Low-complexity random access channel coding and decoding method based on sparse regression code

Abstract

The invention discloses a low-complexity random access channel coding and decoding method based on a sparse regression code, which belongs to the technical field of random access channel coding and decoding, and comprises the following steps of: acquiring to-be-sent information of all active users through a sending end; wherein the information to be sent comprises identity identification information and message information; encoding and transmitting the to-be-sent information, and repeating a plurality of stages; receiving signals are obtained through a receiving end, and whether the whole transmission process is finished after the tth stage is completed or not is judged; wherein the obtained receiving signal transmitted in the first stage is the superposition of a plurality of identity identification sub-code words and noise; the received signals transmitted from the second stage to the t-th stage are all superposition of a plurality of message sub-code words and noise; if the transmission is finished, determining the number of active users according to the number of the transmission stages, and decoding the received signals of the first stage to obtain identity information of all the active users; and decoding the received signals transmitted from the second stage to the t-th stage to obtain message information of all active users.

Description

Technical Field

[0001] The present invention belongs to the technical field of random access channel encoding and decoding, and particularly relates to a low-complexity random access channel encoding and decoding method based on sparse regression codes. Background Art

[0002] In modern communication scenarios such as the Internet of Things, random access technology has extensive applications. In a large-scale communication system, user activities are often unpredictable, and it is impossible to determine the number and identities of active users in each time slot. This situation can be modeled by a random access channel (RAC). There are a total of potential users in the system sending messages to a receiver, where only active users send messages at a certain time, and the number and identities of the active users are unknown. At the receiving end, it is necessary to correctly judge the number and identities of active users based on the received signals and decode the messages they send;

[0003] In a large-scale communication system, the number of connected devices is large, and the number of simultaneously communicating users is also large. Currently, a large number of devices such as Internet of Things devices and mobile terminals that use random access channels are usually battery-powered and require low-complexity coding implementation schemes to reduce energy consumption. However, existing time-division multiplexing-based random access schemes need to dynamically adjust time slot allocation according to the number of users and message loads, and the algorithm complexity is relatively high. If the number of users changes greatly, there may be a situation of wasted or insufficient time slot resources. The frequency-division multiplexing-based random access scheme has high requirements for frequency synchronization, and the algorithm complexity of frequency band resource allocation and management is relatively high, requiring a large communication resource overhead. Therefore, the algorithm complexity of existing random access methods is relatively high, and a large amount of communication resource overhead is also required, which may fail in a large-scale communication system. Summary of the Invention

[0004] In view of this, the present invention provides a low-complexity random access channel encoding and decoding method based on sparse regression codes to solve the problem that the algorithm complexity of existing random access methods is relatively high and may fail in a large-scale communication system.

[0005] To achieve the above object, the present invention provides a low-complexity random access channel encoding and decoding method based on sparse regression codes, including the following steps:

[0006] S1: Obtain the information to be sent by all active users through the sending end; wherein, the information to be sent includes: identity identification information and message information;

[0007] S2: Encode the information to be sent and transmit it, repeating multiple stages; among them, use the same identity dictionary matrix to encode the identity identification information to obtain identity identification sub-codewords, and use the message dictionary matrix corresponding to the active users to encode the message information to obtain message sub-codewords;

[0008] S3: Obtain the received signal through the receiving end and determine whether the entire transmission process ends after the t-th stage; among them, the received signal transmitted in the first stage is the superposition of multiple identity identification sub-codewords and noise; the received signals transmitted in the second stage to the t-th stage are all the superposition of message sub-codewords and noise;

[0009] S4: If the transmission ends, determine the number of active users according to the number of transmission stages, and decode the received signal in the first stage to obtain the identity identification information of all active users;

[0010] S5: Decode the received signals transmitted in the second stage to the t-th stage to obtain the message information of all active users.

[0011] As an embodiment of the present invention, encoding the information to be sent and performing multi-stage transmission includes:

[0012] S21: In the first stage, encode the identity identification information of all active users according to the same identity dictionary matrix to obtain identity identification sub-codewords with a code length of n0 and transmit them, as follows:

[0013]

[0014] where x i0 is the identity identification sub-codeword of the i-th active user, A0 is the identity dictionary matrix, is the identity identification information vector of the i-th active user, and K is the total number of users corresponding to the sending end;

[0015] S22: In the t-th stage, encode the message information according to the message dictionary matrix corresponding to each active user in this stage to obtain message sub-codewords with a code length of n t-1 -n t-2 and transmit them, as follows:

[0016] x i(t-1) = A i(t-1) β i

[0017] where x i(t-1) is the message sub-codeword transmitted in the t-th stage, A i(t-1) is the message dictionary matrix used by the i-th active user during encoding in the t-th stage, β i is the information vector corresponding to the message information of the i-th active user, t ∈ [2:K + 1], and t is a positive integer.

