CN108093435B - Cellular downlink network energy efficiency optimization system and method based on cached popular content - Google Patents

Cellular downlink network energy efficiency optimization system and method based on cached popular content Download PDF

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CN108093435B
CN108093435B CN201711361320.XA CN201711361320A CN108093435B CN 108093435 B CN108093435 B CN 108093435B CN 201711361320 A CN201711361320 A CN 201711361320A CN 108093435 B CN108093435 B CN 108093435B
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base station
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power consumption
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CN108093435A (en
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嵇介曲
朱琨
王然
代陈
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Nanjing University of Aeronautics and Astronautics
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    • 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
    • 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
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/0231—Traffic management, e.g. flow control or congestion control based on communication conditions
    • H04W28/0236—Traffic management, e.g. flow control or congestion control based on communication conditions radio quality, e.g. interference, losses or delay
    • 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
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
    • 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
    • H04W28/08—Load balancing or load distribution
    • 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
    • H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
    • H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
    • 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

Abstract

The invention discloses a cellular downlink network energy efficiency optimization system and method based on cached popular content. SGW caches files requested by BSs, each BS has a cacheable NcBuffer device for individual content, with a capacity of CbhWhen the file requested by the user is cached in the local base station, the base station directly acquires the file and transmits the file to the user; otherwise, the file is acquired from the SGW through the backhaul link and transmitted to the user. The popular content is at time T0The downlink energy efficiency gain is the ratio of the number of bits transmitted to the power consumed in the entire downlink network. The distributed cache is that 4 adjacent base stations cache different contents, and when each user requests the contents, the closest base station which caches the contents is selected to be connected.

Description

Cellular downlink network energy efficiency optimization system and method based on cached popular content
Technical Field
The invention belongs to the technical field of mobile networks, and particularly relates to a cellular downlink network energy efficiency optimization system and method based on cache popular content, in particular to a cellular downlink network energy efficiency optimization method based on cache popular content in a 5G cellular network, which is mainly applied to a fifth generation mobile communication network with explosive throughput requirements.
Background
With the rise of intelligent terminals, tablet computers, social networks and the like, the mobile service demand will increase explosively, and wireless data traffic and signaling bring unprecedented impact on mobile communication networks. According to the international telecommunications union, the data traffic capacity demand of mobile communication networks will reach 1000 times that of 4G commercial networks by 2020. The 4G technology has difficulty in meeting the development requirements of mobile communication technology. In addition, smart grids, unmanned vehicles, and the like have been studied at present. Therefore, with the rapid development of the internet of things, mobile communication needs to solve the communication between people and things, and between things and things, in addition to the current communication between people and people. In summary, future mobile communication needs to adapt to not only ultra-dense wireless communication with multiple users and multiple base stations, but also to adapt to diversified mobile services and scenes.
In response to the above requirements, the development work of the fifth generation mobile communication system (5G) has been in a test phase. Although research on the 5G system and key technologies has been gradually developed, the evolution of the core technologies of the 5G system, especially the core technologies of the ultra-dense heterogeneous network, the ad hoc networking, the content distribution network, etc., still has not been clearly and consistently recognized by the academia and the industry. In order to meet the 1000-time data service capacity requirement in a future mobile communication network, the current research idea mainly solves the problems in three aspects of wireless transmission, spectrum resources and network architecture. The future network architecture mainly includes the following features: the method comprises the following steps of super-dense heterogeneous network fusion, heterogeneous cells with different coverage areas (overlapping coverage areas), and multiple types of access networks. While these technologies may enable increased capacity, they also present new technical challenges for mobile communication networks.
Because the problems of channel interference, uncertainty, power control, channel overhead and the like need to be considered in the wireless network virtualization design, the wireless network is more complex than a wired network, and therefore, the virtualization technology of the wired network cannot be directly applied to the wireless virtualization network. Therefore, the wireless virtual network is realized, communication, storage resources and the like are fused, and the frequency spectrum benefit and the energy consumption benefit can be improved. Wireless virtualization technology has become an important development direction for future networks.
