Code caching method in Internet of Vehicles user cooperation scene based on grouping strategy

Abstract

The invention discloses a coding caching method in an Internet of Vehicles user cooperation scene based on a grouping strategy, and belongs to the technical field of Internet of Vehicles communication, and the method comprises the steps: S1, constructing a system model which comprises a plurality of road side units, each road side unit is connected with a plurality of users, and each user is provided with a cache for caching M files; s2, constructing a decentralized coding cache system and running the decentralized coding cache system, including a placement stage in which each user independently and randomly selects a part of each file for caching; and in the service stage, each user is connected to one or more road side units according to own conditions, and the user decodes and acquires the required file content according to the received encoding information. According to the coding caching method in the Internet of Vehicles user cooperation scene based on the grouping strategy, the condition of communication between the users is considered for the Internet of Vehicles scene, and the condition that the vehicle can be connected to a plurality of road side units at the same time is considered, so that the coding caching method is more consistent with the actual application scene.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking communication technology, and in particular to a coding caching method in a vehicle networking user cooperation scenario based on a grouping strategy. Background Art

[0002] Caching is a technology that reduces the peak rate during network peak hours by placing some content near users in advance, transferring some content that needs to be transmitted during network peak hours to off-peak hours, thereby reducing the peak network load and improving the utilization of network resources during off-peak hours. Encoding caching further reduces the peak rate by encoding the information placed and sent.

[0003] In the prior art, a classic coding cache model includes a central server storing all N files of size F bits, a noiseless multicast shared link connecting the server and all users, and K users equipped with caches, and the cache size of each user is equal, all of which are MF bits, and assuming that the probability of the above N files being requested is consistent, then such a coding cache system is called a uniform system. The operation of the coding cache system is divided into two stages. The first is the placement stage, which occurs during the off-peak period of the network. In this stage, the user pre-caches a part of the file content into his own cache space. According to the strategy of caching files in this stage, the coding cache system is divided into two types. One is a centralized system, that is, the central server determines the content pre-cached by the user; the other is a decentralized system, in which the user independently and randomly selects the pre-cached content. After the placement stage, the system enters the service stage, which occurs during the peak period of the network. In this stage, the user makes a file request to the server, and the server sends information to the user according to the requests of all users. The user obtains the requested file based on the received information and the content pre-cached in the placement stage.

[0004] The expected bit rate R(M) is used to describe the performance of a decentralized coding cache scheme, that is, the relationship between the expected bit rate on the noise-free shared link and the user cache size under the condition of fixed system parameters N and K. The expected bit rate is the sum of the expected lengths of the information sent by the server on the noise-free shared link. The expected bit rate of the decentralized coding cache scheme is

[0005]

[0006] Multi-Access Code Caching (MACC) is a code cache system that allows users to access multiple cache spaces. In a MACC system, user i can access cache i,<i+1> ,…,<i+L-1> There are L cache spaces in total, of which

[0007]

[0008] like Figure 1 As shown, a MACC system model with L=2 is given. In addition, when L=1, the MACC system degenerates into the above-mentioned classic coding cache system.

[0009] A code rate upper bound for a decentralized MACC system

[0010]

[0011] Since this upper bound does not take into account the situation where the information generated by some user cooperation sets is invalid for the users in them, this upper bound is relatively loose. Summary of the invention

[0012] The purpose of the present invention is to provide a coding caching method in a user cooperation scenario of an Internet of Vehicles based on a grouping strategy to solve the problems existing in the above-mentioned background technology.

[0013] To achieve the above object, the present invention provides a coding caching method in a user cooperation scenario of an Internet of Vehicles based on a grouping strategy, comprising the following steps:

[0014] S1. Build a system model, including several roadside units, each of which accesses a file set containing all N files The size of each file is F bits. Each roadside unit is connected to several users. Each user is equipped with a cache for caching M files. Each user can be connected to one or more roadside units during the service phase. At the same time, adjacent users can communicate with each other, that is, each user can access the caches of its two adjacent users. We focus on a roadside unit in the system and the users it is connected to. Let the number of users connected to this roadside unit be K;

[0015] S2. Build and run a decentralized encoding cache system, including

[0016] During the placement phase, each user independently and randomly selects a portion of each file to cache;

[0017] During the service phase, each user connects to several roadside units according to its own conditions, and synchronizes its cache information, request information and connection information with the roadside units. The roadside units generate corresponding coding information based on the above information and send it to the user. The user decodes the received coding information and obtains the required file content.