[0018] As an embodiment of the present invention, after the receiving end obtains the received signal and determines whether the entire transmission process ends after the t-th stage, it includes:

[0019] S31: Concatenate the received signals of the receiving end from the 1st stage to the t-th stage to obtain a target concatenation result;

[0020] S32: Determine whether the target concatenation result meets the transmission stop condition, and the transmission stop condition is as follows:

[0021]

[0022] Among them, is the target concatenation result, λ t-1 ∈[λ1, λ2, …, λ K , [λ1, λ2, …, λ K is a set of positive real numbers, P x is the codebook power;

[0023] S33: If the transmission stop condition is met, the receiver broadcasts a 1-bit signal 1 to all users, and the transmission ends; if the transmission stop condition is not met, the receiver broadcasts a 1-bit signal 0 to all users, and the transmission continues. Repeat steps S22, S31, and S32 until the transmission ends.

[0024] As an embodiment of the present invention, if the transmission ends, determine the number of active users according to the number of transmission stages, and decode the received signal in the 1st stage to obtain the identity information of all active users, including:

[0025] S41: When the transmission stop condition is met, it means that the number of active users is k; among them, k = t - 1;

[0026] S42: Take the received signal obtained by the receiving end in the 1st stage as the target signal, that is

[0027] S43: Decode the target signal according to the approximate message passing decoding algorithm to obtain the decoding result, as follows:

[0028]

[0029] info0 = {L0, M0, P x , P z}

[0030] Among them, is the decoding result, y is the target signal, A0 is the identity dictionary matrix, g AMP() is the approximate message passing decoding algorithm, info0 is the parameter information required for identity identification information decoding, L0 = 1 is fixedly taken as the number of regions of the identity dictionary matrix, M0 is the number of columns per region of the identity dictionary matrix, P x is the codebook power, P z is the channel noise power;

[0031] S44: Suppose The position index of the non-zero element in is a, which indicates that user a is an active user in the current transmission process. Subtract the identity identification sub-codeword of active user a from the target signal as the new target signal, as follows:

[0032]

[0033] Among them, is the identity identification information vector of active user a;

[0034] S45: Repeat the above steps S43 and S44 k - 1 times until the identities of all k active users are determined;

[0035] As an embodiment of the present invention, decode the received signals transmitted in the second stage to the t-th stage to obtain the message information of all active users, including:

[0036] S51: Concatenate the message dictionary matrices used by k active users during the transmission in the second stage to the t-th stage to obtain the concatenated dictionary matrix A k ;

[0037] S52: According to the concatenated dictionary matrix and the approximate message passing decoding algorithm, decode the concatenated result of the received signals received in the second stage to the t-th stage to obtain the decoding result, as follows:

[0038]

[0039] info = {L, M, P x , P z}

[0040] Among them, is the decoding result, corresponding to the message information of the active user; g AMP () is the approximate message passing decoding algorithm, is the concatenated result of the received signals received in the second stage to the t-th stage, A k is the concatenated dictionary matrix, info is the parameter information required for message information decoding, L is the number of regions of the message dictionary matrix, M is the number of columns per region of the message dictionary matrix, P x is the codebook power, P z is the channel noise power.

[0041] The beneficial effects of the present invention are as follows: The joint decoding scheme based on the approximate message passing decoding algorithm effectively reduces the decoding computational complexity. While the performance is close to that of the theoretical optimal decoding scheme, the time overhead is significantly reduced, and it has a low computational complexity, making it feasible in practical applications. It solves the problem that the algorithm complexity of the existing random access method is relatively high and may fail in large-scale communication systems.

