AmBC-NOMA communication resource joint optimization method for high mobility V2X scene

Abstract

The invention 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. Aiming at channel dynamic change caused by high-speed motion of a vehicle and the influence of the channel dynamic change on short packet communication reliability and rate performance, the method combines reflection type communication and non-orthogonal multiple access technologies, and on the basis of fully considering channel time-varying characteristics, a reflection coefficient, a power distribution coefficient and block length resource distribution in a system are jointly optimized, so that the reliability and the rate performance of short packet communication are improved. Therefore, the communication rate performance and the 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 remarkably improved under the conditions of limited time delay and channel uncertainty, and the actual application requirements of the future Internet of Vehicles system for efficient, low-time-delay and high-reliability short packet communication are met.

Description

Technical Field

[0001] The present invention relates to the technical field of joint optimization of communication resources, and particularly to a method for jointly optimizing communication resources of AmBC-NOMA for high-mobility V2X scenarios. Background Art

[0002] With the rapid development of intelligent transportation technologies, vehicle-to-everything (V2X) communication, as an important supporting means for realizing safe and efficient traffic management, has been widely applied. V2X communication significantly improves the safety, traffic efficiency, and user experience of the transportation system by enabling information interaction between vehicles (V2V), between vehicles and infrastructure (V2I), and between vehicles and other traffic participants. However, as the scale of the V2X network continues to expand, traditional communication methods based on the orthogonal multiple access (OMA) mechanism gradually expose problems such as low spectrum resource utilization, congestion prone to occur in large-scale access, and insufficient energy efficiency, making it difficult to meet the higher requirements for communication systems in future complex traffic environments. To address the above problems, non-orthogonal multiple access (NOMA) technology has received extensive attention in recent years. By using superposition coding on the same frequency resources and combining successive interference cancellation (SIC) technology, NOMA can support multiple users to communicate simultaneously, effectively improving the spectrum efficiency and enhancing the large-scale user access ability. At the same time, ambient backscatter communication (AmBC) technology has also gradually emerged. AmBC realizes communication by reflecting the existing radio frequency (RF) signals in the environment and superimposing its own modulation information during the reflection process, without the need for active RF transmitting devices, thus significantly reducing the energy consumption of communication terminals and improving the green communication ability and deployment flexibility of the system. Therefore, the AmBC-NOMA scheme formed by combining AmBC technology and NOMA technology is considered an effective technical path to further improve the spectrum efficiency and energy efficiency of V2X communication systems.

[0003] Existing research on AmBC-NOMA communication systems mainly focuses on improving the system's performance in aspects such as spectrum utilization efficiency, energy efficiency, and communication concealment. Existing studies generally believe that introducing the backscatter communication mechanism 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. On this basis, to further improve the system's performance, existing research generally conducts optimization modeling around resource allocation strategies and designs joint resource allocation schemes between base stations, roadside units, and reflection devices for different system objectives, thus achieving significant improvements in aspects such as spectrum efficiency, energy efficiency, and quality of service. In the paper "Backscatter-enabled efficient V2X communication with non-orthogonal multiple access," the authors established a max-min capacity optimization model, transformed the problem into a standard convex optimization problem, and jointly optimized the power allocation of base stations and roadside units using the KKT conditions and subgradient method to improve the system's minimum achievable capacity and effectively enhance spectrum efficiency and communication performance. In the paper "Energy efficiency optimization for backscatter enhanced NOMA cooperative V2X communications under imperfect CSI," the authors proposed an energy efficiency optimization framework considering imperfect channel state information (CSI). By modeling the problem of minimizing the total transmit power of the system as a non-convex optimization problem and using problem decomposition and iterative subgradient methods to jointly optimize the base station power allocation, roadside unit power allocation, and reflection coefficient of the reflection device, the overall energy efficiency of the system was significantly improved. In the paper "Energy-efficient backscatter aided uplink NOMA roadside sensor communications under channel estimation errors," the authors proposed an energy efficiency maximization optimization framework under the actual conditions of channel estimation errors. The problem was modeled as a fractional programming problem for energy efficiency maximization, and a two-stage alternating optimization algorithm was used. In the first stage, the transmit power of the carrier transmitter was iteratively optimized using the Dinkelbach method combined with subgradient, and in the second stage, the reflection coefficient of the roadside sensor was directly optimized using a closed-form solution. On the premise of ensuring the quality of service (QoS) requirements, the overall energy efficiency of the system was further improved, enhancing the feasibility of large-scale applications in future intelligent transportation systems.

[0004] Although existing research has extensively explored the application of backscatter-enhanced non-orthogonal multiple access (AmBC-NOMA) technology in V2X communication and proposed various solutions such as capacity optimization, energy efficiency improvement, and communication concealment enhancement, there are still certain limitations. Specifically, existing work mostly conducts joint optimization based on ideal channel conditions or on the premise that some system parameters are fixed, and fails to fully consider the requirements of the rapid channel changes in high-mobility V2X scenarios for the dynamic adaptability of resource allocation. In addition, some research only optimizes at the single-parameter level, lacking the joint dynamic optimization design of multi-dimensional parameters such as power allocation coefficient, reflection coefficient, and block length resources, making it difficult to comprehensively improve the overall system performance under the short-packet communication requirements of low latency and high reliability. Therefore, for the AmBC-NOMA V2X system under the characteristics of high mobility and short-packet communication, it is still necessary to further study and improve the multi-parameter joint dynamic optimization method to better meet the performance requirements of future intelligent transportation communication systems.

