IOV (Internet of Vehicles) multi-time scale content caching and transmission optimization method based on NOMA

Abstract

The invention discloses an Internet of Vehicles multi-time scale content caching and transmission optimization method based on NOMA (Non-Orthogonal Multiple Access). The method comprises the steps that a V2X Internet of Vehicles scene is constructed based on NOMA, then a transmission model of vehicles is established in the V2X Internet of Vehicles scene, the V2X Internet of Vehicles scene comprises a macro base station, a sub base station and a vehicle provider with a cache function, then a service quality model is constructed based on the transmission model of the vehicles and an optimization target is established, and finally, according to the optimization target, the service quality of the vehicles is optimized. And the cache state, the transmission state and the power of each video content provider are allocated, and an optimal allocation scheme is obtained. By introducing the optimization model of the mixed time scale, the cooperative transmission scheme and the pre-caching technology, the problems that in the prior art, multi-connection adaptability is poor, transmission is unstable, and the burden of a central network is too heavy are solved, the service quality and stability of video stream transmission in the Internet of Vehicles are remarkably improved, and the method is suitable for high-density dynamic scenes.

Description

Technical Field

[0001] The present invention belongs to vehicle networking technology, and specifically relates to a NOMA-based vehicle networking multi-time scale content caching and transmission optimization method. Background Art

[0002] With the rapid development of 5G technology, the Internet of Vehicles has become an important part of the intelligent transportation system. The surge in the number of vehicles has caused a large amount of data inflow and an increasing demand for network connections. Given the dynamic and multi-connected characteristics of the Internet of Vehicles, traditional mobile network topologies may cause transmission failures due to resource limitations and data congestion. However, it is costly to expand network capacity by adding new base stations, so non-orthogonal multiple access (NOMA) technology has been proposed to provide a solution for future multi-connection scenarios. The difference between NOMA and traditional OMA technology is that it allows multiple users to share the same frequency band channel, thereby improving spectrum utilization and effectively reducing access conflicts. When applying NOMA technology for data transmission in dense Internet of Vehicles (in this process, multiple transmission sources collaborate to provide video transmission services to vehicle requesters), many new challenges will be faced. First, considering the joint decoding nature of NOMA technology, power allocation must be considered. Second, different connection strategies cause users to form different groups, which in turn causes inter-group and intra-group interference. All of the above factors will affect transmission efficiency. Therefore, compared with traditional transmission methods, the NOMA framework constitutes a more complex scenario.

[0003] Although there are some works on caching and NOMA technology, most of these works focus on frequency allocation from the perspective of resource saving, for example, aiming to minimize average delay, increase network throughput, and improve cache hit rate, but do not fully consider the impact of cache placement on video transmission performance. These works are not applicable to the optimization problem of video transmission in dense network scenarios. At the same time, the existing work on static scene content transmission based on NOMA is not applicable to the vehicle framework with frequent connection switching. In addition, previous studies have ignored the collaboration between transmission sources and the use of different infrastructure providers (InPs) resources to improve transmission gain. Existing wireless caching technology cannot be directly applied to specific high-density vehicle network environments. The multi-connection problem of NOMA-based networks in dense transmission scenarios has attracted widespread attention, but existing studies have not fully considered the problem of frequent switching between users and providers. In addition, these studies have not adopted pre-caching technology to reduce the burden on the central network. Inspired by the advantages of content caching and NOMA technology, some works have explored cache-assisted transmission optimization in NOMA networks and studied two wireless caching strategies suitable for NOMA environments, especially updating content caches during peak hours. However, these methods have limited effect on improving video transmission quality in dynamic scenes. Summary of the invention

[0004] In order to solve the problems existing in the background technology, the purpose of the present invention is to provide a multi-time scale content caching and transmission optimization method for Internet of Vehicles based on NOMA, aiming to maximize the long-term average service quality of vehicle video streams.

[0005] The technical solution adopted by the present invention comprises the following steps: Step S1: First, a V2X vehicle networking scenario is constructed in a computer based on NOMA, and then a vehicle transmission model is established in the V2X vehicle networking scenario; Step S2: Next, the processor in the computer constructs a service quality model of the vehicle based on the transmission model of the vehicle, and then the processor establishes an optimization target based on the service quality model of the vehicle; Step S3: Finally, the processor processes the transmission model according to the optimization target to obtain the optimal solution for cache status, transmission status and power allocation of each video content provider in the V2X vehicle networking scenario.

[0006] The V2X vehicle networking scenario of step S1 is constructed based on non-orthogonal multiple access NOMA, including a macro base station, several sub base stations and a vehicle provider equipped with a caching function. The macro base station is connected to a content server storing a video file library. The V2X vehicle networking network covers several infrastructure providers, each of which manages several sub base stations and vehicle providers. The sub base stations and vehicle providers managed under the same infrastructure provider serve as content providers under the infrastructure provider.

[0007] The content providers under the same infrastructure provider share the bandwidth resources of the infrastructure provider. The bandwidth resources of one infrastructure provider are orthogonal to the bandwidth resources of other infrastructure providers. The content provider stores video files and is used to provide the required video files to the vehicle requester.

[0008] The vehicle transmission model in step S1 is mainly composed of a vehicle request cache model, a vehicle distribution model and an on-board network grouping model. The vehicle request cache model is used to represent the cache status of the content provider, the vehicle distribution model is used to represent the transmission status of the video file between the content provider and the vehicle requester, and the on-board network grouping model is used to represent the data bit rate received by the vehicle requester on the infrastructure provider.

[0009] In step S2, the average service quality of the vehicle requester is taken as the optimization target, and the specific steps of step S3 are: obtaining the maximum value of the average service quality, and then taking the cache state, transmission state and power allocation of the content provider when the average service quality reaches the maximum value as the optimal Internet of Vehicles content cache and transmission solution.

[0010] The expression of the vehicle request cache model in step S1 is as follows: C p ={ c p 1 , c p 2 ,…, c p f ,…, c p F}, c p f ∈{0,1} ∑ F f=1 c p f ≤C pmax Among them, C p Indicates the cache status of content provider p; c p f Indicates the cache status. When content provider p caches video file f, then c p f =1, when content provider p does not cache video file f, then c p f =0; C pmax represents the cache capacity limit of content provider p; f represents the requested video file; F represents the total number of video files in the video file library; The expression of the vehicle distribution model is as follows: a i,p,r f (t) ≤c p f ∑ I i=1 ∑ Pi p=1 a i,p,r f (t) ≤A max ∑ R r=1 a i,p,r f ≤B i,p max Among them, a i,p,r f (t) represents the number of infrastructure providers InP in time slot t. i The transmission status of the video file f between the content provider p and the vehicle requester r under iThe content provider p in the following situation transmits the cached video file f to the vehicle requester r, then a i,p,r f (t)=1, otherwise a i,p,r f (t) = 0; I represents the total number of infrastructure providers; Pi represents the i-th infrastructure provider InP i The total number of content providers under; R represents the total number of vehicle requesters; A max Indicates the upper limit of the number of content providers that can be associated with each vehicle requester at the same time; B i,p max InP stands for Infrastructure Provider i The upper limit of the number of vehicle requesters that the content provider p can serve; a i,p,r f represents the number of infrastructure providers InP in all time slots i The transmission status of the video file f between the content provider p and the vehicle requester r under The expression of the vehicle network grouping model is as follows: R r (t) = ∑ I i=1 R i,r (t)+ r bs R i,r (t) = W i ×log2(1+γ i,r (t)) γ i,r (t) = s i,r (t) / ( I i,r IGI (t)+ I i,r ICI (t)+ σ i,r (t) 2 ) s i,r (t) = ∑ F f=1 po f ∑ p∈Pi a i,p,r f (t) × p i,p,r ×h i,p,r R c ≤R r (t) ≤R max Among them, R r (t) represents the total data bit rate received by vehicle requester r in time slot t; r bsrepresents the data transmission bit rate that depends on the remote macro base station; R c Indicates the bit rate of the base layer of the SVC video stream; R max Indicates the bit rate corresponding to the highest video quality in the SVC video stream; R i,r (t) represents the vehicle requester r in the infrastructure provider InP in time slot t i The data bit rate received on i represents the i-th infrastructure provider InP i Bandwidth resources; γ i,r (t) represents the number of nodes from the infrastructure provider InP in time slot t. i The transmission signal-to-noise ratio between the vehicle requester r and s i,r (t) represents the bandwidth resource W used by the vehicle requester r in time slot t i Received signal expectation; I i,r IGI (t) represents the intra-group interference signal received by the vehicle requester r via the bandwidth resource Wi in time slot t; i,r ICI (t) represents the bandwidth resource W used by the vehicle requester r in time slot t i The inter-group interference signal received; σ i,r (t) represents the bandwidth resource W used by the vehicle requester r in time slot t i Received noise power; p i,p,r represents the i-th infrastructure provider InP i The power allocated by content provider p to vehicle requester r under i,p,r represents the i-th infrastructure provider InP i The channel gain allocated by the content provider p to the vehicle requester r under f represents the probability that the vehicle requester requests the video file f.