[0042] Other advantages, objectives, and features of the present invention will be elaborated in the subsequent description, and to some extent, they are obvious to those skilled in the art, or those skilled in the art can obtain teachings from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the following description. Brief Description of the Drawings

[0043] To make the objectives, technical solutions, and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0044] Figure 1 It is a schematic flowchart of the present invention;

[0045] Figure 2 It is a schematic flowchart of the AMP algorithm of the present invention;

[0046] Figure 3 It is a performance comparison diagram of the encoding and decoding scheme of the present invention under different distribution noises;

[0047] Figure 4 It is the first comparison diagram of the encoding and decoding scheme of the present invention with the performance of a multiple access channel system under the same conditions;

[0048] Figure 5 It is the second comparison diagram of the encoding and decoding scheme of the present invention with the performance of a multiple access channel system under the same conditions;

[0049] Figure 6 It is a performance comparison diagram of the AMP joint decoding scheme of the present invention with the theoretical optimal decoding scheme;

[0050] Figure 7 It is a time complexity comparison diagram of the AMP joint decoding scheme of the present invention with the theoretical optimal decoding scheme. Detailed Embodiments

[0051] As Figures 1 - 2 shown, the present invention provides a low-complexity random access channel encoding and decoding method based on sparse regression codes, including:

[0052] S1: Obtain the information to be sent of all active users through the sending end; where the information to be sent includes: identity identification information and message information;

[0053] S2: Encode the information to be sent and transmit it, repeating multiple phases; among them, use the same identity dictionary matrix to encode the identity identification information to obtain identity identification sub-codewords, and use the message dictionary matrix corresponding to the active users to encode the message information to obtain message sub-codewords;

[0054] S3: Obtain the received signal through the receiving end and determine whether the entire transmission process ends after the t-th phase; among them, the received signal transmitted in the first phase is the superposition of multiple identity identification sub-codewords and noise; the received signals transmitted in the second phase to the t-th phase are all the superposition of message sub-codewords and noise;

[0055] S4: If the transmission ends, determine the number of active users according to the number of transmission phases, and decode the received signal in the first phase to obtain the identity identification information of multiple active users;

[0056] S5: Decode the received signals transmitted in the second phase to the t-th phase to obtain the message information of all active users.

[0057] The working principle of the above technical solution: Before communication, it is necessary to complete the codebook design and determine the codebook parameters. Specifically, the size of the dictionary matrix A in the sparse regression code is n×ML, where n is the code length, and M and L satisfy M L = 2 nR , that is, the total number of codewords is M L ; the size of the information vector β is ML×1, which can be regarded as divided into L regions, each region has M elements, and only one element in each region is non-zero, and the value of the non-zero element is where P x is the codebook power of the user; each element in A independently follows a Gaussian distribution with a mean of 0 and a variance of ; the codeword x is generated by multiplying the dictionary matrix A by the information vector β, that is, x = Aβ, and the set of all codewords that can be generated by the same dictionary matrix is the codebook N corresponding to this dictionary matrix. M, L, and P x of K users are the same, the A matrices used for encoding are different, and the A matrices used by the same user in different transmission phases are different; A ij represents the A matrix used by the i-th user in the j-th transmission phase, and x ij represents the word codeword generated by the i-th user in the j-th transmission phase, and the same applies to other parameters;

[0058] There are a total of K potential users at the sender side. In a certain time slot, only k active users send messages to a receiver, and the remaining K - k users do not send messages in the current time slot; the receiver does not know the number k and identities of the active users; therefore, the transmission process is divided into at most K + 1 stages: in the first stage, each active user sends an identity identification sub - codeword with a code length of n0, and what the receiver receives is the superposition result of k identity identification sub - codewords and noise. In the t - th (t ∈ [2:K + 1]) stage, the active users encode and generate the (t - 1)-th message sub - codeword with a code length of n t-1 -n t-2 and send it. The receiver judges whether the number of active users is t - 1 according to the number of active users. If it is, it broadcasts a 1 - bit signal 1 to all users, and the transmission ends; otherwise, it broadcasts a 1 - bit signal 0 to all users, and the transmission continues until a total of K + 1 sub - codewords are received; the total length of the codewords received by the receiver is n k , which includes an identity identification sub - codeword with a length of n0 and a transmission message sub - codeword with a length of n k -n0; the receiver first judges the identities of the active users according to the identity identification decoding scheme designed using the Approximate Message Passing (AMP) algorithm; then decodes the k different messages according to the joint decoding scheme designed using the AMP algorithm; specifically, splice the message dictionary matrices corresponding to the message sub - codewords with a length of n k -n0 sent by the k active users into a new spliced dictionary matrix, and use the AMP algorithm to decode to obtain the decoding results of the k transmission messages.

[0059] The beneficial effects of the above - mentioned technical solution: Through the above - mentioned technical solution, the joint decoding scheme based on the approximate message passing decoding algorithm effectively reduces the decoding calculation complexity. While the performance is close to the theoretical optimal decoding scheme, the time overhead is significantly reduced, and it has a low computational complexity and is feasible in practical applications; it solves the problem that the algorithm complexity of the existing random access method is relatively high and may fail in a large - scale communication system.