[0005] In summary, although existing research on the AmBC-NOMA V2X communication system has made certain progress in network capacity, energy efficiency, and communication concealment optimization, there are still obvious deficiencies for the high-mobility short-packet communication scenario. Under short-packet communication conditions, the system's comprehensive requirements for low latency, high reliability, and high data rate are more stringent, and existing optimization strategies are difficult to fully improve the system transmission rate while ensuring the reliability of short-packet transmission. Therefore, for the high-mobility short-packet communication scenario, it is urgent to design a joint dynamic optimization method for transmit power, reflection coefficient, and block length resources to maximize the rate performance of the AmBC-NOMA V2X system under short-packet communication conditions and meet the urgent needs of future intelligent transportation systems for efficient short-time communication. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a joint optimization method for AmBC-NOMA communication resources for high-mobility V2X scenarios;

[0007] A joint optimization method for AmBC-NOMA communication resources for high-mobility V2X scenarios, comprising 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 reflection device BD, a proximal user U N and a distal user U F , where all nodes are equipped with single antennas;

[0010] Among them, the RSU serves as the main transmitter. Through the non-orthogonal multiple access technology NOMA, it uses the power domain multiplexing method to simultaneously send superimposed signals to U N and U F ; while receiving the RSU signal, the BD carries its own information through the backscattering mechanism to assist the RSU in achieving coverage and transmission enhancement for vehicle users; specifically as follows:

[0011] All links consider Rayleigh fading and are modeled using complex Gaussian distribution; the channels from the RSU to U N 、U F 、BD are respectively denoted as h RN 、h RF 、h RB , and their statistical distributions are The links from the BD to U N 、U F are respectively denoted as h BN 、h BF , and their statistical distributions are

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

[0013]

[0014] Among them, is an independent and identically distributed complex Gaussian noise term, representing the time-varying component of the channel, reflects the average power of this noise component, ρ k is the channel autocorrelation coefficient, and its calculation formula is: ρ k =J0(2πf D T s ) Among them, J0(·) is the zero-order Bessel function, is the Doppler frequency shift, v is the vehicle speed, f c is the carrier frequency, T S is the symbol period, and c is the speed of light;

[0015] The minimum mean square error method MMSE is used to perform a one-time estimation of the channel state information CSI at the beginning of each coherence time; the initial channel is expressed as:

[0016]

[0017] Among them, is the MMSE estimate value, is the estimation error, and the two are independent, represents the average power of the channel estimate value, represents the average power of the estimation error;

[0018] Based on the initial channel, recursive calculations are performed in combination with the first-order autoregressive model AR, and the channel state at any moment is uniformly expressed as:

[0019]

[0020] where ε k reflects the complex Gaussian noise perturbation caused by the estimation error and user mobility to the channel, and satisfies: where represents the average power of the estimation error;

[0021] The RSU uses the non-orthogonal multiple access technology NOMA to simultaneously send the superimposed signal s(t) to U N and U F The superimposed signal s(t) is expressed as:

[0022]

[0023] where P is the total transmission power, s N (t), s F (t) are the target signals of the proximal user and the distal user respectively, and satisfy E[|s N (t)| 2 = E[|s F (t)| 2 = 1, where E[·] represents the statistical expectation; a N and a F are the power allocation coefficients assigned by the base station to the proximal user U N and the distal user U F respectively, and satisfy a N +a F = 1 and a F >a N ;

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

[0025] Finally, 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] Wherein, h RN (t), h RF (t), h RB , h BN (t), h BF (t) respectively represent the channel gains from the RSU to U N , from the RSU to U F , from the RSU to the BD, from the BD to U N , from the BD to U F . n N (t) and n F (t) are additive white Gaussian noise, satisfying the distribution:

[0031] Step 2: Before the operation of the AmBC-NOMA communication system, preset the resource allocation parameters and establish the communication constraint conditions;

[0032] The resource allocation parameters include: the power allocation coefficients a N and a F allocated by the base station to the proximal user U N and the distal user U F , the reflection coefficient β of the reflection device BD, and the block length resource configuration of each communication node;

[0033] The communication constraint conditions specifically include: the power allocation coefficients satisfy the relationship:

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

[0035] The reflection coefficient of the reflection device satisfies the physical limitation condition:

[0036] 0 < β < 1

[0037] The block length resource configuration meets the total block length resource upper limit requirement, that is:

[0038]

[0039] Among them, and respectively represent the block - length resources allocated to the proximal user, the distal user, and the reflection link. And the block - length resources allocated to each communication node should be positive. In addition, the system needs to ensure that the initial rate of all communication links is not lower than the set minimum rate threshold.

[0040] Step 3: The base station combines the AmBC - NOMA communication system with the communication constraint conditions to jointly optimize the power - allocation coefficient a N , a F , the block - length resource and the reflection coefficient β of the reflection device; the optimization process aims to maximize the system transmission rate in the short - packet communication scenario, comprehensively considering the channel conditions, resource - allocation constraints, and system stability requirements, and solves for the resource - allocation scheme that meets the optimal rate performance;

[0041] Specifically, first establish the signal - to - noise ratio SINR expressions of each link in the system, which are respectively expressed as:

[0042]

[0043]

[0044] Among them, the parameters A i , B i , C i , D3, i = 1, 2, 3, are determined by the system power, reflection characteristics, and channel gain, specifically:

[0045]

[0046] B1 = B2 = B3 = γΩ εRN +1

[0047]

[0048]

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

[0050] The short-packet transmission rates of each link under the finite block length condition are respectively defined as:

[0051]

[0052]

[0053]

[0054] where, and respectively represent the short-packet transmission rates of the proximal user, reflection link, and remote user under the finite block length condition, V(γ j ) = 2γ j (1 + γ j ) -1 represents the channel dispersion, Q -1 (·) represents the inverse function of the Gaussian Q function, μ j is 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] Regarding the squared term β 2 of the reflection coefficient as a whole variable and denoting it as θ, only a N is used as the optimization variable for solution, and the power of the remote user is represented through the equivalent relationship of a F = 1 - a N , and the resource joint optimization problem is modeled as the following optimization objective:

[0058]

[0059] Satisfy the following constraints:

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

[0061] 0 ≤ θ ≤ 1

[0062]

[0063]

[0064]

[0065] Specifically, in the process of joint resource optimization of the present invention, the optimization of the power allocation coefficient a F 、a N and the reflection coefficient β and the optimization of the block length resource are divided into two sub-problems and solved separately by an alternating iteration method.

