Simplified iterative detection decoding method, device, electronic device and storage medium

Abstract

The present invention provides a simplified iterative detection and decoding method, device, electronic device, and storage medium, wherein the method comprises: obtaining a received signal of a MIMO system and an original constellation point set used to transmit the signal in the MIMO system; screening the constellation points in the original constellation point set based on the cavity distribution and prior probability of the constellation points in the original constellation point set to obtain a screened constellation point set; based on the screened constellation point set, combining external information detected and prior information of the decoder, updating the prior parameters of the detector, and iterating the detection and decoding process to obtain an iterative detection and decoding result. Screening the constellation points in the joint processing of internal and external iterations of detection can reduce the number of constellation points that need to be traversed, thereby significantly reducing the computational complexity, making the received signal solution process more efficient, and realizing a low-complexity iterative detection and decoding process.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a simplified iterative detection decoding method, device, electronic device and storage medium. Background Art

[0002] Massive Multiple-Input-Multiple-Output (MIMO) wireless communications are a key enabling technology for future communications. Leveraging the spatial dimension, they significantly enhance system diversity or multiplexing gain, significantly increasing system capacity and improving energy efficiency. However, the large-scale antenna arrays in MIMO systems lead to a surge in the amount of data required for signal processing. The iterative detection and decoding process used to improve signal detection accuracy in MIMO systems involves complex computational operations such as matrix inversion and exponential calculations. These large amounts of data increase the computational complexity of this process.

[0003] How to implement low-complexity iterative signal detection and decoding methods in MIMO wireless communications is an important issue that needs to be urgently addressed in the industry. Summary of the Invention

[0004] The present invention provides a simplified iterative detection decoding method, device, electronic device and storage medium for realizing low-complexity signal detection in MIMO wireless communication.

[0005] The present invention provides a simplified iterative detection decoding method, comprising the following steps:

[0006] Acquire a received signal of a multiple-input multiple-output (MIMO) system and an original constellation point set used for transmitting signals in the MIMO system;

[0007] In an inner iteration of the detection of approximate expected propagation, constellation points in the original constellation point set are screened based on the cavity distribution of the constellation points in the original constellation point set to obtain a screened constellation point set;

[0008] performing approximate expected propagation detection based on the constellation points in the screened constellation point set to update the cavity distribution of the constellation points in the screened constellation point set;

[0009] In the outer iterative detection and decoding joint processing, the constellation points in the screened constellation point set are screened based on the updated cavity distribution and the prior probabilities of the constellation points in the screened constellation point set to obtain two further screened constellation point sets. The final constellation point set is selected in combination with the detected outer information and the prior information of the decoder to update the prior parameters of the detector. The detection and decoding processes are then iterated to obtain an iterative detection and decoding result.

[0010] According to a simplified iterative detection decoding method provided by the present invention, constellation points in the filtered constellation point set are filtered based on the updated cavity distribution and the prior probabilities of the constellation points in the filtered constellation point set to obtain two further filtered constellation point sets, including:

[0011] Based on the Gaussian distribution characteristics of the updated cavity distribution, the constellation points in the filtered constellation point set are filtered to obtain a filtered point set based on the cavity distribution;

[0012] sorting and screening the constellation points in the filtered constellation point set based on the prior probabilities of the constellation points in the filtered constellation point set output by the decoder to obtain a constellation point set based on the prior probabilities;

[0013] The screening point set based on cavity distribution and the constellation point set based on prior probability are used as the two constellation point sets after further screening.

[0014] According to a simplified iterative detection decoding method provided by the present invention, the constellation points in the filtered constellation point set are filtered based on the Gaussian distribution characteristics of the updated cavity distribution to obtain a filtered point set based on the cavity distribution, including:

[0015] Based on the Gaussian distribution characteristics of the updated cavity distribution, the values ​​of each constellation point in the filtered constellation point set are compared with the mean value of the updated cavity distribution, and the constellation points in the filtered constellation point set are filtered to obtain a filtered point set based on the cavity distribution.

[0016] According to a simplified iterative detection decoding method provided by the present invention, the constellation points in the filtered constellation point set are filtered to obtain a filtered point set based on cavity distribution, including:

[0017] The constellation points in the screened constellation point set are screened according to the updated cavity distribution obtained by the detector to obtain top N constellation points in the screened constellation point set whose cavity distribution probabilities are sorted from largest to smallest.

[0018] According to a simplified iterative detection decoding method provided by the present invention, based on the prior probabilities of the constellation points in the filtered constellation point set output by a decoder, the constellation points in the filtered constellation point set are sorted and filtered to obtain a constellation point set based on the prior probabilities, including:

[0019] extracting the probability of each bit value from the bit sequence of each constellation point in the filtered constellation point set, and constructing a bit vector of the constellation point with the maximum probability in the filtered constellation point set based on the probability of each bit value;

[0020] dividing the filtered constellation point set into a plurality of constellation point subsets based on a Hamming distance between a bit value of each constellation point in the filtered constellation point set and the bit vector;

[0021] Based on the size of the prior probability, the constellation point subsets obtained by division are sorted and screened to obtain a constellation point set based on the prior probability.

[0022] According to a simplified iterative detection decoding method provided by the present invention, based on the size of the prior probability, the constellation point subsets obtained by division are sorted and screened to obtain a constellation point set based on the prior probability, including:

[0023] The constellation point subsets obtained by division are screened, and the top N constellation points sorted from large to small in prior probability are retained to obtain a constellation point set based on prior probability.

