CN109474664A - A kind of active pre-cache method and device in heterogeneous wireless network - Google Patents
A kind of active pre-cache method and device in heterogeneous wireless network Download PDFInfo
- Publication number
- CN109474664A CN109474664A CN201811136165.6A CN201811136165A CN109474664A CN 109474664 A CN109474664 A CN 109474664A CN 201811136165 A CN201811136165 A CN 201811136165A CN 109474664 A CN109474664 A CN 109474664A
- Authority
- CN
- China
- Prior art keywords
- content
- push
- base station
- energy
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/10—Flow control between communication endpoints
- H04W28/14—Flow control between communication endpoints using intermediate storage
-
- 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
- H04L67/55—Push-based network services
-
- 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
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0212—Power saving arrangements in terminal devices managed by the network, e.g. network or access point is master and terminal is slave
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The present invention relates to the field of resource allocation of wireless network, the active pre-cache method and device in especially a kind of heterogeneous wireless network causes the concern to content pre-cache in wireless communication to utilize renewable energy and save traditional energy.The present invention is absorbed in content push and caching, for the purpose of improving green energy resource utilization rate and save traditional energy.State transition probability and the following reward in mobile environment are unknown.Therefore, the present invention solves the problems, such as green energy resource distribution and content push using intensified learning.Q study is a kind of reinforcement learning technology of model-free, and optimal movement selection strategy can be found in MDP problem.ANALOGY OF BOLTZMANN DISTRIBUTION method is used for more new strategy, and after Q table is stablized, the present invention can be according to Q table to obtain best movement in each state.
Description
Technical field
Content pre-cache the present invention relates to the field of resource allocation of wireless network, in especially a kind of heterogeneous wireless network
Method and its system.
Background technique
Due to the rapid growth of multimedia service and sharply increasing for CO2 emissions, green communications are to solve this
The effective measures of class problem.Green wireless access there is many ways in which, such as collection of energy, multicast and heterogeneous network.Using the sun
Can, wind energy, the EH technology of the natural energy resources such as kinetic energy can substantially reduce the power consumption of conventional power source wireless communication, to reduce dioxy
Change carbon emission.It is considered as one of the candidate technologies for implementing green communications.Wireless multicast passes through while to provide multi-user usual
Interested multimedia content, while individual traffic is expanded into different user, so that the repeated retransmission of identical content is avoided,
To obtain huge energy advantages.Heterogeneous network uses the small base station (SBS) of dense deployment, by reducing user and base station
The distance between higher user rate is provided.But every kind of technology has its limitation.
On the one hand due to limited battery capacity, it may occur however that energy and request reach unmatched situation, cause the energy unrestrained
Take or short.On the other hand, in order to realize wireless multicast, some users request to need to be delayed by wait concurrent transmission, this can
The service quality of early stage demand can seriously be damaged.Finally, due to which the deployment of micro-base station is inflexible, power line and height are supported in deployment
The cost is relatively high for fast backhaul link.
Summary of the invention
The present invention overcomes disadvantages mentioned above, a kind of active pre-cache method in heterogeneous wireless network is provided.
The technical scheme adopted by the invention to solve the technical problem is that: the active pre-cache in a kind of heterogeneous wireless network
Method, include the following steps: to consider the available green energy of node, subscriber data file request, different file popularity with
And the energy consumption of different transmission mechanisms, each node is depicted and is selected brought by Different Strategies in cache resources transmission process
Different incomes.Construct the content push optimal model based on intensified learning;The content push optimal model includes shape
State-movement pair.
Content push optimal model is solved, content push optimisation strategy is obtained, according to the content push prioritization scheme
Carry out content.
The available green energy for considering node, subscriber data file request, different file popularity and difference
The energy consumption of transmission mechanism is depicted each node and difference brought by Different Strategies is selected to receive in cache resources transmission process
Benefit.It further include average with fixed target according to each content before constructing the content push optimal model based on intensified learning
Data rate, bandwidth, small scale rapid fading coefficient, path loss constant and path loss index, noise plus interference power are found out
Power function.
It is described according to each content with fixed target average number according to rate, bandwidth, small scale rapid fading coefficient, path damage
Consumption constant and path loss index, noise plus interference power find out power function and further comprise
W is the bandwidth of SBS, and h is small scale rapid fading coefficient, and β and α respectively indicate path loss constant and path loss refers to
Number, d is transmission range, σ2+PIIt is noise plus interference power.
Consider available green energy, subscriber data file request, the popularity of different files and the different transmission of node
The energy consumption of mechanism is depicted each node and selects difference income brought by Different Strategies in cache resources transmission process.