[0066] For the optimization of the block length resource allocation problem, a method based on successive convex approximation (SCA) is specifically proposed; in each iteration, with the current solution as the expansion point, a first-order Taylor approximation is performed on the term 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 sub-problems and using an iterative optimization method to gradually approximate the optimal block length resource allocation scheme;

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

[0068]

[0069] In the optimization process, for the non-linear coupling relationship between t i and z i , a first-order Taylor approximation expansion is performed on the part containing non-convex terms, thereby constructing a convex approximation expression. The specific approximation form is as follows:

[0070]

[0071] Among them, where \(j = S\) N , \(S\) C , \(S\) F , \(i = 1, 2, 3\); the following constraints are introduced on the auxiliary variables:

[0072] t i \(\geq R\) min , \(i = 1, 2, 3\)

[0073] z i \(\leq \gamma\) i , \(i = 1, 2, 3\)

[0074] where \(\gamma\) i is the upper bound parameter calculated according to the current iteration point; introduce the first-order Taylor expansion form, and perform local linearization on the product term of the power allocation coefficient \(a\) N and the square term \(\theta\) of the reflection coefficient in the signal-to-noise ratio term. Taking the current iteration point as the expansion center, they are respectively denoted as respectively represent the variables \(z\) i , \(\theta\), \(a\) N at the values of the previous iteration point, and perform a first-order Taylor approximation on the product structure at the current point to obtain the following three approximate constraint expressions:

[0075]

[0076] In the overall iteration process, first, under the conditions of fixed power allocation coefficient \(a\) N and reflection coefficient \(\beta\), optimize the block length resource \(m\) j ; then, based on the updated block length resource, jointly optimize the power allocation coefficient \(a\) N and reflection coefficient \(\beta\), and update the auxiliary variables \(z\) i and \(t\) i . This alternating optimization process is continuously iterated until the total system rate converges or reaches the preset stop condition;

[0077] Step 4: Based on the jointly optimized resource allocation results, the base station completes the weighted superposition of multi-user signals according to the optimized power allocation coefficients \(a\) N , \(a\) F and the block length resource allocation, and simultaneously sends to the proximal user \(U\) N and the distal user \(U\) F through the wireless channel. At the same time, the reflection device BD receives the superimposed signal sent by the base station, and performs passive modulation and reflection according to the control signaling sent by the base station based on the optimized reflection coefficient \(\beta\), and embeds its own information signal during the reflection process to achieve synchronous information transmission;

[0078] Step 5: Each user receives the superimposed signal from the base station and the signal modulated and reflected by the reflection device BD, and uses the Successive Interference Cancellation (SIC) technique to decode them in sequence according to the power allocation order. Specifically, the user first detects and decodes the signals of remote users with power higher than the set threshold, and then decodes the signals of proximal users and the scattering information of the reflection device in sequence, so as to extract its own communication information and the information content transmitted by BD respectively, and achieve complete data recovery.

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

[0080] The present invention provides a joint optimization method for AmBC-NOMA communication resources for high-mobility V2X scenarios. By constructing a short-packet communication system including a base station, a reflection device and multiple user nodes, an optimization mechanism for joint power allocation, reflection coefficient adjustment and block-length resource configuration is proposed, which effectively improves the short-packet transmission rate of the system under the condition of finite block length. The proposed method aims to maximize the system transmission rate. Through alternating iterative optimization, it fully considers actual factors such as channel estimation error, high-speed mobility and resource limitation, and achieves a significant improvement in short-delay and high-reliability communication performance. The simulation results verify that the present invention has excellent transmission performance and system robustness under different block-length resources, different SNRs and different vehicle speed changes. Compared with traditional methods, the present invention can effectively approach the infinite block-length Shannon capacity rate in a limited resource environment and maintain the stable operation of the system in a high-speed mobile environment. In addition, the method of the present invention has good flexibility and scalability, is applicable to multi-user and large-scale high-speed mobile communication networks, and is particularly suitable for application scenarios with extremely high requirements for short delay, low bit error rate and high reliability in future 6G, Intelligent Transportation Systems (ITS) and Internet of Things (IoT). Description of the Drawings

[0081] Figure 1 It is a flowchart of the joint optimization method for AmBC-NOMA communication resources for high-mobility V2X scenarios provided by an embodiment of the present invention;

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

[0083] Figure 3 It is a simulation diagram of the short-packet transmission rate optimization comparison of the AmBC-NOMA system under different block-length resources provided by an embodiment of the present invention;

[0084] Figure 4 It is a simulation diagram of the short-packet transmission rate of the optimized AmBC-NOMA system varying with SNR under different vehicle speeds provided by an embodiment of the present invention. Detailed Embodiments

[0085] The following will further describe in detail the specific implementation manners of the present invention in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention, but not to limit the scope of the present invention.

