CN112995979B - Wireless network cache recommendation method for QoE (quality of experience) requirements of user - Google Patents

Wireless network cache recommendation method for QoE (quality of experience) requirements of user Download PDF

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CN112995979B
CN112995979B CN202110238653.3A CN202110238653A CN112995979B CN 112995979 B CN112995979 B CN 112995979B CN 202110238653 A CN202110238653 A CN 202110238653A CN 112995979 B CN112995979 B CN 112995979B
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experience
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CN112995979A (en
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刘越
周一青
刘玲
崔新雨
石晶林
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Institute of Computing Technology of CAS
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W8/00—Network data management
    • H04W8/18—Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/56—Provisioning of proxy services
    • H04L67/568—Storing data temporarily at an intermediate stage, e.g. caching
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/51—Allocation or scheduling criteria for wireless resources based on terminal or device properties

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Abstract

The invention provides a wireless network cache recommendation method, wherein files cached by a base station of a wireless network are integrated into an FcacheFor user u's pair file
Figure DDA0002961277730000011
If the file request is made
Figure DDA0002961277730000012
Not belonging to the set FcacheFrom FcacheIn selecting file fuRecommending to a user u, wherein the recommending method comprises the following steps: step 100: from FcacheSelecting a file set L meeting the requirements of preference degree and quality of experience QoEuWherein the quality of experience QoE is obtained based on fluency experience and content experience; step 200: determination of LuThe number of base stations in the minimum serving cluster for each file; step 300: selecting the recommended file with the least number of service cluster base stations as the recommended file f of the useru. Based on the embodiment of the invention, the bandwidth requirement of a transmission network can be reduced.

Description

Wireless network cache recommendation method for QoE (quality of experience) requirements of user
Technical Field
The present invention relates to wireless communication systems, and more particularly to satisfying caching recommendations for quality of experience (qoe) of a user.
Background
Giving the base station buffering capability is an effective way to generate wireless cellular network traffic pressure against video multimedia traffic. When the user request file is hit in the base station cache, the base station does not need to extract the request file from the core network file library via the transmission network, so that the method based on storage and communication fusion is adopted to reduce the transmission bandwidth requirement of the cellular network. However, due to the limited buffer capacity of the base station, different users have different file preferences, and the hit rate of the user request is low. Meanwhile, the invisibility of the base station cache files to the user is also one of the reasons for the low cache hit rate. Assuming that the file of interest of the user is cached in the base station, but because the content of the base station cache file is unknown to the user and the user request file has a preference space, the user may request another preferred file, and the cache hit rate is reduced. Therefore, how to make the user clearly know the content of the base station cache file, and increasing the visibility of the content of the base station cache file to the user is one of means for improving the cache hit rate of the wireless cellular network.
Considering that a space exists in the user preference file, when the user request file is not cached and hit in the base station, the cache recommendation system recommends the file cached in the base station to the user, so that the visibility of the content of the base station cache file to the user is increased, and the cache hit rate is expected to be improved. However, the cache recommendation system may generate a preference bias between the recommended file and the most preferred file of the user, and when the recommended file does not conform to the user's preference, the user refuses to accept the cache recommendation. Furthermore, even if the recommended file conforms to the user's preference, the user may not receive the recommended file because of poor channel quality and low transmission rate due to interference and fading in wireless transmission. Especially, when the transmission of the cache file is mainly of the video multimedia file type, the user needs to watch the video file smoothly, and the transmission rate must meet the service requirement of the user. Therefore, in order to meet the user service requirements and reduce the transmission bandwidth of the wireless cellular network, the user requirements on the file transmission rate and the preference must be met at the same time. How to recommend files preferred by a user and construct good wireless transmission conditions to improve the file transmission rate is an urgent problem to be solved by a cache recommendation system in a wireless cellular network.
Disclosure of Invention
In view of the above problems, according to a first aspect of the present invention, a method for recommending a wireless network cache is provided, where a set of files cached by a base station of the wireless network is FcacheFor user u's pair file
Figure BDA0002961277710000021
If the file request is made
Figure BDA0002961277710000022
Not belonging to the set FcacheFrom FcacheIn selecting file fuRecommending to a user u, wherein the recommending method comprises the following steps:
step 100: from FcacheSelecting a file set L meeting the requirements of preference degree and quality of experience QoEuWherein the quality of experience QoE is obtained based on fluency experience and content experience;
step 200: determination of LuThe number of base stations in the minimum serving cluster for each file;
step 300: selecting the recommended file with the least number of service cluster base stations as the recommended file f of the useru。
In one embodiment of the invention, wherein said quality of experience QoE is expressed as
Figure BDA0002961277710000023
Wherein, Q (f)u) Receiving a recommendation file f for a user uuQuality of experience QoE, Qr(fu) Is fuExperience of fluency, Qc(fu) Is fuIs used to provide a content experience of (1),
Figure BDA0002961277710000024
to weight fluency experience and content experience in the quality of experience for the user,
Figure BDA0002961277710000025
in one embodiment of the invention, the content experience Q, in which the quality of experience QoEc(fu) Calculated using the following formula:
Qc(fu)＝θ1·ln(θ2·(1-Δu))
wherein, DeltauAs a recommendation file fuResulting deviation of preference, θ1And theta2Is a constant number, theta1＞0，θ2＞0。
In one embodiment of the present invention, wherein L is selected in step 100uAny file f in (2) satisfies the following condition:
1) f is not less than a predetermined psychological threshold theta,
2) content experience Qc(f) Need to be greater than or equal to QminAnd is less than or equal to Qmax，
3) Minimum allowable value of fluency experience
Figure BDA0002961277710000026
Less than or equal to its maximum threshold QmaxWherein
Figure BDA0002961277710000027
Wherein QthTo meet a predetermined threshold of user QoE requirements,
Figure BDA0002961277710000028
to weight fluency experience and content experience in the quality of experience for the user,
Figure BDA0002961277710000029
wherein QminIs Qr(f) And Qc(f) Predetermined minimum allowable value, Qmax＝θ1·lnθ2，θ1And theta2Is a constant.