[0011] The step S2 is specifically as follows: Step S2.1: First, the expression of the service quality model is obtained according to the following formula: QoS r (t) = a n ln(b n R r (t) / r r desir ) Among them, QoS r (t) represents the service quality corresponding to the vehicle requester r in time slot t; a n and b n Both represent the preset service quality model parameters; r r desirR represents the video data bit rate expected by vehicle requester r; r (t) represents the total data bit rate received by vehicle requester r in time slot t; Step S2.2: Next, maximizing the average service quality of vehicle requesters is taken as the optimization goal. The optimization goal is obtained according to the following formula based on the service quality model. The function expression of the optimization goal is as follows: QoSa'=max{ QoSa}= max{(1 / R)∑ R r=1 QoS r (t)} Among them, QoSa' represents the maximum value of the average service quality, and QoSa represents the average service quality; In the above formula, the maximum value of the average service quality is obtained based on the SCA algorithm. In the SCA algorithm, the function expression of the average service quality QoSa is as follows: QoSa=(1 / T) (1 / R)∑ T t=1 ∑ R r=1 a n ln(b n (r r (g z ,d z ,e z )+ h r (k)) / r r desir ) -η(M(a z )- N(a z-1 )-▽ a N(a z-1 ) (a z –a z-1 ))-η(O(c z )- P(c z-1 )-▽ c P(c z-1 ) (c z –c z -1 )) Where T represents the total number of time slots; R represents the total number of vehicle requesters; r r (g z ,d z ,e z ) represents the amount of data obtained by vehicle user r in the zth iteration through transmission from surrounding vehicles; h r (k) represents the preset linear function; η represents the penalty parameter; M(a) represents the transmission state variable a i,p,r fThe first function of M(a z ) represents the value of the z-th iteration function M(a); N(a) represents the value of the transmission state variable a i,p,r f The second function of z ) represents the value of the z-th iteration function N(a); O(c) represents the value of the cache state variable c p f The third function of O(c z ) represents the value of the function O(c) at the zth iteration; P(c) represents the value of the cache state variable c p f The fourth function of P(c z ) represents the value of the z-th iteration function P(c); ▽ a N(a) represents the gradient of function N(a); a N(a z ) represents the zth iteration gradient ▽ a The value of N(a); c P(c) represents the gradient of the function P(c); ▽ c P(c z ) represents the zth iteration gradient ▽ c The value of P(c); vector a represents all transmission state variables a i,p,r f The vector c represents all cache state variables c p f A collection of .

[0012] In step S2, the expressions of the first function M(a), the second function N(a), the third function O(c), and the fourth function P(c) are as follows: M(a)=∑ I i=1 ∑ Pi p=1 ∑ R r=1 ∑ F f=1 a i,p,r f N(a)=∑ I i=1 ∑ Pi p=1 ∑ R r=1 ∑ F f=1 (a i,p,r f ) 2 O(c)=∑ Pi p=1 ∑F f=1 c p f P(c)=∑ Pi p=1 ∑ F f=1 (c p f ) 2 ▽ a N(a)= 2a i,p,r f ▽ c P(c) = 2 c p f Where I represents the total number of infrastructure providers; Pi represents the number of infrastructure providers of the i-th infrastructure provider InP i The total number of content providers under the same file system; R represents the total number of vehicle requesters; F represents the total number of video files in the video file library; a i,p,r f represents the number of infrastructure providers InP in all time slots i The transmission status of the video file f between the content provider p and the vehicle requester r under p f Indicates the cache status.

[0013] The vehicle networking content caching and transmission optimization system used in the present invention: The system comprises a model building module for building a transmission model of a vehicle in a V2X vehicle networking scenario, and building a service quality model of the vehicle based on the transmission model; It includes an optimization target building module for establishing optimization targets based on a vehicle's service quality model; It includes an optimal solution processing module, which is used to process the transmission model according to the optimization target to obtain the cache state, transmission state and power allocation solution of each video content provider.

[0014] Precaching is a technology that stores content on edge nodes or terminal devices in advance before actual demand occurs. In the Internet of Vehicles, precaching can effectively alleviate the pressure on the central network during peak hours. By caching popular or high-demand content to locations close to users (such as roadside units or vehicle terminals) during non-peak hours, the demand for real-time transmission can be reduced, thereby improving transmission efficiency and response speed. This method is particularly effective in in-vehicle video transmission with high traffic and low latency requirements, which can significantly reduce network latency and improve user experience. Collaborative distribution is a strategy to achieve content transmission through the collaboration of multiple network nodes or service providers. In the NOMA Internet of Vehicles, due to the high-speed mobility and frequent switching of vehicle users, traditional single service provision solutions are difficult to meet stability requirements. Collaborative distribution allows content requesters to be provided with content services by different service providers InP, thereby utilizing the collaboration of multiple nodes to improve the flexibility and robustness of transmission. In practical applications, this collaboration can significantly improve bandwidth utilization, reduce transmission interruptions caused by frequent switching, and ensure the quality of service QoS of video streaming.

[0015] The sample average method is an approximation method widely used in stochastic optimization, and is usually used to solve optimization problems involving future uncertainties. Sample average approximation SAA generates a large number of random samples to simulate possible future scenarios, and uses the expectations of these samples to approximate long-term goals, thereby simplifying the originally difficult-to-handle long-term optimization problem into a sample-based deterministic problem. In the present invention, SAA is used for long-term cache optimization, so that the cache strategy can be reasonably approximated based on future user preferences and channel information, thereby better improving the quality of service. Continuous convex approximation is an iterative algorithm for solving non-convex optimization problems. This method solves the non-convex problem by decomposing it into a series of convex sub-problems. In each iteration, the non-convex objective function is approximated as a convex function, thereby gradually approaching the optimal solution. In the present invention, due to the logarithmic form of the average quality of service QoS and the complexity of power allocation in the NOMA environment, the problem is modeled as a mixed integer nonlinear programming MINLP problem. The continuous convex approximation SCA algorithm solves the MINLP problem by converting it into multiple convex sub-problems, ensuring the convergence of the calculation process and improving the computational efficiency of the algorithm, making it suitable for dynamically changing Internet of Vehicles environments.

[0016] The present invention constructs a vehicle network scenario, which is characterized by high dynamics, asynchronous updates and multi-connectivity. The present invention carefully considers multiple key factors: including the actual SVC encoded video bit rate, cache capacity, power constraints, and complex interference in NOMA. At the same time, in order to ensure video quality, the present invention designs a collaborative transmission scheme so that vehicle requesters can be served by providers from the same or different service providers. Cache location, collaborative content distribution and power allocation are jointly optimized on different time scales to ensure optimal transmission performance. In the long-term caching stage, the present invention uses the sample average approximation SAA method to approximate the long-term transmission performance by the expectation of random samples; in the short-term distribution stage, an iterative algorithm based on continuous convex approximation SCA is used to transform the logarithmic form of the average service quality QoS from a mixed integer nonlinear programming MINLP problem to a convex problem. The computational complexity of the proposed algorithm is mathematically analyzed, and a rigorous theoretical proof is provided to prove its convergence; The present invention is applicable to highly dynamic, asynchronously updated and multi-connected Internet of Vehicles scenarios to maximize the long-term average quality of service (QoS) of in-vehicle video streams. The present invention designs a collaborative transmission strategy that comprehensively considers SVC encoded video bit rate, cache capacity, power constraints and NOMA interference, so that requesters can be served by providers of the same or different service providers InP, thereby improving the video stream QoS in NOMA Internet of Vehicles. Multi-time scale joint optimization: The sample average approximation SAA method is used to solve the challenge that the cache strategy needs to rely on future user preferences and transmission channel information, and optimize long-term cache performance. In the short term, an iterative algorithm based on continuous convex approximation SCA is used to transform the mixed integer nonlinear programming MINLP problem in the logarithmic form of QoS into a convex problem, thereby achieving efficient content distribution and power allocation in dynamic scenarios. Algorithm convergence and complexity analysis: The mathematical complexity analysis and theoretical convergence proof of the proposed algorithm are provided to verify the computational efficiency and practical applicability of the optimization method.