[0086] This embodiment proposes a joint optimization method for AmBC-NOMA communication resources in a high-speed mobile V2X scenario, aiming to solve the performance degradation problems brought about by finite block lengths, fast channel variations, and resource limitations in short-packet communication systems. This method comprehensively considers base station power allocation, reflection coefficient control of reflection devices, and block length resource allocation for each communication link, and effectively improves the transmission rate of the system under short-delay communication requirements through an alternating iterative optimization mechanism. For different SNR conditions, user moving speeds, and available resource limitations, the present invention constructs a scalable optimization model and performs equivalent transformation and solution of non-convex problems through the successive convex approximation (SCA) method, ensuring the convergence and engineering feasibility of the algorithm. The simulation results verify that this method has better system throughput performance compared with traditional strategies under finite block length conditions, and can still maintain strong robustness and adaptability in a high-speed mobile environment. This method is applicable to actual vehicle-to-everything (V2X) communication scenarios, especially in short-packet communication applications that require low latency, high reliability, and high-frequency mobility, and has broad engineering value. The above research provides a new idea for resource allocation and joint optimization in reflection-assisted NOMA systems, and 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 a high-mobile V2X scenario, as Figure 1 shown, includes the following steps:

[0088] Step 1: Construct an AmBC-NOMA communication system for a high-mobile V2X scenario, as Figure 2 shown.

[0089] The AmBC-NOMA communication system for a high-mobile V2X scenario includes a roadside unit (RSU), a reflection device (BD), a proximal user U N [[ID=1 eighty]]and a distal user U F , where all nodes are equipped with a single antenna;

[0090] Among them, the RSU serves as the main transmitter, and through non-orthogonal multiple access (NOMA) technology, it simultaneously sends a superimposed signal to U N and U F using power domain multiplexing; while receiving the RSU signal, the BD carries its own information through the 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 complex Gaussian distribution is used for modeling; the channels from RSU to U N and U F to BD are respectively denoted as h RN and h RF and h RB , and their statistical distributions are The links from BD to U N and U F are respectively denoted as h BN and h BF , and their statistical distributions are

[0092] Regarding the high-speed mobile characteristics of vehicle users, the present invention considers that the channel has significant time selectivity and uses a first-order autoregressive model to model the channel variation; the dynamic evolution form of the channel h k (t) is:

[0093]

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

[0095] Due to the rapid change of the channel caused by high-speed movement, to reduce system complexity and improve tracking stability, the present invention uses the minimum mean square error method MMSE to perform a one-time estimation of the channel state information CSI at the start of each coherence time; the initial channel is expressed as:

[0096]

[0097] wherein, is the MMSE estimated value, is the estimation error, and the two are independent, represents the average power of the channel estimated value, reflecting the estimation accuracy; Represents the average power of the estimation error and measures the estimation uncertainty.

[0098] Based on the initial channel state, the subsequent channel state is recursively calculated by combining the first-order autoregressive model AR, and the channel state at any moment is uniformly expressed as:

[0099]

[0100] where ε k reflects the complex Gaussian noise perturbation caused by the estimation error and user mobility to the channel, which is convenient for simplifying the analysis and receiver design and satisfies: where represents the average power of the estimation error;

[0101] In terms of signal transmission, the RSU uses the non-orthogonal multiple access technology NOMA to simultaneously send the superimposed signal s(t) to U N and U F The superimposed signal s(t) is expressed as:

[0102]

[0103] where P is the total transmit power, s N (t), s F (t) are the target signals of 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 are the power allocation coefficients assigned by the base station to the near-end user U N and the far-end user U F respectively, satisfying a N +a F = 1 and a F > a N to ensure the communication quality of the far-end user.

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

[0105] Finally, 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 the RSU to U 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 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-term, low-latency vehicle-to-vehicle communication. Furthermore, complex factors such as the power allocation of the system model's base station, the reflection coefficient of the reflector, the time-varying fading channel, and estimation errors all determine the system's short-packet communication performance.

[0112] Step 2: Before the AmBC-NOMA communication system is put into operation, resource allocation parameters are preset and communication constraints are established as the input basis for the subsequent joint optimization module.

[0113] The resource allocation parameters include: N and remote user U F Allocated power allocation coefficient a N and a F, the reflection coefficient β of the reflection device BD, and the block length resource allocation of each communication node; all preset parameters need to meet the power, reflection efficiency, and block length constraint conditions 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 constraint conditions specifically include: the power allocation coefficient satisfies the relationship:

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

[0116] To ensure that the proximal user obtains relatively more power resources, thereby improving the overall performance of the system.

[0117] The reflection coefficient of the reflection device satisfies the physical constraint condition:

[0118] 0 < β < 1

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

[0120]

[0121] Wherein, and respectively represent the block length resources allocated to the proximal user, the distal user, and the reflection link. And the block length resources allocated to each communication node should be positive. In addition, the system needs to 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 constraint conditions, it can ensure the feasibility and basic performance guarantee of the system in the startup phase, thereby providing effective support for subsequent resource joint optimization iterations.

[0123] Step 3: The base station combines the AmBC-NOMA communication system with the communication constraint conditions to jointly optimize the power allocation coefficient a N 、a F 、the block length resources and the reflection coefficient β of the reflection device; the optimization process aims to maximize the system transmission rate in the short-packet communication scenario, comprehensively consider the channel conditions, resource allocation constraints, and system stability requirements, and solve the resource allocation scheme that meets the optimal rate performance; to achieve the maximization of the overall transmission rate of the short-packet communication system while ensuring the system stability and performance requirements.