[0018] Preferably, during the service phase, each user is connected to several roadside units, and adjacent users communicate with each other, and each user can access the caches of its two adjacent users.

[0019] Preferably, since each user may be connected to a different number of roadside units, a user grouping strategy and grouping precision adjustment are used in the service phase, wherein the user grouping strategy is to divide users into different groups according to the number of roadside units they are connected to, and the number of roadside units connected to users in each group is the same, and multicast opportunities are explored in each user group; the grouping precision is adjusted to reduce the grouping precision, so that some users connected to different but close numbers of roadside units are grouped in the same group to explore more multicast opportunities. In the service phase, users connected to multiple roadside units can evenly send requests to different roadside units to reduce the load of a single roadside unit, that is, the number of bits requested by user k in the service phase is F(1-M / N), and the number of roadside units connected to user k is I k , then the number of bits it requests from each RSU is F(1-M / N) / I k For RSUs, since the number of bits requested by users connected to different numbers of RSUs is different, we group users according to the number of RSUs they are connected to and explore multicast opportunities in each user group. Suppose among the users connected to RSUs, the number of users connected to i RSUs is K i ,i∈{1,2,…,I}, where I is the number of roadside units connected to the user with the largest number of roadside units. We divide the users into I groups, and the users in the i-th group are connected to P i =i roadside units, denoted as The number of users in each user group is K i .

[0020] Considering that adjacent users can share their caches, and each user group contains adjacent or non-adjacent users, let a user group The user in According to whether these users are consecutively adjacent in the system, they can be further divided into multiple user groups, and all consecutively adjacent users are grouped together. That is, after numbering the users according to their positions during the service phase, all users with consecutive integer numbers are grouped together. If the user group Can be divided into J i user groups, and these user groups are

[0021] In the service phase, we generate a message for each user cooperation set and send it to the users in it, except for those user cooperation sets that only consist of several neighboring users, because the users in them can always get the corresponding information from their neighboring users.

[0022] Preferably, the user grouping strategy is as follows: suppose that among the users connected to the roadside unit, the number of users connected to i roadside units is K i,i∈{1,2,…,I}, where I is the number of roadside units connected to the user with the largest number of roadside units; the users are divided into I groups, and the users in the i-th group are connected to P i =i roadside units, denoted as The number of users in each user group is K i ; Since adjacent users share their caches, and there are adjacent or non-adjacent users in each user group, let a user group The user in According to whether these users are consecutively adjacent in the system, they are further divided into several user groups. All consecutively adjacent users are grouped together. After numbering the users according to their positions during the service phase, all users with consecutive integer numbers are grouped together. If the user group Subdivided into J i user groups, and these user groups are

[0023] In the service phase, a message is generated for each user cooperation set and sent to the users in it, except for the user cooperation set consisting of only several neighboring users.

[0024] Preferably, the upper bound of the expected bit rate of the encoding cache system in step S2 is:

[0025]

[0026] Among them, p i (s) and User Groups The number of invalid user cooperation sets of size s that span multiple user groups and the number of user groups The size of s is the number of invalid user cooperation sets.

[0027] Preferably, since the information lengths corresponding to user cooperation sets of different sizes are also different, the number of invalid user cooperation sets should be calculated for all possible user cooperation set lengths s. After selecting h user groups, the ath user group is recorded as Assign the user cooperation set size s to each user group, and the length assigned to the a-th user group is recorded as l a ,for

[0028]

[0029] For each user group, the length is l a The number of invalid user cooperation sets is Then the number of invalid user cooperation sets of size s across multiple user groups contributed by these h user groups is

[0030]

[0031] For all h and other choices, and all distributions of user cooperation set lengths, summing up the results yields the number p of invalid user cooperation sets of size s across multiple user groups. i (s);

[0032] Therefore, the effective user cooperation set of size s has a total of

[0033]

[0034] For all user cooperation sets of size s∈{K i ,K i -1,…,2}, get the length of the information sent by the server

[0035]

[0036] When s=1, each user cooperation set is the user itself, and the server transmits to user k That is, there is a total of K parts that no user has cached. i users, the total size of this part of information is

