A resource allocation and service value optimization method based on matching theory algorithm

Abstract

The application provides a resource allocation and service value optimization method based on a matching theory algorithm, comprising the following steps: S1, defining a matching matrix and an allocation matrix; S2, calculating a communication service value for each vehicle user; S3, calculating a perceived service value for each vehicle user; S4, performing weighted summation on the communication service value and the perceived service value to obtain a total service value, constructing a target function original problem for maximizing the value based on the total service value, and configuring a limited condition for the target function original problem; S5, decoupling the target function original problem into a vehicle user-base station association problem and a sub-channel allocation problem according to variables of the matching matrix and the allocation matrix, and solving the vehicle user-base station association problem and the sub-channel allocation problem based on the limited conditions to optimize the total service value to maximization. The beneficial effect is that the application simultaneously balances the performance of perception and communication, and improves the overall system performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Vehicles communication and perception, and in particular to a resource allocation and service value optimization method based on matching theory algorithm. BACKGROUND

[0002] With the popularity of 5G mobile communication systems and the rapid development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, Intelligent Traffic System (ITS) is rapidly emerging and gradually changing people's travel mode. As an important application scenario of 5G communication, the emerging application scenarios of Intelligent Traffic System require high-precision perception capability and high-speed data communication services under complex weather conditions.

[0003] Therefore, the base station needs to balance the perception and communication performance of the vehicle user under the condition of limited wireless resources. In order to achieve this goal, researchers have proposed an Integrated Sensing and Communication (ISAC) system that allows communication and perception to be performed simultaneously on a unified wireless platform. ISAC system is very promising in the next generation of wireless communication networks, and radar and communication functions can be performed simultaneously on a single platform in a common frequency band. In recent years, it has inspired extensive research in the industry and academia, such as the development of electronic warfare and intelligent traffic systems.

[0004] In the ISAC system, waveform design is crucial to improving radar and communication performance. So far, there are two main categories of ISAC waveform design, namely multiplexed waveforms and homomorphic waveforms. For multiplexed waveforms, although radar and communication waveforms are multiplexed, the resource utilization efficiency is low. In contrast, for homomorphic waveforms, radar and communication functions share wireless resources, and the resource utilization efficiency is high, so joint allocation of resources for dual functions is achieved.

[0005] The communication and perception processes require the shared use of hardware platforms and radio resources. The perception process and data transmission occur simultaneously on the RF front end, which means that both perception and communication compete for resources to improve their own performance. Without a unified design goal, two independent resource allocation targets will lead to a one-sided emphasis on perception accuracy or communication data rate, thereby reducing the performance of the entire system. In addition, once the needs of vehicle users are met, allocating additional resources to achieve higher data transmission rates or perception accuracy will significantly reduce system performance. Therefore, a comprehensive evaluation metric is urgently needed to balance the performance of perception and communication according to the needs of different users. Finally, designing a resource allocation algorithm based on a performance metric for evaluating both functions also faces challenges. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to simultaneously balance the performance of perception and communication and improve the overall performance of the system. In order to overcome the defects of the above-mentioned existing technologies (or related technologies), the present invention provides a resource allocation and service value optimization method based on a matching theory algorithm.

[0007] The present invention provides a resource allocation and service value optimization method based on matching theory algorithm, which pre-configures a group of base stations for the scenario of a two-lane one-way highway. All base stations m share the same orthogonal subchannel The vehicle users in the one-way highway scenario are The resource allocation and service value optimization method comprises the following steps:

[0008] Step S1, defining a matching matrix for characterizing the association between vehicle user i and base station m and an allocation matrix for characterizing the allocation of subchannel v;

[0009] Step S2: for each vehicle user i, when the vehicle user i sends a task requirement to associate with one of the base stations and the orthogonal subchannel is allocated to the vehicle user i, the communication service value of the vehicle user i is obtained based on the matching matrix, the allocation matrix, and the interaction data of the association process between the vehicle user i and the base station;

[0010] Step S3: For each vehicle user i, an OFDM integrated waveform generated when the vehicle user i performs radar detection on a preceding vehicle user and an integrated symbol of the OFDM integrated waveform on the orthogonal subchannel are obtained; an impulse response of the vehicle user i on the orthogonal subchannel and a received reflected signal are obtained; and the perceived service value of the vehicle user i is obtained based on the integrated symbol, the impulse response, and the reflected signal.

[0011] Step S4: performing a weighted summation of the communication service value and the perception service value to obtain a total service value, constructing an objective function original problem for maximizing the total service value based on the total service value, and configuring a plurality of limiting conditions for the objective function original problem;

[0012] Step S5, decoupling the original objective function problem into a vehicle user-base station association problem and a sub-channel allocation problem according to the variables of the matching matrix and the variables of the allocation matrix, and solving the vehicle user-base station association problem and the sub-channel allocation problem respectively based on the respective limiting conditions to optimize the total service value to maximize.

[0013] Compared with the existing technology, the resource allocation and service value optimization method based on the matching theory algorithm in this application has the following advantages:

[0014] In this application, the matching matrix and the allocation matrix are defined through step S1, the communication service value is calculated through step S2, the perception service value is calculated through step S3, the total service value is calculated and the original objective function problem is constructed through step S4, and the original objective function problem is decoupled and solved through step S5. The service value is used as a performance indicator to guide the matching algorithm to allocate resources for communication and perception. The matching algorithm includes two parts: the vehicle user-base station association problem and the sub-channel allocation problem, which effectively reduces the complexity of solving the original objective function problem, and associates different base stations and different sub-channels for different vehicle users. This differentiated resource allocation strategy helps to improve the overall performance of the system.