[0124] Specifically, first establish the signal-to-noise ratio SINR expressions of each link in the system, which are respectively expressed as:

[0125]

[0126] Among them, parameter A i , B i , C i , D3, where 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 expressions, γ = P / σ 2 ; represents the signal-to-noise ratio, where P is the total transmit power and σ 2 is the noise power; a N 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) are jointly determined by factors such as system power, channel correlation, and reflection characteristics: A1, A2, and A3 represent the effective channel gains of the direct link, reflection link, and far-end user link of the near-end user, respectively, and are specifically determined by the MMSE estimate of the initial channel gain, channel correlation coefficient, and power factor; B1 = B2 = B3 = γΩ εRN +1 is the normalized noise term, where Ω εRN 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, and are specifically composed of the reflection link gain, channel correlation, and error term; D3 is the effective channel gain of the far-end user direct link; ρ XY represents the channel correlation coefficient of link XY, is the squared modulus of the MMSE estimate of the initial channel gain, Ω εXY is the average power of the channel estimation error, |h RB | 2 and |h RF | 2 are the squared moduli of the channel gains from the base station to the reflection device and from the base station to the far-end user link, respectively;

[0131] The short-packet transmission rates of each link under the finite block length condition are respectively defined as:

[0132]

[0133]

[0134] Among them, and respectively represent the short - packet transmission rates of the proximal user, the reflection link, and the distal user under the condition of finite block length. \(V(\gamma j ) = 2\gamma j (1 + \gamma j ) -1 represents the channel dispersion, \(Q -1 (\cdot)\) represents the inverse function of the Gaussian \(Q\) - function, \(\mu j is 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, the present invention regards the square term \(\beta 2 of the reflection coefficient as an overall variable, denoted as \(\theta\), and uses it as a new optimization variable to participate in the optimization process. In addition, considering that the power - allocation coefficient satisfies the constraint relationship \(a N +a F = 1\), where \(a N and \(a F respectively represent the power ratios allocated to the proximal user and the distal user. To simplify the dimension of the optimization variables, in the modeling process, this article only uses \(a N as the optimization variable for solution, and represents the power of the distal user through the equivalent relationship \(a F = 1 - a N \), so as to reduce the number of variables and the optimization complexity.

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

[0139]

[0140] Subject to the following constraints:

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

[0142] 0\leq\theta\leq1

[0143]

[0144]

[0145]

[0146] Specifically, in the process of joint resource optimization of the present invention, the power allocation coefficient a F and a N The optimization of the reflection coefficient β and the block length resources The optimization is divided into two sub-problems and solved separately by an alternating iteration method.

[0147] For the optimization of the block length resource allocation problem, a method based on Successive Convex Approximation (SCA) is specifically proposed. Since the short-packet communication rate expression contains non-convex function terms of the block length variable, it is difficult to directly obtain the global optimal solution by direct solution. Specifically, the transmission rate expression of the user under short-packet communication is non-convex with respect to the block length resources and is difficult to directly optimize. Therefore, in each iteration of the present invention, with the current solution as the expansion point, for terms, a first-order Taylor approximation is performed 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 sub-problems and using an iterative optimization method to gradually approach the optimal block length resource allocation scheme. It is verified that this method has good convergence and feasibility.

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

[0149]

[0150] In the optimization process, for the non-linear coupling relationship between t i and z i a first-order Taylor approximation expansion is performed on the part containing non-convex terms, thereby constructing a convex approximation expression. The specific approximation form is as follows:

[0151]

[0152] Among them, where j = S N and S C and S F and i = 1, 2, 3. Since log2(1 + z i) The function itself is concave, and the function itself is a concave function, so its original form can be directly retained. The latter part of the constraint expression is a non-convex expression. For the convenience of optimization and solution, the present invention only linearly approximates the non-convex part by using the first-order Taylor expansion, so as to transform the overall rate constraint into a convex form and ensure that the optimization problem has good solvability.

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

[0154] t i ≥R min , i = 1, 2, 3

[0155] z i ≤γ i , i = 1, 2, 3

[0156] where γ i is the upper bound parameter calculated according to the current iteration point. To further handle the non-convexity problem caused by the product of variables in the signal-to-noise ratio fractional expression, the present invention introduces the first-order Taylor expansion form to locally linearize the product term of the power allocation coefficient a N and the square term θ of the reflection coefficient, so as to ensure that the sub-problems generated in each round of iteration are all convex optimization problems and are convenient for efficient solution. Specifically, taking the current iteration point as the expansion center, denoted as respectively represent the values of the variables z i , θ, a N at the previous iteration point. The product structure is approximately linearly approximated at the current point to obtain the following three approximate constraint expressions:

[0157]

[0158] In the overall iteration process, first, under the conditions of fixing the power allocation coefficient a N and the reflection coefficient β, the block length resource m j is optimized; then, based on the updated block length resource, the power allocation coefficient a N and the reflection coefficient β are jointly optimized, and the auxiliary variables z i and t i are updated. This alternating optimization process iterates continuously until the total system rate converges or reaches the preset stop condition; finally, the present invention can achieve the joint optimal allocation of power, reflection, and block length resources, effectively improving the rate performance and stability of the short-packet communication system in the high-speed mobile V2X scenario.

[0159] It can be concluded from Step 2 and Step 3 that the reasonable presetting and dynamic joint optimization of resource parameters can significantly improve the transmission rate of the short-packet communication system. By ensuring system feasibility through initial resource configuration and combining the alternating optimization of block length resources with 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 achieve the optimization of overall communication quality.

[0160] Step 4: Based on the jointly optimized resource configuration results, the base station, according to the optimized power allocation coefficients a N , a F and the block length resource configuration, completes the weighted superposition of multi-user signals and simultaneously transmits them to the proximal user U N and the distal user U F through the wireless channel. At the same time, the reflection device BD receives the superimposed signal sent by the base station and, according to the control signaling sent by the base station, performs passive modulation and reflection based on the optimized reflection coefficient β, embedding its own information signal during the reflection process to achieve synchronous information transmission.