[0017] The beneficial effects of the present invention are as follows: 1. The present invention solves the difficult problem of balancing real-time response and long-term service in dynamic scenarios by optimizing caching and content distribution at different time scales.

[0018] 2. Collaborative transmission strategy and multi-connection adaptability: The collaborative transmission strategy designed by the present invention enables the requester to be provided with services by the same or different service providers InP, and improves resource utilization in multi-connection scenarios through collaboration across service providers, thereby significantly improving transmission stability and flexibility.

[0019] 3. Use pre-caching technology to reduce network burden: In the long-term caching stage, adopt a pre-caching strategy to reduce dependence on the central network during peak hours, optimize resource allocation and improve service quality.

[0020] 4. Improve the quality of service QoS: Through the optimization strategy of mixed time scales, the present invention can meet the requirements of real-time video streaming transmission in the short term, while optimizing the cache distribution in the long term, thereby effectively improving the average service quality of vehicle-mounted video transmission.

[0021] 5. Enhanced transmission stability: Since the collaborative transmission solution of the present invention allows requesters to obtain content from multiple service providers, it overcomes the service interruption problem caused by frequent user switching and ensures the continuity and stability of the video stream; Reduce network congestion and delay: The pre-caching strategy effectively reduces the burden on the central network during peak hours, thereby alleviating network congestion, improving overall transmission efficiency, and reducing transmission delay.

[0022] 6. Optimization algorithm adapted to dynamic environment: Through algorithms such as sample average approximation SAA and continuous convex approximation SCA, the present invention takes into account both computational complexity and real-time performance in the optimization algorithm, ensuring that it can quickly respond to and adapt to environmental changes in dynamic Internet of Vehicles scenarios, and has high practicality.

[0023] In summary, the present invention overcomes the problems of poor multi-connection adaptability, unstable transmission and excessive burden on the central network in the prior art by introducing a hybrid time scale optimization model, a collaborative transmission scheme and a pre-caching technology, significantly improves the service quality and stability of video stream transmission in the Internet of Vehicles, and is suitable for high-density dynamic scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a scenario diagram of the NOMA-based Internet of Vehicles collaborative transmission system; Figure 2 It is a multi-time scale framework diagram; Figure 3 This is a schematic diagram of the optimization process for the long-term content caching phase; Figure 4 This is a schematic diagram of the optimization process for the short-term content distribution phase; Figure 5 This is a schematic diagram of the overall process of optimizing the average service quality using sample average approximation and continuous convex approximation methods in the present invention. DETAILED DESCRIPTION

[0025] The present invention is described in detail below in conjunction with specific implementation cases. The following implementation cases will help those skilled in the art to further understand the present invention, but will not limit the present invention in any form.

[0026] The present invention adopts a V2X (vehicle-to-everything) communication solution that assists caching technology in urban scenarios with dense vehicle requests. Figure 1As shown, the scenario includes a macro base station MBS, several roadside small base stations SBSs and vehicles equipped with caching functions, which work together to provide services to many vehicle requesters. The macro base station MBS is connected to a content server that stores a complete video file library. The V2X network covers multiple infrastructure providers InPs. Each infrastructure provider InP manages several small base stations SBSs and vehicle providers, and can share the bandwidth resources of the infrastructure provider InP. The bandwidth resources are orthogonal to the resources of other multiple infrastructure providers InPs, avoiding interference between wireless frequency bands of different multiple infrastructure providers InPs. By sharing InPs resources, multiple InPs not only improve system performance, but also enhance the feasibility of multiple connections. The InPs set is defined as I'={1,2,…,I}, and the set of content providers belonging to the i-th infrastructure provider InPi is P i '={1,2,…,q,…,Pi}, for the content providers of the i-th infrastructure provider InPi, the content providers from 1 to q are several small base stations SBSs, and the content providers from q+1 to Pi are vehicle providers. In addition, there are R vehicle requesters who have demand for video content, and the set of vehicle requesters is represented as R'={1,2,…,R}. In order to cope with the dense connection requirements, NOMA (non-orthogonal multiple access) technology is adopted to support multiple connections between vehicle requesters and content providers under the same InP. At the same time, the vehicle requester can also receive collaborative services from multiple content providers belonging to different InPs, and the video content in the video file library is represented.

[0027] In the NOMA-based auxiliary cache transmission network, the cache strategy and transmission strategy are crucial to improving the system performance. All content providers collaboratively update the content cache to improve the cache hit rate. At the same time, the transmission strategy is determined based on the cache status and channel information to ensure that user needs are met. Given the high mobility of vehicles, the switching frequency of wireless connections changes more frequently than the popularity of video content, so the cache and transmission stages are updated at different time scales. The cache strategy is updated at long time intervals and is divided into multiple short time slots T'={1,2,…,T} for content distribution updates. Figure 2 The hybrid time scale framework for content caching and distribution is presented in x，yRepresents the yth short time slot within the xth long time scale, where each long time scale updates the content cache, and each short time slot is responsible for updating the distribution strategy, power allocation, and vehicle grouping. At the beginning of each long-term phase, the content provider obtains the video content to be cached from the remote server, and the cached content will not be updated until the next long-term phase. On this basis, based on the cache strategy optimized in the long-term phase, the transmission strategy, power allocation, and vehicle grouping are optimized in each short time slot to improve the transmission quality. However, future user requests and transmission link status have an impact on the strategy decision. To solve this anti-causal problem, the long-term cache phase is approximated by the average value method of random samples. Next, the models of content request, cache, distribution, link status, interference, and power allocation will be introduced in detail.

[0028] The embodiment of the present invention includes the following steps: Step S1: First, a V2X vehicle networking scenario is constructed in a computer based on NOMA, and then a vehicle transmission model is established in the V2X vehicle networking scenario; Step S2: Next, the processor in the computer constructs a service quality model of the vehicle based on the transmission model of the vehicle, and then the processor establishes an optimization target based on the service quality model of the vehicle; Step S3: Finally, the processor processes the transmission model according to the optimization target to obtain the optimal solution for the cache status, transmission status and power allocation of each video content provider in the V2X vehicle networking scenario.

[0029] The V2X vehicle networking scenario of step S1 is constructed based on non-orthogonal multiple access NOMA, including a macro base station, several sub-base stations and a vehicle provider equipped with a caching function. The sub-base station, the vehicle provider and the macro base station are all connected to a content server storing a complete video file library. The V2X vehicle networking network covers several infrastructure providers, each infrastructure provider manages several sub-base stations and vehicle providers, and the sub-base stations and vehicle providers managed by the same infrastructure provider serve as content providers under the infrastructure provider. The content providers under the same infrastructure provider share the bandwidth resources of the infrastructure provider. The bandwidth resources of one infrastructure provider are orthogonal to the bandwidth resources of other infrastructure providers. The content provider stores video files, and the content provider is used to provide the required video files to the vehicle requester (ie, the user).

[0030] The vehicle transmission model in step S1 is mainly composed of a vehicle request cache model, a vehicle distribution model and a vehicle network grouping model. The construction method of the vehicle request cache model is as follows: In the V2X vehicle networking scenario, the time slot under consideration is denoted as t. Each vehicle requester only requests one video file in the video file library in each time slot t. The probability of a video file being requested is obtained according to the following formula: po f =f –a / (∑ F n=1 n –a ) Among them, po f represents the probability that the vehicle requester requests the video file f; f represents the requested video file; a represents the parameter of the vehicle requester's request concentration; F represents the total number of video files in the video file library; n represents the index variable for summation; The expressions and constraints of the content provider cache status in the V2X vehicle networking scenario are as follows: C p ={ c p 1 , c p 2 ,…, c p f ,…, c p F}, c p f ∈{0,1} ∑ F f=1 c p f ≤C pmax Among them, C p Indicates the cache status of content provider p; c p f Indicates the cache status. When content provider p caches video file f, then c p f =1, when content provider p does not cache video file f, then c p f =0; C pmax Represents the cache capacity limit of content provider p.

[0031] Specifically, the video files in the complete video file library are sorted in descending order according to the popularity of the video files. Generally, the popularity of a video file can be regarded as the requested probability po of a vehicle requester requesting a video file. f . Assume that each vehicle requester can only request one video file in each time slot. The popularity can be modeled by the Zipf distribution model, and po1>po2>…po FIn the video content caching stage, content providers will pre-cache popular video files in their own cache space to reduce the load of the central server. However, due to the limitation of cache capacity, content providers can only cache a part of the files in the video file library.

[0032] The method for constructing the vehicle distribution model in step S1 is as follows: in a V2X vehicle networking scenario, constructing the transmission status of the video file between the content provider and the vehicle requester, and the transmission status between the content provider and the vehicle requester; The constraints on the transmission status of the video file f between the content provider and the vehicle requester are as follows: a i,p,r f (t) ≤c p f , c p f ∈{0,1} ∑ I i=1 ∑ Pi p=1 a i,p,r f (t) ≤A max , f∈F,p∈Pi ∑ R r=1 a i,p,r f ≤B i,p max , i∈I,f∈F, p∈Pi Among them, a i,p,r f (t) represents the number of infrastructure providers InP in time slot t. i The transmission status of the video file f between the content provider p and the vehicle requester r under i The content provider p in the following situation transmits the cached video file f to the vehicle requester r, then a i,p,r f (t)=1, otherwise a i,p,r f (t) = 0; I represents the total number of infrastructure providers; Pi represents the i-th infrastructure provider InP i The total number of content providers under; R represents the total number of vehicle requesters; A max Indicates the upper limit of the number of content providers that can be associated with each vehicle requester at the same time; B i,p max InP stands for Infrastructure Provider i The upper limit of the number of vehicle requesters that the content provider p can serve; a i,p,rf represents the number of infrastructure providers InP in all time slots i The transmission status of the video file f between the content provider p and the vehicle requester r under The constraints on the state transferred between the content provider and the vehicle requester are as follows: a i,p,r =max{ a i,p,r f}= min{∑ F f=1 a i,p,r f ,1} max{ a i,p,r f (t)}≤a i,p,r (t) ≤∑ F f=1 a i,p,r f (t) a i,p,r f (t)∈{0,1}, a i,p,r (t)∈{0,1} Among them, a i,p,r represents the number of infrastructure providers InP in all time slots i Whether there is a transmission link between the content provider p and the vehicle requester r, regardless of whether a specific video file f is transmitted, if there is a transmission link, then a i,p,r =1, otherwise a i,p,r =0,a i,p,r (t) represents the number of infrastructure providers InP in time slot t. i Is there a transmission link between the content provider p and the vehicle requester r? If there is a transmission link, then a i,p,r (t)=1, otherwise a i,p,r (t)=0; max{} represents the maximum value function; min{} represents the minimum value function.

[0033] The NOMA distribution model of the vehicle of the present invention is based on NOMA technology. In the transmission stage, NOMA technology is adopted to adapt to large-scale connection needs. In the present invention, NOMA grouping is determined only by channel gain, and NOMA grouping is dynamically adjusted based on cache status, vehicle movement and power allocation. And NOMA grouping will affect the interference between different transmission links, thereby affecting the efficiency of transmission. In order to avoid transmission waste and simplify the complexity of the system, the number of content providers that transmit video files f to a single vehicle requester is limited. In addition, the number of vehicle requesters that a single content provider can serve in each time slot t is also limited. In the proposed NOMA vehicle distribution model, each vehicle requester can obtain services from multiple vehicle providers and sub-base stations under different infrastructure providers. This vehicle distribution model makes full use of bandwidth resources and breaks the boundaries between different network operators. At the same time, vehicle requesters can dynamically form NOMA groups based on their own content needs and wireless channel status.

[0034] The method for constructing the vehicle network grouping model in step S1 is as follows: First, in the V2X vehicle networking scenario, the following formula is used to obtain the vehicle requester r in the time slot t at the infrastructure provider InP i Received data bit rate: R i,r (t) = W i ×log2(1+γ i,r (t)) γ i,r (t) = s i,r (t) / ( I i,r IGI (t)+ I i,r ICI (t)+ σ i,r (t) 2 ) s i,r (t) = ∑ F f=1 po f ∑ p∈Pi a i,p,r f (t) × p i,p,r ×h i,p,r Among them, R i,r (t) represents the vehicle requester r in the infrastructure provider InP in time slot t i The data bit rate received on i represents the i-th infrastructure provider InP i Bandwidth resources; γ i,r (t) represents the number of nodes from the infrastructure provider InP in time slot t.i The transmission signal-to-noise ratio between the vehicle requester r and s i,r (t) represents the bandwidth resource W used by the vehicle requester r in time slot t i Received signal expectation; I i,r IGI (t) represents the intra-group interference signal received by the vehicle requester r via the bandwidth resource Wi in time slot t; i,r ICI (t) represents the bandwidth resource W used by the vehicle requester r in time slot t i The inter-group interference signal received; σ i,r (t) represents the bandwidth resource W used by the vehicle requester r in time slot t i Received noise power; p i,p,r represents the i-th infrastructure provider InP i The power allocated by content provider p to vehicle requester r under i,p,r represents the i-th infrastructure provider InP i The channel gain allocated by the content provider p to the vehicle requester r under Next, in the V2X vehicle networking scenario, the total data throughput received by the vehicle requester r in time slot t is obtained according to the following formula: R r (t) = ∑ I i=1 R i,r (t)+ r bs r bs =∑ F f=1 po f (1- min{∑ i∈I ∑ p∈Pi a i,p,r ,1}v0) R c ≤R r (t) ≤R max Among them, R r (t) represents the total data bit rate (i.e., throughput) received by vehicle requester r in time slot t; bs represents the data transmission bit rate that depends on the remote macro base station; v0 represents the transmission rate provided by the remote macro base station to the vehicle requester; R c Indicates the bit rate of the base layer of the SVC (Scalable Video Coding) video stream; R max Indicates the bit rate corresponding to the highest video quality in the SVC video stream.

[0035] Specifically, in the NOMA-based cooperative transmission network, the vehicle requester uses the successive interference cancellation (SIC) technique to decode the required information and remove the interference signal. According to the principle of SIC technology, the vehicle requester in each NOMA group decodes and excludes the signals of the vehicle requesters with a lower decoding order than itself, and regards the signals of the vehicle requesters with a higher decoding order than itself as noise (i.e., intra-group interference IGI). At the same time, the signals of other vehicle requesters who are not in the same NOMA group as the vehicle requester are regarded as inter-group interference ICI. The decoding order of the signal is jointly affected by the channel gain, noise level and power allocation. As Figure 1 As shown in Figure 2, vehicles and sub-base stations SBS of the same color belong to the same infrastructure provider and share the bandwidth resources of the infrastructure provider. In each time slot t, the vehicle requesters sharing the same bandwidth resources are arranged in descending order according to the channel interference elimination decoding order, i.e., λ i,1 >λ i,2 >…λ i,r …>λ i,R , where λ i,r Represents the bandwidth resource W i The SIC decoding order of vehicle requester r is shown in Figure 1. Assume that each content provider has complete user channel state information (CSI), which can be obtained through channel estimation technology: The expression of channel gain is as follows: h i,p,r =G ×h i,p,r ' ×β p,r ×d p,r –a0 Where G represents the power gain factor brought by the amplifier and antenna; h i,p,r '~CN(0,1),h i,p,r ' represents the complex zero-mean Gaussian variable of Rayleigh fading; β p,r follows a log-normal distribution, indicating the shadow fading effect; d p,r represents the distance between content provider p and vehicle requester r in time slot t, and a0 represents the path loss exponent. The location of the vehicle can be predicted based on driving information and is considered unchanged during short time slots.