To meet the explosive demands for throughput, support sustainable development and reduce global carbon dioxide emissions, Energy Efficiency (EE) has become a major performance indicator for 5 th generation cellular networks. The network EE can be promoted from the aspects of introducing a new network framework, optimizing network deployment, allocating resources and the like. It can be observed from recent research that repeated downloads of many popular content produce a large portion of the mobile multimedia service stream. This reflects the transition of the network from traditional transmitter-receiver communication to the main goal of content dissemination. On the other hand, the storage capacity of today's storage devices is also growing rapidly. Therefore, the caching device of the Base Station (BS) provides a promising method to free up the potential of the cellular network.
Caching is a technique that improves performance in many areas of wired networks, such as content-centric networking (CCN). In cellular networks, caching popular content can reduce backhaul costs, access delays, and energy consumption at the network edge, and improve throughput. From previous studies it can be noted that backhaul becomes a bottleneck in small networks (SCNs), such as 5G Ultra Dense Networks (UDNs), where disk size is growing rapidly at relatively low cost, and in the face of this can be cached at the BS by a caching device instead of the backhaul link.
For highly skewed requirements, the cache should be pushed to the edge, such as the SGW or BS of the cellular network.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a cellular downlink network energy efficiency optimization system and method based on caching popular content, which are applied to a fifth generation mobile communication network with explosive throughput requirements, ensure the service quality and the flow load of users, improve the energy benefit of a wireless mobile network, and reduce the emission of carbon dioxide. By introducing the method of adopting distributed cache popular content in the base station, the downlink sending rate of the wireless network, the user service quality, the wireless network energy efficiency and the like are greatly improved.
Cellular downlink network energy efficiency optimization system based on cached popular content, and cellular downlinkIn the network, the contents are classified according to the popularity of the contents, and different contents are stored in the high-speed caches of different base stations by adopting a distributed cache strategy; the cellular downlink network is divided into three layers: the core network, namely SGW, the base stations, namely BS, the users, namely UE, the SGW caches files requested by all the BSs, and each BS can cache NcBuffer device for individual content, with a capacity of CbhThe backhaul link of the network element is connected with the SGW and connected with the UE through a wireless link; the content is graded according to the content popularity, namely, the content is analyzed according to the request frequency, the access times and the content updating time of the mobile user to the content, and is divided into different popularity grades.
According to the method for the cellular downlink network energy efficiency optimization system based on the cached popular content, when a file requested by a user is cached in a local base station, the base station directly acquires the file from the cache device and transmits the file to the user, and the file is called as a cache hit user; otherwise, the base station acquires the file from the SGW through a backhaul link and then transmits the file to the user, namely the user is not hit in cache; the local base station means that each user is connected with the nearest base station;
optimizing downlink energy efficiency gain through a plurality of parameters involved in the transmission process, wherein the downlink energy efficiency gain refers to the ratio of the transmitted bit number to the consumed power, and the parameters comprise: general content directory FfCache hit rate H of user request content, average throughput R of user request content, and base station dormancy rate P based on user distributionsPower consumption P generated by backhaul link4Power consumption P for caching popular content3Average circuit power consumption of base station in sleep and active modes is PtAverage transmission power P of base station in dormant and active modesc(ii) a Introducing buffer capacity eta equal to Nc/FfThe method specifically comprises the following steps:
step 1: estimating the number of users in each cell according to the distribution of the poisson point process of the users;
step 2: realizing popularity distribution according to Zipf-link distribution;
and step 3: according to the content popularity distribution, considering the cache hit rate H of the content requested by the user;
and 4, step 4: designing a distributed cache strategy to improve the cache hit rate H;
and 5: designing a sleep mode of each base station according to the Poisson distribution of users;
step 6: according to the content popularity distribution, considering the average throughput R of the content requested by the user;
and 7: discussing the average Circuit Power consumption P of the base station in sleep and active modestAverage transmission power P of base station in sleep and active modesc；
And 8: calculating power consumption P for caching popular content according to the caching device and the backhaul device3And power consumption P generated by backhaul link4；
And step 9: optimizing the cache capacity to achieve maximum energy efficiency under the optimal cache capacity, with average total power consumption of
Figure BDA0001511665520000031
The downlink energy efficiency gain is equivalent to the average throughput R of the network and the average total power consumption of the base station
Figure BDA0001511665520000032
The ratio of (A) to (B):
Figure BDA0001511665520000033
solving and optimizing problem maxη∈(0,1)EE (η), η represents the cache capacity; when eta is eta ═ eta0The energy efficiency is the greatest.