In one embodiment of the present invention, wherein step 200 comprises:
calculating a minimum allowable transmission rate Rmin(f) According to Rmin(f) And maximum transmission rate R of service clusteru(n) calculating the number n of base stations of the minimum service cluster according to the relation, wherein the number of the base stations satisfies Ru(n)≥Rmin(f) Is a positive integer.
In one embodiment of the invention, wherein the minimum allowed transmission rate Rmin(f) Is calculated by the following formula
Figure BDA0002961277710000031
Wherein R isidealFor an ideal transmission rate, theta, recognized by the user when receiving the recommended file1And theta2Is a constant number, theta1＞0，θ2＞0，
Figure BDA0002961277710000032
In one embodiment of the present invention, wherein step 300 comprises:
step 305: determining whether LuWherein, each file f has service cluster conflict, wherein the service cluster conflict is that a base station which determines to transmit the file exists in the service cluster and the transmission file is different from the f;
step 310: if L isuEach file f in the file system has service cluster conflict, and no file can be recommended;
step 320: if L isuIf the file does not have service cluster conflict, determining whether the only recommended file with the minimum number of service cluster base stations exists in the recommended files without service cluster conflict;
step 330: if the only recommended file with the minimum number of the service cluster base stations exists, selecting the recommended file with the minimum number of the service cluster base stations as the recommended file f of the useru；
Step 340: and if a plurality of recommended files with the minimum number of the base stations in the service cluster are available, selecting files which are possibly received by adjacent users to recommend the files to the users.
In one embodiment of the present invention, wherein step 340 comprises: in the set UnothitThe user U 'nearest to the user U is searched, if the user U' does not determine the service cluster transmission file, the file with the highest preference degree of the user U 'is selected as the recommended file, if the user U' determines the service cluster transmission file, the file with the highest popularity degree is selected as the recommended file, wherein the U isnothitRepresenting a requested file
Figure BDA0002961277710000033
Not belonging to the set FcacheAll of the users of (1).
According to a second aspect of the present invention, there is provided a computer readable storage medium having stored therein one or more computer programs which, when executed, are for implementing the wireless network cache recommendation method of the present invention.
According to a third aspect of the invention there is provided a computing system comprising: a storage device, and one or more processors; wherein the storage device is configured to store one or more computer programs, which when executed by the processor are configured to implement the wireless network cache recommendation method of the present invention.
Compared with the conventional cache content recommendation method, the method provided by the invention can effectively reduce the bandwidth requirement of a transmission network. As the density of base station distribution increases, the reduction in transmission network bandwidth requirements becomes more pronounced. Compared with the method without adopting the cache recommendation, the method can reduce the bandwidth requirement of the transmission network by 35.99 percent.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
figure 1 shows a schematic diagram of a user centric wireless cellular network architecture according to an embodiment of the invention;
FIG. 2 illustrates a QAT method flow diagram according to an embodiment of the present invention;
FIG. 3 illustrates a schematic diagram of service cluster overlap according to an embodiment of the invention;
FIG. 4 shows a transmission bandwidth requirement versus a graph of buffer capacity for different base stations;
fig. 5 shows a transmission bandwidth requirement versus a graph for different base station distribution densities.
Detailed Description
In view of the problems in the background art, the inventors of the present application have studied and proposed a cache recommendation method. In a wireless communication network, a base station usually caches some files frequently requested by a user, when the user requests the files from the base station, if the requested files are cached in the base station, the files are transmitted to the user, if the requested files are not cached in the base station, the base station recommends other files cached in the base station to the user, the recommended files need to meet a QoE condition, the condition comprises a file transmission rate and the preference degree of the user for the recommended files, if no files meeting the condition can be recommended to the user, the request files need to be extracted from a cloud library by a transmission network, and at the moment, the transmission network bandwidth needs to be used. The invention designs a user QoE (quality of experience) demand-oriented cache content recommendation method, particularly considers fading and interference experienced by a recommendation file during transmission in a wireless cellular network, and minimizes the number of users who do not cache the request file in a base station and cannot receive the recommendation file meeting the QoE demand, thereby reducing the bandwidth demand of a transmission network.
Consider a wireless cellular network architecture as shown in fig. 1, consisting of a core network, a base station and a transport network between them. The core network comprises a cloud server and a cloud file library. Suppose there are F video files of the same size in the file library, which constitute a file set F ═ 1, 2. There are R base stations in the cellular network, which form a set of base stations R ═ 1, 2.. R }, and the location compliance parameter of the base stations is λRThe Poisson Point Process (PPP) distribution of (1). Each base station has a cache function, at most omega files can be cached, and omega is less than F due to limited cache capacity. In addition, there are U users in the network, which form a user set U ═ 1,2UPPP distribution of (1). Assume that all R base stations and U users in the network have a single antenna.
