Short packet rate segmentation multiple access optimization method

Abstract

The invention discloses a short packet rate segmentation multiple access optimization method. The method comprises the following steps of: segmenting a message into a public message and a private message; public messages and private messages of all users are coded to a public data stream and a private data stream respectively; performing linear precoding on the public data stream and the private data stream; deducing corresponding traversal and rate lower bounds of the public data stream and the private data stream according to the moving speeds of all users and statistical characteristics of channels, and optimizing a public data stream power distribution factor, a private data stream power distribution factor and a public data stream rate distribution proportion; multiplying each data stream by a corresponding precoding matrix and a power distribution factor, and superposing all signals to obtain a base station transmitting signal; and sequentially decoding the public data stream and the private data stream of the user, and reconstructing to obtain all information of the user. According to the method, the spectrum utilization rate can be remarkably improved under the non-ideal CSI, and the strict service quality requirement in ultra-reliable low-delay communication is met.

Description

Technical Field

[0001] The invention relates to the technical field of wireless communications, and in particular to a short packet rate division multiple access optimization method. Background Art

[0002] As self-driving cars must operate in complex and dynamic environments, the need for high-reliability, low-latency communications has become particularly urgent. Ultra-reliable low-latency communications (xURLLC) in the sixth-generation (6G) communication technology can provide almost perfect communication reliability and extremely low end-to-end latency, which is essential to support real-time decision-making in self-driving cars. To meet this demand, short data packets and finite block length (FBL) coding technology have become an effective solution, which can significantly reduce communication latency while ensuring reliability, and is particularly suitable for real-time data transmission in autonomous driving environments.

[0003] In practical applications, it is particularly difficult to obtain accurate channel state information (CSI) in high-mobility environments, especially in communications between base stations and vehicles; the Doppler effect and signal delay caused by the high-speed movement of vehicles will cause a significant decrease in signal quality, thereby affecting the stability and reliability of communications. In this case, the use of advanced technologies such as rate split multiple access (RSMA) can effectively deal with interference and optimize signal transmission. Among the existing technologies related to short packet rate split multiple access, such as the short packet rate split multiple access method and system suitable for high mobility scenarios with patent publication number CN117858234A, it is intended to solve the problem that the existing technology cannot effectively deal with the degradation of communication quality caused by the high mobility of vehicles in the vehicle network communication of short packet rate split multiple access. The problem with this solution is that the system model assumes that the large-scale fading of all vehicles is the same, which ignores the change in the distance between the vehicle and the base station in actual applications, as well as the signal fading effect at different locations; it also uses an exhaustive method to optimize resource allocation, resulting in its high computational complexity and cannot be efficiently applied in actual systems. In addition, the uneven distribution of private data streams and the distribution ratio of public data streams are not considered, which may lead to uneven traffic distribution, further affecting communication efficiency and fairness. If it is not specially designed for large-scale fading and dynamic channel conditions at different vehicle locations, the communication quality will be seriously affected.

[0004] Because the existing technology currently fails to provide a practical, effective and operational solution, especially in dealing with problems such as vehicle position changes, channel fading and traffic distribution in high-mobility systems, there is still room for further improvement. Summary of the invention

[0005] In order to overcome the defects and shortcomings of the prior art, the present invention provides a short packet rate division multiple access optimization method, which adopts a rate division multiple access technical solution based on short packets, improves the parameter settings such as power allocation and rate allocation of public and private data streams, solves the technical problems of ultra-low delay and ultra-low block error rate in communication, and achieves the technical effect of significantly improving spectrum utilization under non-ideal CSI. It can meet the strict quality of service (QoS) requirements in ultra-reliable low-latency communication (URLLC) under the premise of effectively improving system performance and rate.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention provides a short packet rate segmentation multiple access optimization method, comprising the following steps:

[0008] Obtain messages sent by the base station to the user, and divide the messages into public messages and private messages;

[0009] Encode all users' public messages into a public data stream, and encode each user's private messages into a private data stream;

[0010] Linearly precoding the public data stream and the private data stream to obtain a corresponding precoding matrix;

[0011] According to the mobile speed of all users and the statistical characteristics of the channel, the corresponding traversal and rate lower bounds of the public data stream and the private data stream are derived, and the power allocation factor of the public data stream, the power allocation factor of the private data stream, and the rate allocation ratio of the public data stream are optimized to obtain the power allocation factor corresponding to each data stream and the rate allocation of the public data stream;

[0012] Each data stream is multiplied by the corresponding precoding matrix and power allocation factor, and all signals are superimposed to obtain the base station transmission signal;

[0013] After receiving the signal, the receiving end regards all private data streams as noise, decodes the public data stream, and separates the public part information of the receiving end itself;

[0014] Subtracting the portion corresponding to the public data stream from the received signal, and according to the subtracted signal, treating the private data stream from other receiving ends as noise to decode the private data stream of the receiving end, and obtaining the private part information;

[0015] The public part information and the private part information of the user are reconstructed to obtain all the information of the user.