[0024] The present invention also provides a simplified iterative detection decoding device, comprising the following modules:

[0025] A receiving module, configured to obtain a received signal of a multiple-input multiple-output (MIMO) system and an original constellation point set used to transmit a signal in the MIMO system;

[0026] A screening module is configured to screen the constellation points in the original constellation point set based on the cavity distribution of the constellation points in the original constellation point set in an inner iteration of the detection of the approximate expected propagation, so as to obtain a screened constellation point set;

[0027] an updating module, configured to perform approximate expected propagation detection based on the constellation points in the screened constellation point set, so as to update the cavity distribution of the constellation points in the screened constellation point set;

[0028] a joint processing module for screening the constellation points in the screened constellation point set in the outer iterative detection and decoding joint processing based on the updated cavity distribution and the prior probabilities of the constellation points in the screened constellation point set to obtain two further screened constellation point sets, and selecting a final constellation point set in combination with the detected outer information and the prior information of the decoder to update the prior parameters of the detector, and iterating the detection and decoding processes to obtain an iterative detection and decoding result.

[0029] The present invention also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the simplified iterative detection decoding method described above is implemented.

[0030] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the simplified iterative detection and decoding method described above is implemented.

[0031] The present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the simplified iterative detection and decoding methods described above.

[0032] The simplified iterative detection and decoding method, device, electronic device, and storage medium provided by the present invention screen the constellation points in the original constellation point set based on the cavity distribution of the constellation points in the original constellation point set and the prior probabilities of the constellation points in the screened constellation point set to obtain a screened constellation point set. Based on the screened constellation point set, the prior parameters of the detector are updated in combination with external information detected and prior information of the decoder. The detection and decoding processes are iterated, and the constellation points are screened in the joint processing of internal and external iterations of detection. This can reduce the number of constellation points that need to be traversed during the iterative detection and decoding process, thereby significantly reducing computational complexity, making the signal solution process more efficient, and realizing a low-complexity iterative detection and decoding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 It is a flow chart of the simplified iterative detection and decoding method provided by the present invention.

[0035] Figure 2 This is a schematic diagram of bit error rate performance comparison provided by the present invention.

[0036] Figure 3 It is a structural diagram of a receiver applying the simplified iterative detection decoding method provided by the present invention.

[0037] Figure 4 This is a schematic diagram of the architecture corresponding to the EMU provided by the present invention.

[0038] Figure 5 This is a schematic diagram of the bit-based PDU architecture provided by the present invention.

[0039] Figure 6 This is a schematic diagram of the timing arrangement of the receiver provided by the present invention.

[0040] Figure 7 It is a schematic diagram of the quantitative performance under different outer loop times provided by the present invention.

[0041] Figure 8It is a structural diagram of a simplified iterative detection decoding device provided by the present invention.

[0042] Figure 9 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0044] Figure 1 This is a flow chart of a simplified iterative detection decoding method provided by the present invention, such as Figure 1 As shown, the method includes the following:

[0045] Step 110: obtaining a received signal of a multiple-input multiple-output (MIMO) system and an original constellation point set used for transmitting signals in the MIMO system;

[0046] Step 120, in an inner iteration of the detection of approximate expected propagation, screening the constellation points in the original constellation point set based on the cavity distribution of the constellation points in the original constellation point set to obtain a screened constellation point set;

[0047] Step 130: performing approximate expected propagation detection based on the constellation points in the screened constellation point set to update the cavity distribution of the constellation points in the screened constellation point set;

[0048] Step 140: In the outer iterative detection and decoding joint processing, based on the updated cavity distribution and the prior probabilities of the constellation points in the screened constellation point set, the constellation points in the screened constellation point set are screened to obtain two further screened constellation point sets. The final constellation point set is selected to update the prior parameters of the detector by combining the detected outer information and the prior information of the decoder. The detection and decoding processes are then iterated to obtain an iterative detection and decoding result.

[0049] The simplified iterative detection and decoding method provided by the present invention can be executed by an electronic device, a component of an electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. Exemplary mobile electronic devices include mobile phones, tablet computers, laptop computers, PDAs, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), while non-mobile electronic devices include servers, network attached storage (NAS), or personal computers (PCs), although this invention does not impose specific limitations thereon.

[0050] The following takes a computer executing the simplified iterative detection decoding method provided by the present invention as an example to describe the technical solution of the present invention in detail.

[0051] MIMO systems use multiple transmit and receive antennas for signal transmission. In a typical M×N MIMO system, the transmitter has M transmit antennas and the receiver has N receive antennas. The core advantage of MIMO technology lies in its ability to enhance system performance through spatial multiplexing and spatial diversity.

[0052] At the transmitter, data first undergoes modulation. The goal of modulation is to map the bit sequence to specific signal constellation points. These constellation points belong to a set called the original constellation point set. Modulation methods include QPSK (Quaternary Phase Shift Keying) and 16-QAM (16-QAM).

[0053] The original constellation point set is a set of predefined signal points, each of which corresponds to a bit sequence. For example, for 16-QAM, the constellation point set contains 16 different complex values, each of which corresponds to a 4-bit sequence.

[0054] Specifically, in step 110, a system model equation of the MIMO system is pre-constructed.

[0055] Assume that the MIMO system uses polarization code encoding and the number of transmitting antennas is , the number of receiving antennas is At the receiving end, the source code is generated by the matrix The codeword is obtained by encoding, and after interleaving and modulation, the transmitted symbol vector is obtained Based on the real domain model, the channel matrix Size , for vector, , each symbol in the vector is from -QAM modulation constellation point set Select , .in, represents the modulation order, M represents the number of constellation points, is the original constellation point set used to send signals in the MIMO system.