Further, the specific method of step 2 is as follows: carrying out popularity grading on the content, caching the popular content, adopting a Zipf-like distribution model, and defining the content popularity as follows:
wherein, Ff＝{1,…,FfThe whole content catalog is represented, f represents that the user requests the f content, and the value of delta is between 0.5 and 1.0;
the specific method of the step 3 comprises the following steps: according to the distribution model of the content popularity, defining the cache hit rate as follows:
wherein
Figure BDA0001511665520000042
Indicates the buffer capacity, NcIs the size of each base station buffer, Ff＝{1,…,FfRepresents the entire content directory, f represents the user's request for the f-th content, and δ has a value between 0.5 and 1.0.
Further, in step 4, the distributed caching strategy is to divide the base stations into four categories a, b, c, and d in the cellular downlink network, and cache different contents in each base station adjacent to each other, where the base station of category a caches 4 th, 8 th, and 4N thcClass b base stations buffer class 3, 7, 4Nc-1 popular content, class c base station buffer 2, 6, 4Nc-2 popular content, class d base station buffer 1, 5, 4Nc-3 popular content, NcIs the size of each base station buffer.
Further, the specific method of step 5 is as follows: setting a sleep range time of the BS based on the sleep rate of the base stations distributed by users, namely setting the sleep range time of the BS to be in a sleep mode once no user service exists, and otherwise, operating the BS in an active mode; according to the distribution of users, the sleep rate of the BS is as follows:
Figure BDA0001511665520000043
wherein λ isuIndicating the density of the users in a Poisson distribution, with N in the cellbNumber of base stations.
Further, the specific method of step 6 is as follows: the average sending rate of the user request, that is, the network average throughput, needs to be obtained through the backhaul link when the base station does not cache the content of the user request, and the backhaul traffic load is constrained by the backhaul capacity, so the average instantaneous downlink throughput of the b-th cell is represented as:
Figure BDA0001511665520000044
wherein
Figure BDA0001511665520000045
Is the instantaneous received signal-to-interference ratio, CbhIs the backhaul capacity of the backhaul link, ubTotal number of users, ucIndicating the number of users whose requested content is cached at the base station.
Further, in step 7, the average power consumption of the circuit when the base station is in the sleep or active state is:
wherein ζBSζ 1 represents the active state of the base station BS0 represents the sleep state of the base station, P1Is the base station power supply power in active mode, P2Is the base station power supply in sleep mode;
the average transmission power consumption of the base station in the dormant or active state is respectively as follows:
Figure BDA0001511665520000051
where P represents the transmit power in the base station active mode and P is a factor affecting the power amplifier.
Further, the backhaul link power consumption P in step 84The specific calculation method comprises the following steps: the power consumption generated by the backhaul link, i.e. when the content requested by the user is not cached locally, and the content is acquired through the high-speed backhaul link, the backhaul power will be generated in the whole downlinkConsumption, cell average backhaul power consumption P4Comprises the following steps:
Figure BDA0001511665520000052
wherein, ω is2Is the power factor of the backhaul device,
Figure BDA0001511665520000053
representing the average rate of cache misses.
Further, the cache power consumption P in step 83The specific method comprises the following steps: the power consumption resulting from caching popular content, i.e. the power consumption that would occur if a cache were used at the base station to cache a portion of popular content, is the average cache power consumption P3Comprises the following steps:
P3＝ω1ηFFf
where F is the size of each content, Mbyte, ω1Is the power parameter of the cache, and η represents the cache capacity.
Further, in step 9, solving and optimizing the problem specifically includes:
Figure BDA0001511665520000054
Figure BDA0001511665520000056
Figure BDA0001511665520000057
where eta represents the buffer capacity, Ff＝{1,…,FfRepresents the entire content directory, f represents the user's request for the f-th content, δ has a value between 0.5 and 1.0, CbhIs the backhaul capacity of the backhaul link, NbIndicating a base station within a cellNumber, delta, value between 0.5 and 1.0, NcIs the size of each base station buffer, ω1Representing the power coefficient, ω, of a caching hardware device2Representing the power coefficient of the backhaul device.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
has the advantages that: compared with the prior art, the cellular downlink network energy efficiency optimization system and method based on the cached popular content have the following advantages: the invention firstly adopts a distributed cache mode to cache the content on the base station through the content popularity distribution to improve the energy efficiency. The method is applied to the fifth generation mobile communication network with explosive throughput requirements, ensures the service quality and the flow load of the user, improves the energy benefit of the wireless mobile network, and reduces the emission of carbon dioxide. Meanwhile, the method for caching the popular content in a distributed manner in the base station is introduced in the invention, so that the downlink sending rate of the wireless network, the user service quality, the wireless network energy efficiency and the like are greatly improved.