[0015] In a possible implementation, in step S1, define Y=[y i,m ] I×M As the matching matrix, define X = [x v,i ] V×I is the allocation matrix. When the vehicle user i is associated with the base station m, then y i,m =1, zero if not associated; when the orthogonal subchannel v is allocated to the vehicle user i, then x v,i =1, and zero if not allocated.

[0016] In a possible implementation, step S2 includes:

[0017] In step S21, for each vehicle user i, when the vehicle user i sends a task requirement associated with one of the base stations m and the orthogonal subchannel v is allocated to the vehicle user i, based on the Shannon theorem, the transmission rate generated by the vehicle user i is obtained according to the matching matrix, the allocation matrix, and the interaction data:

[0018]

[0019] wherein, denotes the bandwidth allocated by the base station m to the vehicle user i on the orthogonal sub-channel v; y i,m denotes the matching matrix of the vehicle user i; x v,i denotes the allocation matrix of the vehicle user i; P i denotes the power when the vehicle user i sends a task demand; denotes the small-scale Rayleigh fading coefficient between the base station m and the vehicle user i when sending a task demand, subject to a complex Gaussian distribution CN(0, 1); d i,m denotes the distance between the vehicle user i and the base station m; a denotes a large-scale fading factor; denotes the power of additive white Gaussian noise; i' denotes an interfering vehicle; x v,i' denotes the allocation matrix of the interfering vehicle i'; denotes the gain of interfering signal transmission; P i′ denotes the transmission power of the interfering echo;

[0020] Step S22, calculating the transmission rate that the vehicle user i can achieve:

[0021]

[0022] Step S23, calculating the communication service value of the vehicle user i:

[0023]

[0024] wherein, VC i denotes the communication service value; denotes a preset threshold of the communication service value.

[0025] In one possible implementation, the step S3 comprises:

[0026] Step S31, for each vehicle user i, obtaining an OFDM integrated waveform generated by the vehicle user i when performing radar detection on a front vehicle user and K consecutive integrated symbols of the OFDM integrated waveform on the orthogonal sub-channel v:

[0027]

[0028] wherein, denotes the integrated symbol; f c denotes the center frequency of the orthogonal sub-channel v; T0 denotes the duration of a single integrated symbol with a band period prefix, T0 = T + T g, T = 1 / Δf represents the duration of the basic code element of the integrated symbol, T g represents a cyclic prefix; represents the amplitude of the waveform of the vehicle user i on the orthogonal sub-channel v; represents the phase encoding of the modulation symbol τ of the vehicle user i on the orthogonal subchannel v; Δf represents the subcarrier spacing of the duration of the OFDM symbol T; rect[x] represents a rectangular function, which is 1 when 0≤x≤1 and 0 otherwise; t represents the current time;

[0029] Step S32, setting the impulse response of the vehicle user i on the orthogonal sub-channel v is a Gaussian random process, and the reflected signal received by the vehicle user i on the orthogonal sub-channel v is expressed as:

[0030]

[0031] Among them, y i (t) represents the reflected signal; ψ is the intermediate variable of time t; d is the differential symbol; z i (t) represents additive white Gaussian noise;

[0032] Step S33: For radar target detection and evaluation, the conditional mutual information (MI) of the target impulse response is evaluated using the conditional mutual information (MI). When the orthogonal subchannel v of the base station m is assigned to the vehicle user i, the conditional mutual information (CMI) of the vehicle user i on the orthogonal subchannel v is:

[0033]

[0034] Among them, T p =KT0 represents the total duration of the OFDM integrated waveform; K represents the number of OFDM symbols; express Fourier transform of P i represents the transmission power of the vehicle user i; i′ represents the interfering vehicle; P i′ Indicates the transmission power of the interference echo; Indicates the gain of the interference echo; represents the power of additive white Gaussian noise;

[0035] Step S34: During the total duration of the OFDM integrated waveform, the average mutual information that can be achieved by the vehicle user i is:

[0036]

[0037] in, represents the average mutual information;

[0038] Step S35, calculating the perception service value of the vehicle user i:

[0039]

[0040] wherein, VS i represents the perception service value; represents a preset threshold of the perception service value.

[0041] In a possible implementation, in the step S4, the total service value is obtained by and the following formula:

[0042]

[0043] wherein, VoS(Y, X) represents the total service value; a i represents the weight of the communication service value; VC i represents the communication service value; b i represents the weight of the perception service value; VS i represents the perception service value; a i + b i = 1.

[0044] In a possible implementation, in the step S4, the objective function original problem is represented by the following expression:

[0045]

[0046] In a possible implementation, in the step S4, each of the defined conditions is represented by the following expression:

[0047]

[0048] wherein, C1 and C2 are used to define the minimum perception performance and communication performance of each vehicle; C3 and C4 are used to define that each vehicle user i can only match one of the orthogonal sub-channels v; C5 and C6 are used to define that each vehicle user i can only match one of the base stations m; C7 is used to define that a maximum of I m vehicles can be associated with the communication range of the base station m.