[0161] After completing the joint optimization of resources, the base station, according to the optimized power allocation coefficients a N , a F and the block length resource configuration of each communication node, performs the weighted superposition processing of multi-user signals. Specifically, according to the power allocation result, the base station weights the information signals of the proximal user U N and the distal user U F respectively and then superimposes them to form a comprehensive transmission signal, which is simultaneously sent to the two users through the wireless channel to achieve efficient sharing of spectrum resources. At the same time, BD receives the superimposed signal from the base station and, according to the configuration parameters sent by the base station through the control signaling, performs passive modulation and reflection on the received signal according to the optimized reflection coefficient β. During the reflection process, the reflection device embeds its own information signal, modulates the original signal through the passive reflection mechanism, and then synchronously completes information transmission. This process does not require additional transmission power, makes full use of the reflection path resources, and improves the overall energy efficiency and connection performance of the system.

[0162] In specific implementation, before sending the superimposed signal, the base station controls the sending duration and coding strategy of each user data block according to the optimized allocated block length resource m i to ensure the short-packet communication characteristics and reliability requirements. When BD completes the reflection modulation, it 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 synchronously demodulated at the receiving end, maximizing the effective rate and stability of the short-packet communication of the system.

[0163] Step 5: Each user receives the superimposed signal from the base station and the signal modulated and reflected by the reflection device BD, and uses the Successive Interference Cancellation (SIC) technique to perform decoding in sequence according to the power allocation order. Specifically, the user first detects and decodes the signal of the remote user with power higher than the set threshold, and then decodes the signal of the proximal user and the scattering information of the reflection device in sequence, so as to extract its own communication information and the information content transmitted by BD respectively, and achieve complete data recovery.

[0164] After the base station completes the transmission of the superimposed signal and BD completes the 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 corresponding information content, the user adopts the Successive Interference Cancellation (SIC) technique to perform decoding and interference cancellation operations in sequence according to the pre-set power allocation order.

[0165] Specifically, the receiving end first detects and decodes the signal of the remote user U F with the highest power. Since the remote user is allocated a larger power and the signal strength is relatively high, it is easy to detect and reliably decode first. After decoding the signal of the remote user, the receiving end reconstructs the interference component of the remote user signal in the received signal according to the decoding result, and eliminates this interference component from the received signal to reduce the interference impact in the subsequent decoding process.

[0166] After successfully removing the interference of the remote user signal, the receiving end continues to detect and decode the signal of the proximal user U N . The signal of the proximal user must be reliably decoded after removing the interference of the remote user because of the lower power allocation and the interference of the remote signal during the initial reception. After decoding, the influence of the proximal user signal on the subsequent processing is further eliminated by means of reconstruction and cancellation in the same way.

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

[0168] The purpose of the method proposed by the present invention is to improve the system transmission rate and overall reliability under short-packet communication conditions in an AmBC-NOMA communication system for high-mobility V2X scenarios, especially in an environment with rapid channel changes and resource constraints. By presetting initial resource parameters and combining an alternating iterative joint optimization strategy, the method dynamically adjusts the power allocation coefficient, the reflection coefficient of the reflection device, and the block-length resource configuration respectively, so as to achieve efficient resource sharing and synchronous communication among multiple users. By means of the above, the present invention aims to improve the information transmission performance of legitimate users and the data transfer efficiency of reflection devices, enhance the adaptability of the system in a high-speed mobile environment, and finally maximize the transmission rate of the short-packet communication system and significantly improve the system performance.

[0169] Simulation conditions: In the simulation of the present 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). The minimum rate requirement is set to R min = [0.3, 0.1, 0.2] (bit / s / Hz), corresponding to the rate lower limits of each communication node respectively. The system time slot number is set to t = 2. In terms of channel parameters, the channel variance of the link from the base station to the reflection device (RSU-BD) is set to Ω RB = 1, and the estimated channel variances of the links from the base station to the proximal user (RSU-UN) and from the base station to the distal user (RSU-UF) are Ω RN = 20 and Ω RF = 5 respectively. The estimated channel variances of the links from the reflection device to the proximal user (BD-UN) and from the reflection device to the distal user (BD-UF) are Ω BN = 1.5 and Ω BF = 0.5 respectively. The reflection link error variance is set to 0.07. Initial value setting, during the simulation process, all parameters are evaluated based on the Monte Carlo simulation method, and independent simulations are carried out 5000 times under each parameter configuration to ensure the statistical reliability of the results.

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

[0171] Figure 3It can be seen that the comparison of the overall system rate is presented among the 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 resources. The simulation results show that as the total block length resources increase, the communication rates of both the optimized method and the unoptimized method increase. Moreover, under any block length condition, the Proposed-FBL method is always superior to the Benchmark-FBL method, demonstrating better short packet transmission performance. At the same time, the performance of Proposed-FBL gradually approaches the rate upper bound of Proposed-IBL as the block length increases, verifying the efficiency and asymptotic optimality of the proposed optimization strategy under finite block length conditions. The above results fully illustrate that the joint optimization mechanism constructed in this invention can effectively improve the short packet communication performance of the system, with good resource adaptability and application promotion value.

