An ambc-noma communication resource joint optimization method for high mobile v2x scenarios

Abstract

The application provides an AmBC-NOMA communication resource joint optimization method for a high-mobility V2X scene, and relates to the technical field of communication resource joint optimization. In view of the channel dynamic change caused by high-speed movement of vehicles and the influence of the channel dynamic change on short packet communication reliability and rate performance, the application combines reflective communication and non-orthogonal multiple access technology, fully considers the time-varying characteristics of the channel, and jointly optimizes the reflection coefficient, the power distribution coefficient and the block length resource allocation in the system, so that the communication rate performance and resource utilization efficiency under the requirements of short time delay and low error rate are improved. Through the method, the short packet transmission rate of the system can be significantly improved under the conditions of limited time delay and channel uncertainty, and the practical application requirements of the future vehicle networking system for efficient, low time delay, high reliable short packet communication are met.

Description

Technical Field

[0001] This invention relates to the field of joint optimization technology of communication resources, and in particular to an AmBC-NOMA joint optimization method for high-mobility V2X scenarios. Background Technology

[0002] With the rapid development of intelligent transportation technologies, vehicle-to-everything (V2X) communication has been widely used as a crucial support for achieving safe and efficient traffic management. V2X communication significantly improves the safety, efficiency, and user experience of traffic systems by enabling information exchange between vehicles (V2V), between vehicles and infrastructure (V2I), and between vehicles and other traffic participants. However, as V2X networks continue to expand, traditional communication methods based on orthogonal multiple access (OMA) mechanisms have gradually revealed problems such as low spectrum resource utilization, congestion during large-scale access, and insufficient energy efficiency, making it difficult to meet the higher requirements of communication systems in future complex traffic environments. To address these issues, non-orthogonal multiple access (NOMA) technology has received widespread attention in recent years. NOMA, by employing superposition coding on the same frequency resources and combining it with continuous interference cancellation (SIC) technology, can support simultaneous communication by multiple users, effectively improving spectrum efficiency and enhancing the capacity for large-scale user access. At the same time, environmental backscatter communication (AmBC) technology is also gradually emerging. AmBC achieves communication by reflecting existing radio frequency (RF) signals in the environment and superimposing its own modulation information during the reflection process. It eliminates the need for active RF transmitters, significantly reducing the energy consumption of communication terminals and enhancing the system's green communication capabilities and deployment flexibility. Therefore, the AmBC-NOMA scheme, which combines AmBC and NOMA technologies, is considered an effective technical path to further improve the spectral and energy efficiency of V2X communication systems.

[0003] Existing research on AmBC-NOMA communication systems primarily focuses on improving system performance in terms of spectrum utilization efficiency, energy efficiency, and communication concealment. Previous studies generally agree that introducing backscatter communication mechanisms into the NOMA architecture not only helps reduce node energy consumption but also enables efficient reuse of spectrum resources in multi-user scenarios, providing technical support for building large-scale, low-power wireless communication systems. Building upon this foundation, to further enhance system performance, existing research generally focuses on optimization modeling around resource allocation strategies. Joint resource allocation schemes among base stations, roadside units, and reflecting devices have been designed for different system objectives, resulting in significant improvements in spectral efficiency, energy efficiency, and quality of service. The paper "Backscatter-enabled efficient V2X communication with non-orthogonal multiple access" establishes a maximum-minimum capacity optimization model, transforming the problem into a standard convex optimization problem. It employs KKT conditions and sub-gradient methods to jointly optimize the power allocation between base stations and roadside units, thereby increasing the minimum achievable capacity of the system and effectively enhancing spectral efficiency and communication performance. The paper "Energy efficiency optimization for backscatter enhanced NOMA cooperative V2X communications under imperfect CSI" proposes an energy efficiency optimization framework considering imperfect channel state information (CSI). It models the problem of minimizing the total system transmit power as a non-convex optimization problem and uses problem decomposition and iterative sub-gradient methods to jointly optimize base station power allocation, roadside unit power allocation, and the reflection coefficient of reflecting devices, thus significantly improving the overall energy efficiency of the system. The paper "Energy-efficient backscatter aided uplink NOMA roadside sensor communications under channel..." To address the issue of estimation errors, the authors propose an energy efficiency maximization optimization framework. This framework models the problem as a fractional programming problem maximizing energy efficiency and employs a two-stage alternating optimization algorithm. In the first stage, the Dinkelbach method combined with subgradient iteration is used to optimize the carrier transmitter transmit power. In the second stage, the reflection coefficient of the roadside sensor is directly optimized using a closed-form solution. This approach further improves the overall energy efficiency of the system while ensuring Quality of Service (QoS) requirements, thus enhancing the feasibility of large-scale application in future intelligent transportation systems.

[0004] Although existing research has extensively explored the application of backscatter-enhanced nonorthogonal multiple access (AmBC-NOMA) technology in V2X communication and proposed various schemes such as capacity optimization, energy efficiency improvement, and enhanced communication concealment, certain limitations still exist. Specifically, existing work is mostly based on ideal channel conditions or joint optimization under the premise of fixed system parameters, failing to fully consider the requirements of rapid channel changes in high-mobility V2X scenarios for dynamic adaptation of resource allocation. In addition, some studies only optimize at the single parameter level, lacking joint dynamic optimization design for multi-dimensional parameters such as power allocation coefficient, reflection coefficient, and block length resources, making it difficult to achieve a comprehensive improvement in overall system performance under the requirements of low latency and high reliability short packet communication. Therefore, for AmBC-NOMA V2X systems with high mobility and short packet communication characteristics, further research and improvement of multi-parameter joint dynamic optimization methods are still needed to better meet the performance requirements of future intelligent transportation communication systems.

[0005] In summary, while existing research on AmBC-NOMA V2X communication systems has made some progress in optimizing network capacity, energy efficiency, and communication concealment, significant shortcomings remain for high-mobility short-packet communication scenarios. Under short-packet communication conditions, the system faces more stringent requirements for low latency, high reliability, and high data rates. Existing optimization strategies struggle to simultaneously ensure reliable short-packet transmission while maximizing system transmission rates. Therefore, for high-mobility short-packet communication scenarios, it is urgent to design a joint dynamic optimization method considering transmit power, reflection coefficient, and block length resources to maximize the rate performance of the AmBC-NOMA V2X system under short-packet communication conditions, meeting the pressing needs of future intelligent transportation systems for efficient short-time communication. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a joint optimization method for AmBC-NOMA communication resources in high-mobility V2X scenarios;

[0007] A joint optimization method for AmBC-NOMA communication resources in high-mobility V2X scenarios includes the following steps:

[0008] Step 1: Construct an AmBC-NOMA communication system for high-mobility V2X scenarios;

[0009] The AmBC-NOMA communication system for high-mobility V2X scenarios includes a roadside unit (RSU), a reflector (BD), and a near-end user unit (U). N and a remote user U F Each node is equipped with a single antenna.