[0056] Assume that the received signal of the MIMO system is , for Received signal vector, Indicates that the mean is 0 and the variance The additive white Gaussian noise is the channel noise, and the constructed system model equation is shown in formula (1):

[0057] (1);

[0058] In step 120, the specific process of the approximate expected propagation (EP) detection algorithm is as follows:

[0059] According to Bayes' theorem, and The joint posterior probability distribution of is:

[0060] (2);

[0061] in Indicates the mean , the variance is Gaussian distribution, It means that I is the identity matrix with diagonal 1 and dimension is ; is the prior distribution, initialized to , is the indicator function, expressed as:

[0062] (3);

[0063] The indicator function of formula (3) represents the value of the transmitted symbol The value is 1 when the constellation point is in the set, and 0 when the constellation point is not in the set. In subsequent iterations, the prior distribution is updated by the prior LLR output by the decoder. EP replaces the prior with a non-normalized Gaussian distribution , for The variance of for The mean of , then the posterior probability is approximately a Gaussian distribution family function:

[0064] (4);

[0065] in, , The approximate joint posterior distribution probability of x and y is The mean and variance It can be calculated as:

[0066] (5);

[0067] Among them, the intermediate , intermediate amount Then the marginal posterior probability ,in, yes The mean of yes The variance of .

[0068] First, calculate the first constellation point in the original constellation point set. dimensional initial cavity distribution , superscript 1 indicates the initial cavity distribution, , and It is The mean and variance of the dimensional initial cavity distribution are expressed as follows:

[0069] (6);

[0070] Optionally, based on the Find function, the constellation points in the original constellation point set may be screened according to the cavity distribution of the constellation points in the original constellation point set. The specific implementation process of the constellation point screening may be:

[0071] (7);

[0072] function Reservation Medium probability The largest N It is understandable that according to the Gaussian distribution characteristics, the closer the constellation point is to the mean, the better the cavity distribution is. Therefore, the function in formula (7) It can be simplified to:

[0073] (8);

[0074] Select constellation points constitute the filtered constellation point set , making Minimum.

[0075] In step 130, based on the constellation point set obtained in step 120 , perform inner iteration of approximate expected propagation detection, and update the cavity distribution of constellation points in the screened constellation point set during the inner iteration process.

[0076] Specifically, assuming the number of iterations is s , the specific process of inner iteration is:

[0077] Combined with discrete prior distribution Get an approximate distribution , superscript s Indicates the s Inner iterations, while calculating the first moment of the approximate distribution , the expression is as follows:

[0078] (9);

[0079] in, , for The index of the constellation point.

[0080] Subsequently, the cavity distribution is updated , that is, to calculate the first-order moment of the cavity distribution and second-order moment , the expression is as follows:

[0081] (10);

[0082] Among them, the intermediate quantity v=diag(A), is the damping factor.

[0083] According to the updated cavity distribution , according to formula (8) in step 120, the constellation points are screened and the obtained ,Right now:

[0084] (11);

[0085] Repeat step 130 to perform iteration within the approximate expected propagation detection to update the cavity distribution of the constellation points in the screened constellation point set.

[0086] In step 140, in the outer iterative detection and decoding joint process, the updated cavity distribution outputted by the approximate expected propagation detection is and the prior probability , respectively filter the constellation points in the filtered constellation point set to obtain two further filtered constellation point sets.

[0087] First, based on the updated cavity distribution Further screening can obtain the screening point set based on cavity distribution. The specific process is as follows:

[0088] Further filter the constellation point set The constellation points in , namely:

[0089] (12);

[0090] Based on calculate and ,in:

[0091] (13);

[0092] in, , for The index of the constellation point.

[0093] The decoder performs the decoding process and outputs the prior information , express No. bits, so the prior probability It can be calculated as:

[0094] (14);

[0095] Then, according to Filter constellation point set , , further screen the constellation point set to obtain the constellation point set based on prior probability ,Right now:

[0096] (15);

[0097] The constellation point set based on cavity distribution and the constellation point set based on prior probability As the two constellation point sets obtained after further screening.

[0098] Furthermore, in order to reduce the comparison complexity, a reduced set of candidate constellation points can be constructed based on bits. The specific process is as follows:

[0099] A constellation point is The bit vector is mapped to 0 or 1 corresponding to each bit. is the constellation point with the highest mapping probability ,Right now Record The most probable bit value:

[0100] (16);

[0101] (17);

[0102] in, is a hard decision function, which is obtained by Log-likelihood ratio Make a hard judgment, you can get vector.

[0103] According to the constellation point bit value and The Hamming distance can be Constellation points are divided into disjoint subsets , :

[0104] (18);

[0105] in, Indicates constellation points and The Hamming distance of The smaller, The constellation points in The smaller the Hamming distance of is, the greater the probability is. , , then formula (15) can be simplified to:

[0106] (19);

[0107] Therefore, when selecting constellation points, Sort instead of , comparing the complexity from Reduce to ,in Indicates the complexity order.

[0108] The calculated value in formula (13) and the detector output For comparison, if , then the final constellation point set is selected as ,Depend on and Update the detector's prior parameters and , calculated as follows,

[0109] (20);

[0110] if , then the final constellation point set is selected as , first calculate the two intermediate quantities of Gaussian projection and ,

[0111] (twenty one);

[0112] in, , for The index of the constellation point.

[0113] Then update the prior parameters of the detector and , calculated as follows:

[0114] (twenty two);

[0115] in is the scaling factor.

[0116] At present, an external iteration process has been completed, and the updated prior parameters and Substitute into formula (5), return to step 120 to perform the next outer iteration, and after the iteration is completed, obtain the iterative detection decoding result.

[0117] The performance and complexity of the iterative detection decoding process are analyzed. The specific analysis process is as follows:

[0118] Located in the inner ring EPA detector, The number of constellation points is set to In the joint processing, and The number of constellation points is set to and The number of inner iterations is , the number of outer iterations is . Then the number of selected constellation points can be expressed as The calculation of constellation moment information in the original EPA IDD includes Sub-exponential operations and multiplication operations.