[0049] In a possible implementation, the step S5 includes:

[0050] Step S51, decoupling the objective function original problem into the vehicle user-base station association problem and the sub-channel allocation problem according to the variables of the matching matrix and the variables of the allocation matrix;

[0051] Step S52, solving the vehicle user-base station association problem based on the limiting conditions C2, C5, C6 and C7 to optimize Y;

[0052] Step S53, solving the subchannel allocation problem based on the limiting conditions C1, C2, C3 and C4 to optimize X;

[0053] Step S54: Set a maximum number of iterations, and repeatedly execute steps S52 and S53 to optimize X and Y. When the maximum number of iterations is reached, the optimization stops.

[0054] In a possible implementation, step S52 includes:

[0055] Step S521, solving the vehicle user-base station association problem, the solution formula is expressed as:

[0056]

[0057] st:C2,C5,C6,C7

[0058] From the perspective of the base station, define E m is the set of users accessing the mth base station, then Equivalently rewritten as:

[0059]

[0060] C7-1:|E m |≤I m ;

[0061] in, Convert the optimization of Y into the optimization of E m Optimization;

[0062] Step S522: define a base station set of M base stations as B m Represents the mth base station; similarly, the vehicle user set of I vehicle users is defined as V i Representing the i-th vehicle user, the vehicle user set and the base station set are matched using a many-to-one matching function model. The expression of the many-to-one matching function model is:

[0063] M(x)∈{V1,V2,...V i ...,V I}∪{B1,B2,...B m ...,B M},x∈{V1,V2,...V i ...,V I}∪{B1,B2,...B m ...,B M}

[0064] in,

[0065] and|M(V i )|=1;

[0066] and|M(B m )|≤I m ;

[0067] M(V i )=B m , if and only if V i ∈M(B m );

[0068] Step S523: For the vehicle user V i , which is about any of the base stations B m The preference degree is defined as when the vehicle user V i Access the base station B m The base station B m The performance gain is expressed as:

[0069] G i,m =O m (E′ m ∪i)-O m (E′ m )

[0070] Among them, E′ m Indicates that except V i Other access base station B m The set of vehicle users;

[0071] Step S524: Each of the base stations B m Initialize the set of vehicle users that access itself as And each vehicle user V i Calculate its own m The preference of the available base station set can be initialized as and not connected to base station B m The vehicle user set is

[0072] Step S525: Each non-connected base station B m Vehicle users V i Calculate the value of itself and the base station B m The preference between them and the available base station B with the largest preference m Make an access request;

[0073] Step S526: Based on the received access request, each base station B m Accept the vehicle user V that has the highest preference i And add it to the review collection and reject other vehicle users V i At the same time, the base station B m Accessed vehicle users V i The number is increased by 1. If a base station B m Access vehicle user V i If the number reaches the upper limit, the base station B will be added to the available base station set. m Remove;

[0074] Step S527, each rejected vehicle user V i The preference is recalculated and updated, and the process returns to step S525. When the remaining vehicle user set becomes an empty set, the iteration ends and the optimal allocation is obtained.

[0075] In a possible implementation, step S53 includes:

[0076] Step S531, solving the sub-channel allocation problem, the solution formula is expressed as:

[0077]

[0078] stC1~C4

[0079] From the perspective of orthogonal sub-channels, define A v is the set of users served by the vth orthogonal subchannel, then the problem Can be rewritten equivalently as:

[0080]

[0081] stC1-C2

[0082]

[0083] in, Convert the optimization of X into the optimization of A v Optimization;

[0084] Step S532: define a vehicle user set of I vehicle users as u i ={u1, u2, ..., u I},u i Represents the i-th vehicle user; similarly, the orthogonal subchannel set of V orthogonal subchannels is defined as c v ={c1, c2, ..., c V}, cv representing the fifth orthogonal sub-channel, matching the set of vehicle users and the set of orthogonal sub-channels by a many-to-one matching function model, the expression of the many-to-one matching function model is:

[0085]

[0086] wherein,

[0087] M(u i )∈{c1,c2,...,c V}, and |M(u i )|=1;

[0088] M(c v )∈{u1,u2,...,u I};

[0089] M(u i )=c v if and only if u i ∈M(c v );

[0090] Step S533, for the vehicle user u i , the preference degree about any of the orthogonal sub-channels c v is defined as the performance gain brought to the orthogonal sub-channel c i when the vehicle user u v accesses the orthogonal sub-channel c v , the expression is:

[0091] G i,v =O v (A′ v ∪u i )-O ν (A′ ν )

[0092] wherein, A′ v represents the set of vehicle users accessing the orthogonal sub-channel c i except the vehicle user u v ;

[0093] Step S534, each of the orthogonal sub-channels c v initializes the vehicle user u i accessing itself, each of the vehicle users u i calculates the preference degree of all the orthogonal sub-channels c v based on the performance and the constraint condition, the set of orthogonal sub-channels is the set of vehicle users not accessing the orthogonal sub-channel is initialized as

[0094] Step S535: Each vehicle user u that is not connected to the orthogonal sub-channel i Towards the orthogonal subchannel c with the largest preference v Make an allocation request;

[0095] Step S536: Each orthogonal sub-channel c v Upon receipt of the vehicle user u i The vehicle user u with a high preference is allowed in the access request i Access and reject other vehicle users u i After each successful access, the vehicle user set that has not accessed will assign the successfully allocated vehicle user u i Remove, if the vehicle user set is an empty set, the loop ends;

[0096] Step S537: Based on the latest access and pairing results, each non-accessed vehicle user recalculates the orthogonal sub-channel c v preference, and returns to step S535. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 A schematic diagram of a one-way highway scenario of the present invention;

[0098] Figure 2 is a flow chart of the steps of the present invention;

[0099] Figure 3 This is a schematic diagram of the total service value under different numbers of vehicle users in Example 1 of the present invention. DETAILED DESCRIPTION

[0100] First, those skilled in the art should understand that these embodiments are merely used to explain the technical principles of the embodiments of the present application and are not intended to limit the scope of protection of the embodiments of the present application. Those skilled in the art may adjust them as needed to suit specific application scenarios.

