A cellular vehicle-to-network communication resource selection method, system, device and medium

Abstract

The application discloses a kind of cellular vehicle networking communication resource selection method, system, equipment and medium, is related to cellular vehicle networking communication field, method includes obtaining the message frame candidate resource list of vehicle cluster in vehicle-mounted user terminal and constructs candidate resource list matrix according to the message frame candidate resource list;The message frame candidate resource list is the time-frequency resource block set that is constituted by multiple subframes and multiple subcarriers;According to the candidate resource list matrix, the optimization iteration equation of optimal resource list is constructed using PSO algorithm;According to resource perception window, the fitness function of the optimal resource block in the subframe of subcarrier is constructed;According to the optimization iteration equation is iterated according to the fitness function, and optimal selection resource list is obtained.The application reduces the contention access conflict in resource selection process, improves the utilization rate of limited resources.

Description

Technical Field

[0001] This invention relates to the field of cellular vehicle-to-everything (V2X) communication, and in particular to a method, system, device, and medium for selecting cellular V2X communication resources. Background Technology

[0002] Cellular Vehicle to Everything (C-V2X) is a typical application of the Internet of Things (IoT) in cellular communication networks and one of the ways to realize intelligent transportation and smart city construction. It includes 4G cellular vehicle to everything (Long Term Evolution-V2X, LTE-V2X) and its evolution, 5G cellular vehicle to everything (New Radio-V2X, NR-V2X). In C-V2X, the vehicle user equipment (VUE) and the cell user equipment (CUE) share limited spectrum resources for wireless communication. It supports centralized resource allocation modes (Mode3 / Mode1) for communication between the terminal and the base station, and distributed communication resource allocation modes (Mode4 / Mode2) for vehicle-to-vehicle (V2V) communication. Currently, the Mode4 / Mode2 communication resource allocation mode uses a Sensing-Based Semi-Persistent Scheduling (SB-SPS) mechanism to allocate available communication resources. This mechanism includes three processes: resource awareness, candidate resource selection, and resource selection (or resource reselection). During the resource selection (or resource reselection) process, factors such as the highly dynamic topology of the vehicular network and the random resource selection method of the SPS mechanism can lead to contention conflicts when multiple VUEs simultaneously access the same spectrum resources. This can cause communication access and transmission failures for all competing VUEs. Therefore, selecting communication resources through computational methods to reduce contention conflicts is beneficial for improving the utilization of limited spectrum resources and enhancing end-to-end transmission performance of vehicle-to-vehicle communication.

[0003] Currently, SB-SPS resource scheduling mechanisms generally use random selection methods to choose communication resources from a candidate resource pool to transmit message data. The probability of conflict in random resource selection is reduced by using the minimum signal strength selection method (Mode4) or by expanding the candidate resource pool (Mode2), but this reduces communication reliability or increases the probability of communication interference in co-frequency networking. Cluster-based resource selection mechanisms continuously monitor communication signal energy to select communication resources with good channel conditions for workshop communication. Their main advantage is improved communication reliability and some degree of mitigation of contention conflicts, but the strong dynamic nature of vehicles requires the VUE to estimate the channel state within a very short channel sensing time. Short-period sensing-based resource selection methods first divide resource subframes into sensing intervals and message transmission intervals, and use sensing timers of different durations to achieve differentiated access for different priority services, reducing contention conflicts to some extent, but with high computational overhead. Reinforcement learning-based joint optimization schemes for resource selection and power control use deep neural networks to iteratively select communication power adaptively to the channel state, utilizing channel state differences caused by vehicle location differences to reduce contention conflicts and improve communication reliability, but require a large number of effective samples to train the neural network. Intelligent computing methods can improve the performance of resource allocation mechanisms. By utilizing the target information sharing capability, location correlation, and mutual influence of neighboring nodes of the Particle Swarm Optimization (PSO) algorithm to select communication resources, competitive conflicts can be avoided in the resource selection or reselection process. Pre-computation reduces the real-time computation load of the resource allocation mechanism, making it possible to implement the algorithm online. However, no relevant methods have been found yet. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device and medium for selecting cellular vehicle network communication resources, which can reduce competitive access conflicts in the resource selection process and improve the utilization rate of limited resources.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for selecting communication resources in a cellular vehicle-to-everything (V2X) network includes:

[0007] Obtain the message frame candidate resource list of the vehicle user terminal in the vehicle cluster and construct a candidate resource list matrix based on the message frame candidate resource list; the message frame candidate resource list is a set of time-frequency resource blocks composed of multiple subframes and multiple subcarriers;

[0008] Based on the candidate resource list matrix, the PSO algorithm is used to construct the optimization iterative equation for the optimal resource list;

[0009] The fitness function of the optimal resource block within a subframe of a subcarrier is constructed based on the resource-aware window;

[0010] The optimization iterative equation is iterated based on the fitness function to obtain the optimal resource selection list.

[0011] Optionally, the expression for the candidate resource list matrix is:

[0012]

[0013] Where L is the candidate resource list matrix, j∈[1,F] is the subcarrier index, and i∈[1,S] is the subframe index. Let F be the resource block on the j-th subcarrier within the i-th time slot, where F is the total number of subcarriers and S is the total number of subframes.

[0014] Optionally, the expression for the optimization iterative equation is:

[0015]

[0016]

[0017] Among them, L k (t) represents the set of resource blocks currently selected by the k-th vehicle-mounted user terminal within the t-th subframe. Let this be the set of resource blocks optimally selected by the k-th vehicle-mounted user terminal within the t-th subframe. For the k-th vehicle-mounted user terminal in the t-th subframe, the speed at which the selected resource block set is updated is... L represents the optimal set of resource blocks for the current k vehicle-mounted user terminal clusters; k (t+1) represents the set of resource blocks currently selected by the k-th vehicle-mounted user terminal within the (t+1)-th subframe. Let ω be the speed at which the k-th vehicle-mounted user terminal updates the selected resource block set within the (t+1)-th subframe, where ω is the inertia weight, c1 is the individual learning factor, c2 is the group learning factor, and r1 and r2 are both weighted random numbers in the interval [0,1].

[0018] Optionally, the expression for the fitness function is:

[0019]

[0020] in, Let RSRP be the linear mean of the reference signal received power of the optimal resource block in the i-th subframe of the j-th subcarrier within the sensing window, H be the total number of measurement periods within the sensing window, h be the h-th measurement period, and RSRP be the reference signal received power. For the (n+i)th subcarrier s +(h-1)·RRI subframe number, N sThe duration of the sensing window during resource sensing, i is the subframe number, j is the subcarrier number, n is the subframe interval number, and RRI is the resource reservation interval.

[0021] Optionally, the optimization iterative equation is iterated according to the fitness function to obtain the optimal resource selection list, specifically including:

[0022] The optimization iterative equation is iterated based on the fitness function to obtain the update speed of the selected resource block set in the (t+1)th subframe;

[0023] Calculate the optimal resource list selected by the k-th vehicle user terminal in the (t+1)-th subframe based on the update speed;

[0024] The optimal resource selection list is determined based on the optimal resource list selected by the k-th vehicle-mounted user terminal in the (t+1)-th subframe and the set of service request resource blocks.

[0025] Optionally, the optimal resource selection list is determined based on the optimal resource list selected by the k-th vehicle-mounted user terminal in the (t+1)-th subframe and the set of service request resource blocks, specifically including:

[0026] The optimal resource list initially selected by the k-th vehicle user terminal is assigned a value based on the optimal resource list selected by the k-th vehicle user terminal in the (t+1)-th subframe;

[0027] Determine whether the set of resource modules selected in the optimal resource list initially selected by the k-th vehicle user terminal after assignment is greater than the set of resource blocks requested by the business, and obtain the judgment result.

