CN115086427B - Edge cache content placement method for satellite-ground integrated collaborative sharing network - Google Patents

Abstract

The present invention provides a method for placing edge cache content oriented to a satellite-ground integrated collaborative sharing network, which includes the following steps: Step 1: first set the cache matrix of the auxiliary satellite to 0, and when the number of SBS n and the number of cache code packets of the main satellite is equal to When the sum is less than K , the ground sharing link is enabled, and then the content file placement matrix m _i,n of SBSs is obtained by maximizing the overall transmission traffic, and then the main satellite cache placement matrix is obtained by minimizing the overall transmission energy consumption; Step 2: Get SBSs After placing the matrix with the cache of the main satellite, it is extended to the multi-satellite satellite-ground integrated collaborative sharing network, and the shared variable ɑ _i is introduced. When the value of the shared variable ɑ _i is 0, it means that only the ground shared link is enabled. At this time, the cache in the auxiliary satellite The placement matrix is cleared. When the value of the shared variable ɑ _i is 1, it means that only the inter-satellite shared link is enabled. At this time, the cache placement matrix in the SBSs is cleared. The beneficial effects of the invention are: the invention realizes the maximization of the energy efficiency of the satellite-ground integrated collaborative sharing network.

Description

Edge cache content placement method for satellite-ground integrated collaborative sharing network

technical field

The invention relates to the field of satellite technology, in particular to an edge cache content placement method for a satellite-terrestrial integrated collaborative sharing network.

Background technique

With the explosive growth of mobile data traffic, the 6G vision further expands the scope and depth of network coverage, which is a challenge for traditional terrestrial cellular networks. The satellite-to-ground integrated network has wide coverage and seamless access capabilities, which can significantly improve the performance of terrestrial cellular networks, and is an inevitable trend in future technology development.

According to the characteristics of data traffic, that is, the main part of the increased mobile communication traffic is the repeated download of some popular content items from remote servers, using Mobile Edge Caching and delivery technology (Mobile Edge Caching, MEC), the popular content items can be cached In the intermediate server, the content is brought closer to the user, resulting in less delivery delay, so as to significantly improve the quality of user experience, and save the transmission resource consumption of the backhaul and the core network.

The terrestrial backhaul is a multi-hop unicast network, so cached content must traverse multiple links and must be transmitted to each base station individually. Satellite systems, on the other hand, can provide broadband backhaul links and operate in multi/broadcast mode, covering a wide area. Therefore, combining caching and satellite communication technology to deal with typical application scenarios such as wide-area or global hotspot video push and popular content distribution can further alleviate the load pressure on the ground core network and improve the seamlessness of cross-domain real-time information sharing. seam links and broadband communication capabilities.

However, the satellite-ground integrated network cache and energy resources are limited, and the infrastructure is complex, which seriously increases the burden on the backhaul link and reduces energy efficiency. Encoding caching is an emerging technology to improve caching efficiency. Using encoding technology in the caching process, each file can be divided into multiple segments, and different segments of the file can be strategically cached at the edge nodes. Compared with traditional coding schemes, rateless encoding has lower coding redundancy. The so-called rateless means that the encoded data blocks can be continuously generated like a fountain, so the encoded data does not have a specific rate.

With the gradual implementation of plans for Low Earth Orbit (LEO) satellite constellations such as Oneweb and Starlink, LEO satellites with low latency and high bandwidth will serve as access satellites for the future 6G space-space-ground integrated network to communicate with ground base stations. More and more researches consider low-Earth orbit satellite constellations when designing and implementing high-performance radio access networks (Radio Access Networks, RANs), but most studies focus on the transmission of multi-satellite satellite-ground integrated networks. Latency and transmission cost, and the transmission is a complete file, does not involve encoding cache technology, or only applies encoding cache technology to a single-satellite satellite-ground integrated network.

Contents of the invention

The present invention provides an edge cache content placement method oriented to a satellite-ground integrated collaborative sharing network, comprising the following steps:

达到K-m_i,n时，文件f_i地面共享链路被禁用，再调整SBSs的内容文件放置矩阵m_i,n使总体传输能效达到最优。Step 1: First, set the cache matrix of the auxiliary satellite to 0, and when the sum of SBS n and the number of cached coded packets of the primary satellite is less than K, enable the ground sharing link, and then obtain the content file placement matrix of SBSs by maximizing the overall transmission traffic m _i,n , and then obtain the main satellite cache placement matrix by minimizing the overall transmission energy consumption, when the number of encoded packets cached in the main satellite

When Km _i,n is reached, the file f _i ground sharing link is disabled, and then the content file placement matrix m _i,n of SBSs is adjusted to optimize the overall transmission energy efficiency.