[0036] According to the NOMA model, the conditions indicating that vehicle requester r and vehicle requester r0 belong to the same NOMA group are as follows: ∑ Pi p=1 a i,p,r ×a i,p,r0 ≥1 Indicates that it is in the same NOMA group as the vehicle requester r and shares the bandwidth W i The vehicle requester set S i,rThe definition is as follows: S i,r ={ r0∈R||∑ Pi p=1 a i,p,r ×a i,p,r0 ≥1} Among them, || represents the definition of a set, which is used to indicate that the set S i,r The contained elements r0 satisfy the following conditions: ∑ Pi p= 1a i,p,r ×a i,p,r0 ≥1; The intra-group interference IGI set S of vehicle requester r i,r IGI It is expressed as: S i,r IGI ={ r0∈R||∑ Pi p=1 a i,p,r ×a i,p,r0 ≥1,λ i,r0 >λ i,r} The inter-group interference ICI set of vehicle requester r is represented by S i,r ICI for: S i,r ICI ={ r0∈R||∑ Pi p=1 a i,p,r ×a i,p,r0 =0} In order to calculate the received bit rate for each vehicle requester and achieve successful decoding, a series of constraints related to power allocation and signal reception must be met. Here, p i,p,r (t) represents the infrastructure provider InP in time slot t i The power allocated by content provider p to vehicle requester r under the above conditions, the total power received by the connected vehicle requester complies with the following maximum power limit conditions: ∑ R r=1 p i,p,r (t) ≤P i,p ma Among them, P i,p ma On behalf of infrastructure provider InP i The maximum transmission power of content provider p is given by: s i,r (t) = ∑ F f=1 pof ∑ p∈Pi c p f × a i,p,r f (t) × p i,p,r ×h i,p,r = ∑ F f=1 po f ∑ p∈Pi a i,p,r f (t) × p i,p,r ×h i,p,r The intra-group interference IGI signal and inter-group interference ICI signal received by vehicle requester r are expressed as: I i,r IGI (t) = ∑ r0∈R , λ i,r0 >λ i,r ,min{∑ p∈Pi a i,p,r ×a i,p,r0 ,1}×∑ p∈Pi a i,p,r0 ×p i,p,r0 ×h i,p,,r I i,r ICI (t) = ∑ r0∈R, r0≠r ,(1- min{∑ p∈Pi a i,p,r ×a i,p,r0 ,1}) ×∑ p∈Pi a i,p,r0 ×p i,p,r0 ×h i,p,,r In the NOMA network environment of cooperative transmission, the decoding sequence is affected by many factors, which makes the NOMA network decision process more complicated. In order to simplify the signal decoding process and make the NOMA network decision based only on the channel gain, the following constraints must be met to ensure that when λ i,j >λ i,r When , the signal of vehicle requester j is decoded first: min{∑ p∈Pi a i,p,r a i,p,r0 ,1}log2(1+γ i,r ) ≤log2(1+ S i,r,j VP (t) / (I i,r,j VP (t)+I i,rICI (t)+ σ i,r (t) 2 )) S i,r,j VP (t) = ∑ p∈Pi a i,p,r f × p i,p,r ×h i,p,j I i,r,j VP (t) = ∑ r0∈R , λ i,r0 >λ i,r ,min{∑ p∈Pi a i,p,r ×a i,p,r0 ,1}×∑ p∈Pi a i,p,r0 ×p i,p,r0 ×h i,p,j Among them, γ i,r Indicates that all time slots are from the infrastructure provider InP i The transmission signal-to-noise ratio between the vehicle requester r and S i,r,j VP (t) represents the bandwidth resource W of vehicle requester j in time slot t i The decoded signal power received from the vehicle requester r; i,r,j VP (t) represents the bandwidth resource W of vehicle requester j in time slot t i The intra-group interference IGI power received from vehicle requester r; In addition, using infrastructure provider InP i The signal-to-noise ratio (SINR) of vehicle requester r is defined by the following formula: γ i,r (t) = s i,r (t) / ( I i,r IGI (t)+ I i,r ICI (t)+ σ i,r (t) 2 ) Therefore, the corresponding received bit rate obtained by vehicle requester r in the tth time slot can be calculated. The data bit rate obtained by vehicle requester r from the surrounding content providers is given by R r (t) indicates that for those niche video content requests that are not cached locally, they will have to rely on the remote base station BS to obtain data. The data transmission bit rate in this case is represented by r bs To identify.

[0037] Step S2 is specifically as follows: Step S2.1: First, the expression of the service quality model is obtained according to the following formula: QoS r (t) = a n ln(b n R r (t) / r r desir ) Among them, QoS r (t) represents the service quality corresponding to the vehicle requester r in time slot t; a n and b n Both represent the preset service quality model parameters, a n >0, b n >0;r r desir represents the video data bit rate expected by vehicle requester r; In a specific implementation, the quality of service (QoS) is a key indicator to measure the satisfaction of the vehicle requester (i.e., user) and the transmission effect. Considering the characteristics of the video stream, the quality of service (QoS) can be evaluated based on the received throughput. The value of the video data bit rate is based on the requested SVC encoded video type. The present invention uses a logarithmic model to formally express the quality of service perceived by the requester. When the throughput received by the vehicle requester r is less than the expected video data bit rate, as the data throughput R increases, the QoS increases. r (t) increases, the service quality value QoS of vehicle requester r r The value will rise significantly and quickly, however, if the throughput received by the vehicle requester r is greater than the expected video data bit rate, as the data throughput R r (t) increases, QoS r The growth of the value will become slow. This mechanism will promote the efficient use of infrastructure providers by more vehicle requesters and avoid over-allocation of resources to a few vehicle requesters. Therefore, this service quality model is suitable for designing wireless transmission strategies in practical scenarios.

[0038] Step S2.2: Next, maximizing the average service quality of vehicle requesters is taken as the optimization goal. The optimization goal is obtained according to the following formula based on the service quality model. The function expression of the optimization goal is as follows: QoSa'=max{ QoSa}= max{(1 / R)∑ R r=1 QoS r (t)} Among them, QoSa' represents the maximum value of the average service quality, and QoSa represents the average service quality; In the above formula, the maximum value of the average service quality is obtained based on the SCA iterative algorithm. In the SCA iterative algorithm, the function expression of the average service quality QoSa is as follows: QoSa=(1 / T) (1 / R)∑ T t=1 ∑ R r=1 a n ln(b n (r r (g z ,d z ,e z )+ h r (k)) / r r desir ) -η(M(a z )- N(a z-1 )-▽ a N(a z-1 ) (a z –a z-1 ))-η(O(c z )- P(c z-1 )-▽ c P(c z-1 ) (c z –c z -1 )) Where T represents the total number of time slots; R represents the total number of vehicle requesters; r r (g z ,d z ,e z ) represents the amount of data obtained by vehicle user r through the transmission of surrounding vehicles in the zth iteration, i.e., the objective function; h r (k) represents a preset linear function about vector k; η represents a penalty parameter; M(a) represents a linear function about the transmission state variable a i,p,r f The first function of M(a z ) represents the value of the z-th iteration function M(a); N(a) represents the value of the transmission state variable a i,p,r f The second function of z ) represents the value of the z-th iteration function N(a); O(c) represents the value of the cache state variable c p f The third function of O(c z ) represents the value of the function O(c) at the zth iteration; P(c) represents the value of the cache state variable c p f The fourth function of P(c z) represents the value of the z-th iteration function P(c); ▽ a N(a) represents the gradient of function N(a); a N(a z ) represents the zth iteration gradient ▽ a The value of N(a); c P(c) represents the gradient of the function P(c); ▽ c P(c z ) represents the zth iteration gradient ▽ c The value of P(c); vector a represents all transmission state variables a i,p,r f The vector c represents all cache state variables c p f The vector k represents all seventh variables k r f A collection of .

[0039] In the specific implementation, the custom data volume r r (g z ,d z ,e z )’s initial value r r (g 0 ,d 0 ,e 0 ), and the initial value r r (g 0 ,d 0 ,e 0 ) is used as the basis for the first round of iterations. Subsequently, in each iteration, the function term function g is generated based on the solution of the previous iteration. r (x). Then, by r (g z ,d z ,e z ) is converted to a convex function and a new feasible solution (g z ,d z ,e z ) and the corresponding target value.

[0040] In step S2, the expressions of the first function M(a), the second function N(a), the third function O(c), and the fourth function P(c) are as follows: M(a)=∑ I i=1 ∑ Pi p=1 ∑ R r=1 ∑ F f=1 a i,p,r f N(a)=∑I i=1 ∑ Pi p=1 ∑ R r=1 ∑ F f=1 (a i,p,r f ) 2 O(c)=∑ Pi p=1 ∑ F f=1 c p f P(c)=∑ Pi p=1 ∑ F f=1 (c p f ) 2 ▽ a N(a)= 2a i,p,r f ▽ c P(c) = 2 c p f Where I represents the total number of infrastructure providers; Pi represents the number of infrastructure providers of the ith infrastructure provider InP i The total number of content providers under the same file system; R represents the total number of vehicle requesters; F represents the total number of video files in the video file library; a i,p,r f represents the number of infrastructure providers InP in all time slots i The transmission status of the video file f between the content provider p and the vehicle requester r under p f Indicates the cache status.

[0041] Step S3 specifically includes: obtaining the maximum value of the average service quality based on the SCA iterative algorithm, and then calculating the cache status c of the content provider when the average service quality reaches the maximum value. p f , transmission status a i,p,r f and power distribution p i,p,r As the optimal solution for caching and transmitting content in the Internet of Vehicles.