[0016] As a preferred technical solution, the linear precoding of the public data stream and the private data stream specifically includes:

[0017] The public data stream is precoded by maximizing the eigenvalue, and the private data stream is precoded by forcing zero to obtain the corresponding precoding matrix.

[0018] As a preferred technical solution, the corresponding traversal and rate lower bounds of the public data stream and the private data stream are derived according to the moving speeds of all users and the statistical characteristics of the channel, specifically including:

[0019] Traversal and rate lower bounds for public data flows It is expressed as:

[0020]

[0021] in, represents the expectation of the random variable, C represents the Shannon capacity, V represents the channel dispersion, Q -1 (·) represents the inverse Gaussian Q function, Γ c，k represents the signal-to-interference-noise ratio of the public data stream at user k, β c,k represents the block error rate of the public data stream, l c represents the block length of the public data stream, k represents the kth user, ε = J 0 (2πf D T) represents the time correlation coefficient of the Jakes model, f D =v k f c / c is the given carrier frequency f c and user speed v k The maximum Doppler frequency, c is the speed of light, ζ k represents the large-scale fading from the base station to user k, P represents the maximum transmit power, t represents the common power coefficient, K represents the number of users, represents the generalized n-th order exponential integral;

[0022] Traversal and rate lower bounds for private data flows It is expressed as:

[0023]

[0024] Among them, Γ p，k represents the signal-to-interference-noise ratio of the private data stream at user k, l k represents the block length of the private data stream, β p,k represents the block error rate of the private data stream, is the spatially uncorrelated Rayleigh flat fading channel vector between user k and the base station at time m, N t represents the number of base station antennas, P represents the precoding matrix, μk represents the private power coefficient, Represents parameter information about the gamma distribution The derivation of

[0025] As a preferred technical solution, the public data flow power allocation factor, the private data flow power allocation factor, and the public data flow rate allocation ratio are optimized, specifically including:

[0026] Taking the maximum communication rate as the optimization target and introducing the minimum communication rate indicator, the optimization problem is constructed as follows:

[0027]

[0028] Among them, C k represents the portion of the public data flow rate allocated to user k, R c and R k They represent the expected achievable rates of public data flows and private data flows, R min represents the QoS constraint of each vehicle, 1-t represents the power ratio of the public data flow, t·μ k represents the power ratio of the kth private data stream, t represents the public power coefficient, μ = [μ 1 ,μ 2 ,...,μ K ] represents the private power coefficient, c = [C 1 ,C 2 ,...,C K ] indicates the public data flow rate allocation ratio;

[0029] First, optimize the public power coefficient under the average condition of private data flow, and judge whether the public data flow is turned on in the current state according to the value of the public power coefficient. The optimization problem is solved as follows:

[0030]

[0031] Use sequential quadratic programming to find the local optimal solution of the problem * ;

[0032] If * <<1, the optimization problem is:

[0033]

[0034] Initial point setting ζ k represents the large-scale fading from the base station to user k;

[0035] By maximizing the minimum traversal and rate to achieve the minimum communication rate QoS requirement, the optimization problem is solved as follows:

[0036]

[0037] in, j decreases from K to 1. If Then the cycle will be broken and exit;

[0038] If t * →1, set t * =1, the optimization problem is:

[0039]

[0040] Set the initial point K represents the number of users, and the sequential quadratic programming method is used to solve the optimal solution of the optimization problem.

[0041] As a preferred technical solution, each data stream is multiplied by the corresponding precoding matrix and power allocation factor, and all signals are superimposed to obtain the base station transmission signal, which is specifically expressed as:

[0042]

[0043] Where x represents the base station transmitting signal, p c represents the linear precoding of the common data stream, p k represents the linear precoding of the private data stream, P represents the maximum transmit power, t represents the public power coefficient, μ k represents the private power factor, s c Indicates public data flow, s k Indicates a private data stream.

[0044] As a preferred technical solution, the signal received by the receiving end is expressed as:

[0045]

[0046] in, represents the noise-normalized received signal at user k, is the spatially uncorrelated Rayleigh flat fading channel vector between user k and BS at time m, N t represents the number of base station antennas, ζ k represents the large-scale fading from the base station to user k, n k ~CN(0,1) is the additive white Gaussian noise at user k.

[0047] As a preferred technical solution, after receiving the signal, the receiving end regards all private data streams as noise and decodes the public data stream. The signal-to-noise ratio of the public data stream is expressed as:

[0048]

[0049] Where P represents the maximum transmit power, t represents the common power coefficient, ζ k represents the large-scale fading from the base station to user k, p c represents the linear precoding of the common data stream, is the spatially uncorrelated Rayleigh flat fading channel vector between user k and BS at time m, N t Indicates the number of base station antennas, μ j represents the private power factor, p j represents the linear precoding of the private data stream, K represents the number of users.

[0050] As a preferred technical solution, the private data stream from other receiving ends is regarded as noise to decode the private data stream of the receiving end. The signal to noise ratio of the private data stream decoded at user k is expressed as:

[0051]

[0052] Where P represents the maximum transmit power, t represents the common power coefficient, ζ k represents the large-scale fading from the base station to user k, p k represents the linear precoding of the private data stream, is the spatially uncorrelated Rayleigh flat fading channel vector between user k and BS at time m, N t Indicates the number of base station antennas, μ j represents the private power coefficient, K represents the number of users.

[0053] As a preferred technical solution, the public part information and private part information of the user are reconstructed to obtain all the information of the user, and the instantaneous maximum achievable rate of the public data flow and the private data flow is for:

[0054]

[0055] Among them, Γ c，k represents the signal-to-interference-to-noise ratio of the public data stream at user k, C represents the Shannon capacity, V represents the channel dispersion, Q -1 (·) represents the inverse Gaussian Q function, l c represents the block length of the public data stream, β c,k represents the block error rate of the common data stream, Γ p，k represents the signal-to-interference-noise ratio of the private data stream at user k, l k represents the block length of the private data stream, β p,kIndicates the block error rate of the private data stream.

[0056] The present invention also provides a short packet rate division multiple access optimization system, comprising: a base station with multiple antennas, a receiving end, a message segmentation module, a data stream encoding module, a linear precoding module, a power allocation optimization module, a transmission signal construction module, a public data stream decoding module, a private data stream decoding module and a reconstruction module;

[0057] The base station sends a message to the receiving end;

[0058] The message segmentation module is used to segment the message into public messages and private messages;

[0059] The data stream encoding module is used to encode the public messages of all users into a public data stream, and encode the private messages of each user into a private data stream;

[0060] The linear precoding module is used to perform linear precoding on the public data stream and the private data stream to obtain a corresponding precoding matrix;

[0061] The power allocation optimization module is used to derive the corresponding traversal and rate lower bounds of the public data stream and the private data stream according to the mobile speeds of all users and the statistical characteristics of the channels, optimize the power allocation factor of the public data stream, the power allocation factor of the private data stream, and the rate allocation ratio of the public data stream, and obtain the power allocation factor corresponding to each data stream and the rate allocation of the public data stream;

[0062] The transmission signal construction module is used to multiply each data stream by the corresponding precoding matrix and power allocation factor, and obtain the base station transmission signal after all signals are superimposed;

[0063] The public data stream decoding module is used to decode all private data streams as noise to obtain public data streams, and separate the public part information of the receiving end itself;

[0064] The private data stream decoding module is used to subtract the corresponding part of the public data stream from the received signal, and according to the subtracted signal, regard the private data stream from other receiving ends as noise to decode the private data stream of the receiving end to obtain the private part information;

[0065] The reconstruction module is used to reconstruct the public part information and the private part information of the user to obtain all the information of the user.

[0066] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0067] (1) The present invention adopts a rate-segmented multiple access technology solution based on short packets, which solves the technical problems of ultra-low latency and ultra-low block error rate in communications, and achieves the technical effect of significantly improving spectrum utilization under non-ideal CSI.

[0068] (2) The present invention can implement a short packet rate splitting scheme with relatively low complexity. Aiming at complex mobility environments, the present invention improves the parameter settings such as power allocation and rate allocation of public and private data flows. Compared with multiple access technologies such as RSMA with average settings, traditional NOMA, and SDMA, the present invention can effectively improve system performance and rate while meeting the strict quality of service (QoS) requirements in ultra-reliable low-latency communication (URLLC). BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of the process of the short packet rate division multiple access optimization method of the present invention;

[0070] Figure 2 This is a schematic diagram of the overall architecture of the present invention applied to a vehicle-mounted scenario;

[0071] Figure 3 It is a schematic diagram of system and rate comparison of RSMA of the present invention with RSMA under average setting, traditional SDMA and NOMA under different transmission power conditions;

[0072] Figure 4 It is a schematic diagram of system and rate comparison of RSMA of the present invention with RSMA under average setting, traditional SDMA and NOMA under different vehicle speeds;

[0073] Figure 5 It is a schematic diagram of system and rate comparison between RSMA of the present invention and RSMA under average setting, traditional SDMA, and NOMA under different block lengths and block error rates. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] Example 1

[0076] like Figure 1 As shown, this embodiment provides a short packet rate division multiple access optimization method, which can be used to deal with problems such as vehicle position change, channel fading and traffic distribution in a high mobility system. Figure 2 As shown in the figure, a system with K users is further explained. A layer of rate division multiple access scheme is used to serve K users. In the downlink MISO broadcast system, a system with Nt A single base station (BS) with a root antenna and K single-antenna high-mobility users, the user set is K represents the number of users. The method specifically comprises the following steps:

[0077] S1: The base station sends K messages W to K users k , the message W k It is divided into two parts, namely public messages and private messages;

[0078] Specifically, the message W k is divided into two parts {W c,k ,W p,k}, named public message and private message respectively, The public messages of K users {W c,1 ,...,W c,K} are encoded together into the common data stream s c In the example, all K users decode the private message W of user k. p,k Encoded into private data stream s k In the example, the entire data stream vector to be sent is represented by s=[s c ,s 1 ,s 2 ,...,s K ] T express;

[0079] In this embodiment, linear precoding is applied to all data streams, and the precoding matrix satisfy Zero-forcing precoding (ZF-precoding) is used for private data streams, and maximizing eigenvalue precoding is used for public data streams. c represents the linear precoding of the common data stream, p k represents the linear precoding of the private data stream of user k.

[0080] S2: Based on the channel information of high-mobility users, the base station solves the proposed optimization problems P1-P4 by deriving the corresponding traversals and rate lower bounds of the public data stream and private data stream, and determines the power allocation factor and beamforming scheme of each data stream, as well as the rate allocation ratio of the public data stream;

[0081] In this embodiment, the public parts of the above users are encoded and merged into a public data stream, wherein the encoding ratio is set according to the calculated public data stream rate allocation ratio, and the private parts of each user are encoded into their own private data streams;

[0082] In this embodiment, the base station divides the information of the high-mobility user into a public part and a private part and performs short packet processing;

[0083] In this embodiment, the base station derives the traversal and rate lower bounds of the system based on the moving speeds of K users and the statistical characteristics of the channel. Based on the derived traversal and rate lower bounds, the power allocation factor of the public data stream, the power allocation factor of the private data stream, and the rate allocation ratio of the public data stream are optimized in sequence to obtain the power allocation factor corresponding to each data stream and the rate allocation of the public data stream.

[0084] In step S2, the specific operations of optimization based on the derived lower bound traversal and rate expression are as follows:

[0085] Firstly, the private data flow is assumed to adopt an average power allocation, and the public power allocation factor is solved by using the sequential quadratic programming (SQP) method based on the traversal and rate lower bound expressions.

[0086] Based on the solved public power allocation factor, the solution will automatically decide whether to enable public data flow;

[0087] If the public data flow is enabled, first optimize the private data flow power allocation factor based on the private data flow traversal and rate lower bound expression, and then optimize the public data flow rate allocation ratio based on the power setting of the given public and private data flow scheme to complete the configuration of all parameters;

[0088] If the public data flow is closed, the public data flow related parameters do not need to be optimized. At this time, by optimizing the closed-form expression of the lower bound of the private data flow, the private data flow power allocation factor of the scheme is obtained to complete the configuration of all parameters.

[0089] In this embodiment, the steps of optimizing the public data flow power, private data flow power and public data flow rate allocation are specifically explained in combination with the traversal and rate lower bound expressions:

[0090] Final traversal and rate lower bounds for public data flows It can be expressed as:

[0091]

[0092] Among them, the function Q -1 (·) represents the inverse Gaussian Q function, represents the expectation of a random variable,

[0093] represents the generalized n-th order exponential integral;

[0094] Traversal and rate lower bounds for private data flows It can finally be expressed as:

[0095]

[0096] The second line, parameter

[0097] Then in the fourth line of expression, Represents parameter information about the gamma distribution The derivation of

[0098] Furthermore, in order to cope with actual vehicle communication scenarios, the total maximum communication rate of all vehicles is taken as the optimization target. At the same time, in order to meet the communication needs of each vehicle, a minimum communication rate indicator is introduced for each vehicle.

[0099] The optimization problem is constructed as:

[0100]

[0101] Among them, C k represents the portion of the public data flow rate allocated to user k, R c and R k They represent the expected achievable rates of public data flows and private data flows, R min represents the QoS (quality of service) constraints for each vehicle, K represents the number of users, and the optimization variable c=[C 1 ,C 2 ,...,C K ] determines the rate distribution among users in the common data stream. The variables t and μ = [μ 1 ,μ 2 ,...,μ K ] represents the parameters related to the power allocation ratio, where 1-t represents the power ratio of the common data stream, and t·μ k represents the power ratio of the kth private flow. It is necessary to optimize the public power coefficient t, the private power coefficient μ = [μ 1 ,μ 2 ,...,μ K ], and the public data flow rate allocation ratio c = [C 1 ,C 2 ,...,C K ] to maximize the objective function. The interdependence of variables and their ratios in the objective function makes the problem inherently non-convex, and the objective function lacks a direct closed-form expression and can only be approximated by the sample average result of Monte Carlo, which is not conducive to solution.

[0102] Next, using the derived closed-form solution, we provide some simple methods to solve the problem. First, optimize the public power coefficient t under the average condition of the private data flow, and determine whether the public data flow is enabled in the current state based on the value of the public coefficient. To this end, we can solve the following optimization problem instead:

[0103]

[0104] Although there is only one variable, since the objective function is not an elementary function, it is difficult to obtain a direct closed-form solution by directly differentiating it. Considering that the objective function has a continuous first-order derivative, the "fmincon" function in MATLAB is used to solve it. The solution is based on the premise of equal distribution of private data streams, starting at t = 0.5. The function configuration is based on gradient information and uses the sequential quadratic programming (SQP) method to efficiently find the local optimal solution of the problem. * .

[0105] According to the two results of optimizing the common coefficients, there are two corresponding solutions:

[0106] 1)t * <<1, in this case, most of the power will be allocated to the public data flow. Therefore, it can be assumed that the public data flow has a large enough rate to be allocated to meet the QoS requirements of the vehicle. Then, the following optimization problem can be solved alternatively:

[0107]

[0108] Since the objective function is a non-elementary function and the variables are coupled, we continue to use MATLAB's sequential quadratic programming algorithm to obtain a solution. Initial point setting This initial setting allows faster convergence than optimizing from an average.

[0109] After completing the public coefficient allocation and private coefficient allocation, the Shannon achievable rate of all data flows has been determined. By maximizing the minimum traversal and rate, the minimum communication rate QoS requirement can be indirectly achieved, that is, the solution is as follows:

[0110]

[0111] This problem is essentially a water injection problem, which can be calculated by the following expression:

[0112]

[0113] in, j decreases from K to 1 if Cycle exit, R c , Rk It can be obtained by the sample averaging method.

[0114] 2)t * →1, in the actual system, this means closing the common data flow, and the RSMA scheme is downgraded to the SDMA scheme. Therefore, we can directly set t * =1, and no longer consider the public data flow rate allocation, and directly optimize the following problems:

[0115]

[0116] Set the initial point Continue to use the SQP method to solve the problem.

[0117] S3: After multiplying the entire data stream vector by the precoding matrix P and the power allocation factor, all signals are superimposed and sent uniformly by the base station to the above K users;

[0118] Specifically, the base station transmits a signal that can be expressed as: The power sharing coefficient 0≤t≤1 controls the power distribution between the public data flow and the total private data flow, where 1-t is the power ratio of the public data flow. Parameter 0≤μ k ≤1, and The power allocation ratio of the private data stream allocated to the kth vehicle is specified, and the vector μ = [μ 1 ,μ 2 ,...,μ K ], therefore, t·μ k is the power ratio of the kth private data stream. Assuming that all symbols have unit power, The transmitted signal is subject to the maximum transmit power P.

[0119] In this embodiment, the received signal after noise normalization at user k is It can be expressed as: in is the spatially uncorrelated Rayleigh flat fading channel vector between user k and BS at time m, whose elements are independent and obey CN(0,1) distribution, ζ k represents the large-scale fading from the base station to user k, n k ~CN(0,1) is the additive white Gaussian noise at user k. Considering the scenario where non-ideal CSIT occurs due to user mobility and the delay in user reporting CSI, the channel vector at time m is expressed as e k [m] Each element is independent and follows the CN(0,1) distribution. ε=J 0 (2πf D T) represents the time correlation coefficient of the Jakes model, fD =v k f c / c is the given carrier frequency f c and user speed v k (c is the speed of light), and T represents the time interval between channel instantiations. In other words, h k [m] is the instantaneous channel vector observed at time m. Due to the high mobility of the user, the transmitter only knows the channel vector h observed at time m-1 k [m-1], and use h at time m k [m-1] is used to calculate the precoding matrix.