[0119] By screening the constellation point set, the amount of calculation required for constellation point moment information is reduced to Sub-exponential operations and To further reduce the complexity, the matrix inversion is implemented by weighted Neumann series approximation (wNSA).

[0120] The simulation results and analysis are given to verify the effectiveness of iterative detection decoding based on approximate expected propagation (sEPA IDD). Consider a polar code with a code length of 1024 and a code rate of 0.5 and a non-reset BP decoder with two iterations. The number of cycles of all IDD algorithms is set to the outer loop number. T =3 and the number of inner loops S =1. Figure 2 This is a schematic diagram of the bit error rate performance comparison provided by the present invention. Figure 2 The sEPA IDD was compared with MMSE-PIC IDD, MPD IDD and EPA IDD in , 16QAM system and , BER (bit error rate) performance under 256QAM system. And compared the BER performance of sEPA IDD using wNSA. Figure 2 In the left part (a), The sEPA IDD can achieve performance close to that of EPA IDD with negligible loss, while reducing the computational complexity of constellation moment information by 57%. In addition, by introducing three wNSA terms, sEPA IDD maintains performance similar to that of EPA IDD while achieving BER= Under the condition of 0.5dB better than MMSE-PIC IDD. Figure 2 In the right part (b), The sEPA IDD shows performance close to that of EPA IDD while saving 87% of operations. Under 12-term wNSA, the loss of sEPA IDD is about 0.1 dB, but at BER= The loss of sEPA IDD is 0.7dB better than that of MPD IDD in (a) and (b). In addition, sEPA IDD outperforms MPD IDD in both (a) and (b), while MPD IDD fails to converge under heavy antenna loading and high modulation.

[0121] Understandably, in a MIMO (Multiple Input, Multiple Output) system, determining the received signal is a complex process that typically involves traversing all possible transmitted symbols to determine the most likely transmitted signal. Because signals are affected by noise as they propagate through the channel, the probability distribution of the received signal is often concentrated near a few constellation points rather than being evenly distributed across the entire constellation. Therefore, traversing the original set of constellation points can incur unnecessary computational overhead.

[0122] By screening the original constellation point set, a simplified constellation point set can be obtained. The simplified constellation point set only contains constellation points with higher probabilities.

[0123] During the signal solution process, traversing all original constellation points generates a significant computational load, especially in high-order modulation or massive MIMO systems. This computational complexity increases exponentially with the number of constellation points. By selecting high-probability constellation points, the number of constellation points to be traversed can be reduced, significantly reducing computational complexity and making the received signal solution process more efficient.

[0124] Because the streamlined constellation point set contains fewer constellation points, the computational effort required to solve the received signal is significantly reduced, resulting in a faster solution. This is particularly important for communication systems requiring real-time processing or low latency, enabling the system to demodulate the transmitted signal more quickly.

[0125] In high-order modulation and massive MIMO systems, some complex detection algorithms (such as maximum likelihood detection) can be computationally difficult to implement due to their high complexity. Using a streamlined constellation point set can make these complex algorithms more feasible in practical systems, improving their practicality and scalability.

[0126] The simplified iterative detection and decoding method provided by the present invention screens the constellation points in the original constellation point set according to the cavity distribution of the constellation points in the original constellation point set and the prior probabilities of the constellation points in the screened constellation point set to obtain a screened constellation point set. Based on the screened constellation point set, the method combines external information from detection and prior information of the decoder to update the prior parameters of the detector, iterates the detection and decoding processes, and screens the constellation points in the joint processing of internal and external iterations of detection. This method can reduce the number of constellation points that need to be traversed in the iterative detection and decoding process, thereby significantly reducing the computational complexity, making the signal solution process more efficient, and realizing a low-complexity iterative detection and decoding process.

[0127] In one embodiment, based on the updated cavity distribution and the prior probabilities of the constellation points in the filtered constellation point set, constellation points in the filtered constellation point set are filtered to obtain two further filtered constellation point sets, including: filtering the constellation points in the filtered constellation point set based on the Gaussian distribution characteristics of the updated cavity distribution to obtain a filtered point set based on the cavity distribution; sorting and filtering the constellation points in the original constellation point set based on the magnitude of the prior probabilities of the constellation points in the filtered constellation point set output by a decoder to obtain a constellation point set based on the prior probabilities; and using the filtered point set based on the cavity distribution and the constellation point set based on the prior probabilities as the two further filtered constellation point sets.

[0128] In MIMO systems, the distribution of received signal constellation points is often affected by channel characteristics and noise, resulting in the actual received signal points tending to be concentrated in certain areas of the constellation diagram. These areas typically correspond to the original constellation points of the transmitted signal but are perturbed by channel distortion and noise. To optimize the detection and demodulation of the received signal, the Gaussian distribution characteristics of the updated cavity distribution and the prior probabilities of the constellation points can be used to select the constellation point set.

[0129] The parameters of the Gaussian distribution around each original constellation point are estimated by statistical methods (such as maximum likelihood estimation, moment estimation, etc.).

[0130] Based on the parameters corresponding to the estimated Gaussian distribution characteristics, high-probability regions are determined around each original constellation point. These regions are typically circular or elliptical areas centered on the original constellation point and with the variance as the radius. Only received signal points within these high-probability regions are retained as candidate points, forming a screening point set based on the cavity distribution.

[0131] Based on the prior probability of the bit values ​​corresponding to the constellation points, the bit vector corresponding to the constellation point with the highest prior probability can be obtained by hard decision on the log-likelihood ratio of the bits. The original constellation point set is divided into several subsets based on the Hamming distance between the bit sequence of the constellation points in the original constellation point set and the bit vector. Based on the subsets, further screening is performed based on the prior probability of each constellation point. Specifically, only the points with the highest prior probability are retained. constellation points, and obtain a screening point set based on prior probability. In this way, the number of constellation points that need to be traversed can be further reduced, while retaining those constellation points that are most likely to be transmitted.

[0132] The obtained screening point set based on cavity distribution and the screening point set based on prior probability are used as two constellation point sets after screening.

[0133] The simplified iterative detection and decoding method provided by the present invention, based on the Gaussian distribution characteristics and the screening method of prior probability, can effectively reduce redundant calculations in the received signal solution process in the MIMO system and improve the accuracy and efficiency of signal detection and decoding.

[0134] In one embodiment, based on the Gaussian distribution characteristics of the updated cavity distribution, constellation points in the filtered constellation point set are filtered to obtain a filtered point set based on the cavity distribution, including: comparing the value of each constellation point in the filtered constellation point set with the mean value of the updated cavity distribution based on the Gaussian distribution characteristics of the updated cavity distribution, filtering the constellation points in the filtered constellation point set to obtain the filtered point set based on the cavity distribution. The constellation points in the filtered constellation point set are filtered according to the updated cavity distribution obtained by the detector to obtain the filtered point set based on the cavity distribution, the filtered point set based on the cavity distribution being used to retain the top N constellation points in the filtered constellation point set sorted in descending order of cavity distribution probability.

[0135] The constellation points in the filtered constellation point set are filtered according to the Gaussian distribution characteristics of the updated cavity distribution to obtain a filtered point set based on the cavity distribution, which is used to retain the top N constellation points in the filtered constellation point set sorted in descending order of the cavity distribution probability of the constellation points.

[0136] Gaussian distribution, also known as normal distribution, is a common probability distribution. Its characteristics are that the mean and variance of the data point distribution can describe the central tendency and dispersion of the data.

[0137] From the filtered constellation point set, the top N constellation points, ranked by prior probability from highest to lowest, are selected. The probabilities of these points are relative to the updated cavity distribution. By leveraging the properties of the Gaussian distribution and comparing the current signal value with the mean of the updated cavity distribution, a new point set, called the cavity distribution-based filtered point set, can be quickly obtained. Each point in this point set has a high probability, meaning it is more likely to appear under the updated cavity distribution.

[0138] The simplified iterative detection decoding method provided by the present invention screens the constellation points in the screened constellation point set based on the properties of Gaussian distribution to obtain a screening point set based on cavity distribution, thereby realizing a rapid determination process of the screening point set based on cavity distribution.

[0139] In one embodiment, based on the prior probabilities of the constellation points in the filtered constellation point set output by a decoder, the constellation points in the filtered constellation point set are sorted and screened to obtain a constellation point set based on prior probabilities, including: extracting the probability of each bit value from the bit sequence of each constellation point in the filtered constellation point set, and constructing a bit vector of the constellation point with the highest probability in the filtered constellation point set based on the probability of each bit value; dividing the filtered constellation point set into multiple constellation point subsets based on the Hamming distance between the bit values ​​of each constellation point in the filtered constellation point set and the bit vector; sorting and screening the resulting constellation point subsets based on the prior probabilities to obtain a constellation point set based on prior probabilities, the constellation point set based on prior probabilities being used to retain the top N constellation points in the filtered constellation point set sorted from largest to smallest in terms of prior probabilities. The resulting constellation point subsets are screened to retain the top N constellation points in the sorted constellation point set from largest to smallest in terms of prior probabilities, to obtain the constellation point set based on prior probabilities.

[0140] The method of screening the constellation points of the constellation point subset based on the size of the prior probability to obtain a screening point set based on decoding includes: sorting the prior probabilities of the constellation point subset to screen the constellation points in the constellation point subset to obtain a screening point set based on detection, and retaining the constellation points with the largest prior probability. A constellation point.

[0141] For each constellation point in the original constellation point set, it is usually composed of one or more bits (for example, in QPSK, each constellation point is represented by 2 bits; in 16-QAM, each constellation point is represented by 4 bits).

[0142] For each bit, calculate the probability of it being 0 or 1. The probability can be calculated by the distance between the received signal and the constellation point, or by the log-likelihood ratio (LLR) obtained by soft decision.

[0143] After decoding, the decoder outputs the log-likelihood ratio of the bit. After calculating the probability of each bit being 0 or 1, the prior probability of each constellation point in the corresponding constellation point set can be calculated through the modulation mapping relationship.

[0144] Calculation of prior probabilities: The prior probabilities of each constellation point are calculated using the bit value probabilities in the bit vector. Prior probabilities are usually calculated based on the relationship between the probability of the bit vector and the mapping of constellation points.

[0145] By performing a hard decision on the log-likelihood ratio of the bits, the decision is made to be 0 when the log-likelihood ratio is greater than or equal to 0, and to be 1 when the log-likelihood ratio is less than 0. This method obtains the bit vector corresponding to the constellation point with the highest a priori probability. The original constellation point set is divided into several constellation point subsets based on the Hamming distance between the bit vector corresponding to the constellation point in the original constellation point set and the bit vector. The larger the Hamming distance corresponding to the subset, the lower the a priori probability of the constellation point in the subset. The subsets are sorted to select the top N constellation points with the highest a priori probability, thereby obtaining a constellation point set based on the a priori probability.

[0146] The simplified iterative detection decoding method provided by the present invention is based on the fact that by retaining constellation points with high prior probability, the selection of the constellation point set is improved, thereby enhancing the signal transmission effect and improving the reliability of the system.

[0147] The present invention also provides a schematic structural diagram of a receiver using the simplified iterative detection decoding method provided by the present invention, as shown in FIG. Figure 3 As shown, the device is for code length 1024 polar code encoding 16-QAM modulation For massive MIMO systems, a hardware architecture of a receiver based on sEPA IDD is proposed.