[0101] The present application is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0102] See also Figure 2 The present application discloses a method for resource allocation and service value optimization based on a matching theory algorithm, including:

[0103] Step S1, defining a matching matrix for characterizing the association between vehicle user i and base station m and an allocation matrix for characterizing the allocation of subchannel v;

[0104] Step S2, for each vehicle user i, when the vehicle user i sends a task requirement associated with one base station and the orthogonal subchannel is allocated to the vehicle user i, the communication service value of the vehicle user i is obtained according to the matching matrix, the allocation matrix and the interaction data of the vehicle user i and the base station association process;

[0105] Step S3, for each vehicle user i, the OFDM integrated waveform generated when the vehicle user i performs radar detection on the front vehicle user and the integrated symbol of the OFDM integrated waveform on the orthogonal subchannel are obtained, the impulse response of the vehicle user i on the orthogonal subchannel and the received reflection signal are obtained, and the perception service value of the vehicle user i is obtained according to the integrated symbol, the impulse response and the reflection signal;

[0106] Step S4, the communication service value and the perception service value are weighted and summed to obtain a total service value, and a target function original problem for maximizing the evaluation is constructed based on the total service value, and a plurality of limited conditions are configured for the target function original problem;

[0107] Step S5, the target function original problem is decoupled into a vehicle user-base station association problem and a subchannel allocation problem according to the variables of the matching matrix and the variables of the allocation matrix, and the vehicle user-base station association problem and the subchannel allocation problem are solved based on the respective limited conditions to optimize the total service value to maximization.

[0108] Continuing to refer to Figure 2 In the present application, the performance of perception and communication is simultaneously balanced, and finally the overall system performance is improved. The considered scenarios are as shown in Figure 1 A one-way highway scene with two lanes is shown, and vehicles are all driving from right to left. The scene includes a group of base stations All base stations share a unified orthogonal subchannel Vehicle user devices (VUEs) It is assumed that the VUEs all have ISAC technology; the VUEs can use ISAC signals for perceiving the front vehicles, and can also perform vehicle-to-infrastructure (V2I) communication. Considering that each VUE is associated with a computing task, the VUEs need to perceive the surrounding vehicle condition environment through various sensors (including radar), and upload the perception data in a wireless communication manner; In particular, the process of vehicle user i to base station m is defined as a service request in the present application, the communication service value and the perception service value are established based on communication and perception respectively, and finally the total service value (VoS, value of service) of the system is integrated, and the final purpose is to maximize the total service value of the system.

[0109] In the calculation of the communication service value, Y = [y i,m ] I×M is defined as a matching matrix, y i,m = 1 if the vehicle user i is associated with the base station m, otherwise zero; X = [x v,i ] V×I is defined as an allocation matrix, x v,i = 1 if the orthogonal sub-channel v is allocated to the vehicle user i, otherwise zero;

[0110] The system considered in the present application is an OFDM (Orthogonal Frequency Division Multiplexing) based wireless transmission system, and under the premise that the vehicle user i is associated with the base station m, the orthogonal sub-channel v is allocated to the vehicle user i, and the transmission rate generated is:

[0111]

[0112] where P i is the power of the vehicle user i when sending a task requirement; represents the small-scale Rayleigh fading coefficient between the base station m and the vehicle user i when sending a task requirement, which is subject to a complex Gaussian distribution CN(0, 1); d i,m represents the distance between the vehicle user i and the base station m; and a is a large-scale fading factor; is the power of the additive white Gaussian noise; is the gain of the interference signal transmission, and the transmission rate that the VUE i can reach is:

[0113]

[0114] In order to describe the communication process of the vehicle user i, the communication service value of the vehicle user i is defined as:

[0115]

[0116] In the calculation of the perception service value, each vehicle user VUE i in the present application can radiate an OFDM integrated waveform through its dual-function transmitter, and the integrated integrated waveform can perform radar detection on the front vehicle user i, and on the orthogonal sub-channel v, the waveform used by the VUE i has K consecutive integrated symbols, which can be described as:

[0117]

[0118] where T = 1 / Δf is the time duration of the OFDM symbol basic code element; f cis the center frequency of the orthogonal subchannel v; T0is the duration of a single OFDM symbol with cyclic prefix, i.e., the time length of a complete OFDM symbol; specifically, T0= T + T g , T g is the cyclic prefix (CP); represents the amplitude of the waveform of VUEi on the orthogonal subchannel v; represents the phase encoding of the modulation symbol τ of VUEi on the orthogonal subchannel v; rect[x] is a rectangular function, which is equal to 1 when 0≤x≤1, and 0 otherwise;

[0119] Assuming that the impulse response of VUEi on the orthogonal subchannel v is a Gaussian random process, then The received reflected signal on the orthogonal subchannel v can be represented as:

[0120]

[0121] where the received echo signal is affected by additive white Gaussian noise z i (t);