Drawings
FIG. 1 is a flow chart of overall downlink energy efficiency;
fig. 2 is a block diagram of downlink storage and distribution;
fig. 3 is a structural diagram of a distributed cache.
Fig. 4 is a graph of experimental results of energy efficiency and cache capacity.
Detailed Description
The invention provides a downlink energy efficiency optimization system and method based on caching popular content, the method adopts a distributed caching method to cache the content with different popularity in a base station, the whole cellular network is divided into three layers, which are respectively: serving Gateway (SGW), Base Station (BS), mobile (UE). The method comprises the following steps: SGW caches all files possibly requested by BS, each BS has a cacheable NcBuffer device for individual content, with a capacity of CbhWhen the file requested by the user is cached in the local base station, the base station directly acquires the file from the caching device and transmits the file to the userA household; otherwise, the base station will obtain the file from the SGW through the backhaul link and then transmit the file to the user. The popular content is at time T0The content is requested by the internal user for a large number of times. The downlink energy efficiency gain is the ratio of the number of bits transmitted to the power consumed in the overall downlink network. The distributed cache is that 4 adjacent base stations cache different contents, and when each user requests the contents, the nearest base station which caches the contents is selected to be connected. The local base station is connected with the nearest base station by each user.
The invention is further described with reference to the following figures and examples.
FIG. 1 of the drawings
Fig. 1 mainly divides energy efficiency into two parts: throughput of the downlink, average power consumption of the downlink.
Throughput of downlink: content popularity, hit rate of user requested content, average throughput of user request hits, average throughput of user request misses.
Average power consumption of downlink: the base station sleep probability, the base station circuit power and the transmission power in the sleep or active mode, the average cache power consumption and the average backhaul power consumption.
FIG. 2 of the drawings
Fig. 2 mainly shows the distributed caching strategy of the base station. Every four neighboring base stations cache different content, each user being associated with the nearest base station. The distributed caching strategy may reduce redundancy by storing different content in different base stations, and then caching N when each base station uses the distributed caching strategycEach user can access 4N at a time of contentcAnd (4) the content.
FIG. 3 of the drawings
Fig. 3 shows a network architecture diagram of the system. When a file requested by a user is cached in a local base station, the base station directly acquires the file from the caching equipment and transmits the file to the user through a transmission link; otherwise, the base station will acquire the file from the core network (SGW) through the backhaul link and then transmit the file to the user.
Examples
The invention is designed mainly by considering the following problems:
1) construction of a peptide having NbThe full frequency reuse of each base station is a wireless downlink buffer network model of downlink multi-cell multi-user, and the distribution of users and base stations is designed in the network model;
2) realizing popularity distribution of the user request content in T time;
3) considering whether the content requested by the user is cached in the base station or not, and realizing the design of the high-speed cache hit rate;
4) constructing a strategy for caching popular content in a distributed manner;
5) designing a sleep mode of a base station;
6) achieving throughput of a downlink network based on cache hit and miss rates
7) According to the base station sleep mode, circuit power consumption and transmission power consumption of the base station sleep state and the active state are respectively realized;
8) designing cache power consumption and backhaul link power consumption;
in order to solve the above problems, the whole design process is shown in fig. 1 and 2, and the core can be divided into the following steps:
step 1: estimating the number of users in each cell according to the distribution of the poisson point process of the users;
step 2: realizing popularity distribution according to Zipf-link distribution;
and step 3: according to the content popularity distribution, considering the hit rate of the content requested by the user;
and 4, step 4: a distributed cache strategy is designed, and the cache hit rate is improved.
And 5: and designing the sleep mode of each base station according to the Poisson distribution of the users.
Step 6: according to the content popularity distribution, considering the throughput of the user requesting the content;
and 7: circuit power consumption and transmission power consumption of the base station in the base station sleep and active modes are discussed.
And 8: and calculating cache power consumption and backhaul link power consumption according to the cache device and the backhaul device.
And step 9: optimizing the cache capacity and realizing the maximum energy efficiency under the optimal cache capacity.