[0148] Set the number of selected constellation points to The overall architecture of the receiver can be divided into four parts: EPA detector, BP decoder, interface unit and joint processing unit. The detection storage unit is used to store the prior parameters. 、 and external information parameters The internal storage unit of the BP decoder is used to store R / L LLR. In order to achieve high area efficiency and data throughput, the architecture adopts a deep pipeline. By properly configuring the computational parallelism and utilizing module reuse, the data set is divided into 32 data sets. The interface unit is responsible for the information conversion between the detector and the decoder, and the unified external information unit (EMU) is responsible for calculating the external information bit LLR and , bit-based probability distribution unit (PDU) is responsible for In addition, the Serial Input Parallel Output (SIPO) register and the Parallel Input Serial Output (PISO) register are responsible for aligning the decoder data stream with the rest of the data stream. To support hardware sharing, Compress to variable In the case of decoding, the data is decompressed by EMU. Therefore, there is no need to store 32 groups of , only need to store , thus saving a lot of memory overhead. The joint processing unit is responsible for the calculation of moment information and prior parameters 、 Updates.

[0149] Unified EMU is responsible for processing different inputs Message: (1) Calculate the external information bit LLR, and then pass it to the decoder through the SIPO register, such as Figure 3 As shown by the dotted line in . (2) Decompression , which is then passed to the joint processing unit, such as Figure 3 As shown by the dotted line in .

[0150] External information bit LLR It can be approximated by the max-log algorithm, and its expression is as follows:

[0151] (twenty three);

[0152] in, express No. bit value, Indicates the first Rank Column elements, Indicates the The bit value is The constellation point set, formula (23) can be implemented by piecewise function.

[0153] In the MM of the joint information calculation part, since ,set up ,but and The ratio is:

[0154] (twenty four);

[0155] Probability and Can be obtained from According to observation, there are similarities between the calculation formulas of formula (23) and formula (24), which can realize a unified module design. , , and Corresponding to the similar parts in formula (23) and formula (24), the present invention derives that under different modulation and The unified piecewise linear function is shown in Table 1. Among them, the architecture diagram corresponding to EMU is as follows Figure 4 The architecture diagram corresponding to the EMU provided by the present invention is shown as follows.

[0156] Table 1. and Unified function table

[0157]

[0158] Description of the bit-based PDU architecture: Figure 5 The present invention provides a bit-based PDU architecture diagram, which has three main functions: the first is the prior probability calculation, the second is the bit-based constellation point set screening, and the third is the MM prior probability matching. In the GP of joint information calculation, The PDU module calculates the prior , and stored in the buffer. At the same time, the bit LLR is operated to obtain two constellation point subsets and ,in Contains only the constellation points with the maximum prior probability , include and Compare the candidate constellation points of the two second most likely constellation points. and After the probability of , we get the constellation point with the second highest probability At the same time, PDU also needs to provide MM The prior probability of the corresponding constellation point.

[0159] Analysis and comparison of hardware implementation results: Figure 6 The timing arrangement diagram of the receiver provided by the present invention shows the timing arrangement of the sEPA IDD receiver. 32 sets of data are serially input in a pipeline form. After 24 clock cycles, the EPA detector converts the 32 sets of data into the 32 sets of data in a clock cycle. Output to EMU and detection storage unit, at this time EMU is used to calculate the external information bit LLR. After waiting for 34 clock cycles, the SIPO register will input the stored 32 groups of external information bit LLRs in parallel to the BP decoder. The decoder needs 52 clock cycles to complete the decoding, and then the PISO register will input the 32 groups of prior bit LLRs to the PDU. At the same time, the EMU reads the data from the detection storage unit. , external information After 3 clock cycles, the prior probability Foreign Information Input to the joint processing unit to complete the calculation of moment information and prior parameters 、 After 15 clock cycles, the first set of data completes the outer loop and enters the next outer loop. In the next 31 clock cycles, the remaining 31 sets of data also enter the next outer loop. If the number of outer loops is T , then the overall delay of the receiver is 110T+ 18( T -1).

[0160] This receiver adopts a fixed-point quantization strategy, such as Figure 7 The quantization performance diagram of the present invention under different outer loop times is shown, showing the performance of floating point (Fp) and quantization (Qtz) under different outer loop times. The proposed sEPA IDD receiver was synthesized using the Synopsys design compiler in TSMC 65 nm CMOS technology. In order to further improve the hardware throughput, the present invention adopts a matrix-based The core area of ​​the entire receiver is 3.2 , when BER= , the throughput is 3.07 Gb / s at a frequency of 675 MHz and the energy efficiency is 169.6 pJ / b. The overall performance is 0.96 Gb / s / The normalized area efficiency of 100nm is 200nm, which realizes an efficient hardware architecture.

[0161] The simplified iterative detection and decoding device provided by the present invention is described below. The simplified iterative detection and decoding device described below and the simplified iterative detection and decoding method described above can refer to each other.

[0162] like Figure 8 As shown, the device includes:

[0163] A receiving module 810 is configured to obtain a received signal of a multiple-input multiple-output (MIMO) system and an original constellation point set used for transmitting signals in the MIMO system;

[0164] A screening module 820 is configured to screen the constellation points in the original constellation point set based on the cavity distribution of the constellation points in the original constellation point set in an inner iteration of the detection of the approximate expected propagation, to obtain a screened constellation point set;

[0165] An updating module 830 is configured to perform approximate expected propagation detection based on the constellation points in the screened constellation point set to update the cavity distribution of the constellation points in the screened constellation point set;

[0166] The joint processing module 840 is configured to, in the external iterative detection and decoding joint processing, screen the constellation points in the screened constellation point set based on the updated cavity distribution and the prior probabilities of the constellation points in the screened constellation point set to obtain two further screened constellation point sets, select a final constellation point set based on the detected external information and the prior information of the decoder, update the prior parameters of the detector, and iterate the detection and decoding process to obtain an iterative detection and decoding result.