[0060] In one embodiment, encoding the information to be sent and performing multi - stage transmission includes:

[0061] S21: In the first stage, encode the identity identification information of all active users according to the same identity dictionary matrix to obtain an identity identification sub - codeword with a code length of n0 and perform transmission, as follows:

[0062]

[0063] where, xi0 is the identity identification sub - codeword of the \(i\) - th active user, \(A_0\) is the identity dictionary matrix, is the identity identification information vector of the \(i\) - th active user, and \(K\) is the total number of users corresponding to the transmitter;

[0064] S22: In the \(t\) - th stage, encode the message information according to the message dictionary matrix corresponding to each active user in this stage, and obtain a message sub - codeword with a code length of \(n\) t-1 -n t-2 and transmit it as follows:

[0065] x i(t-1) =A i(t-1) β i

[0066] where \(x\) i(t-1) is the message sub - codeword transmitted in the \(t\) - th stage, \(A\) i(t-1) is the message dictionary matrix used by the \(i\) - th active user during encoding in the \(t\) - th stage, and \(β\) i is the information vector corresponding to the message information of the \(i\) - th active user, \(t\in[2:K + 1]\), and \(t\) is a positive integer.

[0067] The receiver obtains the received signal and determines whether the entire transmission process ends after the \(t\) - th stage, including:

[0068] S31: Concatenate the received signals of the receiver from the 1st stage to the \(t\) - th stage to obtain a target concatenation result;

[0069] S32: Determine whether the target concatenation result meets the transmission stop condition. The transmission stop condition is as follows:

[0070]

[0071] where, is the target concatenation result, \(\lambda\) t-1 \(\in[\lambda_1,\lambda_2,\cdots,\lambda\) K , \([\lambda_1,\lambda_2,\cdots,\lambda\) K is a set of positive real numbers, and \(P\) x is the codebook power;

[0072] S33: If the transmission stop condition is met, the receiver broadcasts a 1 - bit signal 1 to all users, and the transmission ends; if the transmission stop condition is not met, the receiver broadcasts a 1 - bit signal 0 to all users, and the transmission continues. Repeat steps S22, S31, and S32 until the transmission ends;

[0073] Working principle and beneficial effects of the above technical solution: During the encoding and transmission of identity identification information and message information: First, perform identity identification sub-codeword encoding according to the identity identification information. Specifically, take M0 = K, L0 = 1, and use the identity identification information vector β of size M0L0×1 K to distinguish K users; only one element in β K is non-zero, and the position of this non-zero element represents the user identity. That is, for the i-th user (i ∈ {1, 2,..., K}), the identity identification information vector is non-zero at the i-th position; all users use the same identity dictionary matrix A0 to encode the identity identification information vector, and the size of A0 is n0×M0L0; the identity identification information vector of the i-th user is The identity identification sub-codeword is x i0 ; when performing message sub-codeword encoding according to the message information, k active users respectively map the messages to be transmitted into information vectors β i and encode and transmit them in the following way; the transmission process is divided into at most K + 1 stages; among them, λ1, λ2,..., λ K is a set of positive real numbers that affect the correctness of system decoding; in the case where the system judgment is correct, the transmission process will end after the (k + 1)-th stage, that is, the receiving end judges that the number of active users is k; the specific process is:

[0074] (1) In the first stage, k active users encode and generate identity identification sub-codewords x of length n0 i0 and send them;

[0075] (2) In the t-th (t ∈ [2:K + 1]) stage, each active user encodes and generates message sub-codewords x of length n t-1 -n t-2 and send them; i(t-1) and send them;

[0076] (3) The receiver receives the result of the superposition of k sub-codewords and noise concatenates all the results received in the current t stages to obtain judges whether the target concatenated result meets the transmission stop condition. Specifically, checks whether the active user number estimation inequality holds. If it holds, it means that the number of active users is t - 1, and the receiver broadcasts a 1-bit signal 1 to all users, and the transmission ends; if it does not hold, it means that the number of active users is not 1, and the receiver broadcasts a 1-bit signal 0 to all users, and the transmission continues, repeating the steps until the transmission ends.