[0037]

[0038] Preferably, the grouping precision adjustment is specifically as follows:

[0039] For P i Smaller user groups prefer to maintain the status quo and are more interested in P i For larger user groups, we prefer to merge them with similar user groups to explore more multicast opportunities. Specifically, we define a grouping accuracy function Δ(i) to determine which user groups will be merged. The input of the function is the user group number i. All user groups with the same output will be merged. Three different grouping accuracy functions are used, namely

[0040] Δ original (i)=i,

[0041]

[0042] Δ non-grouping (i)=1

[0043] Among them, Δ original (i) Keep the original grouping without any merging; Δ α (i) is the logarithmic grouping function, α is an adjustable parameter used to adjust the grouping accuracy. The larger α is, the lower the grouping accuracy is, that is, more user groups will be merged; Δ non-grouping (i) is Δ αIn (i), when α approaches infinity, all user groups are merged without any grouping. After merging the user groups, the users in the user groups are divided into different user groups to explore multicast opportunities, but P must be recalculated. i The value of P i It is the minimum number of roadside units to which users in the merged user group are connected.

[0044] Therefore, the present invention adopts the above-mentioned coding cache method in the scenario of Internet of Vehicles user cooperation based on the grouping strategy. Compared with the prior art, the present application considers the situation of communication between users for the Internet of Vehicles scenario, and considers the situation that the vehicle can be connected to multiple roadside units at the same time, so that it is more consistent with the actual application scenario, and proposes a service phase strategy based on user grouping. For all users connected to a roadside unit, the users are divided into different groups according to the number of roadside units to which the users are connected, so that users connected to multiple roadside units can make requests to each roadside unit at the same time, thereby reducing the transmission rate required by a single roadside unit. In each user group, a calculation method for invalid user cooperation sets is given, thereby obtaining an upper bound of the system bit rate. On the other hand, since user grouping will lead to the loss of multicast opportunities, we propose several grouping strategies, by adjusting the grouping accuracy, merging some user groups, and seeking a compromise between the consistency of the length of the coding information in the user cooperation set and the multicast opportunity, so as to obtain a lower system bit rate. In general, the characteristics of the proposed system model are optimized accordingly, the expected bit rate R(M) required by the system is reduced, and better system performance is obtained.

[0045] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of a multi-access coding cache system model with L=2 in the background technology of the present invention;

[0047] Figure 2 It is a schematic diagram of the system model of the present invention;

[0048] Figure 3 This is a schematic diagram of the results of N=100, M=30 in the system model simulation of the present invention;

[0049] Figure 4 This is a schematic diagram of the number of times that three grouping strategies have the best performance on different user cache sizes M in 1000 simulations of an embodiment of the present invention;

[0050] Figure 5 Schematic diagram of average bit rates under three grouping strategies in 1000 simulations of an embodiment of the present invention;

[0051] Figure 6 This is a schematic diagram of the effect of parameter α in the logarithmic grouping strategy on system performance in 1000 simulations of an embodiment of the present invention;

[0052] Figure 7 This is a schematic diagram of the number of times the parameter α in the logarithmic grouping strategy has the best performance on different user cache sizes M in 1000 simulations of an embodiment of the present invention. DETAILED DESCRIPTION

[0053] Example

[0054] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution and advantages of the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention. Figure 4 In 1000 simulations of the embodiments of the present invention, three grouping strategies are clearly and completely described in different user caching schemes. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Generally, the components of the embodiments of the present invention described and shown in the figures herein can be arranged and designed in various different configurations.

[0055] A coding cache method in a user cooperation scenario of an Internet of Vehicles based on a grouping strategy includes:

[0056] 1. System Model

[0057] This embodiment considers a decentralized, multi-server system model for neighboring user cooperation scenarios, such as Figure 2 As shown, it includes several roadside units, each of which can access a file set containing all N files. The size of each file is F bits, each roadside unit is connected to several users, each user is equipped with a cache that can cache M files, each user can be connected to one or more roadside units during the service phase, and adjacent users can communicate with each other, that is, each user can access the caches of two adjacent users. This embodiment focuses on a roadside unit in the system and the users it is connected to, and the number of users connected to the roadside unit is assumed to be K. Consider a decentralized coding cache system, that is, in the placement phase, each user independently and randomly selects a part of each file for caching. In the service phase, each user can connect to one or more roadside units according to its own conditions, and synchronize its cache information, request information and connection information with the roadside unit. The roadside unit generates corresponding coding information based on this information and sends it to the user. The user decodes and obtains the required file content based on the received coding information.

[0058] 2. Service stage design

[0059] 2.1 User Grouping Strategy

[0060] Since each user may be connected to a different number of RSUs, a grouping strategy is considered in the service phase, that is, users are divided into different groups according to the number of RSUs they are connected to. The users in each group are connected to the same number of RSUs, and multicast opportunities are mined in each user group.

[0061] In the service phase, users connected to multiple RSUs can evenly send requests to different RSUs to reduce the load of a single RSU. That is, the number of bits requested by user k in the service phase is F(1-M / N), and the number of RSUs connected to user k is I. k , then the number of bits it requests from each RSU is F(1-M / N) / I k For RSUs, since the number of bits requested by users connected to different numbers of RSUs is different, we group users according to the number of RSUs they are connected to and explore multicast opportunities in each user group. Suppose among the users connected to RSUs, the number of users connected to i RSUs is K i ,i∈{1,2,…,I}, where I is the number of roadside units connected to the user with the largest number of roadside units. We divide the users into I groups, and the users in the i-th group are connected to P i =i roadside units, denoted as The number of users in each user group is K i .

[0062] Considering that adjacent users can share their caches, and each user group contains adjacent or non-adjacent users, let a user group The user in According to whether these users are consecutively adjacent in the system, they can be further divided into multiple user groups, and all consecutively adjacent users are grouped together. That is, after numbering the users according to their positions during the service phase, all users with consecutive integer numbers are grouped together. If the user group Can be divided into J i user groups, and these user groups are

[0063] In the service phase, a message is generated for each user cooperation set and sent to the users in it, except for those user cooperation sets consisting of only several adjacent users, because the users in them can always obtain the corresponding information from their adjacent users. Table 1 shows the specific implementation process of the service phase.

[0064] Table 1

[0065]

[0066] Here is an expected bit rate upper bound of the system proposed in this embodiment:

[0067]

[0068] Among them, p i (s) and User Groups The number of invalid user cooperation sets of size s that span multiple user groups and the number of user groups The size of s is the number of invalid user cooperation sets, as follows:

[0069] For a user group A user collaboration set satisfy Server transmission information When the file size F is large enough, its expected length is

[0070]

[0071] Considering that there are adjacent users in the user group, when the user cooperation set consists of only a few adjacent users, the information generated by the user cooperation set is useless to all the users in it. Specifically, in a user group, the invalid user cooperation set with a length of s has a total of

[0072]

[0073] , among which

[0074]

[0075] Users in the invalid user cooperation set may also span multiple user groups. In a user group, select any h∈{2,3,…,J i} user groups, and an invalid user cooperation set is randomly selected from each user group. The union of these invalid user cooperation sets is also an invalid user cooperation set. Since the information lengths corresponding to user cooperation sets of different sizes are also different, we need to calculate the number of invalid user cooperation sets for all possible user cooperation set lengths s. That is, after selecting h user groups, the ath user group is recorded as Assign the user cooperation set size s to each user group, and the length assigned to the a-th user group is recorded as l a ,Right now

[0076]

[0077] For each user group, where the length is l a The number of invalid user cooperation sets is Then we can get the number of invalid user cooperation sets of size s across multiple user groups contributed by these h user groups as

[0078]

[0079] For all possible choices of h and all possible ways of allocating the lengths of user cooperation sets, summing up the results gives the number of invalid user cooperation sets p of size s across multiple user groups. i (s), the specific calculation process is given in algorithm \ref{alg:ch4_p}.

[0080]

[0081] In summary, the effective user cooperation set of size s has

[0082]

[0083] For all user cooperation sets of size s∈{K i ,K i -1,…,2}, you can get the length of the information that the server needs to send

[0084]

[0085] When s=1, each user cooperation set is the user itself, and the server transmits to user k That is, there is a total of K parts that no user has cached. i users, the total size of this part of information is

[0086]

[0087] Therefore, for all user groups, the above-mentioned normalized system expected bit rate upper bound in the service phase can be obtained.

