Bit width quantization method, device and equipment for MIMO (Multiple Input Multiple Output) and medium

Abstract

The invention relates to the field of MIMO signal processing, and discloses an MIMO-oriented bit width quantization method, device, equipment and medium, and the method comprises the steps: obtaining a plurality of variables to be quantized, carrying out the quantization of the integer bit width and sign bit width of the variables, obtaining the initial population of the variables, and carrying out the iterative optimization of the initial population, in any iteration process, carrying out the quantization of the integer bit width and sign bit width of the variables, and carrying out the quantization of the integer bit width and sign bit width of the variables. The method comprises the steps of determining a reference individual in a current population, performing decimal bit width updating on other individuals based on the reference individual, performing decimal bit width updating on any individual after the decimal bit width updating based on the target function value difference between the individual and the other individual, and determining a final population after iterative optimization is completed. And determining a target individual in the final population, and quantizing the decimal bit widths of the variables based on the decimal bit width combination of the target individual. According to the technical scheme provided by the invention, the automatic and efficient distribution of the decimal bit width in the MIMO detector is realized, and the calculation complexity and the hardware overhead are reduced.

Description

Technical Field

[0001] This application relates to the field of MIMO signal processing, and in particular to a bit-width quantization method, apparatus, device, and medium for MIMO. Background Technology

[0002] In wireless communication systems, with the large-scale expansion of MIMO (multiple-input multiple-output) detectors, the computational complexity of MIMO detectors increases exponentially with the number of antennas, posing a huge challenge to their hardware deployment.

[0003] In the signal processing of MIMO detectors, quantization is a crucial step in transitioning multiple variables from floating-point representation to fixed-point hardware implementation. It typically employs either uniform quantization or hybrid quantization. Uniform quantization assigns a uniform bit width to all variables, which may lead to precision redundancy and increased hardware overhead. Hybrid quantization, on the other hand, manually allocates bit widths for all variables, further increasing computational complexity.

[0004] Therefore, reducing the computational complexity and hardware overhead of MIMO detectors has become an urgent problem to be solved in the current application research of MIMO detectors. Summary of the Invention

[0005] This application provides a bit-width quantization method, apparatus, device, and medium for MIMO, which can realize automatic quantization of mixed precision of MIMO detectors, and significantly reduce computational complexity and hardware overhead while ensuring detection performance.

[0006] This application provides a bit-width quantization method for MIMO, the method comprising: acquiring multiple variables to be quantized during MIMO signal processing, the variables being configured with a bit width to be quantized, the bit width to be quantized including a fractional bit width, an integer bit width, and a sign bit width; quantizing the integer bit width and the sign bit width of each variable; acquiring an initial population of the multiple variables; iteratively optimizing the initial population, wherein each individual in the initial population represents a set of fractional bit width combinations of the multiple variables; during any iteration of the iterative optimization, determining a reference individual in the current population, updating the fractional bit width of other individuals based on the reference individual, determining the difference in objective function value between the updated individual and another individual for any individual, and updating the fractional bit width of the individual again based on the difference in objective function value; determining a final population after the iterative optimization is completed, determining a target individual in the final population based on the objective function value, and quantizing the fractional bit width of the multiple variables according to the fractional bit width combination of the target individual.

[0007] In one embodiment, quantizing the integer bit width of each of the variables includes: acquiring a sample set generated during MIMO signal processing, wherein each sample in the sample set represents the value of each variable under different signal processing conditions; for any variable, based on the initial integer bit width of the variable, finding the minimum integer bit width that satisfies a preset condition in the sample set, and quantizing the integer bit width of the variable based on the minimum integer bit width, wherein the preset condition is determined based on the quantization value range of the variable and a preset overflow threshold.

[0008] In one implementation, the preset condition is determined as follows: a subset of samples corresponding to the variable is determined in the sample set, and a first sample number and a second sample number of the sample set are determined, wherein the sample subset includes multiple samples that exceed the quantization range of the variable; based on the first sample number and the second sample number, a sample overflow value of the variable is determined, and the sample overflow value being less than or equal to a preset overflow threshold is used as the preset condition.

[0009] In one implementation, the initial population is pre-generated as follows: a preset range of values ​​for the decimal places of each variable is obtained, and a corresponding number of individuals are randomly generated according to the preset population size to form the initial population; wherein each individual is represented by a vector containing multiple elements, each element represents the decimal places of a certain variable, and the value of the element is randomly generated within the preset range of values ​​corresponding to the variable.

[0010] In one implementation, identifying a reference individual in the current population and updating the fractional width of other individuals based on the reference individual includes: obtaining the objective function value of each individual in the current population, and identifying the individual with the smallest objective function value as the reference individual; obtaining the average representation of other individuals in the current population, and updating the fractional width of the other individuals based on the reference individual and the average representation using a teaching factor.

[0011] In one implementation, determining the difference in objective function values ​​between the individual and another individual, and updating the individual again with a fractional width based on the difference in objective function values, includes: randomly selecting another individual different from the individual; obtaining the difference in objective function values ​​between the individual and the other individual; determining the update direction and adaptive factor of the individual based on the difference in objective function values; and updating the individual with a fractional width in the update direction according to the adaptive factor, wherein the update direction includes a first update direction and a second update direction, the first update direction representing a direction closer to the individual, and the second update direction representing a direction farther away from the individual.

[0012] In one implementation, the adaptive factor characterizes the update range of the individual; updating the decimal width of the individual in the update direction according to the adaptive factor includes: determining the update range of the individual according to the adaptive factor, and updating the decimal width of the individual in the update direction with reference to the update range; wherein the update range is dynamically adjusted based on the difference in the objective function value of the individual, characterizing whether to perform exploratory update or convergent update of the decimal width of the individual.