[0042] A vehicle networking content caching and transmission optimization system for the method of the present invention: It includes a model building module, which is used to build a vehicle transmission model in a V2X vehicle networking scenario, and to build a vehicle service quality model based on the transmission model; It includes an optimization target building module for establishing optimization targets based on a vehicle's service quality model; It includes an optimal solution processing module, which is used to process the transmission model according to the optimization target to obtain the cache state, transmission state and power allocation plan of each video content provider.

[0043] The goal of this invention is to maximize the average service quality QoSa of all vehicle requesters in the NOMA-based vehicle networking scenario. To achieve this goal, during T time slots, the cache is placed C = [c p f ] , content transmission strategy A={ a i,p,r f (t),a i,p,r (t)} and power allocation P = [p i,p,r ] for joint optimization. Considering that the content caching and transmission of any two consecutive long-term phases are independent of each other, the system transmission optimization will be performed once in each long-term time interval. For each long-term phase, the service quality-oriented transmission optimization problem will be constructed as the maximization problem of the average service quality during T time slots. The expression for maximizing the average service quality is as follows: max{ (1 / T) (1 / R) ∑ T t=1 ∑ R r=1 QoS r (t)} Among them, QoS r (t) represents the service quality corresponding to the vehicle requester r in time slot t; without loss of generality, it is assumed that the content cache is updated much slower than the content transmission and power allocation. In addition, in order to determine the transmission strategy and power allocation, the cache information must be obtained at the beginning of each time period. However, due to the uncertainty of future user requests and channel states in consecutive T time slots within this time period, the content caching problem cannot be solved directly. In order to deal with this mixed time scale stochastic optimization problem, the sample average approximation SAA method is used to estimate the future expected value of the average service quality. Therefore, the entire optimization process is divided into two stages: the long-term content caching stage and the short-term content distribution stage.

[0044] 1. Long-term content caching stage In the long-term content caching stage, both the popularity of the video and the diversity of the cache must be considered. Although caching the same popular video content in the content provider can ensure multiple transmission sources, it will affect the diversity of the cache. On the contrary, if completely different video files are cached in the surrounding content providers, although the cache diversity and cache hit rate can be improved, the number of content providers serving users at the same time will be reduced. Therefore, under the limitation of cache capacity, formulating a caching strategy requires a trade-off between cache diversity and transmission diversity.

[0045] There is a close relationship between content caching, transmission strategy and power allocation, and they cannot be considered separately. However, at the beginning of each large time interval, it is impossible to obtain user preferences and channel information within T consecutive time slots in this interval. To solve this problem, the sample average approximation SAA method is adopted to decide the cache content by averaging multiple random samples, such as Figure 3 As shown. The key to SAA is to treat the average service quality QoSa as a random sample and approximate the long-term performance by taking the average of T0 random samples. Therefore, the content caching strategy is determined by the following formula: max{ (1 / T0) (1 / R) ∑ T0 t0=1 ∑ R r=1 QoS r (t0)} QoSa =(1 / R)∑ R r=1 QoS r (t) (1 / T0) (1 / R) ∑ T0 t0=1 ∑ R r=1 QoS r (t0)≈E(QoSa) Where T0 represents the total number of samples; t0 represents the sample ordinal number; E( ) represents the mathematical expectation function; where the total number of samples T0 must be large enough to satisfy the accuracy of the mathematical expectation approximation of the average quality of service. Content transmission strategy and power allocation will also be optimized with random samples, but they are not the actual transmission results that are ultimately pursued.

[0046] 2. Short-term content distribution stage In the short-term content distribution phase, based on the content caching decisions obtained in the caching phase, the distribution strategy and power allocation are jointly optimized in the actual transmission scenario. Given the high mobility of vehicle networks, the transmission connections between vehicles are quickly updated in each time slot as the location changes. Therefore, in order to improve system performance, the transmission links and vehicle groups will be adjusted according to the real-time transmission information. Figure 4As shown, the content transmission strategy and power allocation will be updated at each time slot t and optimized by the following formula: max{(1 / R)∑ R r=1 QoS r (t)} The above formulas are classified as mixed integer nonlinear programming MINLP problems, which are difficult to solve. These two formulas cannot be directly solved by conventional optimization methods. In order to find a solution within an acceptable computing time, a series of conversion processes need to be performed. The present invention proposes an iterative algorithm based on SCA (continuous convex approximation) to find the optimal solution. Specifically, the original problem first needs to be equivalently converted into a convex difference algorithm DC problem form. Then, the SCA algorithm linearizes the second term in the convex difference DC problem through Taylor expansion, thereby obtaining a convex objective function and constraints. As the SCA-based iterative method continues, the algorithm gradually converges to a local optimal solution.

[0047] The optimization problem of average service quality QoSa has been equivalently converted into a tractable form. The specific objective function is shown as follows: max{(1 / T) (1 / R) ∑ T t=1 ∑ R r=1 QoS r (t)} Since the constraints are still non-convex, the problem still has complexity. We can use a penalty parameter η>>1 to incorporate the constraints into the objective function. Then, in each iteration, we get a tractable convex problem that can be solved efficiently in polynomial time using the interior point method IPM of the standard solver CVX.

[0048] In order to standardize the expression of the formula, the following variables are introduced: The first variable β i,r, r0 = min{∑ p∈Pi a i,p,r ×a i,p,r0 ,1}; the second variable θ i,p,r, r0 = a i,p,r ×a i,p,r0 ; The third variable c i,p,r = a i,p,r ×p i,p,r ; The fourth variable e i,p, r, r0 =(1-β i,r, r0 ) c i,p,r ; The fifth variable d i,p,r, r0 =β i,r, r0 ×c i,p,r ; The sixth variable gi,p,r f = a i,p,r f ×p i,p,r The seventh variable k r f = min{∑ i∈I ∑ p∈Pi a i,p,r f ,1}; Accordingly, the intra-group interference signal IGI and the inter-group interference signal ICI received by the vehicle requester r are reformulated as: I i,r IGI (t) = ∑ r0∈R , λ i,r0 >λ i,r , ∑ p∈Pi d i,p,r, r0 ×h i,p,,r I i,r,j VP (t) = ∑ r0∈R , λ i,r0 >λ i,r , ∑ p∈Pi d i,p,r, r0 ×h i,p,j s i,r (t) = ∑ F f=1 po f ∑ p∈Pi g i,p,r f ×h i,p,r By introducing substitution variables, the optimization problem P'(P,A,C) can be reformulated as: max{(1 / T)(1 / R)∑ T t=1 ∑ R r=1 a n ln(b n R r (t) / r r desir )} -η∑ I i=1 ∑ Pi p=1 ∑ R r=1 ∑ F f=1 (a i,p,r f - (ai,p,r f ) 2 )-∑ Pi p=1 ∑ F f=1 (c p f - (c p f ) 2 )} R r (t) = f r (x)- g r (x)+ h r (k) h r (k)=∑ F f=1 po f (1-k r f ) v0 Where T represents the total number of time slots; R represents the total number of vehicle requesters; Pi represents the number of i-th infrastructure providers InP i The total number of content providers under the same file system; F represents the total number of video files in the video file library; η represents the penalty parameter; R r (t) represents the total data bit rate received by vehicle requester r in time slot t; r r desir represents the video data bit rate expected by vehicle requester r; f r (x) represents the first convex function; g r (x) represents the second convex function; h r (k) represents a linear function; k r f represents the seventh variable; v0 represents the transmission rate provided by the remote macro base station to the vehicle requester; The function expression of the optimization problem P'(P,A,C) is as follows: max{(1 / T)(1 / R)∑ T t=1 ∑ R r=1 a n ln(b n H(g,d,e,k) / r r desir )} -η∑ I i=1 ∑ Pi p=1 ∑ R r=1 ∑ F f=1 (ai,p,r f - (a i,p,r f ) 2 )-∑ Pi p=1 ∑ F f=1 (c p f - (c p f ) 2 )} Among them, H(g,d,e,k) represents a convex function, which is a convex function for variables d, e, and g; η represents a penalty parameter.

[0049] In order to transform the objective function H(g, d, e, k) into a convex function, for any feasible solution d z-1 、e z-1 , function g r (x) can be approximated by a first-order Taylor expansion. In addition, after applying the first-order Taylor approximation, the functions in the objective and some constraints can be considered as convex functions. For a certain point g z-1 ,d z-1 ,e z-1 ,a z-1 ,c z-1 , by solving the following convex optimization problem, we can obtain the upper bound of the objective function.