[0010] The RSU acts as the main transmitter, simultaneously transmitting to U via non-orthogonal multiple access (NOMA) technology and power domain multiplexing. N and U F The superimposed signal is transmitted; while receiving the RSU signal, the BD carries its own information through a backscattering mechanism to assist the RSU in achieving coverage and transmission enhancement for vehicle users; specifically as follows:

[0011] All links are modeled using a complex Gaussian distribution, considering Rayleigh fading; RSU to U N U F The channels of BD are represented as h respectively. RN h RF h RB Its statistical distribution is BD to U N U F The links are represented as h respectively. BN h BF Its statistical distribution is

[0012] A first-order autoregressive model is used to model channel variations; channel h k The dynamic evolution form of (t) is:

[0013]

[0014] in, The term is an independent and identically distributed complex Gaussian noise term, representing the time-varying component of the channel. This reflects the average power of the noise component, ρ k The channel autocorrelation coefficient is calculated using the following formula: ρ k =J0(2πf D T s Where J0(·) is the zeroth-order Bessel function, The value is the Doppler frequency shift, v is the vehicle speed, and f is the vehicle velocity. c T is the carrier frequency. S Where c is the symbol period and c is the speed of light;

[0015] The minimum mean square error (MMSE) method is used to estimate the channel state information (CSI) once at the beginning of each coherence time; the initial channel is represented as:

[0016]

[0017] in, For MMSE estimates, To estimate the error, the two are independent. This represents the average power of the channel estimate. This represents the average power of the estimation error;

[0018] Based on the initial channel, a first-order autoregressive (AR) model is used for recursive calculation to uniformly represent the channel state at any given time as:

[0019]

[0020] Where, ε k This reflects the complex Gaussian noise disturbance caused by estimation error and user mobility on the channel, satisfying: in, This represents the average power of the estimation error;

[0021] RSU employs Non-Orthogonal Multiple Access (NOMA) technology to simultaneously send signals to U... N and U F Send a superimposed signal s(t), which is represented as:

[0022]

[0023] Where P is the total transmit power, s N (t), s F (t) represent the target signals for the near-end user and the far-end user, respectively, satisfying E[|s N (t)| 2 ]=E[|s F (t)| 2 ] = 1, where E[·] represents the statistical expectation; a N and a F These are the base station to the near-end user U N and remote user U F The allocated power allocation factor satisfies a N +a F =1 and a F >a N ;

[0024] After receiving the superimposed signal s(t) from the RSU, the reflecting device BD superimposes its own information s through passive reflection modulation. C (t), forming a reflected signal; the reflected signal is modeled as: βs(t)s C (t), where β is the reflection coefficient, satisfying |β|<1;

[0025] Ultimately, the signal model received by the vehicle user is as follows:

[0026] For U N :

[0027] y N (t)=h RN (t)s(t)+hRB h BN (t)βs(t)s C (t)+n N (t)

[0028] For U F :

[0029] y F (t)=h RF (t)s(t)+h RB h BN (t)βs(t)s C (t)+n F (t)

[0030] Among them, h RN (t), h RF (t), h RB h BN (t), h BF (t) represents RSU to U respectively N RSU to U F RSU to BD, BD to U N BD to U F The channel gain, n N (t) and n F (t) is additive white Gaussian noise, satisfying the distribution:

[0031] Step 2: Before running the AmBC-NOMA communication system, preset resource allocation parameters and establish communication constraints;

[0032] The resource allocation parameters include: base station to near-end user U N and remote user U F Power allocation factor a N and a F The reflection coefficient β of the reflecting device BD, and the block length resource configuration of each communication node;

[0033] The communication constraints specifically include: the power allocation coefficients satisfying the following relationship:

[0034] a N +a F =1,a F >a N

[0035] The reflection coefficient of the reflecting device meets the physical constraints:

[0036] 0<β<1

[0037] The block length resource allocation meets the maximum requirement of the total block length resource limit, that is:

[0038]

[0039] in, and These represent the block length resources allocated to near-end users, far-end users, and reflection links, respectively. Furthermore, the block length resources allocated to each communication node should be positive numbers. Additionally, the system must ensure that the initial rate of all communication links is not lower than the set minimum rate threshold.

[0040] Step 3: The base station, in conjunction with the AmBC-NOMA communication system and communication constraints, adjusts the power allocation coefficient a. N a F Block length resources The reflection coefficient β of the reflecting device is jointly optimized; the optimization process aims to maximize the system transmission rate in the short packet communication scenario, and comprehensively considers channel conditions, resource allocation constraints and system stability requirements to solve for the resource allocation scheme that satisfies the optimal rate performance.

[0041] Specifically, the signal-to-noise ratio (SINR) expressions for each link in the system are first established, and are expressed as follows:

[0042]

[0043]

[0044] Wherein, parameter A i B i C i D3, i = 1, 2, 3, are determined by the system power, reflection characteristics, and channel gain, specifically as follows:

[0045]

[0046] B1=B2=B3=γΩ εRN +1

[0047]

[0048]

[0049] In the above expression, γ = P / σ 2 ; represents the signal-to-noise ratio, where P is the total transmit power, and σ 2 Noise power; a N β and β represent the power allocation coefficient and reflection coefficient of the near-end user, respectively; A1, A2, and A3 represent the effective channel gain of the direct link, reflection link, and far-end user link of the near-end user, respectively, which are determined by the MMSE estimate of the initial channel gain, the channel correlation coefficient, and the power factor; B1 = B2 = B3 = γΩ εRN+1 represents the normalized noise term, where Ω εRN ρ represents the average power of the channel estimation error; C1, C2, and C3 correspond to the reflection interference or noise enhancement terms in each link, specifically composed of reflection link gain, channel correlation, and error terms; D3 is the effective channel gain of the direct link to the remote user; ρ XY This represents the channel correlation coefficient of link XY. The MMSE estimate of the initial channel gain is given by the modulus squared, Ω. εXY The average power of the channel estimation error, |h RB | 2 and |h RF | 2 These are the squared magnitudes of the channel gain for the links from the base station to the reflecting device and from the base station to the remote user, respectively.