[0088] also, In a user group In the above equation, the number of invalid user cooperation sets of length s, i.e., all users are adjacent to at least one other user in the cooperation set. In fact, the problem can be described as a combinatorial problem, i.e., From the consecutive integers, select s integers to form a set for For every number k in the set, there is at least one adjacent integer, that is, k-1 or k+1 exists in the set Our goal is to obtain all possible sets The problem is equivalent to inserting s elements into In each of the s gaps, at least two elements need to be inserted to satisfy the above conditions. We divide the s elements into i groups, Each group contains at least two elements, so the problem is transformed into inserting i elements into Each gap can only insert one element. For this problem, there are

[0089]

[0090] This is the first term in the equation.

[0091] Secondly, we also need to determine how many options there are when dividing l users into i groups. The result of this problem is the function f, which calculates the number of feasible options when putting b elements into a positions, and each position can hold any number of elements. Obviously, when a = 1, we have only one option, namely

[0092] f(1,b)=1.

[0093] When a>1, we solve the problem recursively, first focusing on the first position. If no element is placed in this position, then all b elements need to be placed in the remaining a-1 positions, that is, there are f(a-1,b) options. If 1 element is placed in this position, then the remaining b-1 elements need to be placed in the remaining a-1 positions, that is, there are f(a-1,b-1) options, and so on until all b elements are placed in the first position. It can be obtained

[0094]

[0095] Note that f(j-1,0)=f(j-2,0)=…=f(1,0)=1, which gives the number of choices when we place all elements in the first position.

[0096] Back to the problem of calculating how many choices there are when dividing l users into i groups, since each group (i.e. each position) requires at least two elements, we need to first place two elements in each position, and then place the remaining s-2i elements, that is, calculate the number of choices of placing s-2i elements in i positions, i.e. f(i,s-2i), which is the second term in the formula. This is the proof.

[0097] 2.2 Grouping Precision Adjustment

[0098] In fact, the grouping method will cause us to lose some multicast opportunities in the service phase, that is, we can only explore multicast opportunities within the user group, while the multicast opportunities between user groups are lost. In the above discussion, we divided the users connected to different numbers of roadside units into different groups. This grouping method will obtain the largest number of groups and ensure that the information lengths corresponding to the user cooperation sets in the user group are equal, but more inter-group multicast opportunities may be lost. Therefore, we consider reducing the grouping accuracy so that some users who are connected to different but close numbers of roadside units are grouped in the same group, thereby exploring more multicast opportunities and further reducing the system bit rate.

[0099] It is easy to see that the more roadside units a user is connected to, the closer the information lengths corresponding to the user cooperation sets in the user group connected to a similar number of roadside units are. That is, when P i and P j When the value is close, P i and P j The larger the value, the closer the ratio of the two is to 1. At this time, the users of the two user groups are divided into one group, and the loss caused by the different message lengths is smaller. For example, when P i =1,P j =2, the ratio of the two is P i / P j is 0.5, and when P i =5,P j When =6, the ratio between the two is 0.83, which is closer to 1.

[0100] Therefore, for P i For smaller user groups, we prefer to maintain the status quo, and for P i For larger user groups, we prefer to merge them with similar user groups to explore more multicast opportunities. Specifically, we define a grouping accuracy function Δ(i) to determine which user groups will be merged. The input of the function is the user group number i. All user groups with the same output will be merged. We give three different grouping accuracy functions, namely

[0101] Δ original (i)=i,

[0102]

[0103] Δ non-grouping (i)=1.

[0104] where Δ original (i) Keep the original grouping, that is, do not perform any merging; Δ α (i) is a logarithmic grouping function, where α is an adjustable parameter used to adjust the grouping accuracy. The larger α is, the lower the grouping accuracy is, that is, more user groups will be merged; Δnon-grouping (i) is Δ α (i) When α approaches infinity, all user groups are merged, that is, no grouping is performed. After merging the user groups, we still divide the users in the user group into different user groups according to the original method to explore multicast opportunities, but at this time, the bit rate P i The value of needs to be recalculated, that is, P i It is the minimum number of roadside units to which users in the merged user group are connected.