The working flow of the wireless cellular network architecture shown in fig. 1 is divided into two phases: a cache file placing stage and a cache file transmission stage. In the stage of placing the cache files, each base station selects files from a file library to place in the cacheIn (1). In the invention, omega files with the highest popularity of the cache of each base station are considered, and the cache files of different base stations are the same, so that on one hand, the probability of the cache hit of the user request file in the base station is improved, and on the other hand, as shown in fig. 1, a cache file transmission method taking the user as the center is considered, and a plurality of base stations jointly transmit the same file to the user, so that the file transmission rate is improved. In the transmission stage of the cache files, U users in the network send requests according to the preference degree of the files in the file library, and the higher the preference degree of the files is, the higher the probability of requesting the files is. Define the user u request file as
Figure BDA0002961277710000051
(u∈U，
Figure BDA0002961277710000052
) Each base station cache file set is Fcache. Therefore, the user set U can be divided into two subsets according to whether the user request file hits in the base station cache, namely the cache hit user set
Figure BDA0002961277710000053
And cache miss user set
Figure BDA0002961277710000054
U＝Uhit∪Unothit. It is assumed that all cache-hit users successfully receive the requested file. For each user U ∈ U with cache missnothitThe system further determines whether the file F can be cached in the base stationcacheFinding out a recommended file f meeting the QoE (quality of experience) of the user uu(fu∈Fcache，
Figure BDA0002961277710000055
QoE of users receiving recommended files is related to fluency experience and content experience). And if the QoE can be found, transmitting a recommendation file to all users meeting the QoE requirement. The invention assumes that all cache miss users have willingness to accept cache recommendation, that is, when the system transmits the information meeting QoE requirement to the cache miss usersWhen the user recommends the file, the user receives the recommended file. For users who cannot receive recommended files meeting QoE requirements, the system extracts user request files from the cloud file library through the transmission network, and transmission bandwidth requirements are increased.
To describe the influence of cache recommendation on network transmission bandwidth requirements, firstly, an expression method of user preference degrees of different files in a file library is introduced. According to an embodiment adopted by the invention, cosine similarity is adopted to describe the similarity of the user U and the file F about all attributes (U belongs to U, F belongs to F), and the larger the cosine similarity is, the higher the degree of the file conforming to the preference of the user is. Definition of Su,fFor the cosine similarity of the user u and the file F, normalizing the cosine similarity of the user u and all F files to obtain the preference degree of the user u to the file F in the file library
Figure BDA0002961277710000061
Figure BDA0002961277710000062
The larger the preference of the user u for the file f, the more frequently the file is requested. Definition of pfThe popularity of the file f is shown, the average preference degree of U users to the file f is shown as
Figure BDA0002961277710000063
Suppose F files in the file library are sorted into p according to the popularity from high to low1≥p2≥...≥pFConsidering the omega files with the highest popularity in the base station cache file library, the base station cache file set FcacheIs shown as FcacheF ≦ f Ω. When the user request file is not cached and hit in the base station, the cache recommendation system recommends the file which is cached in the base station and meets the QoE requirement of the user to the user, so that the file extraction from a cloud file library through a transmission network is avoided, and the bandwidth requirement of the transmission network is reduced. But do notIf so, caching the recommendation may result in a bias in the preference of the recommended file from the user's most preferred file. Definition file
Figure BDA0002961277710000064
Recommending the file F for the file most favored by user u in all F files in the file libraryuResulting deviation of preference ΔuIs shown as
Figure BDA0002961277710000065
Due to the fact that
Figure BDA0002961277710000066
Therefore 0 ≦ Δu＜1。ΔuThe larger the user receives the recommended file fuThe worse the content experience. Defining theta as a psychological threshold of the preference degree of all users to the files, and when the preference degree of the users to the files in the file library is not lower than the psychological threshold, considering that the preference degree of the users to the files is higher. Define the set of user u's favorites as
Figure BDA0002961277710000067
To reduce the preference bias, the invention assumes that the recommendation file f is transmitted to the user uuFrom favorite document set
Figure BDA0002961277710000068
To select.
The following discussion discusses user U ∈ U for cache missesnothitThe process of transmitting the recommended file when the system caches the file set F in the base stationcacheFinding out a recommended file f meeting the u QoE requirement of the useruThen, the file f will be recommendeduAnd transmitting to the user u. The invention selects a cache file transmission method taking a user as a center, and a plurality of base stations closest to the user form a service cluster to jointly transmit the same file to the user, so as to improve the wireless channel condition, improve the file transmission rate and increase the fluency experience of the user in watching the video file. Define the 0-1 variable Sr(u) is equal to {0,1} and is a cache miss for base station rService decision of user u, Sr(u)' 1 indicates that the base station r serves the user u, Sr(u) ═ 0 indicates that the base station r is not serving user u, and the matrix S ═ Sr(u)]R×UAnd making a decision for the service users of all the base stations. At the same time, define the set Φu(n) a set of base stations in the user u serving cluster, set phiuThe base stations in (n) are n base stations (n is more than or equal to 1 and less than or equal to R) which are nearest to the user u, and the recommendation file f is received for the user uuThe set of base stations that generate interference is denoted as R \ phiu(n) of (a). At the same time, according to the service decision Sr(u) is defined byu(n) is represented by phiu(n)＝{r|Sr(u) ═ 1 }. In addition, the variable T is defined to be 0 to 1r(f) E {0,1} is a decision of a transmission file of the base station in the process of cache recommendation, Tr(f) 1 denotes the base station r transmitting the file f, Tr(f) 0 means that the base station r does not transmit the file f, and T is the matrixr(f)]R×FAnd deciding for the transmission files of all the base stations. As the base station in the user u service cluster should transmit the recommendation file f meeting the user u QoE requirementuThus, therefore, it is
Figure BDA0002961277710000071
Tr(fu) 1. Considering that each base station has only a single antenna, each base station is at most buffered file set FcacheIn which a recommended file transmission is selected, i.e.