[0050] The expression of the above function is as follows: r r (g z ,d z ,e z )=∑ I i=1 f i,r (g z ,d z ,e z )-g i,r (g z ,d z ,e z ) -▽ d g i,r (d z-1 ,e z-1 )(d z –d z-1 )-▽ e g i,r (d z-1 ,e z-1 )(e z -e z-1 ) f i,r(g, d, e) = W i × log2(I i,r IGI (t) + I i,r ICI (t) + σ i,r (t) 2 + s i,r (t)) g i,r (d, e) = W i × log2(I i,r IGI (t) + I i,r ICI (t) + σ i,r (t) 2 ) If λ i,r0 > λ i,r , p ∈ Pi, ▽ d g i,r= h i,p,r / ((ln2)(I i,r IGI (t) + I i,r ICI (t) + σ i,r (t) 2 )) Otherwise, ▽ d g i,r = 0 If r0 ∈ R, r0 ≠ r, p ∈ Pi, ▽ e g i,r= h i,p,r / ((ln2)(I i,r IGI (t) + I i,r ICI (t) + σ i,r (t) 2 )) Otherwise, ▽ e g i,r = 0 H(g, d, e, k) = f r (g, d, e) - g r (g, d, e) + h r (k) f r (g, d, e) = ∑ I i=1 f i,r (g, d, e) g r (g, d, e) = ∑ I i=1 g i,r(g,d,e) Among them, r r (g, d, e) represents the second objective function; r r (g z ,d z ,e z ) represents the zth iteration function r r The value of (g, d, e); f i,r (g, d, e) represents the first iteration function, f i,r (g z ,d z ,e z ) represents the zth iteration function f i,r The value of (g, d, e); g i,r (d,e) represents the fourth convex function, g i,r (g z ,d z ,e z ) represents the zth iteration function g i,r The value of (d,e); d g i,r Represents the first gradient function; ▽ e g i,r represents the second gradient function; In the above formula, the average service quality is obtained based on the SCA iterative algorithm. In the SCA iterative algorithm, the function expression of the average service quality is as follows: QoSa=(1 / T) (1 / R)∑ T t=1 ∑ R r=1 a n ln(b n (r r (g z ,d z ,e z )+ h r (k)) / r r desir ) -η(M(a z )- N(a z-1 )-▽ a N(a z-1 ) (a z –a z-1 ))-η(O(c z )- P(c z-1 )-▽ c P(c z-1 ) (c z –c z -1 )) Among them, rr (g z ,d z ,e z ) represents the amount of data obtained by vehicle user r through the transmission of surrounding vehicles in the zth iteration, i.e., the objective function; h r (k) represents the amount of data transmitted by the base station, which is also a linear function of k; M(a) represents the transmission state variable a i,p,r f function; M(a z ) represents the value of M(a) at the zth iteration; N(a) represents the value of the transmission state variable a i,p,r f The quadratic function of N(a z ) represents the value of N(a) at the zth iteration; O(c) represents the value of the cache state variable c p f Function; O(c z ) represents the value of O(c) at the zth iteration; P(c) represents the value of the cache state variable c p f The quadratic function of P(c z ) represents the value of P(c) at the zth iteration; ▽ a N(a) represents the gradient of function N(a); c P(c) represents the gradient of the function P(c); vector a represents all transmission state variables a i,p,r f The vector c represents all cache state variables c p f The vector k represents all seventh variables k r f A collection of .

[0051] From a feasible solution to the optimization problem r (g 0 ,d 0 ,e 0 ) is the initial value and the iteration starts. Each round of iteration generates a feasible solution r r (g z+1 ,d z+1 ,e z+1 ) are all based on the feasible solutions of the previous round.

[0052] In order to solve this complex joint optimization problem, an SCA-based iterative strategy is used to gradually approximate the result. In each round of iteration, the original problem is transformed into a convex problem that can be solved by traditional convex optimization algorithms such as the interior point method. It is worth noting that the solution obtained by optimization is an upper bound of the solution of the initial problem. In order to further improve the suboptimal solution, the algorithm will continue to iterate until the preset stopping condition is met. In order to deal with the converted convex difference DC approximation problem, the low-complexity algorithm applied is as follows: Figure 5 As shown in Figure 1, first, a set of feasible solutions is initialized as the basis for the first round of iterations. Then, in each iteration, the function term function g is generated based on the solution of the previous iteration. r (x) and its corresponding gradient function. Then, by converting the objective function into a convex function, a new feasible solution (g z ,d z ,e z ) and the corresponding target value, which is either improved or unchanged compared to the previous iteration. This process will continue until the convergence condition is met.

[0053] The SCA-based iterative algorithm proposed in this invention has a polynomial time complexity. The optimization problem in each iteration is a convex problem, which can be solved by the interior point method with a computational complexity of O(q 3.5 ), where q represents the total number of variables in the problem. For short-term content distribution strategy optimization, the time complexity of each iteration is O((3IPRR+FIPR+IRR+IPR+RF) 3.5 ), where q=3IPRR+FIPR+IRR+IPR+RF is the number of variables. Similarly, in the long-term content cache optimization process, the cache variable c p f The training is performed during T time slots, and the number of variables is (3IPRR+FIPR+IRR+IPR+RF+PF)T. Overall, the SCA-based iterative algorithm provides a suboptimal solution to the optimization problem that is close to the global optimal solution.

[0054] From a feasible solution of the optimization problem (g 0 ,d 0 ,e 0 ) is the initial value and the iteration starts. Each round of iteration generates a feasible solution (g z+1 ,d z+1 ,e z+1 ) are based on the feasible solution of the previous round. This iterative process will continue until the maximum number of iterations is reached or the predetermined convergence criterion is met. In this process, in order to prove the convergence of the iterative algorithm based on SCA, the lower bound of the algorithm is derived as follows: r i,r = f i,r (g z ,d z ,e z )- g i,r (g z ,d z ,e z ) ≥f i,r (g z ,d z ,e z )- g i,r (d z-1 ,e z-1 )-▽ d g i,r (d z-1 ,e z-1 )(d z –d z-1 )-▽ e g i,r (d z-1 ,e z-1 )(e z -e z-1 ) =max g,d,e f i,r (g,d,e)- g i,r (d,e)-▽ d g i,r (d z-1 ,e z-1 )(d z –d z-1 )-▽ e g i,r (d z-1 ,e z-1 )(e z -e z-1 ) ≥f i,r (g z-1 ,d z-1 ,e z-1 )- g i,r (d z-1 ,e z-1 ) -▽ d g i,r (d z-1 ,e z-1 )(d z –d z-1 )-▽ e g i,r (d z-1 ,e z-1 )(e z -e z-1 ) = f i,r (g z-1 ,d z-1 ,e z-1 )- g i,r (g z-1 ,d z-1 ,e z-1 ) Among them, r i,r represents a convex function f i,r (g,d,e) and convex function g i,r (d, e); therefore, after each iteration, the average service quality QoSa is either improved or remains unchanged compared to the previous result. The feasible solution after iteration (g z ,d z ,e z ) will at least converge to a local optimal solution.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A NOMA-based multi-time-scale content caching and transmission optimization method for Internet of Vehicles, characterized in that: The following steps are involved: Step S1: First, a V2X vehicle networking scenario is constructed in a computer based on NOMA, and then a vehicle transmission model is established in the V2X vehicle networking scenario; Step S2: Next, the processor in the computer constructs a service quality model of the vehicle based on the transmission model of the vehicle, and then the processor establishes an optimization target based on the service quality model of the vehicle; Step S3: Finally, the processor processes the transmission model according to the optimization target to obtain the optimal solution for cache status, transmission status and power allocation of each video content provider in the V2X vehicle networking scenario.

2. According to claim 1, a NOMA-based multi-time scale content caching and transmission optimization method for Internet of Vehicles, characterized in that: The V2X vehicle networking scenario of step S1 is constructed based on non-orthogonal multiple access NOMA, including a macro base station, several sub base stations and a vehicle provider equipped with a caching function. The macro base station is connected to a content server storing a video file library. The V2X vehicle networking network covers several infrastructure providers, each of which manages several sub base stations and vehicle providers. The sub base stations and vehicle providers managed under the same infrastructure provider serve as content providers under the infrastructure provider.

3. According to claim 2, a NOMA-based multi-time scale content caching and transmission optimization method for Internet of Vehicles, characterized in that: The content providers under the same infrastructure provider share the bandwidth resources of the infrastructure provider. The bandwidth resources of one infrastructure provider are orthogonal to the bandwidth resources of other infrastructure providers. The content provider stores video files and is used to provide the required video files to the vehicle requester.