[0120] S4: After receiving the signal, the user first treats all private data streams as noise and decodes the public data stream, and separates the public part of the user's own information;

[0121] Specifically, after receiving the signal, the user first considers all private data streams as noise and decodes the public data stream s c , and separate the public part information of the user. The signal to interference and noise ratio (SINR) of the public data stream at user k is:

[0122] S5: Successfully decoded the public data stream s c Then, the part corresponding to the public data stream in the received signal is subtracted using the successive interference cancellation technique (SIC). Then, user k decodes the private data stream s k , according to the subtracted signal, the private data stream from other users is regarded as noise to decode the private data stream of the user, and then the private part information is obtained.

[0123] In this embodiment, the private data stream s is decoded at user k. k The SINR is:

[0124] S6: reconstruct the public part and private part of the user information obtained in steps S4 and S5, and finally obtain all the information of the user;

[0125] In this embodiment, after successfully decoding the public data stream and the private data stream, user k obtains the decoded W c Extract W c,k Combined with W c,k and W p,k to reconstruct the original message.

[0126] In short packet communication, for sufficiently large block lengths (l>100), at time m, the maximal achievable rate of the public and private data flows is It can be approximated as:

[0127]

[0128]

[0129] In order to ensure that the public data stream is successfully decoded by all users, the maximum achievable rate of the public data stream is The Shannon capacity C(x) = log 2 (1+x), channel dispersion V(x) = (1-(1+x) -2 )(log 2 e) 2 , l c ,l 1 ,...,l K Represents stream s c ,s 1 ,s 2 ,...,s K The block length (blocklengths), β represents the block error rate (BLER);

[0130] In this embodiment, the channel state information is acquired through a pilot-based channel estimation method.

[0131] The present invention takes into account the influence of user mobility on the accuracy of channel state information (CSIT), and derives a closed-form expression of the traversal and rate lower bounds related to user speed. By jointly optimizing the expression, the power allocation and rate scheduling of public and private data streams are determined, which effectively improves the system and rate. In addition, the present invention takes into account the influence of limited packet length in actual communication, further improves system reliability and reduces delay. Through precise power and rate optimization, the present invention achieves higher transmission efficiency and lower delay in a high-speed mobile environment.

[0132] Through the detailed description of the above embodiments, it can be clearly seen that the RSMA scheme proposed in the present invention improves system performance by optimizing the public data flow power allocation coefficient, the private data flow power allocation coefficient and the public data flow rate allocation ratio. Figure 3-Figure 5 As shown in the figure, the trend of the system's ergodic sum rate changes with transmission power, vehicle speed, and block length under different parameters is shown. In all test scenarios, the proposed RSMA scheme always outperforms traditional schemes (including RSMA, SDMA, and NOMA under average settings). Especially in high-speed mobile environments, the system's robustness and transmission efficiency are further improved through precise optimization and power allocation of private data flows.

[0133] Specifically, Figure 3 It is shown that with the change of transmission power, the proposed RSMA scheme exhibits a higher sum rate than the traditional scheme. Figure 4 The influence of vehicle speed on system performance is emphasized. Although the traversal sum rate decreases with the increase of speed, the RSMA scheme of the present invention can still maintain strong robustness and significantly exceeds the traditional scheme under average setting. Figure 5 The performance advantage of the present invention after optimization is further verified from the perspective of block length and block error rate, ensuring the optimal performance gain under strict block error rate and short code transmission conditions.

[0134] Example 2

[0135] This embodiment provides a short packet rate division multiple access optimization system, which is used to implement the short packet rate division multiple access optimization method of the above embodiment 1. The system includes: a base station with multiple antennas, a receiving end, a message segmentation module, a data stream encoding module, a linear precoding module, a power allocation optimization module, a transmission signal construction module, a public data stream decoding module, a private data stream decoding module and a reconstruction module;

[0136] In this embodiment, the base station sends a message to the receiving end;

[0137] In this embodiment, the message segmentation module is used to segment the message into a public message and a private message;

[0138] In this embodiment, the data stream encoding module is used to encode the public messages of all users into the public data stream and encode the private messages of each user into the private data stream;

[0139] In this embodiment, the linear precoding module is used to perform linear precoding on the public data stream and the private data stream to obtain a corresponding precoding matrix;

[0140] In this embodiment, the power allocation optimization module is used to derive the corresponding traversal and rate lower bounds of the public data stream and the private data stream according to the mobile speed of all users and the statistical characteristics of the channel, optimize the power allocation factor of the public data stream, the power allocation factor of the private data stream, and the rate allocation ratio of the public data stream, and obtain the power allocation factor corresponding to each data stream, as well as the rate allocation of the public data stream;

[0141] In this embodiment, the transmission signal construction module is used to multiply each data stream by the corresponding precoding matrix and power allocation factor, and all signals are superimposed to obtain a base station transmission signal;

[0142] In this embodiment, the public data stream decoding module is used to treat all private data streams as noise and decode the public data stream, and separate the public part information of the receiving end itself;

[0143] In this embodiment, the private data stream decoding module is used to subtract the portion corresponding to the public data stream from the received signal, and according to the subtracted signal, the private data stream from other receiving ends is regarded as noise to decode the private data stream of the receiving end to obtain the private part information;

[0144] In this embodiment, the reconstruction module is used to reconstruct the public part information and the private part information of the user to obtain all the information of the user.