Wherein each step is described in detail as follows:
step 1: construction of a peptide having NbThe wireless downlink buffer network model of multiple downlink cells and multiple users of full frequency reuse of each base station is designed, the distribution of the users and the base stations in the network model follows the distribution process of poisson points, and the number of the users in each cell is estimated. The network model for the entire downlink is shown in fig. 2. Suppose one has NbAnd the full frequency reuse of each base station is carried out on the downlink multi-cell multi-user wireless cache network. Each base station has NtA transmitting antenna and a buffer NcA cache device for each content and each base station has a backhaul capacity of CbhIs connected to a core network (SGW). Because the number of transmitting antennas at the base station end is limited, if more users send requests and the number of the transmitting antennas is larger than the number of the antennas, the base station cannot serve all the users at the same time, and at the moment, the polling scheduling is adopted to serve a plurality of users. When cell user density is high, a round robin scheduling method is used to select NtFor each user, base station b serves user ui with a probability of
Figure BDA0001511665520000081
There are several users in each cell and the spatial distribution of the users is modeled as a uniform poisson point process, assuming a density of λ in the entire networkuThen, the probability of u users in each cell is:
Figure BDA0001511665520000082
since full frequency reuse is adopted, Inter-cell Interference (ICI) is one of the major bottlenecks limiting throughput. In order to achieve energy efficiency gain, we assume that each base station knows ideal channel information, and simultaneously serves multiple scheduled users through Zero-Forcing Beamforming (ZFBF), which is a widely used precoder aiming to eliminate multi-user interference and provide equal power allocation among multiple users. Since network energy efficiency is associated with the throughput of the downlink, this depends largely on the interference level. To obtain the essence of the problem and simplify the analysis, we introduce a parameter β to reflect the degree to which ICI can be eliminated after some interference management means such as coordinated beamforming, serial interference suppression, etc. are adopted. The introduced signal-to-interference-and-noise ratio of the u-th user in cell i can be expressed as:
Figure BDA0001511665520000083
where P is the transmit power, rubAnd hubRespectively expressed as the distance from the base station i to the u scheduled users and the channel vector. α is the path loss exponent, σ2Is the variance of Gaussian white noise, and is beta epsilon [0,1 ∈]The ratio of the residual interference value to the total interference when no interference management is performed is reflected, for example, if β ═ 0 reflects an optimistic scenario, all the ICIs are completely eliminated, and if β ═ 1 reflects a pessimistic scenario, the interference coordination situation is not considered between the base stations.
When the content requested by the user is not cached on the base station, the base station needs to acquire from the core network through the backhaul link, and the backhaul traffic load is constrained by the backhaul capacity, and the instantaneous downlink throughput of all ith cells can be represented as:
Figure BDA0001511665520000091
where B is the bandwidth of the downlink, CbhIs the backhaul capacity, NcIs the size of the content buffered at the base station and the min x, y function is the minimum value between the returned x and y.
Step 2: the popularity distribution is achieved according to the Zipf-link distribution.
Rating content according to content popularity, i.e. updating content according to frequency of user requests for content, number of accesses and contentAnd time, carrying out popularity analysis on the content, and dividing the content into different popularity levels. The content popularity distribution changes over time, and in the present invention popular content is considered static, so the energy consumption of refreshing the cached content can be ignored. In the present invention, we consider a composition comprising FfA static catalog of content, ranked according to popularity from most popular to least popular. In the research, the distribution of Zipf-links was widely used to represent many realistic problems. Assuming that each user requests a content from the directory, the probability that the user requests the f-th content is:
Figure BDA0001511665520000092
wherein Ff＝{1,…,FfRepresents the whole content directory, δ has a value between 0.5 and 1.0, which determines the steepness of the popularity distribution curve, which depends on the user's behavior and the base station deployment density.
And step 3: according to the content popularity distribution, the hit rate of the user requesting the content is considered.
According to the popularity distribution of Zipf-link in the step 2, the hit rate of the user request content is completed, and the specific operation is as follows:
when the content requested by the user is cached in a local base station (each user is connected with the closest base station), the base station directly acquires the content from the cache equipment and transmits the content to the user through a wireless link, and the content is called that the cache hits the user; if the content requested by the user is not cached in the local base station, the base station acquires the file from the SGW through a backhaul link, then transmits the file to the base station, and then transmits the file to the user through a wireless link, which is called a cache miss user. According to the distribution model of the content popularity, defining the cache hit rate as follows:
Figure BDA0001511665520000093
wherein
Figure BDA0001511665520000094
Indicating the buffer capacity. N is a radical ofcIs the size of each base station buffer.