[0077] In one embodiment, if the transmission ends, determine the number of active users according to the number of transmission stages, and decode the received signal in the first stage to obtain the identity identification information of all active users, including:

[0078] S41: When the transmission stop condition is met, it indicates that the number of active users is k; where k = t - 1;

[0079] S42: Take the received signal obtained by the receiving end in the first stage as the target signal, that is

[0080] S43: Decode the target signal according to the approximate message passing decoding algorithm to obtain the decoding result, as follows:

[0081]

[0082] info0 = {L0, M0, P x , P z}

[0083] where is the decoding result, y is the target signal, A0 is the identity dictionary matrix, g AMP () is the approximate message passing decoding algorithm, info0 is the parameter information required for decoding the identity identification information, and it is fixed that L0 = 1 is the number of regions of the identity dictionary matrix, M0 is the number of columns in each region of the identity dictionary matrix, P x is the codebook power, P z is the channel noise power;

[0084] S44: Let The position index of the non-zero elements in be a, which indicates that user a is an active user in the current transmission process. Subtract the identity identification sub-codeword of active user a from the target signal as the new target signal, as follows:

[0085]

[0086] where is the identity identification information vector of active user a;

[0087] S45: Repeat the above steps S43 and S44 for k - 1 times until the identities of all k active users are determined;

[0088] The working principle and beneficial effects of the above technical solution: When judging the identity of active users, after the receiving end determines that the number of active users is k, according to Use the identity identification decoding scheme designed based on the AMP algorithm to judge the identity of active users; the specific process is as follows: First, run the decoding of the received signal in the first stage to obtain the decoding result, that is (y, A0, info0), when running for the first time Then, let If the position index of the non - zero element is a, it indicates that user a is an active user in the current transmission process. Denote the identity identification information vector of user a as Subtract the identity sub - codeword of user a from the signal to be decoded as the input for subsequent decoding, that is, the target signal; repeat the above steps k - 1 times until the identities of all k active users are determined.

[0089] In one embodiment, decode the received signals transmitted in the second stage to the t - th stage to obtain the message information of all active users, including:

[0090] S51: Concatenate the message dictionary matrices used by k active users during the transmission in the second stage to the t - th stage to obtain the concatenated dictionary matrix A k ;

[0091] S52: According to the concatenated dictionary matrix and the approximate message passing decoding algorithm, decode the concatenated result of the received signals received in the second stage to the t - th stage to obtain the decoding result, as follows:

[0092]

[0093] info = {L, M, P x , P z}

[0094] Among them, is the decoding result, corresponding to the message information of the active user; g AMP () is the approximate message passing decoding algorithm, is the concatenated result of the received signals received in the second stage to the t - th stage, A k is the concatenated dictionary matrix, info is the parameter information required for message information decoding, L is the number of regions of the message dictionary matrix, M is the number of columns per region of the message dictionary matrix, P x is the codebook power, P z is the channel noise power.

[0095] The working principle and beneficial effects of the above - mentioned technical solution: When decoding the received signal through the AMP algorithm, the specific process is as follows:

[0096] (1) Initialize the iteration number t = 0, the state evolution variable Among them, P z is the channel noise power, P x is the codebook power;

[0097] (2) Calculate the intermediate variable x t+1 , x t+1 The calculation formula of is as follows:

[0098]

[0099] Among them, the size of the estimation matrix U is M×L, denotes the element in the j-th row and l-th column of the matrix. The elements in U are all independently subject to a Gaussian distribution with a mean of 0 and a variance of 1; M is the number of columns in each region of the dictionary matrix, L is the number of regions of the dictionary matrix, n is the code length, and P xl denotes the power allocation of the codebook in the l-th region, and generally takes

[0100] (3): Calculate and store The calculation formula of is as follows:

[0101]

[0102] (4) Let t = t + 1, and repeat the above steps (2)(3) until τ t -τ t+1 < 0.005, and obtain the iteration number T = t + 1;

[0103] (5) Initialize t = 0, v -1 = 0, β 0 = 0; where v is the decoding intermediate variable, β is the decoding estimation result, and the superscript represents the iteration calculation round;

[0104] (6) Calculate v t , v t The calculation formula of is as follows:

[0105]

[0106] (7): Calculate The calculation formula of is as follows:

[0107]

[0108] Among them, j ∈ sec(l) means j ∈ {(l - 1)M + 1, …, lM}, A H denotes the transpose of the A matrix, η() is the estimation function, denotes the i-th bit of the result of the t-th round of iteration calculation, s j is the j-th bit of the input vector s of the estimation function,

[0109] (8) Let t = t + 1, and repeat the above steps (6)(7) until t = T;

[0110] (9) For the L regions of β T+1 , rewrite the largest element in each region as Rewrite other elements as 0. Output β T+1 , which is the decoding result.