[0145] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A short packet rate division multiple access optimization method, characterized in that: The steps include: Obtain messages sent by the base station to the user, and divide the messages into public messages and private messages; Encode all users' public messages into a public data stream, and encode each user's private messages into a private data stream; Linearly precoding the public data stream and the private data stream to obtain a corresponding precoding matrix; According to the mobile speed of all users and the statistical characteristics of the channel, the corresponding traversal and rate lower bounds of the public data stream and the private data stream are derived, and the power allocation factor of the public data stream, the power allocation factor of the private data stream, and the rate allocation ratio of the public data stream are optimized to obtain the power allocation factor corresponding to each data stream and the rate allocation of the public data stream; Each data stream is multiplied by the corresponding precoding matrix and power allocation factor, and all signals are superimposed to obtain the base station transmission signal; After receiving the signal, the receiving end regards all private data streams as noise, decodes the public data stream, and separates the public part information of the receiving end itself; Subtracting the portion corresponding to the public data stream from the received signal, and according to the subtracted signal, treating the private data stream from other receiving ends as noise to decode the private data stream of the receiving end, and obtaining the private part information; The public part information and the private part information of the user are reconstructed to obtain all the information of the user.

2. The short packet rate division multiple access optimization method according to claim 1, characterized in that: The linear precoding of the public data stream and the private data stream specifically includes: The public data stream is precoded by maximizing the eigenvalue, and the private data stream is precoded by forcing zero to obtain the corresponding precoding matrix.

3. The short packet rate division multiple access optimization method according to claim 1, characterized in that: The corresponding traversal and rate lower bounds of public and private data flows are derived based on the mobile speeds of all users and the statistical characteristics of the channels, including: Traversal and rate lower bounds for public data flows It is expressed as: in, represents the expectation of the random variable, C represents the Shannon capacity, V represents the channel dispersion, Q -1 (·) represents the inverse Gaussian Q function, Γ c,k represents the signal-to-interference-noise ratio of the public data stream at user k, β c,k represents the block error rate of the public data stream, l c represents the block length of the public data stream, k represents the kth user, ε=J0(2πf D T) represents the time correlation coefficient of the Jakes model, f D =v k f c / c is the given carrier frequency f c and user speed v k The maximum Doppler frequency, c is the speed of light, ζ k represents the large-scale fading from the base station to user k, P represents the maximum transmit power, t represents the common power coefficient, K represents the number of users, represents the generalized n-th order exponential integral; Traversal and rate lower bounds for private data flows It is expressed as: Among them, Γ p,k represents the signal-to-interference-noise ratio of the private data stream at user k, l k represents the block length of the private data stream, β p,k represents the block error rate of the private data stream, is the spatially uncorrelated Rayleigh flat fading channel vector between user k and the base station at time m, N t represents the number of base station antennas, P represents the precoding matrix, μ k represents the private power coefficient, Represents parameter information about the gamma distribution The derivation of 4. The short packet rate division multiple access optimization method according to claim 1, characterized in that: Optimize the public data flow power allocation factor, private data flow power allocation factor, and public data flow rate allocation ratio, including: Taking the maximum communication rate as the optimization target and introducing the minimum communication rate indicator, the optimization problem is constructed as follows: Among them, C k represents the portion of the public data flow rate allocated to user k, R c and R k They represent the expected achievable rates of public data flows and private data flows, R min represents the QoS constraint of each vehicle, 1-t represents the power ratio of the public data flow, t·μ k represents the power ratio of the kth private data stream, t represents the public power coefficient, μ=[μ1,μ2,…,μ K ] represents the private power coefficient, c=[C1,C2,…,C K ] indicates the public data flow rate allocation ratio; First, optimize the public power coefficient under the average condition of private data flow, and judge whether the public data flow is turned on in the current state according to the value of the public power coefficient. The optimization problem is solved as follows: Use sequential quadratic programming to find the local optimal solution of the problem * ; If * <<1, the optimization problem is: Initial point setting ζ k represents the large-scale fading from the base station to user k; By maximizing the minimum traversal and rate to achieve the minimum communication rate QoS requirement, the optimization problem is solved as follows: in, j decreases from K to 1, if it is determined Then the cycle will be broken and exit; If * →1, set t * =1, the optimization problem is: Set the initial point K represents the number of users, and the sequential quadratic programming method is used to solve the optimal solution of the optimization problem.

5. The short packet rate division multiple access optimization method according to claim 1, characterized in that: Each data stream is multiplied by the corresponding precoding matrix and power allocation factor, and all signals are superimposed to obtain the base station transmission signal, which is specifically expressed as: Where x represents the base station transmitting signal, p c represents the linear precoding of the common data stream, p k represents the linear precoding of the private data stream, P represents the maximum transmit power, t represents the public power coefficient, μ k represents the private power factor, s c Indicates public data flow, s k Indicates a private data stream.

6. The short packet rate division multiple access optimization method according to claim 5, characterized in that: The signal received by the receiving end is expressed as: in, represents the noise-normalized received signal at user k, is the spatially uncorrelated Rayleigh flat fading channel vector between user k and BS at time m, N t represents the number of base station antennas, ζ k represents the large-scale fading from the base station to user k, n k ~CN(0,1) is the additive white Gaussian noise at user k.

7. The short packet rate division multiple access optimization method according to claim 1, characterized in that: After receiving the signal, the receiver regards all private data streams as noise and decodes the public data stream. The signal-to-noise ratio of the public data stream is expressed as: Where P represents the maximum transmit power, t represents the common power coefficient, ζ k represents the large-scale fading from the base station to user k, p c represents the linear precoding of the common data stream, is the spatially uncorrelated Rayleigh flat fading channel vector between user k and BS at time m, N t Indicates the number of base station antennas, μ j represents the private power factor, p j represents the linear precoding of the private data stream, K represents the number of users.

8. The short packet rate division multiple access optimization method according to claim 1, characterized in that: The private data stream from other receiving ends is regarded as noise to decode the private data stream of the receiving end. The signal-to-interference-noise ratio of the private data stream decoded at user k is expressed as: Where P represents the maximum transmit power, t represents the common power coefficient, ζ k represents the large-scale fading from the base station to user k, p k represents the linear precoding of the private data stream, is the spatially uncorrelated Rayleigh flat fading channel vector between user k and BS at time m, N t Indicates the number of base station antennas, μ j represents the private power coefficient, K represents the number of users.

9. The short packet rate division multiple access optimization method according to claim 1, characterized in that: Reconstruct the user's public and private information to obtain all the user's information, the instantaneous maximum achievable rate of the public and private data flows for: Among them, Γ c，k represents the signal-to-interference-to-noise ratio of the public data stream at user k, C represents the Shannon capacity, V represents the channel dispersion, Q -1 (·) represents the inverse Gaussian Q function, l c represents the block length of the public data stream, β c,k represents the block error rate of the common data stream, Γ p，k represents the signal-to-interference-noise ratio of the private data stream at user k, l k represents the block length of the private data stream, β p,k Indicates the block error rate of the private data stream.

10. A short packet rate division multiple access optimization system, characterized in that: include: A base station having multiple antennas, a receiving end, a message segmentation module, a data stream encoding module, a linear precoding module, a power allocation optimization module, a transmission signal construction module, a public data stream decoding module, a private data stream decoding module and a reconstruction module; The base station sends a message to the receiving end; The message segmentation module is used to segment the message into public messages and private messages; The data stream encoding module is used to encode the public messages of all users into a public data stream, and encode the private messages of each user into a private data stream; The linear precoding module is used to perform linear precoding on the public data stream and the private data stream to obtain a corresponding precoding matrix; The power allocation optimization module is used to derive the corresponding traversal and rate lower bounds of the public data stream and the private data stream according to the mobile speeds of all users and the statistical characteristics of the channels, optimize the power allocation factor of the public data stream, the power allocation factor of the private data stream, and the rate allocation ratio of the public data stream, and obtain the power allocation factor corresponding to each data stream and the rate allocation of the public data stream; The transmission signal construction module is used to multiply each data stream by the corresponding precoding matrix and power allocation factor, and obtain the base station transmission signal after all signals are superimposed; The public data stream decoding module is used to decode all private data streams as noise to obtain the public data stream, and separate the public part information of the receiving end itself; The private data stream decoding module is used to subtract the corresponding part of the public data stream from the received signal, and according to the subtracted signal, regard the private data stream from other receiving ends as noise to decode the private data stream of the receiving end to obtain the private part information; The reconstruction module is used to reconstruct the public part information and the private part information of the user to obtain all the information of the user.

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