Figure BDA0002961277710000072
The invention assumes that different base stations use the same time-frequency resource when transmitting the recommendation file to different users, and simultaneously considers large-scale fading and small-scale fading channels between the base stations and the users. Definition PmaxFor maximum transmission power per base station, Du,rIs the transmission distance between user u and base station r, alpha is the path loss coefficient,
Figure BDA0002961277710000073
for a small-scale fading channel between a user u and a base station r, a complex Gaussian distribution with a mean value of 0 and a variance of 1 is obeyed, and a square of zero-mean Gaussian white noise is adoptedAnd (4) poor. Considering that the base stations transmit at the maximum power, when n base stations closest to the user u form a service cluster, the service cluster jointly transmits the recommendation file f to the user uuThen, the user u receives the recommendation file fuHas a maximum signal to interference plus noise ratio of
Figure BDA0002961277710000074
Setting the transmission bandwidth between the base station and the user as W, according to the Shannon formula, the service cluster phiu(n) transmitting the recommendation file f to the user uuHas a maximum transmission rate of
Ru(n)＝W·log2(1+γu(n)) (5)
When the number R of base stations in the network is fixed, the number of elements in the set R is fixed, as shown in (4) and (5), as the number n of base stations in the service cluster increases, the number of interference base stations outside the service cluster decreases, and the maximum transmission rate R of the service clusteru(n) is increased, wherein n is a positive integer. However, considering that each base station transmits at most a single recommended file, different users have differences in file preferences, and when the number n of base stations increases, the probability of collision between service clusters transmitting different recommended files increases, and a user cannot receive recommended files meeting QoE requirements.
As described above, when the user receives the recommended file meeting the QoE requirement due to a cache miss, file extraction from the cloud file repository via the transmission network can be avoided, thereby reducing the transmission bandwidth requirement of the cellular network. When the user receives the cached recommendation file, the quality of experience QoE consists of a fluency experience and a content experience, wherein the fluency experience is related to the transmission rate of the recommendation file, and the content experience is related to the preference bias generated by the recommendation file. Consider a user u receiving a recommendation file fuExperience of fluency Qr(fu) Definition of R (f)u) Transmitting recommendation file f for user u service clusteruRate of (A), RidealAn ideal transmission rate recognized when receiving the recommended file for the user, and subjective cognition (R) for the userideal≥R(fu)). Assuming ideal transmission rate phase for all user approvalAlso, QoE logarithm model, Q, was selectedr(fu) Is shown as
Figure BDA0002961277710000081
Wherein theta is1And theta2As will be explained below. Consider a user u receiving a recommendation file fuContent experience Q ofc(fu) As can be seen from (3), the recommendation file fuResulting deviation of preference ΔuThe smaller, the better the content experience. And Qr(fu) Similarly, the QoE logarithm model, Q, was selectedc(fu) Is shown as
Figure BDA0002961277710000082
In (6) and (7), θ1And theta2Is a constant (theta)1＞0，θ2> 0), the value is according to the recommended video file fuIs determined. Note that R (f)u)≤RidealAnd
Figure BDA0002961277710000083
thus Qr(fu) And Qc(fu) Maximum value of (2) is Qmax＝θ1·lnθ2. At the same time, to ensure that the user receives the recommendation file fuFluency experience and content experience of, define QminIs Qr(fu) And Qc(fu) Is the lowest permissible value of, therefore Qr(fu) And Qc(fu) Should be in the range of QminAnd QmaxIn the meantime. In addition, define
Figure BDA0002961277710000084
To weight fluency experience and content experience in the quality of experience for the user,
Figure BDA0002961277710000085
the larger, the fluency experience pairThe greater the impact of the quality of the user experience. Definition of Q (f)u) Receiving a recommendation file f for a user uuIs expressed as quality of experience QoE of
Figure BDA0002961277710000086
In (8), due to Qmin≤Qr(fu)≤QmaxAnd Qmin≤Qc(fu)≤QmaxThus Q (f)u) In the range of Qmin≤Q(fu)≤Qmax. Set of users U on cache missesnothitIn (1), a user set which receives a recommendation file meeting QoE requirement through a cache recommendation system is defined as
Figure BDA0002961277710000091
When the user request file is not hit in the base station cache and cannot receive the recommended file meeting the QoE requirement, the request file needs to be extracted from the cloud file library through the transmission network, and the transmission bandwidth requirement is increased. Therefore, among the U users, the ratio of the users who extract the requested file via the transmission network is
Figure BDA0002961277710000092
Wherein | UhitI and
Figure BDA0002961277710000093
representation set UhitAnd
Figure BDA0002961277710000094
number of elements in (1). The smaller the value of beta is, the fewer the number of the U users extracting the request file through the transmission network is, and the smaller the transmission bandwidth requirement of the cellular network is. The invention provides a QoE-aware cache recommendation file transmission method QAT (QoE aware transmission), which reduces the value of beta, thereby reducing the bandwidth requirement of a transmission network in figure 1.
From the foregoingWhen the user receives different recommended files in the recommended file set, due to the fact that content experiences of the different recommended files are different, requirements for fluency experience of the recommended files are different. Definition of QthIn order to meet the threshold value of QoE requirement of the user, when the user u receives the recommendation file fuQoE experience of Q (f)u) Not less than threshold QthWhen (Q (f)u)≥Qth) The user receives the recommended file. As can be seen from (8), for a given QthAnd the content experience is Qc(fu) Recommendation file fu，Qr(fu) And Qc(fu) Should be such that Q (f)u)≥QthThus Qr(fu) Minimum of (d) with Qc(fu) Increases and decreases, in particular, Qr(fu) Has a minimum value of
Figure BDA0002961277710000095
While taking into account
Figure BDA0002961277710000096
Is also a variable, using
Figure BDA0002961277710000097
Represents a given Qth、Qc(fu) And
Figure BDA0002961277710000098
the fluency degree experiences Qr(fu) The lowest permissible value of (a) of (b),
Figure BDA0002961277710000099
is composed of
Figure BDA00029612777100000910
Because of the fact that
Figure BDA00029612777100000911
Is Qr(fu) At a given Qth、Qc(fu) And
Figure BDA00029612777100000912
time Qr(fu) Minimum value of value, note Qr(fu) Should satisfy Qr(fu)≤Qmax，
Figure BDA00029612777100000913
Is required to be less than or equal to Qmax，
Figure BDA00029612777100000914
Greater than QmaxCannot be used as a recommended file.