[0028] If the judgment result is yes, then the optimal resource list initially selected by the kth vehicle-mounted user terminal after assignment is taken as the optimal selection resource list;

[0029] If the judgment result is negative, then the subframe is updated and the process returns to the step "Iterate the optimization iterative equation according to the fitness function to obtain the update speed of the selected resource block set in the (t+1)th subframe".

[0030] The present invention also provides a cellular vehicle-to-everything (V2X) communication resource selection system, comprising:

[0031] The acquisition and construction module is used to acquire the message frame candidate resource list of the vehicle user terminal in the vehicle cluster and construct a candidate resource list matrix based on the message frame candidate resource list; the message frame candidate resource list is a set of time-frequency resource blocks composed of multiple subframes and multiple subcarriers;

[0032] An optimization iterative equation construction module is used to construct optimization iterative equations for the optimal resource list based on the candidate resource list matrix using the PSO algorithm.

[0033] The fitness function determination module is used to construct the fitness function of the optimal resource block within a subframe of a subcarrier based on the resource-aware window;

[0034] The iteration module is used to iterate the optimization iterative equation according to the fitness function to obtain the optimal resource selection list.

[0035] The present invention also provides an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method as described.

[0036] The present invention also provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method as described.

[0037] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0038] This invention obtains a candidate resource list of message frames from onboard user terminals in a vehicle cluster and constructs a candidate resource list matrix based on the message frame candidate resource list. The message frame candidate resource list is a set of time-frequency resource blocks composed of multiple subframes and multiple subcarriers. An optimization iterative equation for the optimal resource list is constructed using the PSO algorithm based on the candidate resource list matrix. A fitness function for the optimal resource block within a subframe of a subcarrier is constructed based on a resource awareness window. The optimization iterative equation is iterated using the fitness function to obtain the optimal selected resource list. By leveraging the computing power of the onboard equipment and using the PSO algorithm, the optimal candidate resource list related to the location of different vehicles in the cluster movement is selected, reducing communication access contention between vehicles in the cluster and improving the utilization rate of limited communication resources. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A flowchart of the cellular vehicle-to-everything (V2X) communication resource selection method provided by the present invention;

[0041] Figure 2 This is a schematic diagram of time-frequency resource perception and resource selection. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] The purpose of this invention is to provide a method, system, device and medium for selecting cellular vehicle network communication resources, which can reduce competitive access conflicts in the resource selection process and improve the utilization rate of limited resources.

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] In C-V2X cluster communication scenarios, VUEs use the PSO algorithm to search for the optimal set of candidate resources relative to their own trajectory for transmitting message packets, reducing contention for access during resource selection and improving the utilization of limited resources. For example... Figure 1 As shown, the present invention provides a method for selecting cellular vehicle-to-everything (V2X) communication resources, comprising:

[0046] Step 101: Obtain the message frame candidate resource list of the vehicle user terminal in the vehicle cluster and construct a candidate resource list matrix based on the message frame candidate resource list; the message frame candidate resource list is a set of time-frequency resource blocks composed of multiple subframes and multiple subcarriers.

[0047] Relative motion is the dynamic state of a pair of vehicles in quasi-communication. The two paired vehicles are in different relative positions on the road at different times. This difference will lead to different channel quality of vehicle-to-vehicle communication, and ultimately result in different Reference Signal Received Power (RSRP) values.

[0048] Within the current road segment, assuming there are K VUEs in the vehicle cluster, the message frame candidate resource list is a set of time-frequency resource blocks (RBs) consisting of S subframes (sf) and F subcarriers, such as... Figure 2 As shown, the candidate resources can be mapped to an F×S candidate resource list matrix L, and the expression for the candidate resource list matrix is:

[0049]

[0050] Where subcarrier index j∈[1,F], and subframe index i∈[1,S]. If the candidate resource list for the k-th VUE is... When it is used When using resource blocks, record like Indicates the occupied status, then Indicates an idle state, and has Let F be the resource block on the j-th subcarrier within the i-th time slot, where F is the total number of subcarriers and S is the total number of subframes.