From the cache hit rate and the user distribution density, we deduce that when the ith base station serves ui users, there are uicThe probability that an individual user is a cache hit user is:
and 4, step 4: a distributed cache strategy is designed, and the cache hit rate is improved.
The distributed caching strategy is to cache the 4 th, 8 th and 4N th base stations marked with 'a' in the cellular networkcThe base station marked with 'b' buffers the 3 rd, 7 th and 4 th Nc-1 popular content, base station marked "c" buffers 2 nd, 6 th, 4 th Nc-2 popular content, base station marked "d" buffering 1 st, 5 th, 4 th Nc-3, wherein base stations adjacent to each other cache different content, reducing the redundancy of the base station caches while the hit rate of user requests is increased by a factor of 4. A model diagram of the distributed caching strategy is shown in fig. 3. The distributed cache adopted by the invention is that 4 adjacent base stations cache different contents, and when each user requests the contents, the nearest base station which caches the contents is selected to be connected, so that the method reduces the redundancy of the contents of the base stations and increases the cache hit rate.
And 5: and designing the sleep mode of each base station according to the Poisson distribution of the users.
The energy consumption of a base station depends on the type of base station and the operating state of the base station. When the base station is in an operating state, the power amplifier, the signal processing unit, the antenna, etc. all generate energy consumption. When the base station is in the dormant state, some units will be turned off, thereby saving part of the energy consumption. In order to reduce energy consumption and avoid interference between base stations, the present invention considers that the sleep range of the BS ranges from a very short time (less than 1ms) to a longer time (100ms), the BS becomes sleep mode once there is no user service, otherwise the BS operates in active mode, and the sleep rate of the BS is:
step 6: considering throughput when a user requests content according to content popularity distribution
From step 1 and step 3, we assume (u)i1…uic) The content requested by the user is cached in the ith base station, (u)ic+1…ui) The content requested by the user is not cached in the ith base station, and the average throughput of the ith cell can be obtained as
Figure BDA0001511665520000103
Figure BDA0001511665520000104
Figure BDA0001511665520000111
And 7: circuit power consumption and transmission power consumption of the base station in the base station sleep and active modes are discussed.
The energy consumption of a base station depends on the type of base station and the operating state of the base station. When the base station is in an operating state, the power amplifier, the signal processing unit, the antenna, etc. all generate energy consumption. When the base station is in the dormant state, some units will be turned off, thereby saving part of the energy consumption.
Deriving circuit power consumption and transmit power consumption from the base station sleep mode in step 5.
The average circuit power consumption of the base station in the dormant or active state is respectively:
wherein ζBSζ 1 represents the active state of the base station BS0 stands for dormancy of base stationState, P1Is the base station power supply power in active mode, P2Is the base station circuitry power in sleep mode.
The average circuit power consumption of the base station in the dormant or active state is respectively:
where P represents the transmit power in the base station active mode and P is a factor affecting the power amplifier.
And 8: and calculating cache power consumption and backhaul link power consumption according to the cache device and the backhaul device.
The present invention uses DRAM as caching hardware that will generate some power consumption when caching content at each base station. The average buffer power consumption of the BS may be expressed as:
Figure BDA0001511665520000114
wherein ω is1The unit is (watt/bit) of the power coefficient of the cache hardware. In the present invention, to calculate the energy efficiency more conveniently, we consider that each content is an F bit of the same size.
The present invention uses optical fiber as the backhaul link (capacity of 1 Gbps). When we download content over the backhaul link, the backhaul link will generate power consumption, and the average backhaul power consumption of the BS can be expressed as:
Figure BDA0001511665520000115
wherein ω is2Is the power factor of the backhaul hardware and η is the cache capacity.
And step 9: optimizing the cache capacity and realizing the maximum energy efficiency under the optimal cache capacity.