Because of the fact that
Figure BDA00029612777100000915
Is Qr(fu) At a given Qth、Qc(fu) And
Figure BDA00029612777100000916
time Qr(fu) Minimum value of value, considering Qr(fu) Need to satisfy Qmin≤Qr(fu) Definition of
Figure BDA00029612777100000917
As a recommendation file fuMinimum allowable value of fluency experience, therefore
Figure BDA00029612777100000918
Is shown as
Figure BDA00029612777100000919
Wherein
Figure BDA00029612777100000920
To represent
Figure BDA00029612777100000921
And QminThe larger between the two. Definition of Rmin(fu) As a recommendation file fuIs determined by (6), Rmin(fu) And
Figure BDA0002961277710000101
in a relationship of
Figure BDA0002961277710000102
Considering the transmission process of the recommended files, as described above, the invention selects the recommended file transmission method with the user as the center, and a plurality of base stations around the user form a service cluster to jointly transmit the same recommended files to the user, thereby improving the transmission rate. When the number of base stations of the service cluster of the user u is n, the maximum transmission rate R of the service clusteru(n) can be determined from (4) and (5). Considering that R increases as n increasesu(n) is increased so that when user u serves the cluster to transmit the recommendation file fuTo satisfy the minimum transmission rate R in (12)min(fu) Has a minimum value n, in order to satisfy Ru(n)≥Rmin(fu) The minimum value of n. And defining the service cluster of the user as the minimum service cluster when the number of the base stations is the minimum value.
Then, in the QAT method, the sizes of the recommended files and the service clusters for transmitting the recommended files need to be determined for the cache-miss users in a scenario of multiple users, so as to maximize the number of users receiving the recommended files meeting the QoE requirements. According to the definition of the minimum service cluster, when the service cluster transmits the recommendation file to the user, the number of the base stations of the minimum service cluster is the minimum value of the number of the base stations in the user service cluster under the condition of meeting the QoE requirement. Therefore, in the QAT method, in order to reduce the collision between the service clusters transmitting different recommended files and increase the number of users receiving recommended files satisfying the QoE requirements, the service cluster of the user should be the minimum service cluster. On the basis, the transmission files of the service clusters are sequentially determined for each cache-missed user according to the sequence, and when the transmission files of the service clusters are determined, the conflict between the service clusters is reduced as a determination principle. For simplicity, the present invention determines the order in which the user issues file requests. Considering that different files in the user recommended file set have different minimum allowable transmission rates, when the user service cluster transmits different recommended files to the user, the number of minimum service cluster base stations of different recommended files may be different. To reduce collisions between serving clusters, the recommended file with the least number of base stations in the smallest serving cluster should be selected.
It should be noted that when the base stations in the user service cluster have determined to transmit the file and no service cluster collision occurs, the service clusters overlap, and the base stations having determined to transmit the file should be excluded when considering the number of base stations in the service cluster. Therefore, when selecting a serving cluster to transmit a file, the number of base stations of the serving cluster should be redefined as the difference between the number of base stations of the smallest serving cluster and the number of base stations of the smallest serving cluster for which file transmission has been determined, without collision of the serving clusters. As shown in FIG. 3, there are two users u in the network1And User u2Suppose that the files recommended by the system to these two users are the same, user u1Firstly, the service Cluster of (1) Cluster1 is determined, and the service Cluster is base stations BS 1-BS 3. The number of the base stations BS in the minimum service cluster of the user is 4, that is, the BS 3-BS 6 form the user u2Cluster 2. Has been determined (at the time of determining user u) in view of the transmission file of BS31The time of serving the cluster is determined and compared with the user u2The transmission files of the service cluster are the same), and thus, the user u1And user u2That is, the BS3 simultaneously gives the user u an overlap of service clusters1And user u2Transmitting the same file, user u2The number of serving cluster base stations of (1) should be the difference between the minimum number of serving cluster base stations 4 and the number of base stations 1 for which the file transfer has been determined, i.e. 4-1-3.
In the QAT method of the present invention, when determining the transmission file of the service cluster for each cache-missed user in turn, the recommended file with the least number of base stations in the service cluster should be selected.
When a plurality of files in the recommended file set have the same number of serving cluster base stations, when determining that a serving cluster transmits files, the recommended files which are possibly received by a user who does not determine that the serving cluster transmits files are further considered, and possible conflicts among the serving clusters are changed into overlaps among the serving clusters. Since the closer the user is, the higher the probability of collision of the service cluster, the priority should be given to the user who misses in the cache closest to the user. On the one hand, consider the case where the user has not determined that the serving cluster transmitted the file because of the closest cache miss (the user requests the file later). Since the service cluster tends to recommend a file with a higher preference to the user, a file with a higher preference to the user due to a closest cache miss should be selected as the transmission file of the service cluster. On the other hand, consider the case where the user has determined that the serving cluster transmitted the file because of the closest cache miss. Note that the popularity of a file in equation (2) is the average of the preference of all users to the file in the network. Therefore, a file with a high popularity should be selected as a transmission file of the service cluster.
In summary, the idea of the QAT method proposed by the present invention is to reduce the service cluster collision caused by the transmission of different files by the base stations belonging to different service clusters, increase the number of users receiving recommended files meeting QoE requirements among cache miss users, and reduce the transmission bandwidth requirements of the cellular network. According to an embodiment of the present invention, as shown in fig. 2, first, when determining the size of the user service cluster, the smallest service cluster with the least number of base stations is selected. On this basis, considering that the minimum serving cluster base station number of different recommended files may be different in the user recommended file set, when determining the transmission file of the serving cluster, the recommended file with the minimum serving cluster base station number is selected first. Then, when the plurality of recommended files have the same number of serving cluster base stations, in order to convert possible serving cluster conflicts into serving cluster overlaps, the recommended files that users who do not determine the serving cluster transmission files are likely to receive are selected as the transmission files of the serving clusters. As shown in FIG. 2, the present invention proposes solving an optimization problem P0The QAT method steps are as follows:
step 1: for each cache miss user U ∈ UnothitDetermining a set of recommended files LuFile f recommended to user uuFrom set LuTo select. Set LuShould satisfy the following three conditions simultaneously: firstly
Figure BDA0002961277710000121
②Qmin≤Qc(f)≤Qmax；③
Figure BDA0002961277710000122
Step 2: for set LuIn each possible recommendation file f e LuCalculating a minimum allowable transmission rate R according to (12)min(f) According to Rmin(f) And maximum transmission rate R of service clusteru(n) calculating the number of base stations of the minimum service cluster according to the relation, wherein the number of the base stations satisfies Ru(n)≥Rmin(f) The minimum value of n;
and step 3: for cache miss set UnothitThe user in (1) determines each user U e to U in turn according to the sequence of file requests sent by the usernothitSize of service cluster and recommendation file transmitted by service cluster for each possible recommendation file f ∈ LuAnd setting the number of the base stations of the service cluster as the number of the base stations of the minimum service cluster, wherein when the base stations which determine the transmission file exist in the service cluster and the transmission file is different from the f, the service cluster conflicts. For the recommended files which do not conflict, updating the number of the base stations of the service cluster to be the difference between the number of the base stations of the minimum service cluster and the number of the base stations which determine to transmit the files, and determining whether each recommended file conflicts in the service cluster;
step 3.1: if set LuWherein each recommended file has service cluster conflict, the user u can not receive the recommended file meeting QoE requirement,
Figure BDA0002961277710000123
step 3.2 if not, the step (c),
Figure BDA0002961277710000124
determining whether there is a unique service cluster in a recommended file in which a service cluster conflict does not occurA recommended file with the least number of base stations;
step 3.3, if the only recommended file with the minimum number of the service cluster base stations exists, selecting the recommended file with the minimum number of the service cluster base stations as the recommended file f of the useru；
Step 3.4: if a plurality of recommended files with the least number of the base stations of the service cluster are available, selecting the recommended files by further adopting the following method: in the set UnothitThe user u' nearest to the user u is searched. And if the user u ' does not determine that the file is transmitted by the service cluster (the time when the user u ' requests the file is later than the user u), selecting the file with the highest preference degree of the user u ' as the recommended file. If the user u 'already determines that the file is transmitted by the service cluster (the moment when the user u' requests the file is earlier than the user u), selecting the file with the highest popularity as a recommended file;
and 4, step 4: for users receiving recommendation files meeting QoE requirements
Figure BDA0002961277710000125
To-be-recommended file fuService user decision setting of base station r in minimum service cluster is Sr(u) 1, with the transmission file decision set to Tr(fu)＝1。
Table 1 simulation parameter settings
Figure BDA0002961277710000131
The simulation experiment of the present invention is described below, the experimental parameters are shown in table 1, considering a square area with an area of 1km × 1km, the user and the base station respectively obey the parameters λUAnd λRPPP distribution of (1). In order to improve user experience, a high definition video standard and a QoE threshold Q are selectedth4.5 while assuming Qmin1 and Qmax5. Considering that the number of files in the file library is F equal to 100, the psychological threshold theta of the preference degree of the user for the files is selected to be 0.01, and the psychological threshold theta is the average value of the preference degree of the user for all the files. To compare the performance of the QAT method proposed by the present invention, the comparison method considered in the simulation is:
no cache recommendation, NOREC: for the users with cache misses, the request file is extracted through the transmission network, that is, (9)
Figure BDA0002961277710000132
MPC-MC (most porous content-minimum cluster): missing user U to cache belongs to UnothitRecommended file fuSet L for recommended filesuThe file with the highest user preference degree is used, and the service cluster for transmitting the recommended file is the minimum service cluster;
● MPC-SBS (most porous content-single base station): recommending the file with the highest user preference degree to the cache miss user, and taking the service cluster for transmitting the recommended file as the base station closest to the user;
● RS-MC (random select-minimum cluster): missing user U to cache belongs to UnothitRecommended file fuSet L for recommended filesuThe file is randomly selected, and the service cluster for transmitting the recommended file is the minimum service cluster;
● RS-SBS (random select-single base station): and recommending the randomly selected file to the cache-missing user, and taking the service cluster for transmitting the recommended file as the base station closest to the user.