[0122] For the detection and evaluation of radar targets, the conditional MI can be used to evaluate the estimation accuracy of the target impulse response; in the process of perception, the orthogonal subchannels are shared with communication, so only the allocation process of the orthogonal subchannels needs to be considered; when the orthogonal subchannels of the base station are allocated to the vehicle users, the conditional mutual information of the vehicle users on the orthogonal subchannels at this time is:

[0123]

[0124] where T p = KT0represents the total duration of the OFDM signal; is the Fourier transform of ; P i is the transmit power of vehicle user i; the interference source is the interference of the radar signal, and i' represents the interfering vehicle; P i′ is the transmit power of the interfering echo; is the gain of the interfering echo; is the power of the additive white Gaussian noise; the average mutual information that vehicle user i can achieve in the total duration of the OFDM signal is:

[0125]

[0126] Similar to the value of the communication service, the value of the perception service can also be defined as:

[0127]

[0128] In the calculation process of the total service value, the total service value is obtained by weighted summation of the communication service value and the perceived service value, so the total service value of the system is:

[0129]

[0130] The objective function of the original problem is:

[0131]

[0132] In order to ensure the lowest perceived performance and communication performance of each vehicle:

[0133]

[0134] Each vehicle user can only match one subchannel:

[0135]

[0136] Each vehicle user can only match one base station:

[0137]

[0138] In the communication range of base station m, at most I m vehicles can be associated:

[0139]

[0140] In this application, the service value is used as a performance indicator to guide the matching algorithm for resource allocation of communication and perception. The matching algorithm includes vehicle user-base station association and subchannel allocation, which has the following advantages:

[0141] 1) In the performance measurement stage of communication and perception, this application introduces the service value (Value of Service, VoS) as the standard for resource allocation;

[0142] 2) In the resource allocation stage, different base stations and different subchannels are associated with different vehicle users according to their different preferences after access. This differentiated resource allocation strategy helps to improve the overall performance of the system;

[0143] 3) In addition, this application decouples the original problem into two sub-problems according to two variables, which effectively reduces the complexity of solving the original problem.

[0144] The process of solving the original problem is described in detail as follows:

[0145] 1. First, the original problem is decoupled into vehicle user-base station association problem and subchannel allocation problem according to matching matrix variable and allocation matrix variable.

[0146] 2. Solve the first sub-problem "vehicle user-base station association problem", which is expressed as:

[0147]

[0148] s.t.: C2, C5, C6, C7

[0149] From the perspective of base station, define E m as the set of users accessing the mth base station, then can be rewritten as:

[0150]

[0151] C7-1: |E m |≤I m ;

[0152] where Therefore, the original user matching problem is transformed from the optimization of Y to the optimization of E m , and the set of M base stations is defined as B m represents the mth base station; similarly, the set of I users is defined as V i represents the ith vehicle user;

[0153] Definition 1 (many-to-one matching model): define V i as the ith vehicle user, B m as the mth base station, then the matching between the set of vehicle users and the set of base stations involves a many-to-one matching function model:

[0154] M(x) ∈ {V1, V2,... V i ..., V I}∪{B1, B2,... B m ..., B M}, x ∈ {V1, V2,... V i ..., V I}∪{B1, B2,... B m ..., B M}

[0155] The many-to-one matching function model is defined as follows:

[0156] and |M(V i )| = 1;

[0157] and |M(B m )| ≤ Im ;

[0158] M(V i )=B m , if and only if V i ∈M(B m );

[0159] Definition 2 (Preference): For vehicle user V i , which is about any base station B m The preference of vehicle user V is defined as i Access base station B m The performance gain of the base station is:

[0160] G i,m =O m (E′ m ∪i)-O m (E′ m )

[0161] where E′ m Except V i Other access base station B m The user collection of

[0162] Then proceed as follows:

[0163] The first step is to initialize. Each base station initializes the user set it accesses to And each vehicle user calculates its own preference for base stations (based on performance and whether the constraints are met), and the set of available base stations is initialized as The set of vehicle users that are not connected to the base station is

[0164] In the second step, each vehicle user that has not yet connected to a base station calculates the preference between itself and the base station, and submits an access request to the available base station with the largest preference value.

[0165] In the third step, based on the received requests, each base station accepts the user with the highest preference for itself and adds it to the review set, and "rejects" the access requests of other users. At the same time, the number of vehicle users connected to the base station is increased by 1. If the number of users connected to a base station reaches the upper limit, the base station is removed from the set of available base stations.

[0166] In the fourth step, the preference of each "rejected" vehicle user is recalculated and updated based on the above formula, and the process returns to the second step. When the remaining vehicle user set becomes an empty set, the iteration ends and the optimal allocation is obtained.