Step 2: After obtaining the cache placement matrix of SBSs and main satellites, expand to the multi-satellite satellite-ground integrated cooperative sharing network, and introduce the shared variable a _i , when the value of the shared variable a _i is 0, it means that only the ground shared link is enabled. The cache placement matrix in the time-assisted satellite is cleared, and when the shared variable ai takes a value of 1, it means that only the inter-satellite shared link is enabled. At this time, the cache placement matrix in the SBSs is cleared, and then the genetic algorithm is used to solve the cache of all SBSs and LEO satellites Place the matrix.

As a further improvement of the present invention, in said step 1, it also includes:

Step S1: Initialization.

时，对每个SBS，按照文件集/>

中的顺序，依次放置文件f_i，若m_i,n+1＜K且满足功率大小约束，则m_i,n＝m_i,n+1，其中，/>

代表文件集，M^T代表每个SBS中最多能存储的编码包个数，m_i,n表示缓存在SBS n中的文件f_i的编码包数量，i代表文件集中的文件序号。Step S2: Place the coded packets in the SBS; when the cache size constraint is satisfied

When, for each SBS, according to the document set />

In order, place files f _i sequentially, if mi _,n +1<K and satisfy the power size constraint, then mi _,n =m _i,n +1, where, />

Represents a file set, M ^T represents the maximum number of coded packets that can be stored in each SBS, m _{i, n} represent the number of coded packets of file f _i cached in SBS n, and i represents the file sequence number in the file set.

Step S3: Place the coding packets in the main satellite, and adjust the coding packet placement in the SBS at the same time.

Step S4: Update

Step S5: Repeat steps S1-S4 until the change of η is less than σ and stop iteration.

As a further improvement of the present invention, in the step S1, the initialization specifically includes:

同时置0；其中m_i,n表示缓存在SBS n中的文件f_i的编码包数量，/>

表示缓存在主卫星中的编码包数量。Step S10: set m _i,n ,

Set to 0 at the same time; where m _{i, n} represent the number of coded packets of the file f _i cached in SBS n, />

Indicates the number of encoded packets buffered in the main satellite.

Step S11: Sort the file popularity of each community from high to low to obtain the file set

的值对整片区域的文件流行度由高到低排序，得到文件集/>

其中U_i,n表示SBS n中请求文件f_i的平均用户数，/>

表示小区集，U_i表示整片区域中请求文件f_i的平均用户数。Step S12: According to

Sort the file popularity of the entire area from high to low, and get the file set />

where U _i,n represents the average number of users requesting file f _i in SBS n, />

Represents a cell set, and U _i represents the average number of users requesting file f _i in the entire area.

Step S13: Setting the initial value η ₀ of the total energy efficiency η.

As a further improvement of the present invention, in the step S3, it specifically includes:

中的顺序，在主卫星中依次放置文件f_i，当满足缓存大小约束

时，其中M^S表示卫星中最多能缓存的编码包数，执行如下操作：首先计算SBSs中文件f_i编码包最少缓存数减少1，同时文件f_i'编码包最少缓存数增加1，产生的能耗变化/>

如果/>

且满足

和功率大小约束，则/>

直到/>

计算according to file set

In the sequence, place files f _i sequentially in the main satellite, when the cache size constraint is satisfied

, where M ^S represents the maximum number of coded packets that can be cached in the satellite, and the following operations are performed: first calculate the minimum cached number of file f _i coded packets in the SBSs and decrease by 1, while the minimum cached number of file f _i' coded packets increases by 1, resulting in Changes in energy consumption/>

if />

and satisfied

and power size constraints, then />

until />

calculate

如果δ₂＞ηδ₁，并且/>

满足功率大小约束，则/>

m_i,n＝m_i,n-1，m_i',n＝m_i',n+1。

If δ ₂ >ηδ ₁ , and/>

Satisfy the power size constraint, then />

m _i,n =m _i,n -1, m _i',n =m _i',n +1.

As a further improvement of the present invention, in the steps S2 and S3, the formula of the power size constraint is as follows:

As a further improvement of the present invention, in the step 2, using the genetic algorithm to solve the cache placement matrix specifically includes:

Step 20: Initialize parent population G=0.

Step 21: Calculate the fitness, that is, the total energy efficiency of the system.

Step 22: Randomly select individuals with higher fitness in the parent population, compare the energy efficiency of transferring files f _i among the selected individuals, select a higher value of shared variable a _i , and then under power constraints and cache size constraints, Adjust the number of encoded packages in SBSs and satellites according to the file popularity, and mutate to generate new individuals;

Step 23: Calculate the fitness of the new individual, merge it with the parent population, and then sort according to the fitness from large to small, and keep the top nPop individuals, that is, the next generation population, G=G+1.

Step 24: Determine whether G is greater than the set genetic times GEN, if so, output the individual with the highest fitness, otherwise, repeat steps 22-24.

As a further improvement of the present invention, in the step 20, initializing the parent population specifically includes:

产生第一组共享变量a_i，即当缓存在SBS n中的文件f_i的编码包总数大于K时，共享变量a_i取值0，否则取值1，剩余nPop-1个共享变量a_i随机产生，然后在缓存大小约束和功率约束下，SBSs和辅卫星根据文件流行度由低到高放置剩余所需编码包，产生具有nPop个个体的父代种群。Place vectors m _{i, n} and

Generate the first group of shared variables a _i , that is, when the total number of encoded packets of the file _fi cached in SBS n is greater than K, the shared variable a _i takes the value 0, otherwise it takes the value 1, and the remaining nPop-1 shared variables a _i Random generation, and then under the cache size constraints and power constraints, SBSs and auxiliary satellites place the remaining required encoding packets according to the file popularity from low to high, resulting in a parent population with nPop individuals.

As a further improvement of the present invention, in the steps 20 and 22, the formula of the power constraint is as follows:

The formula of the cache size constraint is as follows:

The beneficial effects of the present invention are: 1. A satellite-ground cooperative sharing transmission strategy proposed by the present invention, and joint placement of ground base stations and cache files on satellites, so as to realize the energy efficiency maximization of the satellite-ground integrated collaborative sharing network; 2. Compared with the single-satellite-satellite integrated cooperative sharing network, the space-based network uses satellite constellations to add an inter-satellite shared link, which can effectively improve the energy efficiency of system transmission.

Description of drawings

Fig. 1 is a schematic diagram of a satellite-ground integrated collaborative sharing network model proposed by the present invention;

Fig. 2 is the comparative schematic diagram of two kinds of transmission strategies and two kinds of caching modes under different popularity parameters of the present invention;

Fig. 3 is the comparative schematic diagram of two kinds of transmission strategies and two kinds of cache modes under the different number of satellites of the present invention;

Fig. 4 is a schematic diagram of the comparison of two transmission strategies and two buffering modes under different inter-satellite distances in the present invention;

Fig. 5 is a comparative schematic diagram of two transmission strategies and two buffering modes under different numbers of SBSs in the present invention;

Fig. 6 is a flowchart of content placement in the present invention.

Detailed ways

In order to make full use of the advantages of the satellite network, the present invention combines the encoding cache technology with the multi-satellite satellite-ground integrated network. Due to the limited resources on the satellite, the present invention considers the sharing of cache resources between the satellite and the terrestrial network, as well as the on-satellite Ground-integrated collaborative transmission, that is, to build a satellite-ground integrated collaborative sharing network, and jointly design the cache content of ground base stations and satellite cache content to ensure accurate push of popular content and improve user service data traffic and overall transmission energy efficiency in different regions . The satellite-ground integrated collaborative sharing network involves two-layer multi-caching nodes, and the design of file caching and transmission strategies is particularly important and complex.

The present invention finds through the research on rateless encoding that its encoding method is to realize data error tolerance by adding redundancy, which needs to meet the MDS characteristics (that is, any k encoded data blocks can restore the original data), and can generate A large number of non-linear data blocks, which provide the possibility for cross-level multi-node caching and transmission of data in a multi-satellite satellite-ground integrated network.

The present invention adopts a rateless encoding method, LT (Luby Transform) encoding, and can obtain infinitely many different encoding packages, and the original file can be restored after receiving K encoding packages. The encoding packets generated by each file are distributed and stored in the ground small base stations (Small Base Stations, SBSs) and LEO satellites.

In the satellite-ground integrated collaborative sharing network, only one main satellite can communicate with the ground equipment, and there are two shared links, one is the ground shared link, and the cached code packets are transmitted to the main satellite through the ground base station, Then the satellite broadcasts to the user; the other is an inter-satellite shared link, which transmits the cached coded packets to the main satellite through the auxiliary satellite (that is, a satellite other than the main satellite). SBSs cooperate with LEO satellites to provide services to ground users, and the remaining required code packets are transmitted through shared links, and then broadcast to ground users through the main satellite.