The simulation parameters in table 1 were selected, and the buffer capacity Ω of the base station was varied from 10 to 30, and the curves of β values with Ω for the different methods are shown in fig. 4. It can be seen that the beta value of NOREC decreases with increasing omega. This is due to the fact that in NOREC
Figure BDA0002961277710000141
When the base station cache capacity Ω increases, the number of users | U that cache hitshitIf | is increased, the value of β is decreased as shown in (9). Due to the adoption of cache recommendation, part of cache misses, and a user receives a recommended file meeting QoE requirements, and a request file does not need to be extracted through a transmission network. Therefore, the performances of the five cache recommendation methods QAT, MPC-MC, RS-MC, MPC-SBS and RS-SBS are all superior to those of NOREC. As can be seen, these cache recommendationsThe beta value of the method also decreases with increasing omega. On the one hand, like NOREC, it can also be analyzed from the viewpoint of an increase in the number of users in cache hits. On the other hand, for a cache miss user, note that the cache recommendation file should be cached in the base station. With the increase of the cache capacity of the base station, the number of files in the user recommended file set is increased, and the selection range of the recommended files is expanded. When determining the recommended files transmitted by the user service clusters, the probability of collision among the service clusters is reduced, and the user is more likely to receive the recommended files meeting the QoE requirement. As can be seen from FIG. 4, MPC-MC and RS-MC perform better than MPC-SBS and RS-SBS in the cache recommendation method. The reason is that in MPC-MC and RS-MC, the user service cluster is the minimum service cluster, and the QoE requirement of the user for receiving the recommended file is met. In MPC-SBS and RS-SBS, users only access the nearest base station, the number of base stations of the service cluster is reduced, the channel quality of the transmission recommendation file is poor, and the number of users receiving the recommendation file meeting QoE requirements is reduced.
Referring to the recommended methods QAT, MPC-MC and RS-MC in FIG. 4, in which the three user service clusters are the minimum service cluster, it can be seen that the MPC-MC has better performance than RS-MC in the three methods. This is because the MPC-MC selects the file with the highest user preference in the recommended file set to recommend to the user, and the number of base stations in the serving cluster is the least among all the files in the recommended file set. From the analysis of the number of the base stations recommending the file service clusters, compared with a method for randomly selecting recommended files by RS-MC, the probability of conflict among the service clusters is reduced, and the beta value is reduced. However, the MPC-MC only selects the recommended file from the viewpoint of the number of base stations in the serving cluster, and does not consider the influence of the contents of the recommended file on the collision of the serving clusters. The method considers the situation that the popularity bias coefficient of the file is low, the difference of different users on the preference of a single file is large, and the preference degree of a file with high preference degree of a certain user is possibly low for adjacent users, so that the content experience requirement of the user cannot be met. Compared with MPC-MC, QAT considers the number of base stations of a service cluster and the preference degree of adjacent users to files under the condition of no conflict of the service cluster, the number of users receiving recommended files meeting QoE requirements is increased, and the beta value is reduced.
Selecting the simulation parameters in fig. 4, fixing the base station buffer capacity Ω to 20, and setting the base station distribution density λRVarying between 20/km2 and 120/km2, beta values for different methods as a function of lambdaRThe curve of the change is shown in fig. 5. It can be seen that with λRThe beta values of the cache recommendation methods QAT, MPC-MC and RS-MC are reduced. This is due to the fact that when λ isRWhen the number of the base stations in the serving cluster is increased, the average distance between the base station and the user is decreased, and it can be known from (4) and (5) that the maximum transmission rate of the serving cluster is increased under the condition that the number of the base stations in the serving cluster is the same. Considering that the minimum allowable transmission rate of the recommended file is not following lambdaRVaries, therefore, with λRWhen the serving cluster transmits the same recommended file to the user, the number of base stations of the serving cluster is reduced, the probability of collision between the serving clusters is reduced, and the beta value is reduced. Also, it can be seen that the beta values of the buffer recommendation methods MPC-SBS and RS-SBS follow the lambdaRIs increased and decreased. This is due to the fact that when λ isRWhen the number of the users is increased, the average distance between the users and the nearest base station is reduced, the transmission rate of the recommended files is improved, and the number of the users receiving the recommended files meeting QoE requirements is increased. However, as the user only accesses the base station with the nearest distance, the number of the base stations of the service cluster is 1, and compared with the condition that the user service cluster in MPC-MC and RS-MC is the minimum service cluster, the beta values of MPC-SBS and RS-SBS are along with lambdaRThe magnitude of the reduction is relatively slow. It can be seen that with λRThe QAT reduces the transmission network bandwidth requirement by up to 35.99% compared to NOREC methods that do not employ the buffer recommendation. Meanwhile, the performance of the NOREC is determined by the cache hit rate, and the cache hit rate is only related to the cache capacity of the base station, so when the lambda is higher than the threshold value, the performance of the NOREC is improvedRThe beta value of NOREC remains constant as it increases.
The previous description is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Moreover, all or a portion of any aspect and/or embodiment may be utilized with all or a portion of any other aspect and/or embodiment, unless stated otherwise. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (8)

1. A wireless network cache recommendation method is characterized in that files cached by a base station of a wireless network are F in setcacheFor user u's pair file
Figure FDA0003335223130000011
If the file request is made
Figure FDA0003335223130000012
Not belonging to the set FcacheFrom FcacheIn selecting file fuRecommending to a user u, wherein the recommending method comprises the following steps:
step 100: from FcacheSelecting a file set L meeting the requirements of preference degree and quality of experience QoEuWherein the quality of experience QoE is obtained based on fluency experience and content experience;
step 200: determination of LuThe number of base stations in the minimum serving cluster of each file f includes:
calculating a minimum allowable transmission rate Rmin(f) According to Rmin(f) And maximum transmission rate R of service clusteru(n) calculating the number n of base stations of the minimum service cluster according to the relation, wherein the number of the base stations satisfies Ru(n)≥Rmin(f) Is the smallest value of n, n being a positive integer,
wherein the minimum allowable transmission rate Rmin(f) Is calculated by the following formula
Figure FDA0003335223130000013
Wherein R isidealFor an ideal transmission rate, theta, recognized by the user when receiving the recommended file1And theta2Is a constant number, theta1＞0，θ2＞0，
Figure FDA0003335223130000014
Wherein,
Figure FDA0003335223130000015
Qthto meet a predetermined threshold of user QoE requirements,
Figure FDA0003335223130000016
to weight fluency experience and content experience in the quality of experience for the user,
Figure FDA0003335223130000017
Qc(f) content experience of f, QminIs Qc(fu) The lowest allowable value of (d);
step 300: selecting the recommended file with the least number of service cluster base stations as the recommended file f of the useru。
2. The method of claim 1, wherein the quality of experience QoE is expressed as
Figure FDA0003335223130000018
Wherein, Q (f)u) Receiving a recommendation file f for a user uuQuality of experience QoE, Qr(fu) Is fuThe fluency experience of.