[0013] In one implementation, the objective function value is determined based on an objective function; the objective function is determined as follows: the relative error of the quantization detector and the floating-point detector is obtained; when the relative error is less than or equal to a target threshold, the objective function is determined based on the average fractional width of the fractional bit width combination; when the relative error is greater than the target threshold, the objective function is determined based on the average fractional bit width and a penalty term, wherein the penalty term is determined based on a scaling factor, and the relative error characterizes the bit error rate of the individual fractional bit width combination.

[0014] A second aspect of this application provides a bit-width quantization apparatus for MIMO, the apparatus comprising: a variable acquisition unit, configured to acquire multiple variables to be quantized during MIMO signal processing, the variables being configured with a bit width to be quantized, the bit width to be quantized including at least a fractional bit width; a population determination unit, configured to generate an initial population of the multiple variables and perform iterative optimization on the initial population, wherein each individual in the initial population represents a set of fractional bit width combinations of the multiple variables; an iterative update unit, configured to, during any iteration of the iterative optimization, determine a reference individual in the current population, update the fractional bit width of other individuals based on the reference individual, determine the difference in objective function value between the updated individual and another individual, and update the fractional bit width of the individual again based on the difference in objective function value; and a bit-width determination unit, configured to, after the iterative optimization is completed, quantize the fractional bit width of the multiple variables based on the target individual in the previous generations of the population, and based on the fractional bit width combination of the target individual.

[0015] A third aspect of this application provides a computer device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the bit-width quantization method for MIMO described in the first aspect above.

[0016] The technical solution provided in one or more embodiments of this application reduces the computational complexity and hardware overhead of MIMO signal processing by iteratively optimizing and optimizing the quantization bit width of the variables to be quantized. Specifically, multiple variables to be quantized with different bit widths are obtained, and an initial population representing the combination of fractional bit widths of each variable is generated and iterative optimization is performed. During the iteration process, the fractional bit width of other individuals is first updated using the best-performing reference individual in the population, and then a second update is performed based on the difference in the objective function values ​​between individuals. After the iteration is completed, the target individual with the best objective function value is selected from the previous generations of the population, and the variable is quantized according to its fractional bit width combination. By accurately allocating the fractional bit width through iterative optimization and algorithm optimization, automatic quantization of the variable bit width of the MIMO detector is realized, including the sign bit width, integer bit width, and fractional bit width. Under the premise of ensuring MIMO detection performance, the required fractional bit width of each variable is minimized, effectively reducing the computational complexity and hardware overhead of MIMO signal processing.

[0017] As can be seen, the technical solution provided in this application can realize the automatic quantization of the mixing accuracy of MIMO detectors, and significantly reduce the computational complexity and hardware overhead while ensuring detection performance. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A step diagram illustrating a bit-width quantization method for MIMO provided for embodiments of this application; Figures 2(a) and 2(b) are schematic diagrams of the objective function values ​​under different MIMO detection systems provided in one embodiment of this application; Figures 3(a) and 3(b) are schematic diagrams of the quantization bit width under different MIMO detection systems provided in one embodiment of this application; Figure 4 A schematic diagram of a bit-width quantization device for MIMO provided in one embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] Furthermore, the use of terms such as "first," "second," etc., in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of embodiments in this application, unless otherwise stated, "multiple" means two or more. Additionally, the use of "based on" or "according to" implies openness and inclusiveness, because processes, steps, calculations, or other actions "based on" or "according to" one or more of the stated conditions or values ​​may in practice be based on additional conditions or beyond the stated values.

[0022] With the large-scale expansion of antenna configurations in MIMO (multiple-input multiple-output) detectors, the computational complexity of MIMO detectors has increased significantly. Currently, hardware efficiency is often improved through algorithm design, but this approach is also computationally complex. In practical applications, quantization, as a key step in transitioning from the floating-point representation of the algorithm to the required fixed-point operations, can reduce computational complexity and hardware costs to some extent without modifying the core algorithm.

[0023] In related technologies, uniform quantization or hybrid quantization methods are commonly used. Uniform quantization assigns a uniform bit width to all variables, but this bit width is usually determined based on limited experience, which may lead to accuracy redundancy and increased hardware overhead. Hybrid quantization, on the other hand, mainly involves manually assigning bit widths to all variables, resulting in extremely high design complexity and difficulty in scaling. Furthermore, there is a non-linear relationship between quantization accuracy and detection performance; insufficient accuracy may lead to a significant performance degradation, while excessive accuracy may lead to performance saturation.

[0024] In view of the above, one or more embodiments of this application provide a bit width quantization method, apparatus, device and medium for MIMO, which can solve the above problems, realize automatic quantization of the mixing accuracy of MIMO detectors, and, while ensuring detection performance, perform fine-grained bit width allocation for multiple variables that need to be quantized in MIMO detectors, thereby significantly reducing computational complexity and hardware overhead.

[0025] First, this application provides an example of a scenario for signal processing using a MIMO detector. In a scenario with... One transmitting antenna and In a MIMO communication system with multiple receiving antennas, independent and identically distributed Rayleigh channels and orthogonal amplitude modulation are used, and a modulation method with a magnitude of [missing information] is employed during the modulation process. A constellation diagram is used to represent the set of signals used in the modulation process. In actual physical systems, signals are real numbers, but complex numbers are needed to represent them during processing. Therefore, it is necessary to convert the complex model into a real number model. ,in, Represents the received signal vector. For the channel matrix, It is the transmitted signal vector. This represents additive white Gaussian noise.

[0026] Furthermore, the received signal is estimated using the MMSE detection method, and the received signal is then utilized. The calculation process is simplified by decomposition. Specifically, MMSE detection aims to achieve mean squared error. Minimize, This is the weight matrix. Represents the transmitted signal vector. Represents the received signal vector, for estimating the transmitted vector To solve this problem, specifically, , , ,in, The autocorrelation matrix represents the received signal vector. This represents a linear combination of the received signal vectors. For the matrix... of Decompose: ,in, It is a lower triangular matrix. any element ,in, ,all Elements form a matrix Therefore, it can be achieved through For estimating the emission vector Solving for the solution, based on the properties of lower triangular matrices, we obtain... , where the matrix any element Similarly, the properties of upper triangular matrices can be used to derive... .

[0027] In this example scenario, the vector representation of the multiple variables to be quantized is: This represents all intermediate variables that need to be converted from floating-point to fixed-point numbers during the MMSE detection process in the MIMO detection system, so that they can be processed in the digital circuit. Among them, , For any variable Its sign part, integer part, and decimal part are represented by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 10 ... and Bit encoding, the quantization result can be expressed as ,in, This indicates the rounding operation. This indicates the precision of quantization, specifically the quantization step size (the interval between two adjacent quantization values). Indicates will Limited to Within the range, of which and They are respectively The lower and upper limits, therefore the result of quantization is... Quantization accuracy closest to the original data within the range Multiples of.

[0028] The above description is merely a scenario example provided in the specification and is not intended to limit the present invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0029] Please see Figure 1 In one aspect, this application provides a bit-width quantization method for MIMO, which may include the following steps: S1: Obtain multiple variables to be quantized during MIMO signal processing. The variables are set with a quantization bit width, which includes a fractional bit width, an integer bit width, and a sign bit width. Quantize the integer bit width and the sign bit width of each variable.

[0030] The variables to be quantized mentioned above are those that require fixed-point conversion during MIMO signal processing. Quantization is the process of converting continuous analog signals or floating-point numbers into discrete digital signals to facilitate processing of these variables in digital circuits. These variables typically include a sign bit, integer bits, and fractional bits, with corresponding quantization bit widths of sign, integer, and fractional bits. Since the sign bit is fixed at 1 bit, the sign bit width is usually quantized to 1 bit. Quantization precision refers to the smallest distinguishable change during quantization, directly affecting the closeness of the quantized value to the original value. The fractional bit width directly determines the quantization precision; a smaller fractional bit width results in lower quantization precision and decreased MIMO detection performance, while a larger fractional bit width leads to higher quantization precision but consumes more hardware resources. Therefore, it is necessary to allocate a reasonable fractional bit width for each variable to significantly reduce computational complexity and hardware overhead while ensuring detection performance.

[0031] S3: Obtain an initial population of the multiple variables, and perform iterative optimization on the initial population, wherein each individual in the initial population represents a set of fractional-bit combinations of the multiple variables.

[0032] In this embodiment, an initial population is randomly generated based on the aforementioned multiple variables. Each individual in the initial population represents a possible combination of fractional bits for the aforementioned multiple variables, i.e., a possible quantization scheme for the aforementioned multiple variables. These individuals are typically represented in vector form. For example, a set of individuals is randomly generated or generated using a heuristic method to form the initial population as the starting point for searching for the optimal solution. The individuals in the initial population are gradually improved through iterative optimization, so that each individual can find a better combination of fractional bits in each iteration. By iteratively optimizing the fractional bit combination represented by each individual, a feasible quantization scheme can be determined after the iterative optimization is completed. This minimizes the required fractional bits for each variable while ensuring MIMO detection performance, thereby reducing computational complexity and hardware overhead.

[0033] S5: In any iteration of the iterative optimization, a reference individual is determined in the current population, and the fractional width of other individuals is updated based on the reference individual. For any individual after the fractional width update, the difference in the objective function value between the individual and another individual is determined, and the fractional width of the individual is updated again based on the difference in the objective function value.

[0034] In this embodiment, during any iteration, a reference bit width update is first performed, that is, the fractional bit width of all individuals is updated using a selected reference individual. The reference individual can be understood as the best-performing individual among all individuals, such as one with an optimal or near-optimal objective function value, used to guide the optimization process of other individuals. The objective function can be established by considering both MIMO detection performance and computational complexity. For other individuals in the population, their fractional bit width combinations are adjusted based on the characteristics of the reference individual, so that all individuals in the current iteration optimize their fractional bit width towards better performance, thereby achieving automatic quantization of the mixing precision of the MIMO detector.

[0035] In this embodiment, during any iteration, for each individual after decimal places, the decimal places are updated again based on the difference in the objective function value. Specifically, for any individual to be updated, another individual (not the current individual) is randomly selected, and the difference in the objective function value between the current individual and the other individual is determined. If the difference in the objective function value indicates that the performance of the current individual is still poor, then based on this difference in the objective function value, the decimal places of the current individual are further updated with reference to the other individual. It should be noted that when the current individual is selected as the reference individual, no further update of the current individual is required.

[0036] The aforementioned difference in objective function values ​​is determined based on the objective function values ​​corresponding to the two individuals. It serves as a quantitative indicator to evaluate the performance of each individual and is typically directly related to the optimization objectives (detection performance, computational complexity), characterizing the performance difference between the current individual and the other individual. By analyzing this difference in objective function values, it is possible to determine whether the fractional bit width of the current individual needs to be updated again, and the direction of the update. This allows for the fine-tuning of the fractional bit width to improve detection performance and reduce computational complexity while simultaneously achieving automatic quantization of the mixed precision of the MIMO detector.

[0037] S7: After the iterative optimization is completed, the final population is determined, and the target individual is determined in the final population based on the objective function value. The decimal width of the multiple variables is quantized according to the decimal width combination of the target individual.

[0038] In this implementation, after all iterations are completed, the resulting population is used as the final population. The objective function value of each individual in the final population is analyzed, and the individual with the optimal objective function value is selected as the target individual. Further, the fractional widths of the aforementioned variables are allocated according to the fractional width combination represented by the target individual; that is, the fractional widths of these variables are allocated according to the configuration of the target individual. Quantization based on the fractional width combination of the target individual ensures the accuracy and effectiveness of the quantization process, allowing the quantized variables to be as close as possible to the original values. This reduces the required computational and hardware resources while maintaining detection performance, thereby lowering the overall computational complexity and hardware overhead.

[0039] In one embodiment, during any iteration, an ATLBO-Q (Adaptive Teaching-Learning-Based Optimization) algorithm is proposed. This ATLBO-Q algorithm is applied to the bit-width quantization method described in the first aspect to update individual values. The ATLBO-Q algorithm is an adaptive heuristic optimization algorithm based on the teaching process. It optimizes the problem-solving process by simulating the interaction between teachers and students in a classroom setting, utilizing the teacher's knowledge transfer and the students' mutual learning. The ATLBO-Q algorithm includes two stages of the teaching process: a teacher stage and a student stage. The teacher stage simulates the teacher imparting knowledge to students, improving the average knowledge level of the group; the student stage simulates mutual learning among students, further enhancing the individual knowledge level.

[0040] In this embodiment, a decimal width update is performed during the teacher phase, which assigns the role of teacher to the reference individual and the role of student to the other individuals. Specifically, before iteration, a group of students is generated as the initial solution. Each student contains a set of possible decimal widths with quantized variables. The student with the best objective function value is called the teacher, representing the globally optimal student. Based on the difference vector between the teacher and the average level of the students, the width scheme of each student is updated, causing the average value of the students to shift towards the teacher, thus guiding the search process. During the teacher phase, each student can quickly and directionally converge to a relatively optimal solution region.

[0041] In this embodiment, the decimal width update is performed again during the student phase, where students enhance their knowledge through interaction. For each student, another different student is randomly selected from the population. The update direction is determined based on their objective function values. Students with better performance (smaller objective function values) move away from students with worse performance, while students with worse performance move towards students with better performance. This adaptive update of the decimal width combination of students enhances diversity and prevents premature convergence.

[0042] Based on the above ideas, the technical solution provided in this embodiment of the application reduces the computational complexity and hardware overhead of MIMO signal processing by iteratively optimizing and optimizing the quantization bit width of the variables to be quantized. Specifically, multiple variables to be quantized with different bit widths are obtained, and an initial population representing the combination of fractional bit widths of each variable is generated and iterative optimization is carried out. During the iteration process, the fractional bit width of other individuals is first updated using the best-performing reference individual in the population, and then a second update is performed based on the difference in the objective function values ​​between individuals. After the iteration is completed, the target individual with the best objective function value is selected from the previous generations of the population, and the variables are quantized according to its fractional bit width combination. By accurately allocating the fractional bit width through iterative optimization and algorithm optimization, the automatic quantization of the mixing precision of the MIMO detector is realized, and the required fractional bit width of each variable is minimized while ensuring the MIMO detection performance, effectively reducing the computational complexity and hardware overhead of MIMO signal processing.

[0043] In one implementation, when quantizing various variables, the integer bit width of the variables needs to be quantized to ensure that the data does not overflow. For example, in a quantization process, when converting a variable represented by a floating-point number to a fixed-point number, the variable is decomposed into a sign bit, an integer bit, and a fractional bit. If the variable x = -5.75, its quantization bit width is 1 sign bit, 3 integer bits, and 4 fractional bits. The corresponding sign bit is represented as '1', the integer bits are represented in binary as '101', and the fractional bits are represented in binary as '1100'. Therefore, the complete quantization representation of the variable x is '11011100'.

[0044] Specifically, the integer bit width can be optimized as follows: Obtain the sample set generated during MIMO signal processing, where each sample in the sample set represents the value of each variable under different signal processing conditions. Further, for any variable, based on the initial integer bit width of the variable, find the minimum integer bit width in the sample set that satisfies a preset condition, and quantize the integer bit width of the variable based on the minimum integer bit width, wherein the preset condition is determined based on the quantization value range of the variable and a preset overflow threshold.

[0045] In this embodiment, the above-mentioned preset conditions can be expressed as: ,in, Representing the above sample set, This represents a subset of samples, specifically the set of samples that exceed the quantized numerical range of the variable, assuming a zero decimal place width. and These represent the number of samples in the set and the preset overflow threshold, respectively. Indicates the number of the first sample. Indicates the number of the second sample. This represents the sample overflow value. For any variable... Its quantized numerical range can be expressed as ,in, , , Indicates integer bit width. This indicates the decimal places. It should be noted that the quantization result of this variable is... The multiple of the quantization precision closest to the original data within the range.

[0046] In this embodiment, finding the minimum integer bit width that satisfies a preset condition based on the initial integer bit width of the variable includes: obtaining the initial integer bit width of the variable, determining whether the initial integer bit width satisfies the preset condition, and if the preset condition is met, obtaining multiple integer bit width samples whose values ​​decrease sequentially from the initial integer bit width, and determining the integer bit width sample with the smallest value that satisfies the preset condition as the minimum integer bit width. Conversely, if the initial integer bit width does not satisfy the preset condition, obtaining multiple integer bit width samples whose values ​​increase sequentially from the initial integer bit width, and determining the integer bit width sample with the smallest value that satisfies the preset condition as the minimum integer bit width. Wherein, if the variable does not have an initial integer bit width, the integer bit width of the variable is randomly sampled to determine multiple integer bit width samples, and the integer bit width sample with the smallest value that satisfies the preset condition is determined as the minimum integer bit width.

[0047] In one embodiment, the variables that need to be quantized during multiple simulated signal processing operations or actual signal processing operations of the MIMO detector can be obtained. Multiple variables requiring quantization each time are treated as a sample, and each sample may have a different quantization bit width in different signal processing operations. For example, to achieve a reliable estimate of the integer bit width of each variable in the MIMO detector, the above sample set is generated using Monte Carlo simulation. Through extensive random sampling, a real communication environment is simulated to obtain the statistical characteristics of the dynamic range of each variable.

[0048] The technical solution provided in this embodiment of the application, based on the aforementioned fractional bit width optimization scheme, extends the quantization bit width to an integer bit width. Specifically, by incrementally increasing or decreasing the integer bit width samples, the minimum integer bit width that satisfies the preset conditions of the corresponding variable is found, and finally, the integer bit width of the variable is quantized based on this minimum integer bit width. By accurately finding the minimum integer bit width that satisfies the overflow requirement, data quantization overflow is completely avoided, and data processing reliability is ensured. This minimizes the hardware resources occupied by the integer bit width, while making the integer bit width allocation more closely match the actual dynamic range of the variable, further reducing the overall hardware overhead and computational complexity, and ensuring the stability and detection performance of MIMO signal processing.

[0049] In one implementation, an initial population representing the aforementioned variables is obtained. This initial population can be pre-generated as follows: a preset range of decimal places for each variable is obtained, and a corresponding number of individuals are randomly generated based on a preset population size to form the initial population. Each individual is represented by a vector containing multiple elements, where each element represents the decimal place of a variable. The values ​​of these elements are randomly generated within the preset range of values ​​corresponding to the variable. The preset population size determines the number of individuals in the initial population.

[0050] For example, the aforementioned preset value range is determined based on the initial decimal width of the variable. Specifically, the initial decimal width is used as the maximum decimal width within the preset value range, and zero is used as the minimum decimal width within the preset value range. For example, an individual can be represented as: ,in, Represented by elements, , This represents the number of individuals in the initial population. Each element can be randomly initialized within a preset value range to form different individuals.

[0051] The technical solution provided in this embodiment of the application ensures the effectiveness and rationality of individuals in the initial population by clearly defining the preset value range of the decimal width. While avoiding the decimal width value from exceeding the actual requirement range, it enriches the diversity of the initial population through random generation, providing multiple search starting points for subsequent iterative optimization. This helps to improve the efficiency and accuracy of finding the optimal decimal width combination, thereby better balancing MIMO detection performance and hardware overhead.

[0052] In one implementation, identifying a reference individual in the current population and updating the fractional width of other individuals based on the reference individual includes: obtaining the objective function value of each individual in the current population, and identifying the individual with the smallest objective function value as the reference individual; obtaining the average representation of other individuals in the current population, and updating the fractional width of the other individuals based on the reference individual and the average representation using a teaching factor.

[0053] In this embodiment, the aforementioned objective function value is used to evaluate the MIMO detection performance and computational complexity of an individual. This objective function value can be determined based on the objective function itself. Since detection performance and computational complexity are mutually conflicting objectives, the objective function aims to unify these two opposing goals into a comprehensive index, guiding the optimization algorithm to find the fractional-bit quantization combination that achieves a relative balance between performance and complexity. Specifically, the individual with the smallest objective function value among the reference individuals is denoted as... ,in, Indicates a reference individual. Describe the objective function. This represents the current population, which includes... There are [number] individuals. The reference individual whose objective function value is minimized is used as the optimal solution to provide an evolutionary reference for other individuals.

[0054] In one embodiment, the average representation of other individuals in the current population is determined as follows: ,in, To represent the average, Indicates the first The iteration of the ... Individuals. Further, according to the following methods... Update the decimal place width combination: ,in, It is a random vector whose elements are in Within the range, As a teaching factor, The aforementioned random vectors and teaching factors enable each individual to optimize towards the reference individual to varying degrees.

[0055] The technical solution provided in this embodiment offers a fractional bit width update method for iterative optimization in MIMO mixed-precision quantization. Specifically, it unifies the conflicting MIMO detection performance and computational complexity into a unified objective function value. Based on the objective function value, a reference individual is determined to provide an evolutionary reference for updating the fractional bit width of other individuals. Guided by the optimal reference individual, and combining the population average level with dynamically adjusted random vectors and teaching factors, each individual can optimize towards the optimal direction to varying degrees. Simultaneously, by comprehensively considering detection performance and computational complexity through the objective function, it ensures that the updated individuals are closer to a balanced state between the two, making the fractional bit width combination represented by the updated individuals more precise and accurate. This reduces overall hardware overhead and computational complexity while ensuring the stability and detection performance of MIMO signal processing.

[0056] In one implementation, by simulating random communication within a population, the difference in objective function values ​​between each individual and other individuals in the population is determined, and the optimization direction of the individual is determined based on this difference. Specifically, for any individual in the population, another individual different from the aforementioned individual is randomly selected, and the objective function values ​​of both the aforementioned individual and the other individual are obtained. Based on the objective function values ​​of the aforementioned individual and the other individual, the difference in objective function values ​​between the aforementioned individual and the other individual is determined. Here, the difference in objective function values ​​can be understood as the absolute difference between two objective function values ​​mathematically represented, or other numerical values ​​determined based on the two objective function values, used to characterize the relative superiority or inferiority of the two individuals.

[0057] In this embodiment, the difference in the objective function value determines the update direction of the individual, allowing for a further decimal-width update of the individual based on this difference. Specifically, based on the difference in the objective function value, the update direction and adaptive factor for the individual are determined, and the individual is updated with a decimal-width based on the adaptive factor and update direction. The adaptive factor dynamically adjusts the update range of the individual based on the difference in the objective function value. The update direction for the individual is determined based on the difference in the objective function value, and includes a first update direction and a second update direction. The first update direction represents the direction closer to the individual, and the second update direction represents the direction farther away from the individual. This can be understood as follows: if the difference in the objective function value indicates that the individual's objective function value is small, the update direction for that individual is closer to itself and farther from the other individual; if the difference in the objective function value indicates that the individual's objective function value is large, the update direction for that individual is farther from itself and closer to the other individual, ensuring that the individual optimizes towards the direction of the optimal objective function value.

[0058] In one embodiment, the fractional-bit width of the individual is updated in the update direction based on the adaptive factor in the following manner: ,in, , Indicates the first The first iteration in the process of the second iteration , An individual refers to the individual before the update and another individual randomly selected for that individual. This represents the updated individual. For student factors, adopting in Random values ​​within a range This represents the adaptive factor.

[0059] In this embodiment, the adaptive factor represents the update range of an individual. This update range can be dynamically adjusted based on the difference in the objective function value, thereby reducing the risk of convergence to a local minimum. The update range of an individual is determined according to the adaptive factor, and the individual is updated with a decimal width based on this update range. The adaptive factor can be determined in the following manner: Among them, the above This can be represented as the difference in objective function values. When the objective function values ​​of two individuals are similar (i.e., the difference in objective function values ​​is small), ς is close to 1, which means that the update range is large, and the algorithm is more inclined to explore new solution spaces. This represents exploring and updating the decimal places of the individuals. When the difference in objective function values ​​is large, ς is close to 0, which means that the update range is small, and the algorithm is more inclined to fine-tune using known high-quality solutions. This represents converging and updating the decimal places of the individuals. By adjusting the update range, while ensuring that the algorithm effectively explores the solution space, the convergence speed is improved and local optima are avoided.

[0060] The technical solution provided in this embodiment of the application determines the optimization direction and update strategy by simulating random communication among individuals within a population, thereby further optimizing the mixed precision quantization effect. Specifically, by comparing the objective function values ​​among individuals, the update direction and adaptive factor are determined. The update range of individuals is dynamically adjusted according to the update direction and adaptive factor, thereby optimizing the decimal width during the iteration process. Through random communication among individuals and dynamic adjustment of the adaptive factor, the algorithm avoids premature convergence to a local optimum, while improving convergence efficiency. It selects the optimal decimal width combination that achieves a precise balance between MIMO detection performance and computational complexity. This combination, while ensuring the stability and detection performance of MIMO signal processing, maximizes the simplification of the decimal width configuration of each variable, thereby effectively reducing the overall hardware overhead and computational complexity of the MIMO system.

[0061] In one implementation, the factors determining the value of the objective function may include the bit error rate (BER) of the fractional-bit combination and the average fractional-bit width. The BER is a primary metric for MIMO detection performance, representing the ratio of errors between the received and transmitted signals. A lower BER implies better detection performance. The average fractional-bit width is a metric for computational complexity, representing the number of bits required for the fractional parts of all variables during quantization; a smaller average fractional-bit width implies lower computational complexity.

[0062] In this embodiment, the objective function value is determined as follows: the relative error between the quantization detector and the floating-point detector is obtained. When the relative error is less than or equal to the target threshold, the objective function is determined based on the average fractional width of the fractional bit width combination. When the relative error is greater than the target threshold, the objective function is determined based on the average fractional bit width and a penalty term, wherein the penalty term is determined based on a scaling factor. This objective function setting considers both low bit error rate and computational complexity. Even if the relative error exceeds a preset threshold in some cases, the objective function value will decrease as long as the bit error rate decreases, thus ensuring the algorithm's convergence performance and ensuring that the algorithm can effectively find the optimal balance between performance and complexity.

[0063] The quantization detector mentioned above is a detector that processes signals using a fractional bit width of quantization, while the floating-point detector is a detector that performs calculations using floating-point numbers. The relative error can be understood as an indicator measuring the performance difference between the quantization detector and the floating-point detector, typically the difference between the bit error rate of the quantization detector and the bit error rate of the floating-point detector. The penalty term is used to introduce additional penalties to improve the objective function value when the performance of the quantization detector does not meet the requirements (i.e., the relative error is greater than the target threshold). The scaling factor is a parameter used to adjust the degree of influence of the penalty term.

[0064] In one embodiment, the above objective function can be expressed as: ,in, This represents the bit error rate of the floating-point detector. This represents the bit error rate of the quantization detector. Indicates relative error. Indicates the target threshold. This indicates the average width of the decimal part. As a penalty item, This is the scaling factor.

[0065] The technical solution provided in this embodiment of the application clarifies the design logic of the objective function and the calculation method of the objective function value. Specifically, by comprehensively evaluating the bit error rate and average fractional bit width of individual fractional bit width combinations, the balance between detection performance and computational complexity is characterized, ensuring the convergence of the algorithm. While ensuring low bit error rate and low computational complexity, a penalty term is used to constrain the performance difference between the quantization detector and the floating-point detector, ensuring that the optimization process always moves towards achieving the performance target and minimizing complexity. Ultimately, the optimal fractional bit width combination can be accurately selected. This combination, while ensuring the stability of MIMO signal processing and detection performance, minimizes the average fractional bit width, thereby effectively reducing the overall hardware overhead and computational complexity of the MIMO system.

[0066] Please refer to Table 1. This application provides an embodiment of a bit-width quantization method for MIMO as described in the first aspect, employing a bit-width quantization method based on the ATLBO-Q algorithm, and using an antenna configuration as follows: 16×32 and A 16×64 MIMO detection system was used, employing 16-QAM modulation, to obtain the multiple variables requiring quantization and their corresponding indices as shown in Table 1. In determining the quantization bit width, the maximum number of iterations was set to 200, the population size to 150, and the initial values ​​for the integer and fractional bit widths were set to 5 and 16, respectively. Using 32-bit floating-point MMSE detection as a benchmark, the ATLBO-Q algorithm was employed to determine the quantization bit width for each variable, thus enabling quantization of each variable. Please refer to Figures 2(a) and 2(b). Figure 2(a) characterizes... The objective function value under a 16×32 MIMO detection system is shown in Figure 2(b). The objective function values ​​in the 16×64 MIMO detection system decreased rapidly in the initial stage, converging within 170 and 44 iterations, respectively. These trends benefited from the adaptive optimization process, demonstrating the effectiveness of the quantization method proposed in this application.

[0067] Table 1. List of multiple variables to be quantified and their corresponding indexes In this embodiment, traditional quantization methods are compared with the quantization methods of this application in various aspects, including quantization bit width comparison, bit error rate comparison, and computational complexity comparison. The aforementioned traditional quantization methods include UQ (Unified Quantization) quantization method and AHPQ (Automatic Hybrid-Precision Quantization) quantization method.

[0068] The quantization bit width comparison results include: please refer to Figure 3(a) and Figure 3(b). Figure 3(a) characterizes... The bit width results under the 16×32 MIMO detection system are shown in Figure 3(b). The bit width results for a 16×64 MIMO detection system show that the AHPQ quantization method yields average bit widths of 11.25 bits and 10.375 bits in 16×32 and 16×64 MIMO detection systems, respectively. The bit width quantization method based on the ATLBO-Q algorithm achieves average bit widths of 8.50 bits and 7.75 bits in the same two systems. Compared to the AHPQ quantization method, the ATLBO-Q algorithm reduces the average bit width by 24.44% and 25.30% in both systems. Compared to the UQ quantization method, it reduces the bit width by 39.29% and 44.64% in both systems.

[0069] The bit error rate comparison results include: using 32-bit floating-point detection results as the performance baseline, and comparing them with BER= Compared to the floating-point baseline, the bit-width quantization method based on the ATLBO-Q algorithm reduced the signal-to-noise ratio (SNR) by 0.34 dB in a 16×32 MIMO detection system and by 0.30 dB in a 16×64 MIMO detection system. A high SNR indicates a strong signal relative to noise and a low bit error rate (BER), while a low SNR indicates the signal is buried by noise and has a high BER. This near-floating-point performance is mainly attributed to an adaptive optimization strategy, which effectively adjusts the update direction and amplitude of the bit width by evaluating the relative objective function values ​​of different learners, thereby achieving efficient quantization bit width allocation.

[0070] The computational complexity comparison results include: compared with the AHPQ quantization method, the bit-width quantization method based on the ATLBO-Q algorithm reduces computation by 41.56% and 42.71% in 16×32 and 16×64 MIMO detection systems, respectively. Compared with the UQ quantization method, the bit-width quantization method based on the ATLBO-Q algorithm reduces computation by 61.71% and 67.88% in both MIMO detection system configurations, respectively.

[0071] Please see Figure 4 This application also provides a bit-width quantization device for MIMO, the device comprising: The variable acquisition unit 100 is used to acquire multiple variables to be quantized during MIMO signal processing. The variables are set with a quantization bit width, which includes at least a fractional bit width, an integer bit width, and a sign bit width. The integer bit width and the sign bit width of each variable are quantized. Population determination unit 200 is used to obtain an initial population of the plurality of variables and to perform iterative optimization on the initial population, wherein each individual in the initial population represents a set of fractional-bit combinations of the plurality of variables; The iterative update unit 300 is used to determine a reference individual in the current population during any iteration of the iterative optimization, update the decimal width of other individuals based on the reference individual, determine the difference in the objective function value between the individual and another individual for any individual after the decimal width update, and update the decimal width of the individual again based on the difference in the objective function value. The bit width determination unit 400 is used to determine the final population after the iterative optimization is completed, and to determine the target individual in the final population based on the objective function value, and to quantize the decimal bit width of the multiple variables according to the decimal bit width combination of the target individual.

[0072] in, The population determination unit 200 is specifically used to obtain the preset value range of the decimal width of each variable, and randomly generate a corresponding number of individuals according to the preset population size to form the initial population. Each individual is represented by a vector containing multiple elements, each element represents the decimal width of a certain variable, and the value of the element is randomly generated within the preset value range corresponding to the variable.

[0073] The iterative update unit 300 is specifically used to obtain the objective function value of each individual in the current population, determine the individual with the smallest objective function value as the reference individual, obtain the average representation of other individuals in the current population, update the other individuals with a decimal width based on the reference individual and the average representation through a teaching factor, randomly select another individual different from the reference individual, obtain the objective function values ​​of the reference individual and the other individual respectively, determine the objective function value difference between the reference individual and the other individual based on the objective function value difference, determine the update direction and adaptive factor of the reference individual based on the objective function value difference, and update the reference individual again with a decimal width based on the adaptive factor and the update direction.

[0074] The device further includes an integer bit width determination unit, specifically used to acquire a sample set generated during MIMO signal processing, wherein each sample in the sample set represents the value of each variable under different signal processing conditions, and for any variable, based on the initial integer bit width of the variable, finds the minimum integer bit width that satisfies a preset condition, and quantizes the integer bit width of the variable based on the minimum integer bit width, wherein the preset condition is determined based on the quantization value range of the variable and a preset overflow threshold.

[0075] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0076] In this application, a bit-width quantization device for MIMO is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, or other devices that can provide the above functions.

[0077] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application, such as... Figure 5 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0078] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0079] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0080] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0081] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0082] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0083] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0084] The apparatus, module, or unit described in the above embodiments can be implemented by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0085] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0086] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatuses, devices, storage media, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, storage media, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0090] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0091] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0092] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0093] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A bit-width quantization method for MIMO, characterized in that, The method includes: Multiple variables to be quantized during MIMO signal processing are acquired. Each variable is set with a quantization bit width, which includes a fractional bit width, an integer bit width, and a sign bit width. The integer bit width and the sign bit width of each variable are quantized. An initial population of the plurality of variables is obtained, and the initial population is iteratively optimized, wherein each individual in the initial population represents a set of fractional-bit combinations of the plurality of variables; In any iteration of the iterative optimization, a reference individual is identified in the current population, and the fractional width of other individuals is updated based on the reference individual. For any individual after the fractional width update, the difference in the objective function value between the individual and another individual is determined, and the fractional width of the individual is updated again based on the difference in the objective function value. After the iterative optimization is completed, the final population is determined, and the target individual is determined in the final population based on the objective function value. The decimal width of the multiple variables is quantized according to the decimal width combination of the target individual.

2. The method according to claim 1, characterized in that, Quantizing the integer bit width of each of the aforementioned variables includes: Obtain a sample set generated during MIMO signal processing, wherein each sample in the sample set represents the value of each variable under different signal processing conditions; For any of the variables, based on the initial integer bit width of the variable, the minimum integer bit width that satisfies the preset conditions is found in the sample set, and the integer bit width of the variable is quantized based on the minimum integer bit width, wherein the preset conditions are determined based on the quantization value range of the variable and a preset overflow threshold.

3. The method according to claim 2, characterized in that, The preset conditions are determined in the following manner: A subset of samples corresponding to the variable is determined in the sample set, and a first sample size and a second sample size of the sample set are determined, wherein the sample subset includes multiple samples that exceed the quantization range of the variable; Based on the first sample size and the second sample size, the sample overflow value of the variable is determined, and the sample overflow value being less than or equal to a preset overflow threshold is used as the preset condition.

4. The method according to claim 1, characterized in that, The initial population is pre-generated in the following manner: Obtain the preset value range of the decimal places of each variable, and randomly generate a corresponding number of individuals according to the preset population size to form the initial population; Each individual is represented by a vector containing multiple elements, each element representing the decimal width of a variable, and the value of the element is randomly generated within a preset value range corresponding to the variable.

5. The method according to claim 1, characterized in that, Identifying a reference individual in the current population and updating the fractional bit width of other individuals based on that reference individual includes: Obtain the objective function value for each individual in the current population, and determine the individual with the smallest objective function value as the reference individual; Obtain the average representation of other individuals in the current population, and update the decimal width of the other individuals based on the reference individual and the average representation using the teaching factor.

6. The method according to claim 1, characterized in that, Determining the difference in objective function values ​​between the individual and another individual, and updating the decimal width of the individual again based on the difference in objective function values, includes: Randomly select another individual different from the stated individual, obtain the difference in objective function values ​​between the stated individual and the other individual, and determine the update direction and adaptive factor of the stated individual based on the difference in objective function values; According to the adaptive factor, the individual is updated with a fractional-bit width in the update direction, wherein the update direction includes a first update direction and a second update direction, the first update direction representing the direction closer to the individual, and the second update direction representing the direction farther away from the individual.

7. The method according to claim 6, characterized in that, The adaptive factor characterizes the update range of the individual; according to the adaptive factor, updating the individual in the update direction with a fractional-bit width includes: The update range of the individual is determined based on the adaptive factor, and the individual is updated in the update direction with a decimal width by referring to the update range; The update range is dynamically adjusted based on the difference in the objective function value of the individual, representing an exploratory update or a convergent update of the decimal width of the individual.

8. The method according to any one of claims 1, 5, or 6, characterized in that, The objective function value is determined based on the objective function; the objective function is determined in the following manner: The relative error between the quantization detector and the floating-point detector is obtained. When the relative error is less than or equal to the target threshold, the objective function is determined based on the average fractional width of the fractional width combination. When the relative error is greater than the target threshold, the objective function is determined based on the average fractional bit width and a penalty term, wherein the penalty term is determined based on a scaling factor, and the relative error characterizes the bit error rate of the individual fractional bit width combination.

9. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the bit-width quantization method for MIMO as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to execute the bit-width quantization method for MIMO as described in any one of claims 1 to 8.

Images