达到K-m_i,n时，文件f_i地面共享链路被禁用，再调整SBSs的内容文件放置矩阵m_i,n使总体传输能效达到最优。Network energy efficiency optimization involves two parts: transmission traffic and transmission energy consumption. The edge cache content placement method for the satellite-ground integrated collaborative sharing network disclosed in the present invention only has ground sharing links, and an iterative algorithm is used to solve the problem. In the first step, the content file placement matrix m _i,n of SBSs is obtained by maximizing the overall transmission traffic. In the second step, the main satellite cache placement matrix is obtained by minimizing the overall transmission energy consumption. When the number of encoded packets cached in the main satellite

When Km _i,n is reached, the file f _i ground sharing link is disabled, and then the content file placement matrix m _i,n of SBSs is adjusted to optimize the overall transmission energy efficiency.

的基础上，随机产生共享变量a_i，在缓存和功率约束下，SBSs和辅卫星根据文件流行度由低到高放置剩余所需编码包；然后计算适应度，即系统总能效，在父代种群中随机选择两个适应度较大的个体，对比两个个体中传输文件f_i的能效，选择更高的共享变量a_i取值，变异产生新的个体，与父代种群合并排序后产生下一代种群；重复有限次遗传操作，输出最优缓存放置矩阵。After obtaining the cache placement matrix of SBSs and main satellites, it is extended to a multi-satellite satellite-ground integrated collaborative sharing network, and the genetic algorithm is used to solve the cache placement matrix of all SBSs and LEO satellites. When the value of the shared variable a _i is 0, it means that the ground sharing link is enabled. At this time, the cache placement matrix in the auxiliary satellite is cleared. When the value of the shared variable a _i is 1, it means that the inter-satellite shared link is enabled. At this time, The cache placement matrix is cleared. First initialize the parent population, the m _{i, n} and

On the basis of , the shared variable a _i is randomly generated. Under the cache and power constraints, SBSs and auxiliary satellites place the remaining required encoding packages according to the file popularity from low to high; then calculate the fitness, that is, the total energy efficiency of the system, in the parent generation Randomly select two individuals with high fitness in the population, compare the energy efficiency of transferring files f _i in the two individuals, select a higher value of the shared variable a _i , mutate to generate new individuals, and merge and sort with the parent population to generate Next-generation population; repeat the genetic operation for a limited number of times, and output the optimal cache placement matrix.

每个SBSs覆盖的小区不重叠，关联用户集为/>

多颗处于相同轨道高度的LEO卫星共同服务一片地面区域，只有一个LEO卫星与地面设备通信，称之为主卫星，其余均为辅卫星，假设只有相邻卫星之间才可以通信，LEO卫星集为/>

文件集为/>

文件在SBS n中的流行度μ_i,n呈Zipf分布(满足Zipf定律的离散概率分布，即一个项目的频率与其在频率表中的排名成反比；Zipf分布中文叫齐夫分布，哈佛大学的语言学家乔治·金斯利·齐夫(George Kingsley Zipf)于1949年发表的实验定律。它可以表述为：在自然语言的语料库里，一个单词出现的频率与它在频率表里的排名成反比。)：As shown in Figure 1, the satellite-ground integrated collaborative sharing network model, the cell set is

The cells covered by each SBSs do not overlap, and the associated user set is />

Multiple LEO satellites at the same orbital height serve a ground area together. Only one LEO satellite communicates with ground equipment, which is called the main satellite, and the rest are auxiliary satellites. Assuming that only adjacent satellites can communicate, the LEO satellite set for />

File set is />

The popularity of files in SBS n μ _i,n is Zipf distribution (discrete probability distribution that satisfies Zipf's law, that is, the frequency of an item is inversely proportional to its ranking in the frequency table; Zipf distribution is called Zipf distribution in Chinese, Harvard University's Linguist George Kingsley Zipf (George Kingsley Zipf) published an experimental law in 1949. It can be expressed as: in a natural language corpus, the frequency of a word is proportional to its rank in the frequency table inversely.):

随机独立选择d(1≤d≤k)个源数据包进行位异或，可以得到无穷多个不同的编码包，下载K＝k(1+ε)个编码包就能以一定概率恢复源文件。将每个文件产生的LT编码包分布式存储在SBSs与LEO卫星中。m_i,n表示缓存在SBS n中的文件f_i的编码包个数，/>

表示缓存在LEO卫星l中的文件f_i的编码包个数。Assuming that all files have the same size s, LT encoding is performed on the file, and an infinite number of different encoding packages can be obtained, and the original file can be recovered by receiving K encoding packages. Divide the file _fi into k source packets, each of size

Randomly and independently select d (1≤d≤k) source data packets for bit XOR, and an infinite number of different encoded packets can be obtained. Downloading K=k (1+ε) encoded packets can restore the source file with a certain probability . The LT encoding package generated by each file is distributed and stored in SBSs and LEO satellites. m _{i, n} represent the number of encoded packets of the file f _i cached in SBS n, />

Indicates the number of encoded packets of file f _i cached in LEO satellite l.