[0111] In one embodiment, the present invention also provides some specific experimental data as follows:

[0112] As Figure 3 shown, when the total number of users K = 4 and the number of active users k = 2 in the system, the comparison of the probability of incorrect decoding after superimposing noises with different distributions using the coding transmission method proposed by the present invention. The identity identification codebook parameter is L0 = 1, M0 = 4, n0 = 64. It is set that the length of the message codeword transmitted in each stage is equal, that is, n2 - n1 = n1 - n0 = n = 2048. The message codebook parameters are L = 64, M = 32. The codebook power P of each user x = 3. The abscissa is the overall signal-to-noise ratio (SNR), and the calculation method is kP x / P z . The ordinate is the joint block error rate (JBLER). When k = 2, all information vectors are β1, β2. When and the number of active users and the identity judgment are correct, the decoding is correct; otherwise, the decoding is incorrect. The number of repetitions at each point during simulation is 50,000 times. The results shown in the graph are the probabilities of incorrect decoding in 50,000 times. Gaussian distributed noise, uniformly distributed noise, and Laplace noise are respectively selected as the channel noise. Other factors such as the noise mean, power, and all codebook parameters are the same during the simulation of the three channels. The results show that the JBLER hardly changes after superimposing different noises, and the communication performance is basically the same. Thus, it can be seen that the encoding and decoding scheme of the present invention has good robustness and has the same good communication performance for different channels.

[0113] As Figure 4 shown, the comparison of the probability of incorrect decoding between the random access channel coding transmission method proposed by the present invention and the multiple access channel (MAC) encoding and decoding method under the same conditions. In the random access channel system, the total number of users K = 4, the number of active users k = 2, and the identity identification codebook parameter is L0 = 1, M0 = 4, n0 = 64. It is set that the length of the message codeword transmitted in each stage is equal, that is, n2 - n1 = n1 - n0 = n = 2048. The message codebook parameters are L = 64, M = 32. The codebook power P of each user x = 3. The abscissa is the overall SNR, and the calculation method is kP x / P z . The ordinate is JBLER. When k = 2, all information vectors are β1, β2. Therefore, when When the number of active users and their identities are correctly determined, the decoding is correct; otherwise, it is incorrect. In a multiple access channel under the same conditions, the number of users and their identities are known, and the number of active users \(k = 2\). The code length \(n'=4160\), the codebook parameters \(L' = 128\), \(M' = 32\). The codebook power \(P\) x ' = 3, and the SNR is calculated as \(kP\) x ' / P z . The transmitted information vectors are \(\beta'_1,\beta'_2\), so when it is correct decoding; otherwise, it is incorrect decoding. During the simulation, the number of repetitions per point is 50,000 times, and the results shown in the graph are the probabilities of incorrect decoding for 50,000 times. The noise mean, power, and other factors are the same in both cases. The results show that the encoding and decoding scheme implemented by the present invention for a random access channel system with unknown number and identities of active users is basically consistent with the communication performance of a multiple access channel system with known number of active users under the same conditions. Thus, it can be seen that the encoding and decoding scheme of the present invention has good reliability and can maintain good communication performance while correctly determining the number and identities of active users.

[0114] As Figure 5 shown, the comparison of the incorrect decoding probabilities between the random access channel coding transmission method proposed by the present invention and the encoding and decoding method of a multiple access channel under the same conditions. In the random access channel system, the total number of users \(K = 4\), the number of active users \(k = 3\), and the identity codebook parameters are \(L_0 = 1\), \(M_0 = 4\), \(n_0 = 64\). It is set that the lengths of the message codewords transmitted in each stage are equal, that is, \(n_3 - n_2=n_2 - n_1=n_1 - n_0=n = 2048\). The message codebook parameters are \(L = 64\), \(M = 32\). The codebook power \(P\) x of each user is 2. The abscissa is the overall SNR, which is calculated as \(kP\) x / P z . The ordinate is the JBLER. In the case of \(k = 3\), all the information vectors are \(\beta_1,\beta_2,\beta_3\), so when and the number of active users and their identities are correctly determined, the decoding is correct; otherwise, it is incorrect decoding. In a multiple access channel under the same conditions, the number of users and their identities are known, and the number of active users \(k = 3\). The code length \(n'=6208\), the codebook parameters \(L' = 192\), \(M' = 32\). The codebook power \(P\) x ' = 2, and the SNR is calculated as \(kP\) x ' / P z . The transmitted information vectors are \(\beta'_1,\beta'_2,\beta'_3\), so when It is considered as correct decoding when [condition], otherwise it is decoding error. During simulation, the number of repetitions for each point is 10,000 times, and the result shown in the graph line is the probability of 10,000 decoding errors. Other factors such as the noise mean and power are the same in both cases. The results show that the encoding and decoding scheme implemented by the present invention for the random access channel system with unknown number and identity of active users is basically consistent with the communication performance of the multiple access channel system with known number of active users under the same conditions. Combining Figure Four and Figure Five the results, the encoding and decoding scheme of the present invention has good reliability for different numbers of active users, and can maintain good communication performance while correctly judging the number and identity of active users.