[0051] Step 102: Construct the optimization iterative equation for the optimal resource list using the PSO algorithm based on the candidate resource list matrix. The optimization iterative equation for the optimal resource list includes the speed of updating the selected resource set and the optimization selection iterative equation for the optimal resource block set of the vehicle user terminal.

[0052] In a certain message frame, let L be the set of resource blocks currently selected by the k-th VUE in the t-th subframe (t∈[1,S]). k (t), whose optimal set of resource blocks is (t). Its update speed for selecting resource block sets is The optimal set of resource blocks for the current K VUE clusters is Within the (t+1)th subframe, the optimal resource block set selected by the kth VUE is L. k (t+1), its speed for updating the selected resource block set is As shown in equation (2-1), the iterative equation for the optimal selection of the k-th VUE optimal resource block set is shown in equation (2-2).

[0053]

[0054]

[0055] Where ω is the inertia weight, representing the particle's spatial search capability. The smaller the ω value, the stronger the local search capability; the larger the ω value, the stronger the global search capability. It is recommended to take a value within the interval [0.4, 2]. c1 is the individual learning factor, and c2 is the group learning factor. The learning factor reflects the learning speed, and it is recommended to take a value within the interval [0, 4]. r1 and r2 are weighted random numbers within the interval [0, 1] to increase the randomness of the search.

[0056] Step 103: Construct the fitness function of the optimal resource block within the subframe of the subcarrier based on the resource-aware window.

[0057] Step 104: Iterate the optimization iterative equation according to the fitness function to obtain the optimal resource selection list.

[0058] Step 104 specifically includes: iterating the optimization iterative equation according to the fitness function to obtain the update speed of the selected resource block set in the (t+1)th subframe; calculating the optimal resource list selected by the kth vehicle user terminal in the (t+1)th subframe according to the update speed; and determining the optimal selected resource list according to the optimal resource list selected by the kth vehicle user terminal in the (t+1)th subframe and the service request resource block set.

[0059] Specifically, determining the optimal resource selection list based on the optimal resource list selected by the k-th vehicle-mounted user terminal and the service request resource block set within the (t+1)-th subframe includes: assigning values ​​to the optimal resource list initially selected by the k-th vehicle-mounted user terminal based on the optimal resource list selected by the k-th vehicle-mounted user terminal within the (t+1)-th subframe; determining whether the set of selected resource modules in the initially selected optimal resource list of the k-th vehicle-mounted user terminal after assignment is greater than the set of service request resource blocks, and obtaining a determination result; if the determination result is yes, then the optimal resource list initially selected by the k-th vehicle-mounted user terminal after assignment is taken as the optimal resource selection list; if the determination result is no, then the subframe is updated and the process returns to step "Iterating the optimization iterative equation according to the fitness function to obtain the update speed of the selected resource block set within the (t+1)-th subframe".

[0060] To avoid interference with the licensed cellular spectrum, workshop communication selects a set of resource blocks with the lowest relative reference signal received power (RSRP) to maximize system throughput. Generally, the RSRP value of the radio signal in a cellular network ranges from -140 to -44 dBm. Assuming the resource allocation is triggered in the nth subframe interval, let sf be the first subframe after the perception window. n The fitness function of the optimal resource block in the i-th subframe of the j-th subcarrier is the linear mean of the RSRP (Reference Signal Received Power) within the sensing window, as shown in Equation (3).

[0061]

[0062] in, Let Rj be the linear mean of the reference signal received power of the optimal resource block in the i-th subframe of the j-th subcarrier within the sensing window, H be the total number of measurement periods within the sensing window, h be the h-th measurement period, and RSRP be the reference signal received power. For the (n+i)th subcarrier s +(h-1)·RRI subframe number, N s This represents the duration of the perception window during resource awareness, and is usually set to an integer multiple of the subframe, as shown in the attached figure. Figure 1 As shown, if n = 0, then [sf0-1000, sf0-1] is the resource-aware interval, and the negative sign only indicates the time sequence before the 0th subframe. h = 1, 2, ..., H. RRI is the resource reservation interval, which randomly selects an integer value in the range [1, 1000] ms according to the corresponding rules.