3. The method according to claim 2, wherein the quality of experience QoE of the content experience Qc(fu) Calculated using the following formula:
Qc(fu)＝θ1·ln(θ2·(1-Δu))
wherein, DeltauAs a recommendation file fuResulting deviation of preference, θ1And theta2Is a constant number, theta1＞0，θ2＞0。
4. Method according to one of claims 1 to 3, wherein L is selected in step 100uAny file f in (2) satisfies the following condition:
1) f is not less than a predetermined psychological threshold theta,
2) content experience Qc(f) Need to be greater than or equal to QminAnd is less than or equal to Qmax，
3) Minimum allowable value of fluency experience
Figure FDA0003335223130000021
Less than or equal to its maximum threshold Qmax，
Wherein QminIs Qr(f) And Qc(f) Predetermined minimum allowable value, Qmax＝θ1·lnθ2，θ1And theta2Is a constant.
5. The method of claim 1, wherein step 300 comprises:
step 305: determining whether LuWherein, each file f has service cluster conflict, wherein the service cluster conflict is that a base station which determines to transmit the file exists in the service cluster and the transmission file is different from the f;
step 310: if L isuEach file f in the file system has service cluster conflict, and no file can be recommended;
step 320: if L isuIf the file does not have service cluster conflict, determining whether the only recommended file with the minimum number of service cluster base stations exists in the recommended files without service cluster conflict;
step 330: if the only recommended file with the minimum number of the service cluster base stations exists, selecting the recommended file with the minimum number of the service cluster base stations as the recommended file f of the useru；
Step 340: and if a plurality of recommended files with the minimum number of the base stations in the service cluster are available, selecting files which are possibly received by adjacent users to recommend the files to the users.
6. The method of claim 5, wherein step 340 comprises: in the set UnothitThe user U 'nearest to the user U is searched, if the user U' does not determine the service cluster transmission file, the file with the highest preference degree of the user U 'is selected as the recommended file, if the user U' determines the service cluster transmission file, the file with the highest popularity degree is selected as the recommended file, wherein the U isnothitRepresenting a requested file
Figure FDA0003335223130000022
Not belonging to the set FcacheAll of the users of (1).
7. A computer-readable storage medium, in which one or more computer programs are stored, which when executed, are for implementing the method of any one of claims 1-6.
8. A computing system, comprising:
a storage device, and one or more processors;
wherein the storage means is for storing one or more computer programs which, when executed by the processor, are for implementing the method of any one of claims 1-6.
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016123497A1 (en) * 2015-01-29 2016-08-04 Vid Scale, Inc. Bandwidth prediction and prefetching for enhancing the qoe of applications over wireless networks
CN107592656A (en) * 2017-08-17 2018-01-16 东南大学 Caching method based on base station cluster
CN109194763A (en) * 2018-09-21 2019-01-11 北京邮电大学 Caching method based on small base station self-organizing cooperative in a kind of super-intensive network
CN109600780A (en) * 2019-02-18 2019-04-09 南京邮电大学 A kind of duplicate of the document caching method based on base station sub-clustering
CN110248206A (en) * 2019-07-29 2019-09-17 北京邮电大学 A kind of resource allocation methods, device and electronic equipment for edge network system
CN111277860A (en) * 2020-01-23 2020-06-12 北京邮电大学 Method, device and equipment for caching video in mobile edge network and readable medium
CN112261668A (en) * 2020-10-20 2021-01-22 北京邮电大学 Content caching method and device in mobile edge network and electronic equipment
CN112291284A (en) * 2019-07-22 2021-01-29 中国移动通信有限公司研究院 Content pushing method and device and computer readable storage medium

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9756142B2 (en) * 2013-03-14 2017-09-05 The Regents Of The University Of California System and method for delivering video data from a server in a wireless network by caching the video data

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016123497A1 (en) * 2015-01-29 2016-08-04 Vid Scale, Inc. Bandwidth prediction and prefetching for enhancing the qoe of applications over wireless networks
CN107592656A (en) * 2017-08-17 2018-01-16 东南大学 Caching method based on base station cluster
CN109194763A (en) * 2018-09-21 2019-01-11 北京邮电大学 Caching method based on small base station self-organizing cooperative in a kind of super-intensive network
CN109600780A (en) * 2019-02-18 2019-04-09 南京邮电大学 A kind of duplicate of the document caching method based on base station sub-clustering
CN112291284A (en) * 2019-07-22 2021-01-29 中国移动通信有限公司研究院 Content pushing method and device and computer readable storage medium
CN110248206A (en) * 2019-07-29 2019-09-17 北京邮电大学 A kind of resource allocation methods, device and electronic equipment for edge network system
CN111277860A (en) * 2020-01-23 2020-06-12 北京邮电大学 Method, device and equipment for caching video in mobile edge network and readable medium
CN112261668A (en) * 2020-10-20 2021-01-22 北京邮电大学 Content caching method and device in mobile edge network and electronic equipment

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
面向移动边缘网络的联合内容推荐与缓存系统研究;罗文彬;《中国优秀硕士学位论文辑》;20180401;全文 *

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