate the channel reception soft value; based on the log-likelihood ratio calculation formula in the pre-constructed channel model, the log-likelihood ratio of the demodulated channel reception soft value is calculated; the log-likelihood ratio is input into the decoder as the initial soft information for iterative decoding until the maximum number of iterations or the preset threshold is reached, the soft information is output, and the original signal is restored.

[0088] For example, if the transmitting end adopts binary phase shift keying modulation, the corresponding demodulation algorithm is usually coherent demodulation or incoherent demodulation. Taking coherent demodulation as an example, first recover the carrier signal with the same frequency and phase as the transmitting end, use a multiplier to multiply the received channel reception soft value with the local carrier, move the modulated signal to the baseband, and then filter out the high-frequency component through a low-pass filter to obtain the baseband signal. Suppose the received channel reception soft value is r(t), and the locally recovered carrier is ,in, is the carrier angular frequency, φ is the phase, and after multiplication, we get r(t)*c(t). The baseband signal after low-pass filtering can be expressed as , effectively removing the carrier component, restoring the baseband signal form carrying information, and completing demodulation.

[0089] The calculation formula for calculating the log-likelihood ratio of the demodulated channel reception soft value based on the pre-built channel model has been described in detail above and the calculation formula has been listed, which will not be repeated here.

[0090] Next, 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, which may include: Select a suitable iterative decoding algorithm, such as the iterative decoding of Turbo codes or the iterative decoding algorithm of low-density parity-check codes (LDPC), and use the calculated log-likelihood ratio as the initial soft information and input it into the corresponding port of the decoder in order.

[0091] Taking Turbo code decoding as an example, the LLR value is distributed to two component decoders and combined with the observation information from the channel as a priori information to start the first round of decoding process. Each component decoder performs processing according to its own decoding algorithm.

[0092] During the iterative decoding process, the component decoders work alternately, continuously updating soft information and transmitting it interactively. In each round of iteration, the prior information of the next round of input is updated according to the soft information output by the current decoder, and key parameters such as likelihood ratio and posterior probability are recalculated to optimize decoding.

[0093] For example, in LDPC decoding, by repeatedly passing messages between check nodes and variable nodes, based on the belief propagation algorithm, the update message is calculated according to the connection relationship between nodes and the current soft information, and the correct decoding result is gradually approached; at the same time, the maximum number of iterations is set as an insurance mechanism to prevent infinite iterations; a preset threshold, such as a bit error rate threshold, can also be set. Once the bit error rate is reached or the maximum number of iterations is completed during the iteration process, the iteration is stopped immediately and the final soft information is output. These soft information are hard-determined according to the decoding algorithm rules, so as to restore the original signal sequence and the original signal.

[0094] According to the embodiments of the present disclosure, the following technical effects are achieved: Through flexible frame header information and structure, quality assessment and threshold matching, adaptive merging mode matching and calculation, demodulation and decoding, an adaptive and efficient signal recovery system is constructed. This system can cope with complex multi-channel transmission environments, adapt to multi-signal quality and data types, effectively improve the data synchronization accuracy of each channel, the soft information merging effect, and the decoding error correction capability, reduce the bit error rate and signal distortion, enhance the stability, reliability and transmission efficiency of the communication system, meet the needs of modern complex communication services, and ensure high-quality information interaction.

[0095] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0096] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0097] Figure 5 FIG. 4 shows a block diagram of a multi-channel adaptive signal recovery device according to an embodiment of the present disclosure; Figure 5 As shown, the multi-channel adaptive signal recovery device 500 may include: The first processing module 510 is used to receive valid information of each channel, capture the 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 channel identifier in the frame header information.

[0098] The second processing module 520 is used to perform quality assessment 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 method according to the quality assessment 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.

[0099] The third processing module 530 is used to perform soft information merging according to the data frame synchronization result and the matched soft information merging mode to obtain the best channel reception soft value.

[0100] The fourth processing module 540 is used to demodulate the channel reception soft value and calculate the log-likelihood ratio; perform iterative decoding based on the log-likelihood ratio to restore the original signal.

[0101] Understandably, Figure 5 The various modules in the multi-channel adaptive signal recovery device 500 shown have the function of implementing the various steps in the multi-channel adaptive signal recovery method 400 provided in the embodiment of the present disclosure, and can achieve the corresponding technical effects. The specific working process of the described modules can refer to the corresponding process in the aforementioned method embodiment, and for the convenience and conciseness of the description, it will not be repeated here.

[0102] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0103] Figure 6 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown.

[0104] The electronic device 600 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0105] The electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory ROM 602 or a computer program loaded from a storage unit 608 into a random access memory RAM 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An I / O interface 605 is also connected to the bus 604.

[0106] Multiple components in the electronic device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a 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.

[0107] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, 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, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method 400 described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the method 400 in any other appropriate manner (e.g., by means of firmware).

[0108] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, 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 implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0109] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0110] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0111] To provide interaction with a user, the systems and techniques described herein 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 trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0112] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may 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.

[0113] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0114] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0115] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection 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 captures the frame header information of each data frame in the valid information; The data frames of each channel are synchronized according to the unique identification code and channel identifier in the frame header information, including: 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, characterized in that The method of performing quality assessment and threshold comparison on the valid information of each channel according to the key information guiding the soft information merging in the frame header information, and adaptively matching the soft information merging mode according to the quality assessment and threshold comparison results, includes: 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 methods according to the comparison results, including: 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, including: 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 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, including: 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, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

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