[0105] 3. Simulation results

[0106] First, we show an intuitive result. The system parameters are N=100, K=30, and the user is connected to a maximum of 7 roadside units. The specific connection conditions and user groups are as follows:

[0107]

[0108] like Figure 3 As shown, the system bit rate under different grouping strategies is shown, where Δ original is the original grouping strategy, that is, no merging is performed, Δ α is a logarithmic grouping strategy, where α is 2, \Δ non-grouping In order to not perform any grouping, all users are merged into one group. As can be seen from the figure, the grouping scheme has better performance than the non-grouping scheme in the area where M is smaller. As M increases, the scheme with lower grouping accuracy performs better until the non-grouping scheme maintains the best performance.

[0109] When M is small, since the cache of neighboring users that each user can access is also small, the performance loss caused by the loss of inter-group multicast opportunities is small. We can think that this part of the performance loss is caused by not considering the cache of users outside the group that some users can access when generating information for some user cooperation sets. However, the load balancing effect brought by the grouping strategy has a greater impact on system performance at this time, so the original grouping strategy has better performance. As M increases, the amount of this part of information gradually increases, and the impact on system performance also gradually increases. Therefore, the strategy with low grouping accuracy has better performance at this time. As M further increases, the impact of this part of information on system performance gradually dominates, so the non-grouping strategy has the best performance.

[0110] Next, we simulated the same system settings multiple times, and the system parameters were set as follows: the number of files N = 50, the number of users K = 20, and the maximum number of roadside units that users can connect to I max =7, the number of RSUs to which each user is connected is randomly generated and follows a normal distribution, i.e.

[0111]

[0112] Take the absolute value of the result, round it off, and limit it within the range of [1, I max to obtain the number of roadside units to which the user is connected. Since N > K, we assume that each user requests a different file. 1000 simulations are performed on this system parameter to obtain the following results.

[0113] Figure 4 Shows the number of times that among the 1000 simulations, the three grouping strategies Δ original , Δ α , Δ non-grouping have the best performance on different user cache sizes M. The same as the above conclusion, when M < 0.02N approximately, the performance of the original grouping strategy is generally the best; when 0.02N < M < 0.14M approximately, the logarithmic grouping strategy with α = 2 can sometimes achieve the best performance; when M > 0.14N, the non-grouping strategy generally has the best performance.

[0114] Figure 5 Shows the average code rate under the three grouping strategies obtained after 1000 simulations. It can be seen that in terms of the average code rate, the performance of the logarithmic grouping strategy with α = 2 is worse than that of the original grouping strategy and the non-grouping strategy. In addition, since in each user group, the number of user cooperation sets grows exponentially with the number of users, as the grouping precision decreases, the computational overhead required by the system in the service phase will also increase, which may lead to an increase in communication delay in practical applications. Therefore, even if the non-grouping strategy has better performance in terms of code rate when M is large, in practical applications, the computational overhead and communication overhead can be comprehensively considered to select a suitable grouping strategy.

[0115] Next, show the influence of the parameter α on the system performance in the logarithmic grouping strategy. To better show its influence, let the maximum value I of the number of roadside units to which the user is connected max = 24, and perform 1000 simulations as well.

[0116] Figure 6 Shows the average code rate of the logarithmic grouping strategy with different α after 1000 simulations, Figure 7 Shows the number of times that the logarithmic grouping strategy with different α has the best performance on different user cache sizes M. It can be seen from the figure that when M is small, the logarithmic grouping strategy with a smaller α has better performance. As M increases, the logarithmic grouping strategy with a larger α has better performance, which is consistent with the above conclusion. The difference is that the strategy with a larger α does not necessarily have better performance, but only has better expected performance in terms of probability, indicating that in practice, it is necessary to select a suitable α value according to specific system parameters such as user connection situation and user cache size.

[0117] Therefore, the present invention adopts the above-mentioned coding caching method based on the grouping strategy in the Internet of Vehicles user cooperation scenario, takes into account the communication between users for the Internet of Vehicles scenario, and considers the situation that the vehicle can be connected to multiple roadside units at the same time, making it more in line with the actual application scenario.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A coding cache method in a user cooperation scenario of an Internet of Vehicles based on a grouping strategy, characterized in that: The following steps are involved: S1. Build a system model, including several roadside units, each of which accesses a file set containing all N files The size of each file is F bits, each roadside unit is connected to several users, and each user is equipped with a cache for caching M files; S2. Build and run a decentralized encoding cache system, including During the placement phase, each user independently and randomly selects a portion of each file to cache; During the service phase, each user connects to several roadside units according to its own conditions, and synchronizes its cache information, request information and connection information with the roadside units. The roadside units generate corresponding coding information based on the above information and send it to the user. The user decodes the received coding information and obtains the required file content.

2. The coding cache method in the scenario of Internet of Vehicles user cooperation based on grouping strategy according to claim 1 is characterized in that: During the service phase, each user is connected to several roadside units, and adjacent users communicate with each other, and each user can access the caches of its two adjacent users.

3. The coding cache method in the scenario of Internet of Vehicles user cooperation based on grouping strategy according to claim 1 is characterized in that: During the service phase, user grouping strategy and grouping precision adjustment are adopted. The user grouping strategy is to divide users into different groups according to the number of roadside units they are connected to. The users in each group are connected to the same number of roadside units, and multicast opportunities are explored in each user group. The grouping precision adjustment is to reduce the grouping precision so that some users who are connected to different but close numbers of roadside units are grouped in the same group to explore more multicast opportunities.

4. The coding cache method in the scenario of Internet of Vehicles user cooperation based on grouping strategy according to claim 3 is characterized in that: The specific user grouping strategy is: suppose that among the users connected to the roadside unit, the number of users connected to i roadside units is K i ,i∈{1,2,…,I}, where I is the number of roadside units connected to the user with the largest number of roadside units; the users are divided into I groups, and the users in the i-th group are connected to P i =i roadside units, denoted as The number of users in each user group is K i ; Since adjacent users share their caches, and there are adjacent or non-adjacent users in each user group, let a user group The user in According to whether these users are consecutively adjacent in the system, they are further divided into several user groups. All consecutively adjacent users are grouped together. After numbering the users according to their positions during the service phase, all users with consecutive integer numbers are grouped together. If the user group Subdivided into J i user groups, and these user groups are In the service phase, a message is generated for each user cooperation set and sent to the users in it, except for the user cooperation set consisting of only several neighboring users.

5. The coding cache method in the scenario of Internet of Vehicles user cooperation based on grouping strategy according to claim 1 is characterized in that: The upper bound of the expected bit rate of the coding cache system in step S2 is: Among them, p i (s) and User Groups The number of invalid user cooperation sets of size s that span multiple user groups and the number of user groups The size of s is the number of invalid user cooperation sets.

6. The coding cache method in the scenario of Internet of Vehicles user cooperation based on grouping strategy according to claim 5 is characterized in that: Since the information lengths corresponding to user cooperation sets of different sizes are also different, the number of invalid user cooperation sets should be calculated for all user cooperation set lengths s. After selecting h user groups, the ath user group is recorded as Assign the user cooperation set size s to each user group, and the length assigned to the a-th user group is recorded as l a ,for For each user group, the length is l a The number of invalid user cooperation sets is Then the number of invalid user cooperation sets of size s across multiple user groups contributed by these h user groups is For all h and other choices, and all distributions of user cooperation set lengths, summing up the results yields the number p of invalid user cooperation sets of size s across multiple user groups. i (s); Therefore, the effective user cooperation set of size s has a total of For all user cooperation sets of size s∈{K i ,K i -1,…,2}, get the length of the information sent by the server When s=1, each user cooperation set is the user itself, and the server transmits to user k That is, there is a total of K parts that no user has cached. i users, the total size of this part of information is 7. The coding cache method in the scenario of Internet of Vehicles user cooperation based on grouping strategy according to claim 3 is characterized in that: The specific adjustment of grouping precision is as follows: Define a grouping accuracy function Δ(i) to determine which user groups will be merged. The input of the function is the user group number i. All user groups with the same output will be merged. Three different grouping accuracy functions are used, namely Δ original (i)=i, D non-grouping (i)=1 Among them, Δ original (i) Keep the original grouping without any merging; Δ α (i) is the logarithmic grouping function, α is an adjustable parameter used to adjust the accuracy of grouping; Δ non-grouping (i) is Δ α In (i), when α approaches infinity, all user groups are merged without any grouping. After merging the user groups, the users in the user groups are divided into different user groups to explore multicast opportunities, but P must be recalculated. i The value of .

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