[0115] As Figure 6 shown, in the case where the total number of users K = 4 and the number of active users k = 2 in the system, the comparison of the incorrect decoding probability between the AMP joint decoding scheme proposed by the present invention and the theoretically optimal decoding scheme. The process of the theoretically optimal decoding scheme is to traverse all combinations of codewords in different user codebooks, and the result with the minimum Euclidean distance from the received signal is the decoding result. The identity identification codebook parameters are L0 = 1, M0 = 4, n0 = 64. It is set that the length of the message codeword transmitted in each stage is equal, that is, n2 - n1 = n1 - n0 = n. The message codebook parameters are L = 4, M = 4, and according to the relationship M L = 2 nR the actual code length n for each point of simulation is calculated. The codebook power P of each user x = 3, and the channel noise power P z = 6. The abscissa is the code rate R. The ordinate is JBLER. In the case of k = 2, all information vectors are β1, β2, so when and the number and identity of active users are correctly judged, it is considered as correct decoding, and other cases are all decoding errors. During simulation, the number of repetitions for each point is 10,000 times, and the result shown in the graph line is the probability of 10,000 decoding errors. The results show that the JBLER of the AMP joint decoding scheme proposed by the present invention is higher than that of the theoretically optimal decoding scheme, but the graph lines are relatively close and the performance difference is not significant.

[0116] As Figure 7 shown, in the case where the total number of users K = 4 and the number of active users k = 2 in the system, the comparison of the simulation time between the AMP joint decoding scheme proposed by the present invention and the theoretically optimal decoding scheme. The identity identification codebook parameters are L0 = 1, M0 = 4, n0 = 16. It is set that the length of the message codeword transmitted in each stage is equal, that is, n2 - n1 = n1 - n0 = n. The message codebook parameter M = 4, and L = 6, 7, 8 are taken respectively. According to the relationship M L = 2 nR the actual code length n for each point of simulation is calculated. The codebook power P of each user x = 3, and the channel noise power Pz = 6. The abscissa is the code length n, and the ordinate is the simulation time for one run of the encoding and decoding scheme. The results show that, under the condition that other factors such as the operating environment are the same, the AMP joint decoding scheme proposed by the present invention has a shorter simulation time and lower computational complexity compared with the theoretically optimal decoding scheme. Moreover, as the code length increases, the gap in the simulation time between the two schemes increases significantly, and the running time of the theoretically optimal decoding scheme is too long to be applied in an actual system. Considering Figure 6 and Figure 7 results, the decoding scheme proposed by the present invention has little difference in performance from the theoretically optimal decoding scheme, but the operation overhead and time overhead are significantly reduced, and it has a lower computational complexity.

[0117] To sum up, through analysis and simulation, it can be obtained that the present invention retains the advantages of sparse regression codes in the random access channel. The codebook design does not depend on the channel noise distribution, and the performance is quite good for different distributions of channel noise, having a certain robustness. In the random access channel system where the number and identity of users are unknown, it can achieve communication performance comparable to that of the multiple access channel system with known user identities. It has a lower computational complexity and meets the requirements of actual applications.

[0118] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A low-complexity random access channel encoding and decoding method based on sparse regression codes, characterized in that It includes the following steps: S1: Obtain the information to be sent of all active users through the sender; among them, the information to be sent includes: identity identification information and message information; S2: Encode and transmit the information to be sent, repeating multiple stages; among them, use the same identity dictionary matrix to encode the identity identification information to obtain identity identification sub-codewords, and use the message dictionary matrix corresponding to each active user in each stage to encode the message information to obtain message sub-codewords; S3: Obtain the received signal through the receiver and determine whether the entire transmission process ends after the t-th stage; among them, the received signal transmitted in the first stage is the superposition of multiple identity identification sub-codewords and noise; the received signals transmitted in the second stage to the t-th stage are all the superposition of multiple message sub-codewords and noise; S4: If the transmission ends, determine the number of active users according to the number of transmission stages, and decode the received signal in the first stage to obtain the identity identification information of all active users; S5: Decode the received signals transmitted in the second stage to the t-th stage to obtain the message information of all active users.