According to the average downlink throughput and the power consumption of the base station in the steps, the energy efficiency of the downlink network in the buffer of the base station is deduced. In the present invention, the energy efficiency gain of the downlink network is defined as the bit rate transmitted to the user by the downlink when the user requests the content in the unit power consumption in the whole downlink network, that is, the ratio of the transmitted bit number to the average consumed energy consumption is equivalent to the ratio of the average throughput of the network to the average total power consumption of the base station, so the energy efficiency gain formula is:
Figure BDA0001511665520000121
Figure BDA0001511665520000122
it is an object of the present invention to maximize the energy efficiency of a downlink wireless network by finding the best buffer content in the local base station. Mathematically, we have the following optimization problem,
Figure BDA0001511665520000123
Figure BDA0001511665520000124
Figure BDA0001511665520000125
Figure BDA0001511665520000126
the process of solving the maximum energy efficiency is as follows:
1) firstly, derivation is carried out on the energy efficiency EE in an interval range, and the derivative is 0, so that the following can be obtained:
Figure BDA0001511665520000127
Figure BDA0001511665520000128
Figure BDA0001511665520000129
wherein Q and M are constants.
2) Order function
Figure BDA00015116655200001210
And the equation f (eta) is derived to obtain:
Figure BDA0001511665520000131
when eta>0, f' (η)<0, so the equation f (η) is a monotonically decreasing function. Because η ═ η0When the equation f (η) is 0, we can get,
Figure BDA0001511665520000132
since f (η) decreases with increasing η, when η<η0When the energy efficiency EE is a monotonically increasing function, when η>η0When it is time, the energy efficiency EE is a monotonically decreasing function, thus at η0Where is the maximum of EE. From this, the cache capacity η can be obtained0The energy efficiency is the greatest.
Results of the experiment
Fig. 4 shows the relationship between energy efficiency and cache capacity, with energy efficiency first increasing and then decreasing with cache capacity. The energy efficiency EE is maximized when the buffer capacity is equal to 0.17. This also means that optimizing the caching strategy and caching capacity maximizes energy efficiency.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (1)

1. A cellular downlink network energy efficiency optimization system based on caching popular content, characterized by: cellular downlink networkIn the network, the content is classified according to the content popularity, and different contents are stored in the high-speed caches of different base stations by adopting a distributed cache strategy; the cellular downlink network is divided into three layers: the core network, namely SGW, the base stations, namely BS, the users, namely UE, the SGW caches files requested by all the BSs, and each BS can cache NcBuffer device for individual content, with a capacity of CbhThe backhaul link of the network element is connected with the SGW and connected with the UE through a wireless link; the content is graded according to the content popularity, namely, the content is analyzed according to the request frequency, the access times and the content updating time of the mobile user to the content, and is divided into different popularity grades;
when a file requested by a user is cached in a local base station, the base station directly acquires the file from the caching equipment and transmits the file to the user, and the file is called a cache hit user; otherwise, the base station acquires the file from the SGW through a backhaul link and then transmits the file to the user, namely the user is not hit in cache; the local base station means that each user is connected with the nearest base station;
optimizing downlink energy efficiency gain through a plurality of parameters involved in the transmission process, wherein the downlink energy efficiency gain refers to the ratio of the transmitted bit number to the consumed power, and the parameters comprise: general content directory FfCache hit rate H of user request content, average throughput R of user request content, and base station dormancy rate P based on user distributionsPower consumption P generated by backhaul link4Power consumption P for caching popular content3Average circuit power consumption of base station in sleep and active modes is PtAverage transmission power P of base station in dormant and active modesc(ii) a Introducing buffer capacity eta equal to Nc/FfThe method specifically comprises the following steps:
step 1: estimating the number of users in each cell according to the distribution of the poisson point process of the users;
step 2: the popularity distribution is realized according to the Zipf-link distribution, and the specific method comprises the following steps: carrying out popularity grading on the content, caching the popular content, adopting a Zipf-like distribution model, and defining the content popularity as follows:
Figure FDA0002202763990000011
wherein, Ff＝{1，…，FfThe whole content catalog is represented, f represents that the user requests the f content, and the value of delta is between 0.5 and 1.0;
and step 3: according to the content popularity distribution, calculating the cache hit rate H of the content requested by the user: according to the distribution model of the content popularity, defining the cache hit rate as follows:
Figure FDA0002202763990000021