[0172] Figure 4 It can be seen that the simulation results of the short packet transmission rate of the system varying with the signal-to-noise ratio (SNR) are presented under different vehicle speed conditions (50 km / h, 70 km / h, 90 km / h). The results show that as the SNR increases, the short packet transmission rate of the system under each vehicle speed shows an upward trend, indicating that increasing the signal-to-noise ratio helps to enhance communication reliability and effective transmission rate. However, there are obvious differences in the system performance under different vehicle speeds. Among them, the higher the vehicle speed, the lower the system transmission rate. This is because high-speed movement will cause the channel to change more violently, further exacerbating the channel estimation error and time-selective fading effect, thus limiting the effective rate improvement space of the system in short packet transmission. On the contrary, the channel is relatively stable at lower vehicle speeds, the channel estimation is more accurate, and the reliability and efficiency of short packet communication are more easily guaranteed. Generally speaking, the simulation results verify that the method proposed in this invention has good short packet transmission performance under various signal-to-noise ratio and vehicle speed conditions. Especially in the high-speed mobile environment, it still shows strong robustness and adaptability, reflecting its practical value in the dynamic communication scenario of vehicle-to-everything (V2X).

[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 speed conditions. By effectively configuring power allocation, reflection coefficient, and block length resources, the optimization method not only enhances the overall throughput of the system but also maintains good communication stability in the high-speed mobile scenario. This method fully exploits the transmission potential under limited resources, provides effective support for short-delay and high-reliability vehicle-to-everything (V2X) communication applications, and demonstrates superior system adaptability and practical value.

[0174] The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) disclosed in the embodiments of the present disclosure that have similar functions.

Claims

1. A joint optimization method for communication resources of AmBC-NOMA in high-mobility V2X scenarios, characterized in that, It 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 reflecting device (BD), a proximal user (U) N and a distal user (U). F All nodes are equipped with a single antenna. Step 2: Before the operation of the AmBC-NOMA communication system, preset resource allocation parameters and establish communication constraint conditions; Step 3: The base station combines the AmBC-NOMA communication system with the communication constraint conditions to jointly optimize the power allocation coefficients a N , a F , the block length resources and the reflection coefficient β of the reflection device; the optimization process aims to maximize the system transmission rate in the short-packet communication scenario, comprehensively considers the channel conditions, resource allocation constraints, and system stability requirements, and solves the resource configuration scheme that meets the optimal rate performance; Step 4: Based on the jointly optimized resource allocation result, the base station completes the weighted superposition of multi-user signals according to the optimized power allocation coefficients a N , a F and the block length resource allocation, and simultaneously sends it to the proximal user U N and the distal user U F through the wireless channel; At the same time, the reflection device BD receives the superimposed signal sent by the base station, and performs passive modulation and reflection according to the optimized reflection coefficient β according to the control signaling sent by the base station, and embeds its own information signal during the reflection process to achieve synchronous information transmission; Step 5: Each user receives the superimposed signal from the base station and the signal modulated and reflected by the reflection device BD, and uses the successive interference cancellation technique SIC to decode sequentially according to the power allocation order.

2. The joint optimization method of AmBC-NOMA communication resources for 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 serves as the primary transmitter. Through non-orthogonal multiple access technology NOMA, it simultaneously transmits a superimposed signal to U N and U F using power domain multiplexing. While receiving the RSU signal, the BD carries its own information through the backscattering mechanism to assist the RSU in achieving coverage and transmission enhancement for vehicle users. Specifically as follows: All links consider Rayleigh fading and are modeled using complex Gaussian distribution; the channels from RSU to U N 、U F 、BD are denoted as h RN 、h RF 、h RB , and their statistical distributions are The links from BD to U N 、U F are denoted as h BN 、h BF , and their statistical distributions are Model the channel variation using a first-order autoregressive model; the channel h k (t) has the following dynamic evolution form: Among them, is an independent and identically distributed complex Gaussian noise term, representing the time-varying component of the channel, reflects the average power of this noise component, ρ k is the channel autocorrelation coefficient, and its calculation formula is: ρ k = J0(2πf D T s ) where J0(·) is the zero-order Bessel function, is the Doppler shift, v is the vehicle speed, f c is the carrier frequency, T S is the symbol period, and c is the speed of light; The minimum mean square error method MMSE is used to estimate the channel state information CSI once at the beginning of each coherence time; the initial channel is expressed as: wherein, is the MMSE estimated value, is the estimation error, and the two are independent, represents the average power of the channel estimated value, represents the average power of the estimation error; Based on the initial channel, recursive calculation is performed in combination with the first-order autoregressive model AR, and the channel state at any moment is uniformly expressed as: Among them, ε k reflects the complex Gaussian noise perturbation caused by the estimation error and user mobility, and satisfies: Among them, represents the average power of the estimation error The RSU uses the non - orthogonal multiple access technology NOMA to simultaneously send the superimposed signal s(t) to U N and U F The superimposed signal s(t) is expressed as: where P is the total transmission power, s N (t) and s F (t) are the target signals of 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 are the power allocation coefficients assigned by the base station to the near - end user U N and the far - end user U F respectively, satisfying a N +a F = 1 and a F >a N ; After the reflection device BD receives the superimposed signal s(t) from the RSU, it superimposes its own information s C (t) through passive reflection modulation to form a reflected signal; the reflected signal is modeled as: βs(t)s C (t), where β is the reflection coefficient and satisfies |β| < 1; Finally, the signal model received by the vehicle user is as follows: For U N : y N y(t) = h RN y(t)s(t) + h RB h BN y(t)βs(t)s C y(t) + n N y(t) For U F : y F y(t) = h RF y(t)s(t) + h RB h BN y(t)βs(t)s C y(t) + n F y(t) where h RN (t), h RF (t), h RB , h BN (t), h BF (t) represent the channel gains from the RSU to U N , from the RSU to U F , from the RSU to BD, from BD to U N , from BD to U F respectively, and n N (t) and n F (t) are additive white Gaussian noise, satisfying the distribution: n N (t), 3. The joint optimization method of AmBC-NOMA communication resources for high-mobility V2X scenarios according to claim 1, wherein, The resource allocation parameters described in step 2 include: the power allocation coefficients a N and a F allocated by the base station to the near-end user U N and the far-end user U F , the reflection coefficient β of the reflection device BD, and the block length resource configuration of each communication node.