[0167] 3. Solve the second sub-problem "sub-channel allocation problem", the formula is expressed as:

[0168]

[0169] s.t.C1~C4

[0170] From the channel perspective, define A v the set of users served by the v-th orthogonal sub-channel, then the problem can be equivalently rewritten as:

[0171]

[0172] s.t.C1-C2

[0173]

[0174] where The total system performance is calculated from the channel perspective, while is calculated from the vehicle user's perspective;

[0175] Definition 1 (Many-to-one assignment model): The matching between the set of vehicle users and the set of channels involves a many-to-one matching function model:

[0176]

[0177] The many-to-one matching function model is defined as follows:

[0178] M(u i )∈{c1,c2,...,c V}, and |M(u i )| = 1;

[0179] M(c v )∈{u1,u2,...,u I};

[0180] M(u i ) = c v if and only if u i ∈ M(c v );

[0181] Definition 2 (Preference degree): For a vehicle user u i , the preference degree of u i about any channel c v is defined as the performance gain brought to the channel when u i,v accesses c v , i.e.,

[0182] G i,v = O v (A′ v ∪u i )-O ν (A′ ν )

[0183] where A′ v For except u i The set of users accessing orthogonal subchannel v other than ;

[0184] Then proceed as follows:

[0185] The first step is initialization: each channel initializes its own access user A v Each user calculates his preference for all channels based on performance and constraints. The set of channels is The set of users who have not accessed the channel is initialized to

[0186] In the second step, each user who has not yet accessed a channel makes an allocation request to the channel with the highest preference;

[0187] In the third step, each channel allows access to users with a high preference score among the received user access requests and rejects other users. After each successful access is allowed, the set of users that have not been allowed access removes the successfully allocated user. If the set of users is empty, the loop ends.

[0188] Step 4, loop: Based on the latest access and pairing results, each non-accessed user recalculates the channel preference, returns to step 2 and continues execution.

[0189] 4. Set a maximum number of iterations and optimize X and Y alternately. In each iteration, calculate the optimal value of the corresponding optimization variable. After reaching the maximum number of iterations, the optimization stops.

[0190] Example 1

[0191] For the communication model, it is assumed that the small-scale fading channel gain for communication follows unit-mean Rayleigh fading, and the uplink path loss is determined by the large-scale loss factor α = 2 and the distance d between the vehicle user and its associated BS. i,m Calculation,For the sensing model, it is assumed that the target frequency response follows a standard normal distribution.,The other parameter settings are shown in Table 1 below:

[0192] Table 1 Simulation parameters

[0193]

[0194] This application compares the proposed algorithm (Matching algorithm of joint base station-User Association and subchannel allocation, MUA) with the following three algorithms:

[0195] 1) MUA-FP (Matching algorithm of joint base station and User Association-Fractional Programming): This method still uses the matching theory method to optimize the first sub-problem, i.e., the base station-vehicle user pairing problem, and uses a fractional programming algorithm for the second sub-problem, i.e., the sub-channel allocation problem;

[0196] 2) IMUA-FP (Improved Matching algorithm of joint base station and User Association-Fractional Programming): Compared with "MUA-FP", this method introduces the Hungarian algorithm to directly solve the first sub-problem on the basis of the original matching theory, which can reduce the number of iterations in the simulation process, but at the cost of poor performance. The second sub-problem still uses a fractional programming algorithm;

[0197] 3) GA (Genetic Algorithm): First, the user matching variable Y and the channel allocation variable X are encoded into chromosomes, and the corresponding population and fitness values are defined. Intelligent search can be performed using natural selection, crossover, mutation, and exploration. In this embodiment, the population size is defined as 100, the maximum number of evolutions is 300, the crossover probability and mutation probability are 0.0001-0.1, respectively, and the genetic algorithm toolbox of MATLAB is used for solving;

[0198] The total service value of the schemes proposed in this application is evaluated in the ISAC system under different numbers of vehicle users, as shown in Figure 3 As shown in the table, when the number of VUEs is less than 7, all four schemes show high VoS. When the number of VUEs is greater than 7, the VoS of the schemes except MUA decreases significantly. When the number of VUEs exceeds 24, the VoS of MUA-FP, IMUA-FP, and GA decreases to below 0.7, while the service value of MUA remains above 0.85.

[0199] Because MUA can well consider the competition relationship between user equipments related to the same resource (base station / channel) in both channel allocation and user-base station matching, while the other three methods cannot fully consider the access correlation, MUA mechanism can obtain the highest performance; GA can obtain higher service value, because it can obtain a result close to brute force search by iterative evolution, constantly saving better variables and discarding crossed variables selection; and the performance of MUA-FP mechanism is slightly higher than that of IMUA-FP mechanism, because for IMUA, in order to ensure that each new user can be associated with a base station in each iteration, some higher user-base station pairs can be prioritized.

[0200] The above experimental results show that MUA can effectively improve network performance and optimize resource utilization when coping with complex network conditions, thereby providing strong support for the implementation of intelligent transportation systems.

[0201] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "in this embodiment", "specific examples" or "some examples" means that the specific features, mechanisms, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, mechanisms, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, different embodiments or examples described in the specification and the features of different embodiments or examples can be combined and combined by those skilled in the art without contradiction.

[0202] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A resource allocation and service value optimization method based on matching theory algorithm, characterized in that: Pre-configure a set of base stations for two-lane one-way highway scenarios All base stations m share the same orthogonal subchannel The vehicle users in the one-way highway scenario are The resource allocation and service value optimization method comprises the following steps: Step S1, defining a matching matrix for characterizing the association between vehicle user i and base station m and an allocation matrix for characterizing the allocation of subchannel v; Step S2: for each vehicle user i, when the vehicle user i sends a task requirement to associate with one of the base stations and the orthogonal subchannel is allocated to the vehicle user i, the communication service value of the vehicle user i is obtained based on the matching matrix, the allocation matrix, and the interaction data of the association process between the vehicle user i and the base station; Step S3: For each vehicle user i, an OFDM integrated waveform generated when the vehicle user i performs radar detection on a preceding vehicle user and an integrated symbol of the OFDM integrated waveform on the orthogonal subchannel are obtained; an impulse response of the vehicle user i on the orthogonal subchannel and a received reflected signal are obtained; and the perceived service value of the vehicle user i is obtained based on the integrated symbol, the impulse response, and the reflected signal. Step S4: performing a weighted summation of the communication service value and the perception service value to obtain a total service value, constructing an objective function original problem for maximizing the total service value based on the total service value, and configuring a plurality of limiting conditions for the objective function original problem; Step S5, decoupling the original objective function problem into a vehicle user-base station association problem and a sub-channel allocation problem according to the variables of the matching matrix and the variables of the allocation matrix, and solving the vehicle user-base station association problem and the sub-channel allocation problem respectively based on the respective limiting conditions to optimize the total service value to maximize.

2. The resource allocation and service value optimization method according to claim 1, characterized in that: In step S1, define Y=[y i,m ] I×M As the matching matrix, define X = [x v,i ] V×I is the allocation matrix. When the vehicle user i is associated with the base station m, then y i,m =1, zero if not associated; when the orthogonal subchannel v is allocated to the vehicle user i, then x v,i =1, and zero if not allocated.

3. The resource allocation and service value optimization method according to claim 2, characterized in that: The step S2 comprises: In step S21, for each vehicle user i, when the vehicle user i sends a task requirement associated with one of the base stations m and the orthogonal subchannel v is allocated to the vehicle user i, based on the Shannon theorem, the transmission rate generated by the vehicle user i is obtained according to the matching matrix, the allocation matrix, and the interaction data: in, represents the bandwidth allocated by base station m to vehicle user i on the orthogonal subchannel v; i,m represents the matching matrix of the vehicle user i; x v,i represents the allocation matrix of the vehicle user i; i represents the power when the vehicle user i sends the task requirement; represents the small-scale Rayleigh fading coefficient between the base station m and the vehicle user i when sending the task requirement, which obeys the complex Gaussian distribution CN(0,1); d i,m represents the distance between the vehicle user i and the base station m; α represents the large-scale fading factor; represents the power of additive white Gaussian noise; i′ represents the interfering vehicle; x v , i' represents the allocation matrix of the interfering vehicle i′; Represents the gain of interference signal transmission; P i′ Indicates the transmission power of the interference echo; Step S22: Calculate the transmission rate that the vehicle user i can achieve: Step S23, calculating the communication service value of the vehicle user i: Among them, VC i Indicates the value of the communication service; Indicates the preset threshold value of communication service value.

4. The resource allocation and service value optimization method according to claim 3, characterized in that: The step S3 comprises: Step S31: For each vehicle user i, obtain the OFDM integrated waveform generated when the vehicle user i performs radar detection on the vehicle user in front and K consecutive integrated symbols of the OFDM integrated waveform on the orthogonal subchannel v: in, Represents the integrated symbol; f c represents the center frequency of the orthogonal subchannel v; T0 represents the duration of a single integrated symbol with a cyclic prefix, T0 = T + T g , T = 1 / Δf represents the duration of the basic code element of the integrated symbol, T g represents a cyclic prefix; represents the amplitude of the waveform of the vehicle user i on the orthogonal sub-channel v; represents the phase encoding of the modulation symbol τ of the vehicle user i on the orthogonal subchannel v; Δf represents the subcarrier spacing of the duration of the OFDM symbol T; rect[x] represents a rectangular function, which is 1 when 0≤x≤1 and 0 otherwise; t represents the current time; Step S32, setting the impulse response of the vehicle user i on the orthogonal sub-channel v is a Gaussian random process, and the reflected signal received by the vehicle user i on the orthogonal sub-channel v is expressed as: Among them, y i (t) represents the reflected signal; ψ is the intermediate variable of time t; d is the differential symbol; z i (t) represents additive white Gaussian noise; Step S33: For radar target detection and evaluation, the conditional mutual information (MI) of the target impulse response is evaluated using the conditional mutual information (MI). When the orthogonal subchannel v of the base station m is assigned to the vehicle user i, the conditional mutual information (CMI) of the vehicle user i on the orthogonal subchannel v is: Among them, T p =KT0 represents the total duration of the OFDM integrated waveform; K represents the number of OFDM symbols; express Fourier transform of P i represents the transmission power of the vehicle user i; i′ represents the interfering vehicle; P i′ Indicates the transmission power of the interference echo; Indicates the gain of the interference echo; represents the power of additive white Gaussian noise; Step S34: During the total duration of the OFDM integrated waveform, the average mutual information that can be achieved by the vehicle user i is: in, represents the average mutual information; Step S35, calculating the perceived service value of the vehicle user i: Among them, VS i represents said perceived service value; Indicates the preset threshold of perceived service value.

5. The resource allocation and service value optimization method according to claim 4, characterized in that: In step S4, the total service value is obtained by the following calculation formula: Wherein, VoS(Y, X) represents the total service value; α i The weight of the communication service value; VC i Represents the value of the communication service; β i Represents the weight of perceived service value; VS i represents the perceived service value; α i +β i =1.

6. The resource allocation and service value optimization method according to claim 5, characterized in that: In step S4, the objective function original problem is expressed by the following expression:

7. The resource allocation and service value optimization method according to claim 6, characterized in that: In step S4, each of the limiting conditions is represented by the following expressions: Among them, C1 and C2 are used to limit the minimum perception performance and communication performance of each vehicle; C3 and C4 are used to limit each vehicle user i to only match one orthogonal subchannel v; C5 and C6 are used to limit each vehicle user i to only match one base station m; C7 is used to limit the maximum number of vehicles that can be associated with I within the communication range of the base station m. m car.

8. The resource allocation and service value optimization method according to claim 7, characterized in that: The step S5 comprises: Step S51, decoupling the original objective function problem into the vehicle user-base station association problem and the subchannel allocation problem according to the variables of the matching matrix and the variables of the allocation matrix; Step S52, solving the vehicle user-base station association problem based on the limiting conditions C2, C5, C6 and C7 to optimize Y; Step S53, solving the subchannel allocation problem based on the limiting conditions C1, C2, C3 and C4 to optimize X; Step S54: Set a maximum number of iterations, and repeatedly execute steps S52 and S53 to optimize X and Y. When the maximum number of iterations is reached, the optimization stops.

9. The resource allocation and service value optimization method according to claim 8, characterized in that: The step S52 includes: Step S521, solving the vehicle user-base station association problem, the solution formula is expressed as: st:C2,C5,C6,C7 From the perspective of the base station, define E m is the set of users accessing the mth base station, then P1.1 can be equivalently rewritten as: C7-1:|E m |≤I m ; in, Convert the optimization of Y into the optimization of E m Optimization; Step S522: define a base station set of M base stations as B m Represents the mth base station; similarly, the vehicle user set of I vehicle users is defined as V i Representing the i-th vehicle user, the vehicle user set and the base station set are matched using a many-to-one matching function model. The expression of the many-to-one matching function model is: M(x)∈{V1,V2,...V i ...,V I }∪{B1,B2,...B m ...,B M },x∈{V1,V2,V i ...,V I }∪{B1,B2,...B m ...,B M } in, And|M(V i )|=1; and|M(B m )|≤I m ; M(V i )=B m , if and only if V i ∈M(B m ); Step S523: For the vehicle user V i , which is about any of the base stations B m The preference degree is defined as when the vehicle user V i Access the base station B m The base station B m The performance gain is expressed as: G i,m =The m (AND' m ∪i)-O m (AND' m ) Among them, E′ m Indicates that except V i Other access base station B m The set of vehicle users; Step S524: Each of the base stations B m Initialize the set of vehicle users that access itself as And each vehicle user V i Calculate its own m The preference of the available base station set can be initialized as and not connected to base station B m The vehicle user set is Step S525: Each non-connected base station B m Vehicle users V i Calculate the value of itself and the base station B m The preference between them and the available base station B with the largest preference m Make an access request; Step S526: Based on the received access request, each base station B m Accept the vehicle user V that has the highest preference i And add it to the review collection and reject other vehicle users V i At the same time, the base station B m Accessed vehicle users V i The number is increased by 1. If a base station B m Access vehicle user V i If the number reaches the upper limit, the base station B will be added to the available base station set. m Remove; Step S527, each rejected vehicle user V i The preference is recalculated and updated, and the process returns to step S525. When the remaining vehicle user set becomes an empty set, the iteration ends and the optimal allocation is obtained.

10. The resource allocation and service value optimization method according to claim 8, characterized in that: The step S53 includes: Step S531, solving the sub-channel allocation problem, the solution formula is expressed as: stC1~C4 From the perspective of orthogonal sub-channels, define A v is the set of users served by the vth orthogonal subchannel, then the problem Can be rewritten equivalently as: stC1-C2 in, Convert the optimization of X into the optimization of A v Optimization; Step S532: define a vehicle user set of I vehicle users as u i ={u1,u2,...,u I },u i Represents the i-th vehicle user; similarly, the orthogonal subchannel set of V orthogonal subchannels is defined as c v ={c1,c2,...,c V }, c v represents the Vth orthogonal sub-channel, and the vehicle user set and the orthogonal sub-channel set are matched by a many-to-one matching function model. The expression of the many-to-one matching function model is: in, M(u i ) ∈ {c1, c2,..., c V}, and |M(u i )| = 1; M(c v )∈{u1,u2,...,u I }; M(u i )=c v If and only if u i ∈M(c v ); Step S533: For the vehicle user u i , which is about any of the orthogonal subchannels c v The preference degree is defined as when the vehicle user u i Access the orthogonal subchannel c v When the orthogonal subchannel c v The performance gain is expressed as: G i,v =The v (THE v ′∪u i )-THE ν (THE' ν ) Among them, A v ′ represents the number of users except vehicle u i Other access orthogonal subchannels c v The set of vehicle users; Step S534: each of the orthogonal sub-channels c v Initialize the vehicle user u that accesses itself i , each of the vehicle users u i Based on the performance and constraints, it calculates the c of all the orthogonal sub-channels v The preference of the orthogonal subchannel set is The set of vehicle users that do not access the orthogonal sub-channel is initialized as Step S535: Each vehicle user u that is not connected to the orthogonal sub-channel i Towards the orthogonal subchannel c with the largest preference v Make an allocation request; Step S536: Each orthogonal sub-channel c v Upon receipt of the vehicle user u i The vehicle user u with a high preference is allowed in the access request i Access and reject other vehicle users u i After each successful access, the vehicle user set that has not accessed will assign the successfully allocated vehicle user u i Remove, if the vehicle user set is an empty set, the loop ends; Step S537: Based on the latest access and pairing results, each non-accessed vehicle user recalculates the orthogonal sub-channel c v preference, and returns to step S535.

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