whereinIndicates the buffer capacity, NcIs the size of each base station buffer, Ff＝{1，…，FfThe whole content catalog is represented, f represents that the user requests the f content, and the value of delta is between 0.5 and 1.0;
and 4, step 4: designing a distributed cache strategy to improve the cache hit rate H; the distributed caching strategy is that in a cellular downlink network, base stations are divided into four types of a, b, c and d, and the base stations adjacent to each other cache different contents, wherein the base station of the type a caches 4 th, 8 th and 4N thcClass b base stations buffer class 3, 7, 4Nc-1 popular content, class c base station buffer 2, 6, 4Nc-2 popular content, class d base station buffer 1, 5, 4Nc-3 popular content, NcIs the size of each base station buffer;
and 5: designing a sleep mode of each base station according to the Poisson distribution of users, wherein the specific method comprises the following steps: setting a sleep range time of the BS based on the sleep rate of the base stations distributed by users, namely setting the sleep range time of the BS to be in a sleep mode once no user service exists, and otherwise, operating the BS in an active mode; according to the distribution of users, the sleep rate of the BS is as follows:
Figure FDA0002202763990000023
wherein λ isuIndicating the density of the users in a Poisson distribution, with N in the cellbThe number of base stations;
step 6: according to the content popularity distribution, calculating the average throughput R of the content requested by the user, wherein the specific method comprises the following steps: the average sending rate of the user request, that is, the network average throughput, needs to be obtained through the backhaul link when the base station does not cache the content of the user request, and the backhaul traffic load is constrained by the backhaul capacity, so the average instantaneous downlink throughput of the b-th cell is represented as:
Figure FDA0002202763990000024
Figure FDA0002202763990000025
wherein
Figure FDA0002202763990000031
Is the instantaneous received signal-to-interference ratio, CbhIs the backhaul capacity of the backhaul link, ubTotal number of users, ucIndicating the number of users whose requested content is cached at the base station;
and 7: calculating average circuit power consumption P of base station in sleep and active modestAverage transmission power P of base station in sleep and active modesc(ii) a The average circuit power consumption of the base station in the dormant or active state is respectively:
Figure FDA0002202763990000032
wherein ζBSζ 1 represents the active state of the base stationBS0 represents the sleep state of the base station, P1Is the base station power supply power in active mode, P2Is the base station power supply in sleep mode;
the average transmission power consumption of the base station in the dormant or active state is respectively as follows:
Figure FDA0002202763990000033
wherein P represents the transmission power in the active mode of the base station, and P is a factor influencing the power amplifier;
and 8: calculating power consumption P for caching popular content according to the caching device and the backhaul device3And power consumption P generated by backhaul link4；
Backhaul link power consumption P4The specific calculation method comprises the following steps: the power consumption generated by the backhaul link, i.e. when the content requested by the user is not cached locally, and the content is obtained through the high-speed backhaul link, the backhaul power consumption will be generated in the whole downlink, and the average backhaul power consumption P of the cell will be generated4Comprises the following steps:
Figure FDA0002202763990000034
wherein, ω is2Is the power factor of the backhaul device,
Figure FDA0002202763990000035
represents the average rate of cache misses;
cache power consumption P3The specific method comprises the following steps: the power consumption resulting from caching popular content, i.e. the power consumption that would occur if a cache were used at the base station to cache a portion of popular content, is the average cache power consumption P3Comprises the following steps:
P3＝ω1ηFFf
where F is the size of each content, Mbyte, ω1Is a power parameter of the cache, η represents the cache capacity;
and step 9: optimizing the cache capacity to achieve maximum energy efficiency under the optimal cache capacity, with average total power consumption of
Figure FDA0002202763990000046
The downlink energy efficiency gain is equivalent to the average throughput R of the network and the average total power consumption of the base station
Figure FDA0002202763990000047
The ratio of (A) to (B):
Figure FDA0002202763990000041
solving and optimizing problem maxη∈(0，1)EE (η), η represents the cache capacity; when eta is eta ═ eta0The energy benefit is maximum; the solving and optimizing problem is specifically as follows:
Figure FDA0002202763990000042
Figure FDA0002202763990000043
Figure FDA0002202763990000044
where eta represents the buffer capacity, Ff＝{1，…，FfRepresents the entire content directory, f represents the user's request for the f-th content, δ has a value between 0.5 and 1.0, CbhIs the backhaul capacity of the backhaul link, NbRepresents the number of base stations in the cell, the value of delta is between 0.5 and 1.0, NcIs the size of each base station buffer, ω1Representing the power coefficient, ω, of a caching hardware device2Representing the power coefficient of the backhaul device.
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