[0050] The short packet transmission rate of each link under the condition of finite block length is defined as follows:

[0051]

[0052]

[0053]

[0054] in, and V(γ) represents the short packet transmission rate for near-end users, reflection link, and far-end users under finite block length conditions, respectively. j )=2γ j (1+γ j ) -1 Q represents the channel dispersion. -1 (·) denotes the inverse function of the Gaussian Q-function, μ j For the target bit error rate, j = S N S C S F ;

[0055] The total transmission rate of the system is defined as:

[0056]

[0057] The square term β of the reflection coefficient 2 Treat it as a whole variable, denoted as θ, and only use a N It is used as an optimization variable for solving, and through a F =1-a N The equivalence relation is used to represent the power of remote users, and the joint resource optimization problem is modeled as the following optimization objective:

[0058]

[0059] The following constraints must be met:

[0060] a N +a F =1,a N >a F

[0061] 0≤θ≤1

[0062]

[0063]

[0064]

[0065] Specifically, in the resource joint optimization process, this invention uses the power allocation coefficient a F a N Optimization of reflection coefficient β and block length resources The optimization is divided into two sub-problems, which are solved separately using an alternating iterative method.

[0066] To optimize the block-length resource allocation problem, a method based on successive convex approximation SCA is proposed. In each iteration, the current solution is used as the basis for optimization. As the starting point, for The term is approximated by a first-order Taylor approximation to construct its linear lower bound, thereby linearizing the original non-convex rate function; by transforming the problem into a series of solvable linear programming subproblems, and using an iterative optimization method to gradually approximate the optimal block length resource allocation scheme;

[0067] For the power allocation coefficient a F a N The joint optimization of the reflection coefficient β, based on the SCA method, introduces the auxiliary variable t. i Let i = 1, 2, 3 represent the short packet transmission rate of each link, and introduce an auxiliary variable z. i This represents the signal-to-noise ratio of each link; the problem of maximizing the total system transmission rate is reconstructed as maximizing the sum of auxiliary rate variables, i.e.:

[0068]

[0069] During the optimization process, for t i With z i To determine the nonlinear coupling relationship between them, a first-order Taylor approximation is applied to the part containing nonconvex terms, thereby constructing a convex approximation expression, the specific approximation form of which is as follows:

[0070]

[0071] in, Where j = S N S C S F , i = 1, 2, 3; introduce the following constraints on the auxiliary variables:

[0072] t i ≥R min i = 1, 2, 3

[0073] z i ≤γ i i = 1, 2, 3

[0074] Where, γ i To obtain the upper bound parameter calculated based on the current iteration point, a first-order Taylor expansion is introduced to adjust the power allocation coefficient 'a' in the signal-to-noise ratio term. N Locally linearize the product term with the squared term θ of the reflection coefficient, and denote it as follows, with the current iteration point as the expansion center: Representing variables z respectively i ,θ,a N Taking the value from the previous iteration point, we apply a first-order Taylor approximation to the product structure at the current point, resulting in the following three approximation constraint expressions:

[0075]

[0076] In the overall iterative process, firstly, with a fixed power allocation coefficient a... N Given the reflection coefficient β, optimize the block length resource m j Subsequently, based on the updated block length resources, the power allocation coefficient 'a' is jointly optimized. N And the reflection coefficient β, and update the auxiliary variable z i and t i This alternating optimization process iterates continuously until the overall system rate converges or a preset stopping condition is met;

[0077] Step 4: Based on the jointly optimized resource allocation results, the base station uses the optimized power allocation coefficient a. N a F With block length resource allocation, weighted superposition of multi-user signals is completed, and simultaneously transmitted to near-end user U via wireless channel. N and remote user U F Meanwhile, the reflector BD receives the superimposed signal sent by the base station, and performs passive modulation and reflection based on the optimized reflection coefficient β according to the control signaling issued by the base station. During the reflection process, it embeds its own information signal to achieve synchronous information transmission.

[0078] Step 5: Each user receives the superimposed signal from the base station and the signal reflected by the BD modulation from the reflecting device, and uses the continuous interference cancellation (SIC) technology to decode them sequentially according to the power allocation order. Specifically, the user first detects and decodes the signal of the far-end user with a power higher than the set threshold, and then decodes the signal of the near-end user and the scattering information of the reflecting device in sequence, thereby extracting its own communication information and the information content transmitted by BD, and realizing complete data recovery.

[0079] The beneficial effects of adopting the above technical solution are as follows:

[0080] This invention provides a joint optimization method for AmBC-NOMA communication resources in high-mobility V2X scenarios. By constructing a short-packet communication system including base stations, reflection devices, and multiple user nodes, it proposes an optimization mechanism for joint power allocation, reflection coefficient adjustment, and block length resource configuration, effectively improving the short-packet transmission rate under limited block length conditions. The proposed method aims to maximize the system transmission rate. Through alternating iterative optimization, it fully considers practical factors such as channel estimation errors, high-speed mobility, and resource constraints, achieving a significant improvement in low-latency, high-reliability communication performance. Simulation results verify that this invention exhibits excellent transmission performance and system robustness under different block length resources, different SNRs, and different vehicle speed variations. Compared to traditional methods, this invention can effectively approach the infinite block length Shannon capacity rate in resource-constrained environments while maintaining stable system operation in high-speed mobile environments. Furthermore, the method of this invention combines good flexibility and scalability, making it suitable for multi-user, large-scale high-speed mobile communication networks, particularly suitable for future 6G, Intelligent Transportation Systems (ITS), and Internet of Things (IoT) applications with extremely high requirements for low latency, low bit error rate, and high reliability. Attached Figure Description

[0081] Figure 1 The flowchart of the AmBC-NOMA joint optimization method for high-mobility V2X scenarios provided in this embodiment of the invention is as follows:

[0082] Figure 2 This is a model diagram of an AmBC-NOMA assisted communication system for high-mobility V2X scenarios provided in an embodiment of the present invention;

[0083] Figure 3 This is a simulation comparison of short packet transmission rate optimization for AmBC-NOMA systems under different block length resources, provided in an embodiment of the present invention.