4. According to claim 2, a NOMA-based multi-time scale content caching and transmission optimization method for Internet of Vehicles, characterized in that: The vehicle transmission model in step S1 is mainly composed of a vehicle request cache model, a vehicle distribution model and an on-board network grouping model. The vehicle request cache model is used to represent the cache status of the content provider, the vehicle distribution model is used to represent the transmission status of the video file between the content provider and the vehicle requester, and the on-board network grouping model is used to represent the data bit rate received by the vehicle requester on the infrastructure provider.

5. According to claim 2, a NOMA-based multi-time scale content caching and transmission optimization method for Internet of Vehicles, characterized in that: In step S2, the average service quality of the vehicle requester is taken as the optimization target, and the specific steps of step S3 are: obtaining the maximum value of the average service quality, and then taking the cache state, transmission state and power allocation of the content provider when the average service quality reaches the maximum value as the optimal Internet of Vehicles content cache and transmission solution.

6. According to claim 4, a NOMA-based multi-time scale content caching and transmission optimization method for Internet of Vehicles, characterized in that: The expression of the vehicle request cache model in step S1 is as follows: C p ={ c p 1 , c p 2 ,…, c p f ,…, c p F }, c p f ∈{0,1} ∑ F f=1 c p f ≤C pmax Among them, C p Indicates the cache status of content provider p; c p f Indicates the cache status. When content provider p caches video file f, then c p f =1, when content provider p does not cache video file f, then c p f =0; C pmax represents the cache capacity limit of content provider p; f represents the requested video file; F represents the total number of video files in the video file library; The expression of the vehicle distribution model is as follows: a i,p,r f (t) ≤c p f ∑ I i=1 ∑ Pi p=1 a i,p,r f (t) ≤A max ∑ R r=1 a i,p,r f ≤B i,p max Among them, a i,p,r f (t) represents the number of infrastructure providers InP in time slot t. i The transmission status of the video file f between the content provider p and the vehicle requester r under i The content provider p in the following situation transmits the cached video file f to the vehicle requester r, then a i,p,r f (t)=1, otherwise a i,p,r f (t) = 0; I represents the total number of infrastructure providers; Pi represents the i-th infrastructure provider InP i The total number of content providers under; R represents the total number of vehicle requesters; A max Indicates the upper limit of the number of content providers that can be associated with each vehicle requester at the same time; B i,p max InP stands for Infrastructure Provider i The upper limit of the number of vehicle requesters that the content provider p can serve; a i,p,r f represents the number of infrastructure providers InP in all time slots i The transmission status of the video file f between the content provider p and the vehicle requester r under The expression of the vehicle network grouping model is as follows: R r (t)= ∑ I i=1 R i,r (t)+ r bs R i,r (t)= W i ×log2(1+γ i,r (t)) γ i,r (t)= s i,r (t) / ( I i,r IGI (t)+ I i,r ICI (t)+ σ i,r (t) 2 ) s i,r (t)= ∑ F f=1 po f ∑ p∈Pi a i,p,r f (t) × p i,p,r ×h i,p,r R c ≤R r (t) ≤R max Among them, R r (t) represents the total data bit rate received by vehicle requester r in time slot t; r bs represents the data transmission bit rate that depends on the remote macro base station; R c Indicates the bit rate of the base layer of the SVC video stream; R max Indicates the bit rate corresponding to the highest video quality in the SVC video stream; R i,r (t) represents the vehicle requester r in the infrastructure provider InP in time slot t i The data bit rate received on i represents the i-th infrastructure provider InP i Bandwidth resources; γ i,r (t) represents the number of nodes from the infrastructure provider InP in time slot t. i The transmission signal-to-noise ratio between the vehicle requester r and s i,r (t) represents the bandwidth resource W used by the vehicle requester r in time slot t i Received signal expectation; I i,r IGI (t) represents the intra-group interference signal received by the vehicle requester r via the bandwidth resource Wi in time slot t; i,r ICI (t) represents the bandwidth resource W used by the vehicle requester r in time slot t i Received inter-group interference signal; σ i,r (t) represents the bandwidth resource W used by the vehicle requester r in time slot t i Received noise power; p i,p,r represents the i-th infrastructure provider InP i The power allocated by content provider p to vehicle requester r under i,p,r represents the i-th infrastructure provider InP i The channel gain allocated by the content provider p to the vehicle requester r under f represents the probability that the vehicle requester requests the video file f.

7. According to claim 2, a NOMA-based multi-time scale content caching and transmission optimization method for Internet of Vehicles, characterized in that: The step S2 is specifically as follows: Step S2.1: First, the expression of the service quality model is obtained according to the following formula: QoS r (t)= a n ln(b n R r (t) / r r desir ) Among them, QoS r (t) represents the service quality corresponding to the vehicle requester r in time slot t; a n and b n Both represent the preset service quality model parameters; r r desir R represents the video data bit rate expected by vehicle requester r; r (t) represents the total data bit rate received by vehicle requester r in time slot t; Step S2.2: Next, maximizing the average service quality of vehicle requesters is taken as the optimization goal. The optimization goal is obtained according to the following formula based on the service quality model. The function expression of the optimization goal is as follows: QoSa'=max{ QoSa}= max{(1 / R)∑ R r=1 QoS r (t)} Among them, QoSa' represents the maximum value of the average service quality, and QoSa represents the average service quality; In the above formula, the maximum value of the average service quality is obtained based on the SCA algorithm. In the SCA algorithm, the function expression of the average service quality QoSa is as follows: QoSa=(1 / T) (1 / R) ∑ T t=1 ∑ R r=1 a n ln(b n (r r (g z ,d z ,e z )+ h r (k)) / r r desir ) -η(M(a z )- No z-1 )–▽ a No z-1 ) (to z -to z-1 ))-η(O(c z )- P(c z-1 )–▽ c P(c z-1 ) (c z –c z-1 )) Where T represents the total number of time slots; R represents the total number of vehicle requesters; r r (g z ,d z ,e z ) represents the amount of data obtained by vehicle user r in the zth iteration through transmission from surrounding vehicles; h r (k) represents the preset linear function; η represents the penalty parameter; M(a) represents the transmission state variable a i,p,r f The first function of M(a z ) represents the value of the z-th iteration function M(a); N(a) represents the value of the transmission state variable a i,p,r f The second function of z ) represents the value of the z-th iteration function N(a); O(c) represents the value of the cache state variable c p f The third function of O(c z ) represents the value of the function O(c) at the zth iteration; P(c) represents the value of the cache state variable c p f The fourth function of P(c z ) represents the value of the z-th iteration function P(c); ▽ a N(a) represents the gradient of function N(a); a N(a z ) represents the zth iteration gradient ▽ a The value of N(a); c P(c) represents the gradient of the function P(c); ▽ c P(c z ) represents the zth iteration gradient ▽ c The value of P(c); vector a represents all transmission state variables a i,p,r f The vector c represents all cache state variables c p f A collection of .

8. The method for optimizing multi-time-scale content caching and transmission of NOMA-based IoV according to claim 7 is characterized in that: In step S2, the expressions of the first function M(a), the second function N(a), the third function O(c), and the fourth function P(c) are as follows: M(a)= ∑ I i=1 ∑ Pi p=1 ∑ R r=1 ∑ F f=1 a i,p,r f N(a)= ∑ I i=1 ∑ Pi p=1 ∑ R r=1 ∑ F f=1 (a i,p,r f ) 2 O(c)= ∑ Pi p=1 ∑ F f=1 c p f P(c)= ∑ Pi p=1 ∑ F f=1 (c p f ) 2 ▽ a N(a)= 2a i,p,r f ▽ c P(c)= 2 c p f Where I represents the total number of infrastructure providers; Pi represents the number of infrastructure providers of the ith infrastructure provider InP i The total number of content providers under the same file system; R represents the total number of vehicle requesters; F represents the total number of video files in the video file library; a i,p,r f represents the number of infrastructure providers InP in all time slots i The transmission status of the video file f between the content provider p and the vehicle requester r under p f Indicates the cache status.

9. A vehicle networking content caching and transmission optimization system for implementing any of the methods described in claims 1-8, characterized in that: The system comprises a model building module for building a transmission model of a vehicle in a V2X vehicle networking scenario, and building a service quality model of the vehicle based on the transmission model; It includes an optimization target building module for establishing optimization targets based on a vehicle's service quality model; It comprises an optimal solution processing module, which is used to process the transmission model according to the optimization target to obtain the cache state, transmission state and power allocation solution of each video content provider.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the content caching and transmission optimization method described in any one of claims 1 to 8 are implemented.

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