2. The low-complexity random access channel encoding and decoding method based on sparse regression code according to claim 1, characterized in that The multi-stage encoding and transmission of the information to be sent includes: S21: In the first stage, encode the identity identification information of all active users according to the same identity dictionary matrix to obtain identity identification sub-codewords with a code length of n0 and transmit them, as follows: where x i0 is the identity identification sub-codeword of the i-th active user, A0 is the identity dictionary matrix, is the identity identification information vector of the i-th active user, and K is the total number of corresponding users at the sending end; S22: In the t-th stage, the message information is encoded according to the message dictionary matrix corresponding to each active user in this stage, and the message sub-codewords with a code length of n t-1 -n t-2 are obtained and transmitted as follows: x i(t-1) = A i(t-1) β i where x i(t-1) is the message sub-codeword transmitted in the t-th phase, A i(t-1) is the message dictionary matrix used by the i-th active user during encoding in the t-th phase, β i is the information vector corresponding to the message information of the i-th active user, t ∈ [2:K+1], and t is a positive integer.

3. The low-complexity random access channel encoding and decoding method based on sparse regression code according to claim 1, characterized in that Obtain the received signal through the receiver and determine whether the entire transmission process ends after the t-th stage, including: S31: Concatenate the received signals in the first stage to the t-th stage of the receiver to obtain a target concatenation result; S32: Determine whether the target concatenation result meets the transmission stop condition, and the transmission stop condition is as follows: Among them, is the target splicing result, λ t-1 ∈[λ1, λ2, …, λ K , [λ1, λ2, …, λ K is a set of positive real numbers, and P x is the codebook power; S33: If the transmission stop condition is met, the receiver broadcasts a 1-bit signal 1 to all users, and the transmission ends; if the transmission stop condition is not met, the receiver broadcasts a 1-bit signal 0 to all users, and the transmission continues. Repeat step S22, step S31, and step S32 until the transmission ends.

4. The low-complexity random access channel encoding and decoding method based on sparse regression code according to claim 1, characterized in that If the transmission ends, determine the number of active users according to the number of transmission stages, and decode the received signal in the first stage to obtain the identity identification information of all active users, including: S41: When the transmission stop condition is met, it means that the number of active users is k; where k = t - 1; S42: Use the received signal obtained by the receiving end in the first stage as the target signal, that is S43: Decode the target signal according to the approximate message passing decoding algorithm to obtain the decoding result, as follows: info0 = {L0, M0, P x , P z} Among them, is the decoding result, y is the target signal, A0 is the identity dictionary matrix, and g AMP () is the approximate message passing decoding algorithm, info0 is the parameter information required for identity identification information decoding, and L0 is fixed to be 1 as the number of regions of the identity dictionary matrix, M0 is the number of columns per region of the identity dictionary matrix, and P x is the codebook power, and P z is the channel noise power; S44: Set If the position index of the non-zero element in it is a, it indicates that user a is an active user in the current transmission process. Subtract the identity identification sub-codeword of active user a from the target signal to obtain a new target signal as follows: Among them, is the identity identification information vector of active user a; S45: Repeat the above steps S43 and S44 k - 1 times until the identities of all k active users are determined.

5. The low-complexity random access channel encoding and decoding method based on sparse regression code according to claim 1, characterized in that Decode the received signals transmitted in the second to the t-th phase to obtain the message information of all active users, including: S51: Concatenate the message dictionary matrices used by k active users during the transmission from the second stage to the t-th stage to obtain the concatenated dictionary matrix A k ; S52: According to the spliced dictionary matrix and the approximate message passing decoding algorithm, decode the splicing result of the received signals received in the second to the t-th phase to obtain the decoding result as follows: info = {L, M, P x , P z} Among them, is the decoding result, corresponding to the message information of the active user; g AMP () is the approximate message passing decoding algorithm, is the concatenation result of the received signals received from the second stage to the t-th stage, A k is the concatenation dictionary matrix, info is the parameter information required for message information decoding, L is the number of regions of the message dictionary matrix, M is the number of columns per region of the message dictionary matrix, P x is the codebook power, P z is the channel noise power.

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