[0084] Figure 4 The simulation diagram shows the variation of the short packet transmission rate as a function of SNR in the AmBC-NOMA system after optimization at different vehicle speeds, as provided in the embodiments of the present invention. Detailed Implementation

[0085] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0086] This embodiment proposes an AmBC-NOMA joint optimization method for communication resources in high-speed mobile V2X scenarios, aiming to address the performance degradation issues caused by finite block length, rapid channel changes, and resource constraints in short packet communication systems. This method comprehensively considers base station power allocation, reflection coefficient control of reflection devices, and block length resource configuration of each communication link. Through an alternating iterative optimization mechanism, it effectively improves the transmission rate of the system under low-latency communication requirements. For different SNR conditions, user movement speeds, and available resource constraints, this invention constructs a scalable optimization model and uses the Successive Convex Approximation (SCA) method to perform equivalent transformations and solutions to the non-convex problem, ensuring the convergence and engineering feasibility of the algorithm. Simulation results verify that this method has superior system throughput performance compared to traditional strategies under finite block length conditions, and maintains strong robustness and adaptability in high-speed mobile environments. This method is applicable to practical vehicle-to-everything (V2X) communication scenarios, and has broad engineering value, especially in short packet communication applications requiring low latency, high reliability, and high-frequency mobility. The above research provides a novel approach to resource allocation and joint optimization in reflection-assisted NOMA systems, which is of great significance for improving the communication efficiency and practicality of future large-scale V2X networks.

[0087] A joint optimization method for AmBC-NOMA communication resources in high-mobility V2X scenarios, such as Figure 1 As shown, it includes the following steps:

[0088] Step 1: Construct an AmBC-NOMA communication system for high-mobility V2X scenarios, such as... Figure 2 As shown.

[0089] The AmBC-NOMA communication system for high-mobility V2X scenarios includes a roadside unit (RSU), a reflector (BD), and a near-end user unit (U). N and a remote user U F Each node is equipped with a single antenna.

[0090] The RSU acts as the main transmitter, using non-orthogonal multiple access (NOMA) technology and power domain multiplexing to simultaneously transmit to U... N and U F The superimposed signal is transmitted; while receiving the RSU signal, the BD carries its own information through a backscattering mechanism to assist the RSU in achieving coverage and transmission enhancement for vehicle users; specifically as follows:

[0091] In terms of channel modeling, Rayleigh fading is considered for all links, and a complex Gaussian distribution is used for modeling; RSU to U N U F The channels of BD are represented as h respectively. RN h RF h RB Its statistical distribution is BD to U N U F The links are represented as h respectively. BN h BF Its statistical distribution is

[0092] Considering the high-speed mobility characteristics of vehicle users, this invention takes into account the significant time selectivity of the channel and employs a first-order autoregressive model to model channel variations; channel h k The dynamic evolution form of (t) is:

[0093]

[0094] in, The term is an independent and identically distributed complex Gaussian noise term, representing the time-varying (fast fading) component of the channel. This reflects the average power of the noise component, i.e., the average fast fading intensity of the channel within a symbol period; ρ k The channel autocorrelation coefficient reflects the vehicle speed v and carrier frequency f. c With symbol period T s The influence of factors such as ρ on channel correlation. Its calculation formula is: ρ k =J0(2πf D T s Where J0(·) is the zeroth-order Bessel function, The value is the Doppler frequency shift, v is the vehicle speed, and f is the vehicle velocity. c T is the carrier frequency. S Where c is the symbol period and c is the speed of light;

[0095] Due to the rapid changes in the channel caused by high-speed movement, this invention employs the Minimum Mean Square Error (MMSE) method to estimate the Channel State Information (CSI) at the beginning of each coherent time step to reduce system complexity and improve tracking stability. The initial channel is represented as follows:

[0096]

[0097] in, This is an estimate of MMSE. To estimate the error, the two are independent. The average power of the channel estimate reflects the accuracy of the estimate. It represents the average power of the estimation error and measures the uncertainty of the estimation.

[0098] Subsequent channel states are recursively calculated based on the initial channel state, using a first-order autoregressive (AR) model, to uniformly represent the channel state at any given time as:

[0099]

[0100] Where, ε k This reflects the complex Gaussian noise disturbance caused by estimation errors and user mobility on the channel, which simplifies analysis and receiver design, and satisfies: in, This represents the average power of the estimation error;

[0101] In terms of signal transmission, the RSU employs Non-Orthogonal Multiple Access (NOMA) technology to simultaneously transmit signals to the U... N and U F Send a superimposed signal s(t), which is represented as:

[0102]

[0103] Where P is the total transmit power, s N (t), s F (t) represent the target signals for the near-end user and the far-end user, respectively, satisfying E[|s N (t)| 2 ]=E[|s F (t)| 2 ] = 1, where E[·] represents the statistical expectation; a N and a F These are the base station to the near-end user U N and remote user U F The allocated power allocation factor satisfies a N +a F =1 and a F >a N To ensure the quality of communication for remote users.

[0104] After receiving the superimposed signal s(t) from the RSU, the reflecting device BD superimposes its own information s through passive reflection modulation. C (t), forming a reflected signal; the reflected signal is modeled as: βs(t)s C (t), where β is the reflection coefficient, reflecting the reflection efficiency, and satisfies |β|<1;

[0105] Ultimately, the signal model received by the vehicle user is as follows:

[0106] For U N :

[0107] y N (t)=h RN (t)s(t)+h RB (t)h BN (t)βs(t)s C (t)+n N (t)

[0108] For U F :

[0109] y F (t)=h RF (t)s(t)+h RB (t)h BN (t)βs(t)s C (t)+n F (t)

[0110] Among them, h RN (t), h RF (t), h RB (t), h BN (t), h BF (t) represents RSU to U respectively N RSU to U F RSU to BD, BD to U N BD to U F The channel gain, n N (t) and n F (t) is additive white Gaussian noise, satisfying the distribution:

[0111] As can be seen from step 1, the system model constructed in this invention effectively addresses the channel estimation and transmission reliability issues in high-speed mobile environments while ensuring spectral efficiency and system coverage, providing a solid theoretical and architectural foundation for short-time low-latency vehicle-to-everything (V2X) communication. Furthermore, complex factors such as the power allocation of the system model base station, the reflection coefficient of the reflecting device, time-varying fading channels, and estimation errors collectively determine the short-packet communication performance of the system.

[0112] Step 2: Before running the AmBC-NOMA communication system, preset resource allocation parameters and establish communication constraints as the input basis for subsequent joint optimization modules.

[0113] The resource allocation parameters include: base station to near-end user U N and remote user U F Power allocation factor a N and a FThe reflection coefficient β of the reflecting device BD, and the block length resource configuration of each communication node; all preset parameters must meet the power, reflection efficiency and block length constraints of the system design to ensure the feasibility of the initial system operation and provide reasonable initial values ​​for subsequent joint optimization.

[0114] The communication constraints specifically include: the power allocation coefficients satisfying the following relationship:

[0115] a N +a F =1,a F >a N

[0116] This ensures that near-end users have access to relatively more power resources, thereby improving the overall system performance.