The content push optimal model based on intensified learning is constructed, further comprises:
It is distributed according to zipf, different file popularities are as follows:
The interested a total of N number of content of user, by list C=(c1,…,cN) indicate, wherein with probability (or pop-out
Mouthful) fiThe content c of i-th of sequence of requesti.Statistical research shows that content popularit distribution meets Zipf distribution.Zipf is distributed
What the linguist Zipf of Harvard had found when studying corpus, it sorts according to the number that word occurs in corpus,
Then the sequence number of the word and its occur frequency in corpus and be inversely proportional, in other words, the two product be a constant.
P (r)=c/rv
Here r indicates the ranking of the frequency of occurrences of a word, and P (r) indicates that ranking is the frequency of occurrences of the word of r.It is single
C is approximately equal to 0.1 in word frequency distribution.
Therefore, content ciPopularity can indicate are as follows:
The interest content of user can change with the time, and over time, part flow content guild is replaced.And
In this model, it is not concerned with content itself, so not considering the process that popular content updates.According to model it is found that always to use
Family push is currently stored in most popular content in small base station.The content pushed can be expressed as Ct=(c1, c2...,
ck), it is remaining not push list and be represented by
The solution content push optimal model obtains content push optimisation strategy, is optimized according to the content push
Scheme carries out content, further comprises:
The content push optimal model is solved based on nitrification enhancement, the minimum scheme of traditional energy amount will be consumed
As content push prioritization scheme, content push is carried out according to the content push prioritization scheme.
It is described that the content push optimal model is solved based on nitrification enhancement, consumption traditional energy amount is minimum
Scheme carries out content push as content push prioritization scheme, according to the content push prioritization scheme, may further comprise:
It include three elements, intelligent body state, motion space, return based on the intensified learning.
The intelligent body state, it is definition status space first that intelligent body, which can select the basis most preferably acted,.Agent can
The different conditions set S system state of its environment is perceived by Sk=Ek,Ck,Xk,Yk) indicate, EkThe current electricity of SBS is represented,
CkRepresent a current push state, Xk,YkIndicate the state that the user of request is issued to SBS, Xk, indicate the energy of SBS unicast
Amount consumption, YkIndicate the energy consumption of BS unicast.
The motion space considers the access point with caching function that the power supply of the pure green energy is added in macro base station,
Access point is according to current electric quantity, cache contents and user's request selecting factum.The action collection of SBS is { a0,a1,a2,
a3}.SBS has 4 kinds of possible behaviors: a0, and suspend mode, user's request at this time is by macro base station service;a1, direct unicast is to user;a2,
To BS request content, updates and cache and issue;a3Push most popular content.
The Reward Program, (s a) is designed based on the traditional energy amount of the system consumption of system Reward Program R, is wished
Hoping the traditional energy amount of system consumption is least, it is specified that Reward Program is negative value.
Active pre-cache method in the heterogeneous wireless network includes: macro base station, small base station and user's request, total
For, SBS executes the content push prioritization scheme, selects suspend mode, unicast, or push.As SBS since the selection of certain factors is stopped
Dormancy perhaps pushes or the request of user is not in SBS push list, and user of the invention at this time requests to need by macro base station
To handle.Macro base station processing user's request needs to consume traditional energy, and it is an object of the present invention to minimize to pass in long period
The consumption of the system energy, improves the utilization rate of green energy resource.
The present invention from save traditional energy consumption and reduce user using time delay angle, SBS active cache and
Content is pushed before user's request.Firstly, since advanced push-mechanism, the present invention can have the longer time into user's transmission
Hold, therefore the content for sending user to can match the arrival of green energy resource for greater flexibility.Secondly as can reasonably use
Collected energy, therefore energy dissipation caused by avoiding due to limited battery capacity.The present invention passes through intensified learning
Method has obtained the optimal policy that SBS is acted under each state, so that the consumption of traditional energy be made to be preferably minimized.
Detailed description of the invention
Fig. 1 is the active pre-cache method flow diagram in heterogeneous wireless network of the present invention;
Fig. 2 is to use deployment scheme schematic diagram of the present invention;
Fig. 3 is the C realized using the present inventionkActive pre-cache scheme schematic diagram when=0;
Fig. 4 is the C realized using the present inventionk=0 active pre-cache scheme schematic diagram;
Fig. 5 is the C realized using the present inventionk=0 active pre-cache scheme schematic diagram;;
Fig. 6 is when it is 0.4 that user, which requests probability, and active pre-cache method and unicast based on intensified learning are preferential and push away
Send the comparison schematic diagram of the mode of priority;
Fig. 7 is when it is 0.9 that user, which requests probability, and active pre-cache method and unicast based on intensified learning are preferential and push away
Send the comparison schematic diagram of the mode of priority;
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Embodiment one: for the preferred embodiment of a kind of active pre-cache method in heterogeneous wireless network, it is as shown in Figure 1
The functional block diagram and process of the present embodiment.