SBSs cooperate with LEO satellites to provide services for ground users. When the number of coded packets is insufficient, the shared link is used to transmit the remaining coded packets to the main satellite. In order to maximize the overall transmission energy efficiency of the network, the transmission strategy and buffer matrix of SBSs and LEO satellites need to be carefully designed.

First, set the cache matrix of the auxiliary satellite to 0, that is, only consider the ground sharing link. When the sum of SBS n and the number of cached code packets of the main satellite is less than K, the ground sharing link is enabled. The transmission traffic of SBSs and LEO satellites is ^BT and ^BS respectively, which should satisfy all user requests as much as possible.

The transmission energy consumption of SBSs is P ^T , which is mainly divided into two parts, the power consumed by transmission to users and the power consumed by shared satellites. The transmission energy consumption of LEO is P ^S .

The total network traffic and total energy consumption are respectively:

B ^total ＝B ^T +B ^S

P ^total = P ^T + P ^S

We formulate an energy efficiency optimization problem:

Problem 1

(2), (3) represent the buffer size constraints of SBSs and LEO satellites, (4) represent the transmission power constraints, (5) ensure that both SBSs and LEO satellites cache at least one encoded packet of file f _i , and (6) represent the sharing to The number of encoded packets for a satellite is non-negative.

It is easy to deduce the transmission traffic of transmitting file f _i in SBS n, and the energy consumed by transmitting file f _i in the entire network, respectively:

The optimization problem is a fractional polynomial, which we transform into a linear polynomial

The specific algorithm steps are as follows:

同时置0；对每个小区的文件流行度由高到低排序，得到文件集/>

根据/>

的值对整片区域的文件流行度由高到低排序，得到文件集

设置总能效η的初值η₀。Step S1: Initialization. Also m _i,n ,

Set to 0 at the same time; sort the file popularity of each community from high to low to get the file set />

According to />

Sort the file popularity of the entire area from high to low by the value of , and get the file set

Set the initial value η ₀ of the total energy efficiency η.

时，对每个SBS，按照文件集/>

中的顺序，依次放置文件f_i，若m_i,n+1＜K且满足功率大小约束(4)，则m_i,n＝m_i,n+1。Step S2: Place the encoded packets in the SBS. When cache size constraints are satisfied

When, for each SBS, according to the document set />

In the order in , files f _i are placed sequentially, if mi _,n +1<K and the power size constraint (4) is satisfied, then mi _,n =m _i,n +1.

中的顺序，在主卫星中依次放置文件f_i，当满足缓存大小约束/>

时，执行如下操作：首先计算SBSs中文件f_i编码包最少缓存数减少1，同时文件f_i'编码包最少缓存数增加1，产生的能耗变化/>

如果

且满足/>

和功率大小约束(4)，则/>

直到

计算Step S3: Place the coding packets in the main satellite, and adjust the coding packet placement in the SBS at the same time. according to file set

In the sequence in the main satellite, file f _i is placed sequentially in the main satellite, when the cache size constraint is satisfied />

, the following operations are performed: first calculate the minimum cache number of encoded packets of file f _i in SBSs is reduced by 1, and at the same time the minimum cached number of encoded packets of file f _i' is increased by 1, resulting in energy consumption changes/>

if

and satisfy />

and the power size constraint (4), then />

until

calculate

如果δ₂＞ηδ₁，并且/>

满足功率大小约束(4)，则/>

m_i,n＝m_i,n-1，m_i',n＝m_i',n+1。

If δ ₂ >ηδ ₁ , and/>

Satisfy the power size constraint (4), then />

m _i,n =m _i,n -1, m _i',n =m _i',n +1.

Step S4: Update

Step S5: Repeat steps S1-S4 until the change of η is less than σ and stop iteration.

Through simulation analysis, the present invention has obtained different user densities θ ₀ , different cache sizes M/sI (M represents the number of files cached, and I represents the total number of files) and different file popularity parameters α, different transmission modes and different cache modes The comparison of transmission energy efficiency is to enable terrestrial sharing link and optimized content placement (TS-OPP), enable terrestrial sharing link and most popular content placement (TS-MPP), not enable terrestrial sharing link and optimized content placement ( NS-OPP) without ground share links and Most Popular Content Placement (NS-MPP).

Figure 2 shows the comparison of two transmission strategies and two cache methods under different popularity parameters (the cache size is 10%, and the user density is 100users/km ² ).

Therefore, it can be obtained that for different cache sizes of cache nodes, different user densities, and different file popularity parameters, the proposed transmission strategy and cache method can effectively improve the performance of the system in terms of transmission energy efficiency.

The above analysis is about the cache content placement of the single-satellite satellite-ground integrated collaborative sharing network, which is extended to the multi-satellite scenario, that is, the inter-satellite sharing link is added. At this time, the sharing vector ai is introduced. When the value is 0, it means that only the ground sharing is enabled At this time, the cache placement matrix in the auxiliary satellite is cleared, and when the shared variable a _i takes a value of 1, it means that only the inter-satellite shared link is enabled, and the cache placement matrix in the SBSs is cleared at this time. Re-analyze the transmission traffic and transmission energy consumption of SBSs and LEO satellites. The transmission traffic formula in the network is the same as that in the single-satellite scenario, while the transmission energy consumption of SBSs is:

表示SBS n下行链路传输功率；/>

表示地面共享链路的传输功率，/>

表示通过地面共享链路向主卫星传输的编码包数目，Pr(U_i,n≥1)表示SBS n中至少有一个用户请求文件f_i的概率。in,

Indicates the SBS n downlink transmission power; />

Indicates the transmission power of the ground shared link, />

Indicates the number of encoded packets transmitted to the main satellite through the ground sharing link, and Pr(U _i,n ≥ 1) indicates the probability that at least one user in SBS n requests file f _i .

The main satellite transmission energy consumption is:

表示LEO下行链路传输功率，/>

表示缓存在SBSs中的文件f_i的最小编码包数量，Pr(U_i≥1)表示所有小区中至少有一个用户请求文件f_i的概率。in,

Indicates the LEO downlink transmission power, />

Indicates the minimum number of encoded packets of file f _i cached in SBSs, and Pr(U _i ≥ 1) represents the probability that at least one user requests file f _i in all cells.

The energy consumption of auxiliary satellite transmission is:

where l represents the number of hops to the main satellite, and satisfies

表示时隙t内传输完/>

个编码包所消耗的星间链路传输功率，W^S是卫星的信道带宽，|σ^S|²表示噪声功率，

Indicates that the transmission is completed within the time slot t />

The inter-satellite link transmission power consumed by a coding packet, W ^S is the channel bandwidth of the satellite, |σ ^S | ² represents the noise power,

表示星间链路衰落系数，仅考虑自由空间损耗，其中H^ISL代表相邻卫星之间的距离，λ是载波的波长。

Indicates the fading coefficient of the inter-satellite link, only free space loss is considered, where H ^ISL represents the distance between adjacent satellites, and λ is the wavelength of the carrier.

Total transmission flow: B ^total ＝B ^T +B ^S

Total transmission energy consumption: P ^total ＝P ^T +P ^S +P ^ISL

Thus the final optimization problem:

s.t.(1)(2)(6)

a _i ∈{0,1} (10)

(7) indicates the buffer size constraint, (8) indicates the transmission power constraint, (9) indicates that at least one coded packet is cached in both the satellite and the SBSs, and (10) indicates that the shared variable takes a value of 0 or 1.

Because it involves two-layer multi-node cache variables, we use the genetic algorithm to solve the cache placement matrix. The specific algorithm steps are as follows:

产生第一组a_i，即当缓存在SBS n中的文件f_i的编码包总数大于K时，a_i取值0，否则取值1，剩余nPop-1个共享变量a_i随机产生。然后在缓存大小约束(7)和功率约束(8)下，SBSs和辅卫星根据文件流行度由低到高放置剩余所需编码包，产生具有nPop个个体的父代种群。Step 20: Initialize parent population G=0. Place vectors m _{i, n} and

Generate the first set of a _i , that is, when the total number of encoded packets of the file _fi cached in SBS n is greater than K, a _i takes the value 0, otherwise it takes the value 1, and the remaining nPop-1 shared variables a _i are randomly generated. Then under the cache size constraint (7) and power constraint (8), SBSs and secondary satellites place the remaining required encoded packets according to the file popularity from low to high, resulting in a parent population with nPop individuals.

Step 21: Calculate the fitness, that is, the total energy efficiency of the system.

Step 22: Randomly select two individuals with higher fitness in the parent population, compare the energy efficiency of transferring files f _i between the two individuals, select a higher shared variable a _i value, and then use the power constraints (8) and Under the cache size constraint (7), the number of encoded packets in SBSs and satellites is adjusted according to the file popularity, and new individuals are mutated.

Step 23: Calculate the fitness of the new individual, merge it with the parent population, and then sort according to the fitness from large to small, and keep the top nPop individuals, that is, the next generation population, G=G+1.

Step 24: Determine whether G is greater than the set genetic times GEN, if so, output the individual with the highest fitness, otherwise, repeat steps 22-24.

Through simulation analysis, the present invention has obtained different satellite parameters, and under different SBSs quantity, does not enable the inter-satellite shared link and enables the inter-satellite shared link, the comparison of different transmission modes and different buffer modes in the transmission energy efficiency, respectively enabling two One Shared Link and Optimized Content Placement (TSS-OPP), Two Shared Links Enabled and Most Popular Content Placement (TSS-MPP), Only Intersatellite Shared Link and Optimized Content Placement (SS-OPP), Intersatellite Only Shared Links and Most Popular Content Placement (SS-MPP). As shown in Figure 3-5, the comparison of two transmission strategies and two buffering methods under different numbers of satellites in Figure 3 (satellite buffer size is 20%, inter-satellite distance is 100km, and the number of SBSs is 5); The comparison of two transmission strategies and two buffering methods under the inter-distance distance (the number of satellites is 5, the satellite buffer size is 20%, and the number of SBSs is 5). Figure 5 is the comparison of two transmission strategies and two buffering methods under different numbers of SBSs (The number of satellites is 5, the satellite cache size is 20%, and the inter-satellite distance is 100km).

Therefore, it can be concluded that for different satellite parameters and different numbers of SBSs, enabling inter-satellite and ground sharing links, and using the proposed cooperative caching method based on genetic algorithm can effectively improve the performance of the system in terms of transmission energy efficiency.

The present invention studies the transmission strategy of the satellite-ground integrated network and the method of placing the edge cache content, considers the limited cache resources and energy resources, and the layered heterogeneity of the system, and combines the coding cache technology to propose a satellite-ground collaboration Share the transmission strategy, and jointly place the cache files on the ground base station and the satellite to maximize the energy efficiency of the satellite-ground integrated collaborative sharing network:

1. First consider the single-satellite satellite-ground integrated network, propose the concept of ground shared link, combine the rateless coding cache, deduce the transmission flow and transmission energy consumption expressions required by the system to meet user needs within a certain period of time, and obtain the system The energy efficiency expression is optimized, and the effectiveness of the proposed cooperative sharing transmission strategy and cache method in improving system transmission energy efficiency is verified.

2. On the basis of the single-satellite scenario, expand to multi-satellite, add the inter-satellite shared link, and then derive a new transmission energy efficiency expression, use the genetic algorithm to strategically select the shared link and cache file, and verify that compared with the single-satellite The satellite-satellite integrated collaborative sharing network, the space-based network uses satellite constellations to add an inter-satellite shared link, which can effectively improve the energy efficiency of system transmission.

Beneficial effects of the present invention: 1. A satellite-ground collaborative sharing transmission strategy proposed by the present invention, and joint placement of ground base stations and cache files on satellites, to realize energy efficiency maximization of satellite-ground integrated collaborative sharing network; 2. The present invention Compared with the single-satellite satellite-ground integrated collaborative sharing network, the space-based network uses satellite constellations to add an inter-satellite shared link, which can effectively improve the energy efficiency of system transmission.

The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be assumed that the specific implementation of the present invention is limited to these descriptions. For those of ordinary skill in the technical field of the present invention, without departing from the concept of the present invention, some simple deduction or replacement can be made, which should be regarded as belonging to the protection scope of the present invention.

Claims (6)

1. The method for placing the edge cache content of the star-to-ground integrated collaboration sharing network is characterized by comprising the following steps of:

step 1: firstly, setting a buffer matrix of an auxiliary satellite to 0, when the sum of the number of the buffer coding packets of the SBSn and the main satellite is smaller than K, starting a ground shared link, and obtaining a content file placement matrix m of the SBSs by maximizing the total transmission flow _i,n Obtaining a main satellite buffer storage placement matrix by minimizing the total transmission energy consumption, and when the number of the coded packets buffered in the main satellite

Reaching K-m _i,n File f _i The ground shared link is disabled and the content file placement matrix m of the SBSs is readjusted _i,n Optimizing the overall transmission energy efficiency;

step 2: after obtaining the buffer memory placement matrix of SBSs and main satellites, expanding the buffer memory placement matrix to a multi-satellite-ground integrated collaboration sharing network, and introducing a sharing variable a _i Shared variable a _i When the value is 0, only the ground shared link is started, at the moment, the buffer memory placement matrix in the auxiliary satellite is cleared, and the variable a is shared _i When the value is 1, only the inter-satellite shared link is started, at the moment, the buffer storage matrix in the SBSs is cleared, and then the buffer storage matrix of all SBSs and LEO satellites is solved by adopting a genetic algorithm;

in the step 1, further includes:

step S1: initializing;

step S2: placing the coding packet in SBS; when the cache size constraint is satisfied

At this time, for each SBS, according to the File set +.>

Sequentially placing files f _i If m _i,n +1 < K and satisfies the power size constraint, then m _i,n ＝m _i,n +1, wherein->

Representing a set of files, M ^T Represents the number of code packets which can be stored at most in each SBS, m _i,n Representing a file f cached in SBSn _i I represents the file sequence number in the file set;

step S3: placing the coding packet in the main satellite, and simultaneously adjusting the placement condition of the coding packet in the SBS;

step S4: updating

Step S5: repeating steps S1-S4 until the variation of eta is less than sigma, stopping iteration;

in the step 2, the method for solving the cache placement matrix by adopting a genetic algorithm specifically comprises the following steps:

step 20: initializing parent population g=0;

step 21: calculating fitness, namely the total energy efficiency of the system;

step 22: randomly selecting individuals with larger adaptability from parent population, and comparing the transmission file f in the selected individuals _i Selecting a higher shared variable a _i Taking values, then adjusting the number of coding packets in SBSs and satellites according to the file popularity under the power constraint and the cache size constraint, and generating new individuals by variation;

step 23: calculating the fitness of new individuals, merging with the parent population, and then sequencing from large to small according to the fitness, and reserving the nPop individuals with the top ranking, namely the next generation population, wherein G=G+1;

step 24: judging whether G is larger than the set genetic times GEN, if so, outputting individuals with the first fitness rank, otherwise, repeating the steps 22-24.

2. The method for placing the content in the edge cache according to claim 1, wherein in the step S1, the initializing specifically includes:

step S10: let m _i,n ,

Simultaneously setting 0; wherein m is _i,n Representing a file f cached in SBSn _i The number of coded packets,/->

Indicating the number of encoded packets buffered in the primary satellite;

step S11: ordering the popularity of the files of each cell from high to low to obtain a file set

Step S12: according to

The popularity of the files in the whole area is sequenced from high to low to obtain a file set

Wherein U is _i,n Representing a request file f in SBSn _i Average number of users, U _i Representing the request file f in the entire region _i Average number of users, +.>

Representing a set of cells;

step S13: setting initial value eta of total energy efficiency eta ₀ 。

3. The method for placing the content in the edge cache according to claim 1, wherein in the step S3, specifically comprising:

according to the file set

Sequentially placing files f in the main satellite _i When the cache size constraint is satisfied

In which M is ^S The number of code packets which can be buffered most in the satellite is represented, and the following operations are performed: first, calculate the file f in SBSs _i The minimum buffering number of the coding packet is reduced by 1, and the file f is simultaneously _i' The minimum buffer number of the coding packet is increased by 1, and the generated energy consumption is changed

If->

And satisfy the following

And a power size constraint, then->

Up to->

Calculation of

If delta ₂ ＞ηδ ₁ And (2) and

satisfying the power size constraint, then +.>

m _i,n ＝m _i,n -1，m _i',n ＝m _i',n +1。

4. The method according to claim 3, wherein in the steps S2 and S3, the formula of the power size constraint is as follows:

5. the method for placing content in an edge cache according to claim 1, wherein in said step 20, initializing a parent population specifically comprises:

according to the buffer storage placement vector m solved under single satellite scene _i,n And

generating a first set of shared variables a _i I.e. when the file f is cached in SBSn _i When the total number of coded packets is greater than K, the variable a is shared _i Take value 0, otherwise take value 1, remaining nPop-1 shared variables a _i Randomly generating, and then under the buffer size constraint and the power constraint, placing the residual needed coding packets by the SBSs and the auxiliary satellites from low to high according to the file popularity, so as to generate a parent population with nPop individuals.

6. The method for edge cache content placement as recited in claim 5, wherein, in said steps 20, 22,

the formula of the buffer size constraint is as follows:

the power constraint is formulated as follows:

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