[0063] Let the optimal resource list selected during the initialization of the k-th VUE be... The fitness function is initialized to a random value. Within the i-th subframe, the average reference signal received power of all subcarriers is... As shown in equation (4).

[0064]

[0065] In S subframes, all subcarriers The set is Let the t-th subframe have the minimum Value, that is Then VUE selects the set of resource blocks within this subframe as the global optimum, denoted as:

[0066]

[0067] On subcarrier j, within all subframes As shown in equation (6).

[0068]

[0069] On F subcarriers, within all subframes The set is Let the j-th subcarrier have the minimum Value, that is Then the VUE selects the set of resource blocks on that subcarrier as the individual optimal set, denoted as:

[0070]

[0071] Substituting equations (5) and (7) into equation (2-1), we calculate the update speed of the selected resource block set within the (t+1)th subframe. Then, the required update speed Substituting into equation (2-2), we calculate the optimal resource list L selected by the k-th VUE within the (t+1)-th subframe. k (t+1), and assign the value L. kopt =L k (t+1). If L kopt If the selected resource block set is greater than the business request resource block set, stop updating the calculation, and the output is... Select the optimal resource list Used for message data transmission.

[0072] This invention can select relatively optimal communication resources based on the vehicle's driving position for message transmission in the workshop. Leveraging the computing power of the onboard equipment, the particle swarm optimization method selects the optimal candidate resource list relevant to the respective positions of different vehicles in the swarm movement, reducing communication access contention conflicts within the swarm and improving the utilization rate of limited communication resources.

[0073] The communication resource selection method provided in this embodiment equips the vehicle with an onboard user terminal (VUE) and a computing unit supporting the C-V2X communication standard. When the VUE generates message data, it obtains candidate resources through a resource awareness process. The SB-SPS resource scheduling mechanism uses a distributed mode to allocate available communication resources for inter-vehicle communication. During the resource selection (or resource reselection) process, the onboard computing unit establishes a candidate resource list matrix, uses the PSO algorithm to establish a resource optimization iterative equation, and updates and iteratively optimizes the candidate resource list based on the established individual optimal and global optimal fitness functions, selecting the optimal resource list for inter-vehicle communication message transmission. Specific implementation steps include:

[0074] (1) Establish a candidate resource list matrix

[0075] Modeling the candidate resource list into a matrix form is similar in form to the resource structure of a new cellular network. From a technological innovation perspective, it provides an achievable resource structure for the two optimal steps in the third step. The existing technology of synchronously selecting two-dimensional resource blocks is decomposed into two steps: selecting resource blocks from two one-dimensional dimensions, namely, from the subframe (communication time slot dimension) and from the subcarrier (communication channel dimension).

[0076] This invention runs only on the vehicle's computer, does not require the use of drones, is compatible with existing international standards, has low computational requirements, and is highly feasible.

[0077] In C-V2X networks, the 3GPP standard describes the communication resources of a message frame as a two-dimensional resource block set consisting of 12 frequency-domain subcarriers and 10 time-domain subframes. Each subframe has a duration of 1ms, and 10 subframes form a 100ms message frame. Therefore, the candidate resource list is constructed as shown in matrix L'. In this embodiment, the superscript ' in all formulas indicates that the two-dimensional resource block set consisting of 12 frequency-domain subcarriers and 10 time-domain subframes is used as an example.

[0078]

[0079] Where subcarrier index j'∈[1,12] and subframe index i'∈[1,10]. If there are K VUEs in the vehicle cluster, and the resource list matrix selected by the k-th VUE is L' k Then L' k A subset of the candidate resource list matrix L', i.e. k∈K. When it is used When using resource blocks, record like Indicates the occupied status, then Indicates an idle state, and has

[0080] (2) Establish the PSO optimization iterative equation

[0081] In a certain message frame, let L' be the resource list currently selected by the k-th VUE in the t-th subframe (t∈[1,10]). k (t), whose optimal resource list is Its update speed for the selected resource list is The optimal resource list for the current K VUE clusters is as follows: Within the (t+1)th subframe, the optimal resource list selected by the kth VUE is L' k (t+1), its speed for updating the selected resource list is As shown in Equation (8-1), the optimization selection iterative process of the kth VUE optimal resource list is shown in Equation (8-2).

[0082]

[0083]

[0084] Wherein, the inertia weight ω = 0.3, the learning factor c1 = c2 = 2, and r1 and r2 are randomly selected in the interval [0,1].

[0085] (3) Methods for selecting the optimal resource set

[0086] This section includes three sub-sections:

[0087] 1. A standard for selecting the best PSO algorithm is defined, namely, the average RSRP is defined as the fitness function of the PSO algorithm. The basis for this is that the resources used in V2V communication between workshops are essentially the spectrum licensed to paid mobile terminals by the cellular network. Therefore, it should select resource blocks with the smallest possible RSRP, so that the transmission distance is minimized and interference with normal licensed user communication signals can be avoided.

[0088] 2. Based on the defined fitness function, the optimal communication time period for each vehicle is first selected from a global perspective (global optimum), which means selecting the optimal subframe. For vehicles that need to communicate, the time period (subframe) with the minimum average RSRP across all subcarriers (each subcarrier is essentially a subchannel) has the least impact on the entire network. In the standard, a message cycle has 10 subframes and 12 subchannels. The average RSRP of each of the 12 subchannels in the 10 subframes is calculated (there are 10 values ​​in total), and the minimum value is the globally optimal value.

[0089] 3. However, the quality of the communication channel in the workshop changes dynamically with the relative position of the vehicles. Therefore, from the perspective of a communication link between two individual vehicles, the channel with the smallest average RSRP over all time periods has the least impact on the cellular network. There are a total of 10 subframes. Therefore, we calculate the average RSRP of each of the 12 sub-channels in the 10 subframes (there are 12 values ​​in total) and select the optimal one.

[0090] The transmission of communication signals ultimately needs to be realized on the spectrum (channel) to form electromagnetic waves (the third stage is channel selection), and the transmission time period (or subframe) is the opportunity for different pairs of workshop communications to transmit signals, involving all vehicles that need workshop communication, which is a global issue (the second stage is subframe selection).

[0091] Through steps 2 and 3, each round of PSO calculation iteration will select one or zero optimal resource blocks, and the selected resource blocks will be assigned to L. kopt The variables are stored on the vehicle's computer. Before the next round of calculation, this resource block is marked as occupied and will not enter the resource pool for iterative optimization. After repeated iterative calculations, under normal circumstances, a sequence of resource blocks will eventually be found. When the resource set composed of these resource blocks meets the vehicle-to-vehicle communication requirements, the calculation terminates, and the entire set of these resource blocks is allocated to the pair of vehicles (only two neighboring vehicles can perform short-distance vehicle-to-vehicle communication, i.e., V2V), for the transmission of vehicle-to-vehicle messages. Under abnormal circumstances (such as under extremely dense congestion), the set of resource blocks found may not be sufficient for vehicle-to-vehicle communication. In this case, the calculation terminates due to iterative calculation timeout, and resource allocation fails.

[0092] Typically, the resource-aware window size N s =1000 subframe duration (i.e., 1000ms), RRI = 100ms, assuming the trigger time for resource allocation is in the 0th subframe interval, i.e., the first subframe after the perception window is denoted as sf0, then the fitness function for modeling the optimal resource block within the i'th subframe of the j'th subcarrier is the linear mean of RSRP within the perception window as follows:

[0093]

[0094] Where h = 1, 2, ..., 10.

[0095] Let the optimal resource list selected during the initialization of the k-th VUE be... The fitness function is initialized with a random value within the range [-140, -44]. Within the i'-th subframe, the fitness function of all subcarriers... as follows:

[0096]

[0097] In 10 subframes, all subcarriers The set is Let the t-th subframe have the minimum Value, that is VUE selects the set of candidate resource blocks within this subframe as the global optimum, denoted as:

[0098]

[0099] On subcarrier j', within all subframes As shown in the following formula:

[0100]

[0101] On 12 subcarriers, in all subframes The set is Let the j'th subcarrier have the minimum Value, that is VUE selects the set of candidate resource blocks on this subcarrier as the individual optimal set, denoted as:

[0102]

[0103] Substituting equations (9) and (10) into equation (8-1), we calculate the update speed of the selected resource block set within the (t+1)th subframe. Then, the required update speed Substituting into equation (8-2), we calculate the optimal resource list L selected by the k-th VUE within the (t+1)-th subframe. k (t+1), and assign the value L. kopt =L k (t+1). If L kopt If the selected resource block set is greater than the business request resource block set, stop updating the calculation, and the output is... This is the optimal list of resources selected for message data transmission.

[0104] In summary, this embodiment provides a cellular vehicle-to-everything (V2X) communication resource selection method based on the PSO algorithm principle. This method associates the selection of time-domain subframes and frequency-domain subcarriers with the vehicle's driving position, and selects the optimal resource list through multiple iterative calculations of individual optimality and global optimality. This provides an easy-to-implement solution for reducing vehicle-to-vehicle communication contention and access collisions under limited spectrum resource sharing.

[0105] The present invention also provides a cellular vehicle-to-everything (V2X) communication resource selection system, comprising:

[0106] The acquisition and construction module is used to acquire the message frame candidate resource list of the vehicle user terminal in the vehicle cluster and construct a candidate resource list matrix based on the message frame candidate resource list; the message frame candidate resource list is a set of time-frequency resource blocks composed of multiple subframes and multiple subcarriers.

[0107] The optimization iterative equation construction module is used to construct the optimization iterative equation for the optimal resource list based on the candidate resource list matrix using the PSO algorithm.

[0108] The fitness function determination module is used to construct the fitness function of the optimal resource block within a subframe of a subcarrier based on the resource-aware window.

[0109] The iteration module is used to iterate the optimization iterative equation according to the fitness function to obtain the optimal resource selection list.

[0110] The present invention also provides an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method as described.

[0111] The present invention also provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method as described above.

[0112] The vehicle-mounted communication terminal of this invention, based on the differences in communication resources at different driving locations, applies the PSO algorithm to search for time-frequency resources with the lowest potential competition to transmit message data. Beneficial effects: It utilizes the uniqueness of associated driving locations to achieve differentiation of relatively optimal resources, minimizes the probability of communication access competition, and improves the transmission performance of vehicle-to-vehicle communication messages and the utilization rate of limited communication resources.

[0113] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0114] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for selecting communication resources in a cellular vehicle-to-everything (V2X) network, characterized in that, include: Obtain the message frame candidate resource list of the vehicle user terminal in the vehicle cluster and construct a candidate resource list matrix based on the message frame candidate resource list; The message frame candidate resource list is a set of time-frequency resource blocks consisting of multiple subframes and multiple subcarriers; Based on the candidate resource list matrix, the PSO algorithm is used to construct the optimization iterative equation for the optimal resource list; The fitness function of the optimal resource block within a subframe of a subcarrier is constructed based on the resource-aware window; The optimization iterative equation is iterated based on the fitness function to obtain the optimal resource selection list.

2. The cellular vehicle-to-everything (V2X) communication resource selection method according to claim 1, characterized in that, The expression for the candidate resource list matrix is: Where L is the candidate resource list matrix, j∈[1,F] is the subcarrier index, and i∈[1,S] is the subframe index. Let F be the resource block on the j-th subcarrier within the i-th time slot, where F is the total number of subcarriers and S is the total number of subframes.

3. The cellular vehicle-to-everything (V2X) communication resource selection method according to claim 1, characterized in that, The expression for the optimization iterative equation is: Among them, L k (t) represents the set of resource blocks currently selected by the k-th vehicle-mounted user terminal within the t-th subframe. Let this be the set of resource blocks optimally selected by the k-th vehicle-mounted user terminal within the t-th subframe. For the k-th vehicle-mounted user terminal in the t-th subframe, the speed at which the selected resource block set is updated is... L represents the optimal set of resource blocks for the current k vehicle-mounted user terminal clusters; k (t+1) represents the set of resource blocks currently selected by the k-th vehicle-mounted user terminal within the (t+1)-th subframe. Let ω be the speed at which the k-th vehicle-mounted user terminal updates the selected resource block set within the (t+1)-th subframe, where ω is the inertia weight, c1 is the individual learning factor, c2 is the group learning factor, and r1 and r2 are both weighted random numbers in the interval [0,1].

4. The cellular vehicle-to-everything (V2X) communication resource selection method according to claim 1, characterized in that, The expression for the fitness function is: in, Let RSRP be the linear mean of the reference signal received power of the optimal resource block in the i-th subframe of the j-th subcarrier within the sensing window, H be the total number of measurement periods within the sensing window, h be the h-th measurement period, and RSRP be the reference signal received power. For the (n+i)th subcarrier s +(h-1)·RRI subframe number, N s The duration of the sensing window during resource sensing, i is the subframe number, j is the subcarrier number, n is the subframe interval number, and RRI is the resource reservation interval.

5. The cellular vehicle-to-everything (V2X) communication resource selection method according to claim 1, characterized in that, The optimization iterative equation is iterated based on the fitness function to obtain the optimal resource selection list, specifically including: The optimization iterative equation is iterated based on the fitness function to obtain the update speed of the selected resource block set in the (t+1)th subframe; Calculate the optimal resource list selected by the k-th vehicle user terminal in the (t+1)-th subframe based on the update speed; The optimal resource selection list is determined based on the optimal resource list selected by the k-th vehicle-mounted user terminal in the (t+1)-th subframe and the set of service request resource blocks.

6. The cellular vehicle-to-everything (V2X) communication resource selection method according to claim 5, characterized in that, The optimal resource selection list is determined based on the optimal resource list selected by the k-th vehicle-mounted user terminal in the (t+1)-th subframe and the set of service request resource blocks, specifically including: The optimal resource list initially selected by the k-th vehicle user terminal is assigned a value based on the optimal resource list selected by the k-th vehicle user terminal in the (t+1)-th subframe; Determine whether the set of resource modules selected in the optimal resource list initially selected by the k-th vehicle user terminal after assignment is greater than the set of resource blocks requested by the business, and obtain the judgment result. If the judgment result is yes, then the optimal resource list initially selected by the kth vehicle-mounted user terminal after assignment is taken as the optimal selection resource list; If the judgment result is negative, then the subframe is updated and the process returns to step "Iterate the optimization iterative equation according to the fitness function to obtain the update speed of the selected resource block set in the (t+1)th subframe".

7. A cellular vehicle-to-everything (V2X) communication resource selection system, characterized in that, include: The acquisition and construction module is used to acquire the message frame candidate resource list of the vehicle user terminal in the vehicle cluster and construct a candidate resource list matrix based on the message frame candidate resource list; The message frame candidate resource list is a set of time-frequency resource blocks consisting of multiple subframes and multiple subcarriers; An optimization iterative equation construction module is used to construct optimization iterative equations for the optimal resource list based on the candidate resource list matrix using the PSO algorithm. The fitness function determination module is used to construct the fitness function of the optimal resource block within a subframe of a subcarrier based on the resource-aware window; The iteration module is used to iterate the optimization iterative equation according to the fitness function to obtain the optimal resource selection list.

8. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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