Step 1: initializing the corresponding movement of small base station corresponding state (initialization Q table)
Step 2: the movement of small base station is selected in behavior aggregate (suspend mode, unicast update caching unicast, push)
Request Q is generated from user side in each time slott.Small base station is acted and is asked accordingly according to battery status selection
Content is sought, U is consumedtEnergy, and capture AtEnergy.The action collection of SBS is { a0,a1,a2,a3}.SBS has 4 kinds of possible behaviors:
a0, suspend mode, user's request at this time is by macro base station service;a1, direct unicast is to user;a2, to BS request content, update caching simultaneously
It issues;a3Push most popular content.Consider the access with caching function that the power supply of the pure green energy is added in macro base station
Point, access point is according to current electric quantity, cache contents and user's request selecting factum.
Step 3: executing the movement of small base station
Small base station selects corresponding movement and request content according to battery status, consumes UtEnergy, and capture AtEnergy.
Meanwhile in user side, each user receives and saves push contents list Ct.Update corresponding parameter
Step 4: estimation return value saves the amount of traditional energy
(s a) is designed based on the traditional energy amount of the system consumption of system Reward Program R, it is desirable to the biography of system consumption
Quantity of energy of uniting is least, it is specified that Reward Program is negative value.
Step 5: updating Q table
It updates according to the following formula
Step 6: finally obtaining stable Q table, i.e., optimum state-movement of small base station is to step 2~step 5 is repeated, directly
Stablize to Q table, obtains optimal Content push strategy.
Fig. 2 is deployment schematic diagram of the invention, and macro base station is by backhaul link Access Core Network, and small base station is as network
The not yet done cache server of fringe node, small base station can be cached with active push in popular content.
Fig. 3, Fig. 4, Fig. 5 are the push strategies that the present invention is obtained by emulation, give user's solicited status and push content
State, optimal policy battery power status show the structure based on threshold value, that is, BS will keep sleep until the energy content of battery is more than certain
A value, then for any battery power status greater than the value, it will not suspend mode.This is because when the energy content of battery is big,
BS tends to wolfishly use it in the case where battery overflows.Secondly, for user's (user's solicited status 1) close to BS,
Always preferred unicast.Since these users consume very small amount of unicast energy and can also enjoy higher communication quality,
Therefore it is more beneficial to transfer a request to MBS for unicast ratio.The content of third, push is more, and system determines that the trend pushed is smaller.It is right
In Ck=0, under most situations, BS is by the popular content of push, the case where in addition to user very close to BS.However, working as
When pushing the quantity of content close to its maximum value, BS will be only in system free time (Qk=0) and the energy content of battery almost expires Shi Caihui
Push.According to Q table, the present invention can find out the rule that SBS is presented when making best decision.Therefore, it can be applied to
In practical application scene.
Fig. 6, Fig. 7 are the result of the present invention as a comparison using Unicast-Only strategy and Push-Only strategy.Then
The present invention assesses the effect of the system model.Fig. 6 and Fig. 7 is illustrated respectively in single time slot TpIn, the request probability of user is pu=
0.5, pu=0.9 Energy Expenditure Levels.As shown in Figure 6 and Figure 7.Unicast-Only is unicast preference strategy.When there is no list
When broadcasting request, system executes push operation.Push-Only is the strategy that will be pushed as highest priority.System Priority is every
Push operation is executed on a time slot.When pushing all popular contents, unicast operation is executed.
The result shows that with only push strategy compared with, by Q-Learning learn push strategy need the more time come
The content in contents list is pushed, but the energy consumed is minimum.Contents list quickly can be pushed to use by Push-Only strategy
Family, cost are to refuse the unitcast request from previous user, but overall energy consumption is very high.Unicast-Only.When each time slot is used
When probability is requested close to 1 in family, SBS there is no that chance executes push operation.When it is 0.5 that user, which requests probability, when not having
When user requests, SBS can execute push operation.During the late stages of developmet, contents list, which can be pushed completely, send and substantially not
Energy is consumed again.Therefore, the performance Yu user's number of requests of Unicast-Only strategy have much relations.In general, by one section
The study of time, Q-learning execute operation according to Q table to obtain optimum.