[0117] The reflection coefficient of the reflecting device meets the physical constraints:

[0118] 0<β<1

[0119] The block length resource allocation meets the maximum requirement of the total block length resource limit, that is:

[0120]

[0121] in, and These represent the block length resources allocated to near-end users, far-end users, and reflection links, respectively. Furthermore, the block length resources allocated to each communication node should be positive numbers. Additionally, the system must ensure that the initial rate of all communication links is not lower than the set minimum rate threshold.

[0122] By setting reasonable initial parameters to meet the above constraints, the feasibility and basic performance of the system can be ensured during the startup phase, thus providing effective support for subsequent resource joint optimization iterations.

[0123] Step 3: The base station, in conjunction with the AmBC-NOMA communication system and communication constraints, adjusts the power allocation coefficient a. N a F Block length resources The reflection coefficient β of the reflecting device is jointly optimized. The optimization process aims to maximize the system transmission rate in the short packet communication scenario. It comprehensively considers channel conditions, resource allocation constraints and system stability requirements to solve for the resource allocation scheme that meets the optimal rate performance. This is to maximize the overall transmission rate of the short packet communication system while ensuring system stability and performance requirements.

[0124] Specifically, the signal-to-noise ratio (SINR) expressions for each link in the system are first established, and are expressed as follows:

[0125]

[0126] Wherein, parameter A i B i C i D3, i = 1, 2, 3, are determined by the system power, reflection characteristics, and channel gain, specifically as follows:

[0127]

[0128] B1=B2=B3=γΩ εRN +1

[0129]

[0130] In the above expression, γ = P / σ 2 ; represents the signal-to-noise ratio, where P is the total transmit power, and σ 2 Noise power; a N β and β are the power allocation coefficient and reflection coefficient of the near-end user, respectively. Parameter A i B i C i D3 (k = 1, 2, 3) is determined by factors such as system power, channel correlation, and reflection characteristics; A1, A2, and A3 represent the effective channel gains of the near-end user direct link, reflected link, and far-end user link, respectively, and are specifically determined by the MMSE estimate of the initial channel gain, the channel correlation coefficient, and the power factor; B1 = B2 = B3 = γΩ εRN +1 represents the normalized noise term, where Ω εRN ρ represents the average power of the channel estimation error; C1, C2, and C3 correspond to the reflection interference or noise enhancement terms in each link, specifically composed of reflection link gain, channel correlation, and error terms; D3 is the effective channel gain of the direct link to the remote user; ρ XY This represents the channel correlation coefficient of link XY. The MMSE estimate of the initial channel gain is given by the modulus squared, Ω. εXY The average power of the channel estimation error, |h RB | 2 and |h RF | 2 These are the squared magnitudes of the channel gain for the links from the base station to the reflecting device and from the base station to the remote user, respectively.

[0131] The short packet transmission rate of each link under the condition of finite block length is defined as follows:

[0132]

[0133]

[0134] in, and V(γ) represents the short packet transmission rate for near-end users, reflection link, and far-end users under finite block length conditions, respectively. j )=2γ j (1+γ j ) -1 Q represents the channel dispersion. -1 (·) denotes the inverse function of the Gaussian Q-function, μ j For the target bit error rate, j = S N S C S F ;

[0135] The total transmission rate of the system is defined as:

[0136]

[0137] To further simplify the coupling relationship between variables and improve the solvability of the optimization problem, this invention uses the square term β of the reflection coefficient. 2 Treating it as a single variable, denoted as θ, it will participate in the optimization process as a new optimization variable. Furthermore, considering that the power allocation coefficients satisfy the constraint relationship a... N +a F =1, where a N With a F These represent the power proportions allocated to near-end users and far-end users, respectively. To simplify the optimization of variable dimensions, this paper uses only a in the modeling process. N It is used as an optimization variable for solving, and through a F =1-a N The equivalence relation is used to represent the power of remote users, thereby reducing the number of variables and reducing the optimization complexity.

[0138] In summary, this invention models the joint resource optimization problem as the following optimization objective:

[0139]

[0140] The following constraints must be met:

[0141] a N +a F =1,a N >a F

[0142] 0≤θ≤1

[0143]

[0144]

[0145]

[0146] Specifically, in the resource joint optimization process, this invention uses the power allocation coefficient a F a N Optimization of reflection coefficient β and block length resources The optimization is divided into two sub-problems, which are solved separately using an alternating iterative method.

[0147] To optimize the block length resource allocation problem, a method based on successive convex approximation SCA is proposed. Since the short packet communication rate expression contains a non-convex functional term with respect to the block length variable, direct solution is difficult to obtain the global optimum. Specifically, the user's transmission rate expression in short packet communication is non-convex with respect to block length resources, making direct optimization difficult. Therefore, in each iteration, this invention uses the current solution... As the starting point, for The method employs a first-order Taylor approximation to construct a linear lower bound, thereby linearizing the original non-convex rate function. By transforming the problem into a series of solvable linear programming subproblems, an iterative optimization method is used to gradually approximate the optimal block-length resource allocation scheme. The method has been verified to possess good convergence and implementability.

[0148] For the power allocation coefficient a F a N The joint optimization of the reflection coefficient β, based on the SCA method, introduces the auxiliary variable t. i Let i = 1, 2, 3 represent the short packet transmission rate of each link, and introduce an auxiliary variable z. i This represents the signal-to-noise ratio of each link; the problem of maximizing the total system transmission rate is reconstructed as maximizing the sum of auxiliary rate variables, i.e.:

[0149]

[0150] During the optimization process, for t i With z i To determine the nonlinear coupling relationship between them, a first-order Taylor approximation is applied to the part containing nonconvex terms, thereby constructing a convex approximation expression, the specific approximation form of which is as follows:

[0151]

[0152] in, Where j = S N S C S F , i = 1, 2, 3. Since log2(1 + z) iThe function itself is concave, and its original form can be directly preserved. However, the latter part of the constraint is a non-convex expression. To facilitate optimization, this invention only uses a first-order Taylor expansion to linearly approximate this non-convex part, thereby transforming the overall rate constraint into a convex form and ensuring that the optimization problem has good solvability.

[0153] Meanwhile, to ensure physical feasibility and system performance, the present invention introduces the following constraints on auxiliary variables:

[0154] t i ≥R min i = 1, 2, 3

[0155] z i ≤γ i i = 1, 2, 3

[0156] Where, γ i The upper bound parameter is calculated based on the current iteration point. To further address the non-convexity issue caused by variable multiplication in the signal-to-noise ratio expression, this invention introduces a first-order Taylor expansion to address the power allocation coefficient 'a' in the signal-to-noise ratio term. N The product term with the squared term θ of the reflection coefficient is locally linearized to ensure that the subproblems generated in each iteration are convex optimization problems, facilitating efficient solution. Specifically, with the current iteration point as the expansion center, they are denoted as follows: Representing the variables z respectively i ,θ,a N Taking the value from the previous iteration point, we apply a first-order Taylor approximation to the product structure at the current point, resulting in the following three approximation constraint expressions:

[0157]

[0158] In the overall iterative process, firstly, with a fixed power allocation coefficient a... N Given the reflection coefficient β, optimize the block length resource m j Subsequently, based on the updated block length resources, the power allocation coefficient 'a' is jointly optimized. N And the reflection coefficient β, and update the auxiliary variable z i and t i This alternating optimization process iterates continuously until the overall system rate converges or a preset stopping condition is met. Ultimately, this invention enables the joint optimal configuration of power, reflection, and block length resources, effectively improving the rate performance and stability of short packet communication systems in high-speed mobile V2X scenarios.

[0159] Steps 2 and 3 demonstrate that reasonable preset resource parameters and dynamic joint optimization can significantly improve the transmission rate of short packet communication systems. By ensuring system feasibility through initial resource configuration and combining alternating optimization of block length resources, power, and reflection coefficient, the system can effectively adapt to channel changes in high-speed mobile V2X scenarios, improve the performance of each link, and thus optimize the overall communication quality.

[0160] Step 4: Based on the jointly optimized resource allocation results, the base station uses the optimized power allocation coefficient a. N a F With block length resource allocation, weighted superposition of multi-user signals is completed, and simultaneously transmitted to near-end user U via wireless channel. N and remote user U F Meanwhile, the reflector BD receives the superimposed signal sent by the base station, and performs passive modulation and reflection based on the optimized reflection coefficient β according to the control signaling issued by the base station. During the reflection process, it embeds its own information signal to achieve synchronous information transmission.

[0161] After completing the joint resource optimization, the base station uses the optimized power allocation coefficient 'a'. N a F The base station configures the block length resources of each communication node and performs weighted superposition processing of multi-user signals. Specifically, the base station allocates power to the near-end user U according to the power allocation result. N and remote user U F The information signals are weighted and superimposed to form a composite transmission signal, which is then simultaneously transmitted to two users via a wireless channel to achieve efficient sharing of spectrum resources. Simultaneously, the BD receives the superimposed signal from the base station and, based on the configuration parameters issued by the base station through control signaling, performs passive modulation and reflection on the received signal according to the optimized reflection coefficient β. During reflection, the reflecting device embeds its own information signal, modulating the original signal through a passive reflection mechanism, thereby synchronously completing information transmission. This process requires no additional transmit power, fully utilizes reflection path resources, and improves the overall energy efficiency and connectivity performance of the system.

[0162] In practical implementation, before transmitting the superimposed signal, the base station determines the optimal block length resource m. i The transmission duration and encoding strategy of each user data block are controlled to ensure the short packet communication characteristics and reliability requirements. When performing reflection modulation, BD also controls the reflection modulation rate based on the preset block length and optimized parameters to ensure that the reflected signal and the direct signal can be correctly synchronized and demodulated at the receiving end, thereby maximizing the effective rate and stability of the system's short packet communication.

[0163] Step 5: Each user receives the superimposed signal from the base station and the signal reflected by the BD modulation from the reflecting device, and uses the continuous interference cancellation (SIC) technology to decode them sequentially according to the power allocation order. Specifically, the user first detects and decodes the signal of the far-end user with a power higher than the set threshold, and then decodes the signal of the near-end user and the scattering information of the reflecting device in sequence, thereby extracting its own communication information and the information content transmitted by BD, and realizing complete data recovery.

[0164] After the base station completes the transmission of the superimposed signal and the BD completes passive reflection modulation, each user terminal receives the superimposed signal directly transmitted by the base station and the signal modulated and reflected by the reflection device. To effectively separate and recover their respective information content, users employ Continuous Interference Cancellation (SIC) technology, performing decoding and interference cancellation operations sequentially according to a pre-set power allocation order.

[0165] Specifically, the receiving end first detects and decodes the remote user U with the highest power. F The signal is relatively strong due to the higher power allocated to the remote user, making it easier to detect and reliably decode initially. After decoding the remote user signal, the receiver reconstructs the interference component of the remote user signal from the received signal based on the decoding result and eliminates this interference component from the received signal to reduce interference in subsequent decoding processes.

[0166] After successfully removing interference from the remote user's signal, the receiver continues to detect and decode the near-end user's U signal. N The signal from the near-end user is subject to interference from the far-end signal during initial reception due to its lower power distribution. Therefore, reliable decoding must be performed only after removing interference from the far-end user. After decoding, the influence of the near-end user signal on subsequent processing is further eliminated through reconstruction and cancellation.

[0167] Finally, after completing two stages of user signal interference cancellation, the receiver detects and decodes the scattered information signal reflected by BD modulation. Since the reflected signal of BD is usually lower in power and has more complex channel characteristics, it is necessary to extract the information content embedded in BD based on the remaining signal components after the interference is completely eliminated in the first two steps, so as to achieve complete recovery of passive communication information.

[0168] The purpose of this invention is to improve the system transmission rate and overall reliability under short packet communication conditions in AmBC-NOMA communication systems for high-mobility V2X scenarios, especially in environments with rapidly changing channels and limited resources. By pre-setting initial resource parameters and combining them with an alternating iterative joint optimization strategy, this method dynamically adjusts the power allocation coefficient, the reflection coefficient of the reflecting device, and the block length resource configuration, thereby achieving efficient resource sharing and synchronous communication among multiple users. Through these means, this invention aims to improve the information transmission performance of legitimate users and the data transmission efficiency of the reflecting device, enhance the system's adaptability in high-speed mobile environments, and ultimately maximize the transmission rate of the short packet communication system and significantly improve system performance.

[0169] Simulation conditions: In the simulation of this invention, the bit error rate threshold is set to μ = 0.01, and the corresponding inverse Q function value is calculated as Q. -1 (μ). Minimum rate requirement is set to R. min = [0.3, 0.1, 0.2] (bit / s / Hz), corresponding to the lower limit of the rate for each communication node. The system time slot number is set to t = 2. Regarding channel parameters, the channel variance of the link from the base station to the reflector device (RSU-BD) is set to Ω. RB =1, and the estimated channel variances for the base station to near-end user (RSU-UN) and base station to far-end user (RSU-UF) links are Ω respectively. RN =20 and Ω RF =5. The estimated channel variances for the reflector-to-near-end user (BD-UN) and reflector-to-far-end user (BD-UF) links are Ω, respectively. BN =1.5 and Ω BF =0.5. The variance of the reflection link error is set to 0.07. Initial values ​​are set, and during the simulation, all parameters are evaluated based on the Monte Carlo simulation method. Each parameter configuration is simulated independently 5000 times to ensure the statistical reliability of the results.

[0170] Simulation Content: This invention verifies the short packet communication performance of the AmBC-NOMA system in a high-speed mobile V2X scenario through simulation. First, under different total block length resource conditions, the transmission rate and performance differences between the proposed resource joint optimization method, the traditional benchmark method, and the traditional infinite block length Shannon rate under short packet communication conditions are compared. Second, under fixed block length resource conditions, the impact of different signal-to-noise ratio levels and different vehicle speeds on system performance is further analyzed. Simulation results show that the proposed resource joint optimization method can effectively improve the short packet transmission rate of the system under various channel conditions and significantly improve the throughput performance in short-latency communication environments.

[0171] Figure 3As can be seen, the system's overall rate is compared between the proposed resource joint optimization method (Proposed-FBL), the traditional unoptimized benchmark method (Benchmark-FBL), and the infinite block length Shannon rate optimization scheme (Proposed-IBL) under different total block length resource conditions. Simulation results show that the communication rate of both the optimized and unoptimized methods improves with the increase of total block length resources. Furthermore, under any block length condition, the Proposed-FBL method consistently outperforms the Benchmark-FBL method, exhibiting superior short packet transmission performance. Simultaneously, the performance of Proposed-FBL gradually approaches the rate upper limit of Proposed-IBL as the block length increases, verifying the efficiency and asymptotic optimality of the proposed optimization strategy under finite block length conditions. These results fully demonstrate that the joint optimization mechanism constructed in this invention can effectively improve the system's short packet communication performance, possessing good resource adaptability and application promotion value.

[0172] Figure 4 The simulation results show the short packet transmission rate of the system as a function of the signal-to-noise ratio (SNR) under different vehicle speeds (50km / h, 70km / h, and 90km / h). The results indicate that the short packet transmission rate increases with increasing SNR at all vehicle speeds, demonstrating that improving the SNR helps enhance communication reliability and effective transmission rate. However, significant differences exist in system performance at different vehicle speeds; the higher the vehicle speed, the lower the system transmission rate. This is because high-speed movement leads to more drastic channel changes, exacerbating channel estimation errors and time-selective fading effects, thus limiting the potential for improving the effective rate under short packet transmission. Conversely, at lower vehicle speeds, the channel is relatively stable, channel estimation is more accurate, and the reliability and efficiency of short packet communication are more easily guaranteed. In summary, the simulation results verify that the proposed method exhibits good short packet transmission performance under various SNR and vehicle speed conditions, especially demonstrating strong robustness and adaptability in high-speed moving environments, showcasing its practical value in dynamic communication scenarios of vehicle-to-everything (V2X) networks.

[0173] In summary, the resource joint optimization method proposed in this invention can significantly improve the short packet transmission rate of the system under different block length resources, different SNR levels, and different vehicle speeds. By effectively configuring power allocation, reflection coefficient, and block length resources, the optimization method not only enhances the overall system throughput but also maintains good communication stability in high-speed mobile scenarios. This method fully leverages the transmission potential under limited resources, providing effective support for low-latency, high-reliability V2X communication applications, and demonstrating superior system adaptability and practical value.

[0174] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A joint optimization method for AmBC-NOMA communication resources in high-mobility V2X scenarios, characterized in that, Includes the following steps: Step 1: Construct an AmBC-NOMA communication system for high-mobility V2X scenarios; The AmBC-NOMA communication system for high-mobility V2X scenarios includes a roadside unit (RSU), a reflector device (BD), and a near-end user. and a remote user Each node is equipped with a single antenna. Step 2: Before running the AmBC-NOMA communication system, preset resource allocation parameters and establish communication constraints; Step 3: The base station, in conjunction with the AmBC-NOMA communication system and communication constraints, adjusts the power allocation coefficient. , Block length resources , , and the reflectivity of the reflecting device Joint optimization is performed; the optimization process aims to maximize the system transmission rate in short packet communication scenarios, taking into account channel conditions, resource allocation constraints and system stability requirements, and solves for a resource allocation scheme that satisfies the optimal rate performance. Specifically, step 3 first establishes the signal-to-noise ratio (SINR) expressions for each link in the system, which are expressed as follows: ； ； ； Among them, parameters , , , The values ​​i = 1, 2, 3 are determined by the system power, reflection characteristics, and channel gain, specifically: 、 、 ； ； ； 、 ； in, ; represents the signal-to-noise ratio, where Total transmission power, Noise power; and These are the power allocation factor and reflection factor for near-end users, respectively; , , These represent the effective channel gains for the near-end user direct link, the reflected link, and the far-end user link, respectively, and are specifically determined by the MMSE estimate of the initial channel gain, the channel correlation coefficient, and the power factor. Here is the normalized noise term, where This represents the average power of the channel estimation error; , , These correspond to the reflection interference or noise enhancement terms in each link, specifically consisting of reflection link gain, channel correlation, and error terms. This is the effective channel gain of the direct link for the remote user; This represents the channel correlation coefficient of link XY. The modulus squared of the MMSE estimate of the initial channel gain. The average power of the channel estimation error. and These are the squared magnitudes of the channel gain for the links from the base station to the reflecting device and from the base station to the remote user, respectively. The short packet transmission rate of each link under the condition of finite block length is defined as follows: ； ； ； in, , and These represent the short packet transmission rates for near-end users, reflection links, and far-end users, respectively, under the condition of finite block length. Indicates channel dispersion. This represents the inverse function of the Gaussian Q-function. For the target bit error rate, j=S N S C S F ; The total transmission rate of the system is defined as: ； The square of the reflection coefficient Treat it as a single variable and denot it as , only It is used as an optimization variable for solving, and through The equivalence relation is used to represent the power of remote users, and the joint resource optimization problem is modeled as the following optimization objective: ； The following constraints must be met: ； ； ； ； ； In the aforementioned joint resource optimization problem, the power allocation coefficients are... , Optimization of reflection coefficient β and block length resources , , The optimization is divided into two sub-problems, which are solved separately using an alternating iterative method; Step 4: Based on the jointly optimized resource allocation results, the base station uses the optimized power allocation coefficients. , By configuring block length resources, weighted superposition of multi-user signals is completed, and signals are simultaneously transmitted to near-end users via wireless channels. and remote users Simultaneously, the reflector BD receives the superimposed signal sent by the base station and, based on the control signaling issued by the base station, determines the superimposed signal according to the optimized reflection coefficient. Passive modulation and reflection are performed, and the information signal is embedded in the reflection process to achieve synchronous information transmission; Step 5: Each user receives the superimposed signal from the base station and the BD modulated reflected signal from the reflection device, and uses the continuous interference cancellation (SIC) technology to decode them sequentially according to the power allocation order.

2. The AmBC-NOMA joint optimization method for communication resources in high-mobility V2X scenarios according to claim 1, characterized in that, In the AmBC-NOMA communication system described in step 1, the roadside unit (RSU) acts as the main transmitter, simultaneously transmitting signals to other devices via non-orthogonal multiple access (NOMA) technology using power domain multiplexing. and The superimposed signal is transmitted; while receiving the RSU signal, the BD carries its own information through a backscattering mechanism to assist the RSU in achieving coverage and transmission enhancement for vehicle users; specifically as follows: All links are modeled using a complex Gaussian distribution, considering Rayleigh fading; RSU to , The channels of BD are respectively represented as , , Its statistical distribution is , , ; BD to , The links are respectively represented as , Its statistical distribution is , ; A first-order autoregressive model is used to model channel variations; channel The dynamic evolutionary form is as follows: ； in, The term is an independent and identically distributed complex Gaussian noise term, representing the time-varying component of the channel. This reflects the average power of the noise component. The channel autocorrelation coefficient is calculated using the following formula: in, It is a zero-order Bessel function. The value is the Doppler frequency shift, v is the vehicle speed, and f is the vehicle velocity. c T is the carrier frequency. S Where c is the symbol period and c is the speed of light; The minimum mean square error (MMSE) method is used to estimate the channel state information (CSI) once at the beginning of each coherence time; the initial channel is represented as: ； in, This is an estimate of MMSE. To estimate the error, the two are independent. This represents the average power of the channel estimate. This represents the average power of the estimation error; Based on the initial channel, and using a first-order autoregressive (AR) model for recursive calculation, the channel state at any given time is uniformly represented as: ； in, This reflects the complex Gaussian noise disturbance caused by estimation error and user mobility on the channel, satisfying: ;in, This represents the average power of the estimation error; RSU employs Non-Orthogonal Multiple Access (NOMA) technology to simultaneously... and Send superimposed signal Superimposed signals Represented as: ； in, Total transmission power, , The target signals for near-end users and far-end users respectively satisfy... ,in Indicates statistical expectation; a N and a F For base stations to near-end users and remote users The allocated power allocation factor satisfies and ; The reflector BD receives the superimposed signal from the RSU. Then, its own information is superimposed through passive reflection modulation. This forms a reflected signal; the reflected signal is modeled as follows: ,in, Let be the reflection coefficient, which satisfies ; Ultimately, the signal model received by the vehicle user is as follows: for : ; for : ； in, , , , , Representing RSU to RSU to RSU to BD, BD to BD to Channel gain, and It is additive white Gaussian noise, satisfying the following distribution: .

3. The AmBC-NOMA joint optimization method for communication resources in high-mobility V2X scenarios according to claim 1, characterized in that, The resource allocation parameters mentioned in step 2 include: base station to near-end users and remote users Power allocation factor and The reflectivity of the reflective device BD And the block length resource configuration of each communication node.

4. The AmBC-NOMA joint optimization method for high-mobility V2X scenarios according to claim 1, characterized in that, The communication constraints described in step 2 specifically include: the power allocation coefficients satisfying the following relationship: ； The reflection coefficient of the reflecting device meets the physical constraints: ； The block length resource allocation meets the maximum requirement of the total block length resource limit, that is: ； in, , and These represent the block length resources allocated to near-end users, far-end users, and reflection links, respectively; and the block length resources allocated to each communication node should be positive numbers. In addition, the system must ensure that the initial rate of all communication links is not lower than the set minimum rate threshold.

5. The AmBC-NOMA joint optimization method for communication resources in high-mobility V2X scenarios according to claim 1, characterized in that, In the resource joint optimization problem, for the block-length resource allocation problem, a method based on successive convex approximation SCA is proposed for optimization; in each iteration, the current solution is used as the basis for optimization. As the starting point, for The term is approximated by a first-order Taylor approximation to construct its linear lower bound, thereby linearizing the original non-convex rate function; by transforming the problem into a series of solvable linear programming subproblems, and using an iterative optimization method to gradually approximate the optimal block length resource allocation scheme; For power allocation coefficient , The joint optimization of the reflection coefficient β, based on the SCA method, introduces auxiliary variables. Let i = 1, 2, 3 represent the short packet transmission rate of each link, and introduce auxiliary variables. This represents the signal-to-noise ratio of each link; the problem of maximizing the total system transmission rate is reconstructed as maximizing the sum of auxiliary rate variables, i.e.: ； During the optimization process, targeting and To determine the nonlinear coupling relationship between them, a first-order Taylor approximation is applied to the part containing nonconvex terms, thereby constructing a convex approximation expression, the specific approximation form of which is as follows: ； in, Where j=S N S C S F Let i = 1, 2, 3; introduce the following constraints on the auxiliary variables: ； ； in, To obtain the upper bound parameters calculated based on the current iteration point, a first-order Taylor expansion is introduced to adjust the power allocation coefficient in the signal-to-noise ratio term. With the square of the reflection coefficient The product terms are locally linearized, with the current iteration point as the expansion center, and denoted as follows: , , , respectively represent variables , , Taking the value from the previous iteration point, we apply a first-order Taylor approximation to the product structure at the current point, resulting in the following three approximation constraint expressions: ； ； ； In the overall iterative process, the power allocation coefficient is first fixed. With reflection coefficient Under the condition of optimizing block length resources Subsequently, based on the updated block length resources, the power allocation coefficient was jointly optimized. With reflection coefficient and update auxiliary variables. and The alternating optimization process iterates continuously until the overall system rate converges or the preset stopping condition is met.

6. The AmBC-NOMA joint optimization method for high-mobility V2X scenarios according to claim 1, characterized in that, Specifically, step 5 involves the user first detecting and decoding the remote user signal whose power exceeds a set threshold, and then sequentially decoding the near-end user signal and the scattering information of the reflecting device, thereby extracting their own communication information and the information content transmitted via BD, thus achieving complete data recovery.

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