4. A joint optimization method for AmBC-NOMA communication resources for high-mobility V2X scenarios according to claim 1, characterized in that The communication constraint conditions described in Step 2 specifically include: the power allocation coefficient satisfies the relationship: a N +a F =1,a F >a N The reflection coefficient of the reflection device satisfies the physical limitation condition: 0<β<1 The block length resource configuration meets the total block length resource upper limit requirement, that is: wherein, and respectively represent the block length resources allocated to the proximal user, the distal user, and the reflection link; and the block length resources allocated to each communication node should be positive. In addition, the system needs to ensure that the initial rate of all communication links is not lower than the set minimum rate threshold.

5. The joint optimization method of AmBC-NOMA communication resources for high-mobility V2X scenarios according to claim 1, characterized in that Specifically for Step 3, first establish the signal-to-noise ratio SINR expressions of each link in the system, which are respectively expressed as: Among them, parameter A i , B i , C i , D3, where i = 1, 2, 3, are determined by the system power, reflection characteristics and channel gain, specifically: B1 = B2 = B3 = γΩ εRN +1 C2 = γ|h RB | 2 Ω εRN where γ = P / σ 2 ; represents the signal-to-noise ratio, where P is the total transmit power and σ 2 is the noise power; a N and β are the power allocation coefficient and reflection coefficient of the proximal user respectively; A1, A2, and A3 represent the effective channel gains of the direct link, reflection link, and remote user link of the proximal user respectively, which are specifically determined by the MMSE estimated value of the channel initial gain, the channel correlation coefficient, and the power factor; B1 = B2 = B3 = γΩ εRN +1 is the normalized noise term, where Ω εRN 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 respectively, which are specifically composed of the reflection link gain, channel correlation, and error terms; D3 is the effective channel gain of the direct link of the remote user; ρ XY represents the channel correlation coefficient of link XY, is the square of the modulus of the MMSE estimated value of the initial channel gain, Ω εXY is the average power of the channel estimation error, |h RB | 2 and |H RF | 2 are the squares of the moduli of the channel gains of the base station to the reflection device and the base station to the remote user link respectively; The short-packet transmission rates of each link under the condition of finite block length are respectively defined as: Among them, and respectively represent the short-packet transmission rates of the proximal user, the reflection link, and the distal user under the condition of finite block length. V(γ j ) = 2γ j (1 + γ j ) -1 represents the channel dispersion, Q -1 (·) represents the inverse function of the Gaussian Q function, μ j is the target bit error rate, j = S N 、S C 、S F ; The total transmission rate of the system is defined as: Regard the square term β of the reflection coefficient 2 as an overall variable, denoted as θ, and only use a N as the optimization variable to solve, and through a F = 1 - a N to represent the power of the remote user by the equivalent relationship, and model the resource joint optimization problem as the following optimization objective: It satisfies the following constraint conditions: a N +a F =1,a N >a F 0 ≤ θ ≤ 1 6. The joint optimization method of AmBC-NOMA communication resources for high-mobility V2X scenarios according to claim 5, characterized in that In the resource joint optimization problem, the power allocation coefficients a F and a N , the optimization of the reflection coefficient β and the optimization of the block length resource are divided into two sub-problems and solved separately by the method of alternating iteration; Optimize the block length resource allocation problem, and specifically propose a method based on successive convex approximation SCA; In each iteration, using the current solution as the expansion point, perform a first-order Taylor approximation on the terms 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 sub-problems and adopting an iterative optimization method to gradually approximate the optimal block length resource allocation scheme; For the joint optimization of the power allocation coefficient a F , a N and the reflection coefficient β, based on the SCA method, introduce the auxiliary variable t i , where i = 1, 2, 3 represents the short-packet transmission rate of each link, and introduce the auxiliary variable z i representing the signal-to-noise ratio of each link; reconstruct the problem of maximizing the total system transmission rate as maximizing the sum of the auxiliary rate variables, that is: During the optimization process, for t i and z i between the non-linear coupling relationship, the part containing non-convex terms is expanded by the first-order Taylor approximation to construct a convex approximation expression. The specific approximation form is as follows: Among them, where j = S N , S C , S F , and i = 1, 2, 3; the following constraints are introduced on the auxiliary variables: t i ≥R min , i = 1, 2, 3 z i ≤γ i , i = 1, 2, 3 where γ i is the upper bound parameter calculated according to the current iteration point; introducing the first-order Taylor expansion form, the product term of the power allocation coefficient a N and the square term θ of the reflection coefficient in the signal-to-noise ratio term is locally linearized, with the current iteration point as the expansion center, denoted as respectively represent the variable z i , θ, a N at the value of the previous iteration point, and the product structure is approximated by the first-order Taylor at the current point, resulting in the following three approximate constraint expressions: In the overall iterative process, first, under the conditions of the fixed power allocation coefficient a N and the reflection coefficient β, optimize the block length resource m j ; Subsequently, based on the updated block length resource, jointly optimize the power allocation coefficient a N and the reflection coefficient β, and update the auxiliary variables z i and t i ; This alternating optimization process is continuously iterated until the total system rate converges or reaches the preset stop condition.

7. A joint optimization method for AmBC-NOMA communication resources for high-mobility V2X scenarios according to claim 1, characterized in that Specifically for Step 5, the user first detects and decodes the signals of remote users with power higher than the set threshold, and then sequentially decodes the signals of proximal users and the scattering information of the reflection device, so as to extract its own communication information and the information content transmitted by BD respectively, and realize complete data recovery.

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