Detailed Jie has been carried out to the active pre-cache method and device in heterogeneous wireless network provided by the present invention above
It continues, used herein a specific example illustrates the principle and implementation of the invention, and the explanation of above embodiments is only
It is to be used to help understand method and its core concept of the invention;At the same time, for those skilled in the art, according to this hair
Bright thought, there will be changes in the specific implementation manner and application range, in conclusion the content of the present specification should not manage
Solution is limitation of the present invention.
Claims (5)
1. a kind of active pre-cache method in heterogeneous wireless network, which comprises the steps of:
Consider available green energy, subscriber data file request, the popularity of different files and the difference transmission mechanism of node
Energy consumption, each node is depicted and selects difference income brought by Different Strategies in cache resources transmission process;Building
Content push optimal model based on intensified learning;
Content push optimal model is solved, content push optimisation strategy is obtained, is carried out according to the content push prioritization scheme
Content push.
2. the active pre-cache method in heterogeneous wireless network according to claim 1, it is characterised in that: building is based on strong
It further include with fixed target average number according to each content according to rate before the content push optimal model that chemistry is practised, bandwidth,
Small scale rapid fading coefficient, path loss constant and path loss index, noise plus interference power find out power function.
3. the active pre-cache method in heterogeneous wireless network according to claim 1, it is characterised in that: consider content stream
The distribution of row degree, content popularit distribution meet Zipf distribution, sort according to the number that word occurs in corpus, then the word
Sequence number and its occur frequency in corpus and be inversely proportional;The interest content of user can change with the time, with the time
Passage, part flow content guild is replaced;In this model, it is not concerned with content itself, so not considering that popular content updates
Process, always to user push be currently stored in most popular content in small base station.
4. the active pre-cache method in heterogeneous wireless network according to claim 1, it is characterised in that: in the solution
Hold push optimal model, obtain content push optimisation strategy, content is carried out according to the content push prioritization scheme, further
Include:
Solve the content push optimal model based on nitrification enhancement, will consume the minimum scheme of traditional energy amount as
Content push prioritization scheme carries out content push according to the content push prioritization scheme;Based on the intensified learning model, intelligence
Energy body state includes base station electricity, push state, the energy consumption of User Status and unicast;Motion space includes suspend mode, unicast
To user, to macro base station request content, the most popular content of push;Reward Program is designed based on the energy consumption of system
's.
5. a kind of active pre-cache method in heterogeneous wireless network characterized by comprising macro base station, small base station and user
Request, generally speaking, small base station execute the content push prioritization scheme, select suspend mode, unicast, or push;When small base station by
It is perhaps pushed in the selection suspend mode of certain factors or the request of user is not in the push list of small base station, use of the invention at this time
Family request needs to handle by macro base station;Macro base station processing user's request needs to consume traditional energy, it is an object of the present invention to
The consumption that traditional energy is minimized in long period, improves the utilization rate of green energy resource.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811136165.6A CN109474664B (en) | 2018-09-28 | 2018-09-28 | Active pre-caching method and device in heterogeneous wireless network |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811136165.6A CN109474664B (en) | 2018-09-28 | 2018-09-28 | Active pre-caching method and device in heterogeneous wireless network |
Publications (2)
Publication Number | Publication Date |
---|---|
CN109474664A true CN109474664A (en) | 2019-03-15 |
CN109474664B CN109474664B (en) | 2020-09-25 |
Family
ID=65664598
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201811136165.6A Active CN109474664B (en) | 2018-09-28 | 2018-09-28 | Active pre-caching method and device in heterogeneous wireless network |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109474664B (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111405646A (en) * | 2020-03-17 | 2020-07-10 | 重庆邮电大学 | Base station dormancy method based on Sarsa learning in heterogeneous cellular network |
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 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102802247A (en) * | 2012-08-10 | 2012-11-28 | 北京邮电大学 | Hierarchical cellular network base station sleep method based on low-power base station |