[0167] The simplified iterative detection and decoding device provided by the present invention screens the constellation points in the original constellation point set based on the cavity distribution of the constellation points in the original constellation point set and the prior probabilities of the constellation points in the screened constellation point set to obtain a screened constellation point set. Based on the screened constellation point set, the device combines external information from detection and prior information of the decoder to update the prior parameters of the detector, iterates the detection and decoding processes, and screens the constellation points in the joint processing of internal and external iterations of detection. This reduces the number of constellation points that need to be traversed during the iterative detection and decoding process, thereby significantly reducing computational complexity, making the signal solution process more efficient, and realizing a low-complexity iterative detection and decoding process.

[0168] In one embodiment, the screening module 820 is specifically configured to:

[0169] Based on the updated cavity distribution and the prior probabilities of the constellation points in the filtered constellation point set, the constellation points in the filtered constellation point set are filtered to obtain two further filtered constellation point sets, including:

[0170] Based on the Gaussian distribution characteristics of the updated cavity distribution, the constellation points in the original constellation point set are screened to obtain a screened point set based on the cavity distribution;

[0171] Based on the prior probabilities of the constellation points in the filtered constellation point set output by the decoder, the constellation points in the filtered constellation point set are sorted and filtered to obtain two constellation point sets based on the prior probabilities.

[0172] In one embodiment, the screening module 820 is further configured to:

[0173] Based on the Gaussian distribution characteristics of the updated cavity distribution, constellation points in the filtered constellation point set are filtered to obtain a filtered point set based on the cavity distribution, including:

[0174] Based on the Gaussian distribution characteristics of the updated cavity distribution, the values ​​of each constellation point in the filtered constellation point set are compared with the mean value of the updated cavity distribution, and the constellation points in the filtered constellation point set are filtered to obtain a filtered point set based on the cavity distribution.

[0175] In one embodiment, the screening module 820 is further configured to:

[0176] Filtering the constellation points in the filtered constellation point set to obtain a filter point set based on cavity distribution, including:

[0177] The constellation points in the screened constellation point set are screened according to the updated cavity distribution obtained by the detector to obtain top N constellation points in the screened constellation point set whose cavity distribution probabilities are sorted from largest to smallest.

[0178] In one embodiment, the screening module 820 is further configured to:

[0179] Based on the prior probabilities of the constellation points in the filtered constellation point set output by the decoder, the constellation points in the filtered constellation point set are sorted and filtered to obtain two constellation point sets based on the prior probabilities, including:

[0180] extracting the probability of each bit value from the bit sequence of each constellation point in the filtered constellation point set, and constructing a bit vector of the constellation point with the maximum probability in the filtered constellation point set based on the probability of each bit value;

[0181] dividing the filtered constellation point set into a plurality of constellation point subsets based on a Hamming distance between a bit value of each constellation point in the filtered constellation point set and the bit vector;

[0182] Based on the size of the prior probability, the constellation point subsets obtained by division are sorted and screened to obtain two constellation point sets based on the prior probability.

[0183] In one embodiment, the screening module 820 is further configured to:

[0184] Based on the magnitude of the prior probability, the constellation point subsets obtained by division are sorted and screened to obtain two constellation point sets based on the prior probability, including:

[0185] The constellation point subsets obtained by division are screened, and the top N constellation points sorted from largest to smallest in terms of prior probability are retained, thereby obtaining two constellation point sets based on prior probability.

[0186] Figure 9 An example of a physical structure diagram of an electronic device is shown below. Figure 9 As shown, the electronic device may include: a processor 910, a communication interface 920, a memory 930, and a communication bus 940, wherein the processor 910, the communication interface 920, and the memory 930 communicate with each other via the communication bus 940. The processor 910 may call logic instructions in the memory 930 to execute a simplified iterative detection decoding method, which includes: obtaining a received signal in a multiple-input multiple-output (MIMO) system and an original constellation point set used to transmit a signal in the MIMO system;

[0187] In an inner iteration of the detection of approximate expected propagation, constellation points in the original constellation point set are screened based on the cavity distribution of the constellation points in the original constellation point set to obtain a screened constellation point set;

[0188] performing approximate expected propagation detection based on the constellation points in the screened constellation point set to update the cavity distribution of the constellation points in the screened constellation point set;

[0189] In the outer iterative detection and decoding joint processing, the constellation points in the screened constellation point set are screened based on the updated cavity distribution and the prior probabilities of the constellation points in the screened constellation point set to obtain two further screened constellation point sets. The final constellation point set is selected in combination with the detected outer information and the prior information of the decoder to update the prior parameters of the detector. The detection and decoding processes are then iterated to obtain an iterative detection and decoding result.

[0190] Furthermore, the logic instructions in the aforementioned memory 930 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0191] On the other hand, the present invention further provides a computer program product, comprising a computer program, which may be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is capable of performing the simplified iterative detection and decoding method provided by the above methods, the method comprising: obtaining a received signal of a multiple-input multiple-output (MIMO) system and an original constellation point set used for transmitting a signal in the MIMO system;

[0192] In an inner iteration of the detection of approximate expected propagation, constellation points in the original constellation point set are screened based on the cavity distribution of the constellation points in the original constellation point set to obtain a screened constellation point set;

[0193] performing approximate expected propagation detection based on the constellation points in the screened constellation point set to update the cavity distribution of the constellation points in the screened constellation point set;

[0194] In the outer iterative detection and decoding joint processing, the constellation points in the screened constellation point set are screened based on the updated cavity distribution and the prior probabilities of the constellation points in the screened constellation point set to obtain two further screened constellation point sets. The final constellation point set is selected in combination with the detected outer information and the prior information of the decoder to update the prior parameters of the detector. The detection and decoding processes are then iterated to obtain an iterative detection and decoding result.

[0195] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the simplified iterative detection decoding method provided by the above methods is implemented, the method comprising: obtaining a received signal in a multiple-input multiple-output (MIMO) system and an original constellation point set used for transmitting a signal in the MIMO system;

[0196] In an inner iteration of the detection of approximate expected propagation, constellation points in the original constellation point set are screened based on the cavity distribution of the constellation points in the original constellation point set to obtain a screened constellation point set;

[0197] performing approximate expected propagation detection based on the constellation points in the screened constellation point set to update the cavity distribution of the constellation points in the screened constellation point set;

[0198] In the outer iterative detection and decoding joint processing, the constellation points in the screened constellation point set are screened based on the updated cavity distribution and the prior probabilities of the constellation points in the screened constellation point set to obtain two further screened constellation point sets. The final constellation point set is selected in combination with the detected outer information and the prior information of the decoder to update the prior parameters of the detector. The detection and decoding processes are then iterated to obtain an iterative detection and decoding result.

[0199] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0200] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A simplified iterative detection decoding method, characterized in that: The method comprises: Acquire a received signal of a multiple-input multiple-output (MIMO) system and an original constellation point set used for transmitting signals in the MIMO system; In an inner iteration of the detection of approximate expected propagation, constellation points in the original constellation point set are screened based on the cavity distribution of the constellation points in the original constellation point set to obtain a screened constellation point set; performing approximate expected propagation detection based on the constellation points in the screened constellation point set to update the cavity distribution of the constellation points in the screened constellation point set; In the outer iterative detection and decoding joint processing, based on the updated cavity distribution and the prior probabilities of the constellation points in the screened constellation point set, the constellation points in the screened constellation point set are screened respectively to obtain two further screened constellation point sets. The final constellation point set is selected in combination with the detected outer information and the prior information of the decoder to update the prior parameters of the detector. The detection and decoding processes are then iterated to obtain an iterative detection and decoding result.

2. The simplified iterative detection decoding method according to claim 1, wherein: Based on the updated cavity distribution and the prior probabilities of the constellation points in the filtered constellation point set, the constellation points in the filtered constellation point set are filtered respectively to obtain two further filtered constellation point sets, including: Based on the Gaussian distribution characteristics of the updated cavity distribution, the constellation points in the filtered constellation point set are filtered to obtain a filtered point set based on the cavity distribution; sorting and screening the constellation points in the filtered constellation point set based on the prior probabilities of the constellation points in the filtered constellation point set output by the decoder to obtain a constellation point set based on the prior probabilities; The screening point set based on cavity distribution and the constellation point set based on prior probability are used as the two constellation point sets after further screening.

3. The simplified iterative detection decoding method according to claim 2, wherein: The step of screening the constellation points in the screened constellation point set based on the Gaussian distribution characteristic of the updated cavity distribution to obtain a screened point set based on the cavity distribution includes: Based on the Gaussian distribution characteristics of the updated cavity distribution, the values ​​of each constellation point in the filtered constellation point set are compared with the mean value of the updated cavity distribution, and the constellation points in the filtered constellation point set are filtered to obtain a filtered point set based on the cavity distribution.

4. The simplified iterative detection decoding method according to claim 3, wherein: Filtering the constellation points in the filtered constellation point set to obtain a filter point set based on cavity distribution, including: The constellation points in the screened constellation point set are screened according to the updated cavity distribution obtained by the detector to obtain top N constellation points in the screened constellation point set whose cavity distribution probabilities are sorted from largest to smallest.

5. The simplified iterative detection decoding method according to claim 2, wherein: Sorting and screening the constellation points in the filtered constellation point set based on the prior probabilities of the constellation points in the filtered constellation point set output by the decoder to obtain a constellation point set based on the prior probabilities includes: extracting the probability of each bit value from the bit sequence of each constellation point in the filtered constellation point set, and constructing a bit vector of the constellation point with the maximum probability in the filtered constellation point set based on the probability of each bit value; dividing the filtered constellation point set into a plurality of constellation point subsets based on a Hamming distance between a bit value of each constellation point in the filtered constellation point set and the bit vector; Based on the size of the prior probability, the constellation point subsets obtained by division are sorted and screened to obtain a constellation point set based on the prior probability.

6. The simplified iterative detection decoding method according to claim 5, wherein: Based on the magnitude of the prior probability, the constellation point subsets obtained by division are sorted and screened to obtain a constellation point set based on the prior probability, including: The constellation point subsets obtained by division are screened, and the top N constellation points sorted from large to small in prior probability are retained to obtain a constellation point set based on prior probability.

7. A simplified iterative detection decoding device, characterized in that: include: A receiving module, configured to obtain a received signal of a multiple-input multiple-output (MIMO) system and an original constellation point set used to transmit a signal in the MIMO system; A screening module is configured to screen the constellation points in the original constellation point set based on the cavity distribution of the constellation points in the original constellation point set in an inner iteration of the detection of the approximate expected propagation, so as to obtain a screened constellation point set; an updating module, configured to perform approximate expected propagation detection based on the constellation points in the screened constellation point set, so as to update the cavity distribution of the constellation points in the screened constellation point set; a joint processing module for, in the external iterative detection and decoding joint processing, filtering the constellation points in the filtered constellation point set based on the updated cavity distribution and the prior probabilities of the constellation points in the filtered constellation point set to obtain two further filtered constellation point sets, selecting a final constellation point set based on the detected external information and the prior information of the decoder to update the prior parameters of the detector, and iterating the detection and decoding processes to obtain an iterative detection and decoding result.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the simplified iterative detection and decoding method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the simplified iterative detection and decoding method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the simplified iterative detection and decoding method according to any one of claims 1 to 6 is implemented.

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