CN102917446A (en) * | 2012-09-29 | 2013-02-06 | 北京邮电大学 | Environmental protection and energy conservation oriented dynamic cell dormancy method |
CN104602329A (en) * | 2015-01-30 | 2015-05-06 | 北京邮电大学 | Base station cooperation dormancy method and system applied to cellular heterogeneous network |
CN105100276A (en) * | 2015-09-01 | 2015-11-25 | 厦门大学 | Regional content caching device for inferior content distribution system and regional content caching method for inferior content distribution system |
CN105246101A (en) * | 2015-09-01 | 2016-01-13 | 厦门大学 | Content recommendation device orienting minor mobile content distribution system and method thereof |
CN107241790A (en) * | 2017-05-24 | 2017-10-10 | 沈阳航空航天大学 | Base station collaboration Energy Saving Strategy based on content caching |
-
2018
- 2018-09-28 CN CN201811136165.6A patent/CN109474664B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102802247A (en) * | 2012-08-10 | 2012-11-28 | 北京邮电大学 | Hierarchical cellular network base station sleep method based on low-power base station |
CN102917446A (en) * | 2012-09-29 | 2013-02-06 | 北京邮电大学 | Environmental protection and energy conservation oriented dynamic cell dormancy method |
CN104602329A (en) * | 2015-01-30 | 2015-05-06 | 北京邮电大学 | Base station cooperation dormancy method and system applied to cellular heterogeneous network |
CN105100276A (en) * | 2015-09-01 | 2015-11-25 | 厦门大学 | Regional content caching device for inferior content distribution system and regional content caching method for inferior content distribution system |
CN105246101A (en) * | 2015-09-01 | 2016-01-13 | 厦门大学 | Content recommendation device orienting minor mobile content distribution system and method thereof |
CN107241790A (en) * | 2017-05-24 | 2017-10-10 | 沈阳航空航天大学 | Base station collaboration Energy Saving Strategy based on content caching |
Non-Patent Citations (1)
Title |
---|
YI ZHANG等: "Fault Recovery Algorithm Based on SDN Network", 《HUMANCENTEREDCOMPUTING》 * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
CN112291284B (en) * | 2019-07-22 | 2023-01-03 | 中国移动通信有限公司研究院 | Content pushing method and device and computer readable storage medium |
CN111405646A (en) * | 2020-03-17 | 2020-07-10 | 重庆邮电大学 | Base station dormancy method based on Sarsa learning in heterogeneous cellular network |
CN111405646B (en) * | 2020-03-17 | 2022-06-03 | 重庆邮电大学 | Base station dormancy method based on Sarsa learning in heterogeneous cellular network |
Also Published As
Publication number | Publication date |
---|---|
CN109474664B (en) | 2020-09-25 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106998353B (en) | Optimal caching configuration method for files in content-centric networking | |
CN108093435B (en) | Cellular downlink network energy efficiency optimization system and method based on cached popular content | |
Ge et al. | Optimization on TEEN routing protocol in cognitive wireless sensor network | |
CN111552564A (en) | Task unloading and resource optimization method based on edge cache | |
CN108600998B (en) | Cache optimization decision method for ultra-density cellular and D2D heterogeneous converged network | |
Li et al. | Deep reinforcement learning for cooperative edge caching in future mobile networks | |
Nassar et al. | Resource allocation in fog RAN for heterogeneous IoT environments based on reinforcement learning | |
CN108322352B (en) | Honeycomb heterogeneous caching method based on inter-group cooperation | |
Feng et al. | Performance analysis of push-based converged networks with limited storage | |
CN109474664A (en) | A kind of active pre-cache method and device in heterogeneous wireless network | |
CN116321307A (en) | Bidirectional cache placement method based on deep reinforcement learning in non-cellular network | |
Zhong et al. | Joint optimal multicast scheduling and caching for improved performance and energy saving in wireless heterogeneous networks | |
Jiang et al. | Cooperative content distribution for 5G systems based on distributed cloud service network | |
CN108093482B (en) | Optimization method for wireless information center network resource allocation | |
Chen et al. | Dynamic task caching and computation offloading for mobile edge computing | |
US20240031427A1 (en) | Cloud-network integration oriented multi-access edge computing architecture | |
WO2021083230A1 (en) | Power adjusting method and access network device | |
Wu et al. | Joint long-term cache allocation and short-term content delivery in green cloud small cell networks | |
Fang et al. | Offloading strategy for edge computing tasks based on cache mechanism | |
Gong et al. | Joint optimization of content caching and push in renewable energy powered small cells | |
CN113672372A (en) | Multi-edge cooperative load balancing task scheduling method based on reinforcement learning | |
Li et al. | Joint scheduling of proactive caching and on-demand transmission traffics over shared spectrum | |
CN108429919B (en) | Caching and transmission optimization method of multi-rate video in wireless network | |
Mi et al. | Joint caching and transmission in the mobile edge network: An multi-agent learning approach | |
Katsaros et al. | Wireless Information Highways |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |