CN108093435A - Cellular downlink network energy efficiency optimization system and method based on caching popular content - Google Patents

Abstract

The invention discloses a cellular downlink network energy efficiency optimization system and method based on caching of popular content. A distributed caching method is used to cache content with different popularity in a base station. The cellular network is divided into SGW, BS, and UE. The SGW caches the files requested by the BS. Each BS has a cache device that can cache N _c contents, and is connected to the SGW through a backhaul link with a capacity of C _bh . When the file requested by the user is cached in the local base station, the base station directly obtains the file and transmits it to user; otherwise, the file is obtained from the SGW through the backhaul link and transmitted to the user. The popular content is the content requested by the user more times in the time T ₀ , and the downlink energy efficiency gain is the ratio of the number of transmitted bits to the consumed power in the entire downlink network. Distributed cache is that four adjacent base stations cache different content. When each user requests content, the nearest base station that caches the content is selected to connect. This method reduces the redundancy of base station content and increases the cache hit rate.

Description

System and method for optimizing cellular downlink network energy efficiency based on buffering popular content

technical field

The invention belongs to the technical field of mobile networks, and in particular relates to a cellular downlink network energy efficiency optimization system and method based on caching popular content, in particular to a cellular downlink network energy efficiency optimization method based on caching popular content in a 5G cellular network. It is mainly used in the fifth-generation mobile communication network with explosive throughput requirements. This method ensures the user service quality and traffic load, improves the energy efficiency of the wireless mobile network, and reduces carbon dioxide emissions at the same time.

Background technique

With the rise of smart terminals, tablet PCs, and social networks, the demand for mobile services will grow explosively, and wireless data traffic and signaling will have an unprecedented impact on mobile communication networks. According to the forecast of the International Telecommunication Union, by 2020, the demand for data service capacity of mobile communication networks will reach 1,000 times that of 4G commercial networks. 4G technology is difficult to meet the development needs of mobile communication technology. In addition, there are already studies on smart grids and driverless cars. 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. In short, future mobile communication needs not only to adapt to ultra-dense wireless communication with multiple users and multiple base stations, but also to adapt to diversified mobile services and scenarios.

In response to the above requirements, the research and development of the fifth generation mobile communication system (5G) is already in the testing stage. Although research on 5G systems and key technologies has been gradually carried out, the evolution of the core technologies of 5G systems, especially ultra-dense heterogeneous networks, self-organizing networks, and content distribution networks, has not yet been well received by the academic and industrial circles. A clear and consistent understanding of the world. In order to realize the 1000 times the data service capacity requirement in the future mobile communication network, the current research ideas mainly solve the problem from three aspects: wireless transmission, spectrum resources, and network architecture. Among them, the future network architecture mainly includes the following features: ultra-dense heterogeneous network integration, heterogeneous cells with different coverage areas (overlapping coverage), and multiple types of access networks. Although these technologies can increase capacity, they also bring new technical challenges to mobile communication networks.

Due to the need to consider channel interference, uncertainty, power control, channel overhead and other issues in the design of wireless network virtualization, the wireless network is more complex than the wired network, so the virtualization technology of the wired network cannot be directly applied to wireless virtualization in the network. Therefore, realizing a wireless virtualized network and integrating communication and storage resources at the same time can improve spectrum efficiency and energy consumption efficiency. Wireless virtualization technology has become an important development direction of future networks.

To meet the explosive demands on throughput, support sustainable development and reduce global CO2 emissions, energy efficiency (EE) has become the main performance indicator of 5th generation cellular networks. The EE of the network can be improved by introducing a new network framework, optimizing network deployment and resource allocation. It can be observed from recent studies that repeated downloads of many popular contents generate most of the mobile multimedia traffic. This reflects the shift of the web from traditional transmitter-receiver communication to the primary goal of content dissemination. On the other hand, the storage capacity of today's storage devices is also increasing rapidly. Therefore, caching devices in base stations (BSs) offer a promising approach to unleash the potential of cellular networks.

Caching is a technique that improves performance in many areas of wired networking, such as content-centric networking (CCN). In cellular networks, caching popular content at the edge of the network reduces backhaul costs, access latency, and energy consumption, and increases throughput. According to previous research, it can be noticed that the backhaul becomes the bottleneck in the small network (SCN) (such as: 5G ultra-dense network (UDN)), and the disk size grows rapidly at a relatively low cost. In the face of this situation, it can be solved by caching The device replaces the backhaul link cache at the BS.

For highly skewed requirements, the cache should be pushed to the edge, such as the SGW or BS of the cellular network.

Contents of the invention

Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a cellular downlink network energy efficiency optimization system and method based on buffering popular content, which is applied to the fifth-generation mobile communication network with explosive throughput requirements. Ensure user service quality and traffic load, improve the energy efficiency of wireless mobile networks, and reduce carbon dioxide emissions. By introducing the method of adopting distributed caching of popular content in the base station, the downlink transmission rate of the wireless network, the user service quality, and the energy efficiency of the wireless network are greatly improved.

A cellular downlink network energy efficiency optimization system based on caching popular content. In the cellular downlink network, the content is classified according to the popularity of the content, and a distributed caching strategy is used to store different content in the caches of different base stations; The cellular downlink network is divided into three layers: the core network is the SGW, the base station is the BS, and the user is the UE. The SGW caches all files requested by the BS. Each BS has a cache device that can cache N _c content, and the throughput capacity is C _bh The backhaul link of the mobile phone is connected to the SGW, and is connected to the UE through a wireless link; the content is graded according to the popularity of the content, that is, the content is popular according to the mobile user's request frequency, access times, and content update time for the content. Level analysis, divided into different prevalence levels.

In the above-mentioned method of the cellular downlink network energy efficiency optimization system based on caching popular content, when the file requested by the user is cached in the local base station, the base station directly obtains the file from the cache device and transmits it to the user, which is called a cache hit user; otherwise , the base station will obtain the file from the SGW through the backhaul link and then transmit it to the user, which is called a cache miss user; the local base station means that each user is connected to the nearest base station;

The downlink energy efficiency gain is optimized through several parameters involved in the transmission process. The downlink energy efficiency gain refers to the ratio of the number of bits sent to the power consumed. The parameters include: the total content directory F _f , the cache hit rate H of user requested content, the average throughput R of user requested content, the base station sleep rate P _s based on user distribution, the power consumption P ₄ generated by the backhaul link, the power consumption P ₃ of caching popular content, sleep and The average circuit power consumption of the base station in active mode is P _t , and the average transmission power consumption of the base station in sleep and active mode is P _c ; the introduction of buffer capacity η=N _c /F _f specifically includes the following steps:

Step 1: Estimate the number of users in each cell according to the distribution of the user's Poisson point process;

Step 2: Realize the popularity distribution according to the Zipf-link distribution;

Step 3: According to the content popularity distribution, consider the cache hit rate H of the content requested by the user;

Step 4: Design a distributed cache strategy to improve the cache hit rate H;

Step 5: According to the Poisson distribution of users, design the sleep mode of each base station;

Step 6: According to the content popularity distribution, consider the average throughput R of content requested by users;

Step 7: Discuss the average circuit power consumption P _t of the base station in sleep and active mode, and the average transmit power consumption P _c of the base station in sleep and active mode;

Step 8: According to the cache device and the backhaul device, calculate the power consumption P ₃ for caching the popular content and the power consumption P ₄ generated by the backhaul link;

Step 9: Optimize the cache capacity to achieve the maximum energy efficiency under the optimal cache capacity, the average total power consumption is 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 The ratio:

Solve and optimize the problem max _η∈(0,1) EE(η), η represents the cache capacity; when η=η ₀ , the energy benefit is the largest.

Further, the specific method of step 2 is: classify the popularity of the content, cache the popular content, adopt the Zipf-like distribution model, and define the popularity of the content as:

Among them, F _f ={1,...,F _f } represents the entire content directory, f indicates that the user requests the fth content, and the value of δ is between 0.5-1.0;

The specific method of said step 3 is: according to the distribution model of content popularity, the cache hit rate is defined as:

in Indicates the cache capacity, N _c is the cache size of each base station, F _f = {1,...,F _f } represents the entire content directory, f represents the user request for the fth content, and the value of δ is between 0.5-1.0 .

Further, in the 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 each base station adjacent to each other caches different content, Among them, the base station of type a caches the popular content of the 4th, 8th, and 4N _c , the base station of type b caches the popular content of the 3rd, 7th, and 4N _c -1, and the base station of type c caches the popular content of the 2nd, 6th, and 4N _c -2 , the base station of class d caches the popular contents of the 1st, 5th, and 4th N _c -3, and N _c is the size of each base station cache.

Further, the specific method of step 5 is: base station sleep rate based on user distribution, that is, in order to reduce energy consumption and avoid interference between base stations, set the BS sleep range time, once there is no user service, the BS will become sleep mode , otherwise the BS operates in active mode; according to the distribution of users, the BS dormancy rate is:

Among them, _λu represents the density of users adopting Poisson distribution, and there are N _b base station numbers in the cell.

Further, the specific method of step 6 is: the average sending rate of the user request is the average throughput of the network. When the content requested by the user is not cached on the base station, it needs to be obtained through the backhaul link, and the backhaul traffic load is constrained by the backhaul capacity , so the average instantaneous downlink throughput of the bth cell is expressed as:

in is the instantaneous received signal-to-interference ratio, _{C bh} is the backhaul capacity of the backhaul link, _ub the total number of users, and uc represents the number of users whose requested content is cached in the base station.

Further, in step 7, the average circuit power consumption of the base station in the dormant or active state is respectively:

Wherein ζ _BS =1 represents the active state of the base station, ζ _BS =0 represents the dormant state of the base station, P ₁ is the power supply of the base station in the active mode, and P ₂ is the power supply of the base station in the dormant mode;

The average transmit power consumption of the base station in sleep or active state is:

Among them, P represents the transmit power in the active mode of the base station, and ρ is a factor affecting the power amplifier.

Further, the specific calculation method of the backhaul link power consumption P4 in step ₈ is: the power consumption generated by the backhaul link, that is, when the content requested by the user is not cached in the local cache, the content is obtained through the high-speed backhaul link , then the backhaul power consumption will be generated in the entire downlink, and the average backhaul power consumption _P4 of the cell is:

where _ω2 is the power coefficient of the backhaul equipment, Indicates the average rate of cache misses.

Further, the specific method of the cache power consumption _P3 in the step 8 is: the power consumption produced by the cache popular content, that is, the power consumption will be generated when the cache memory is used to cache a part of the popular content at the base station, then the average cache power consumption _P3 is:

P ₃ =ω1ηFF _f

Wherein, F is the size of each content, i.e. Mbyte, ω ₁ is the power parameter of the cache, and η represents the cache capacity.

Further, in the step 9, the solution and optimization problem is specifically:

Among them, η represents the cache capacity, F _f ={1,...,F _f } represents the entire content directory, f represents the user’s request for the fth content, the value of δ is between 0.5-1.0, and C _bh is the backhaul link N _b represents the number of base stations in the cell, the value of δ is between 0.5 and 1.0, N _c is the buffer size of each base station, ω ₁ represents the power coefficient of the cache hardware device, and ω ₂ represents the power of the backhaul device coefficient.

Technical scheme: in order to achieve the above object, the technical scheme adopted in the present invention is:

Beneficial effects: the present invention provides a cellular downlink network energy efficiency optimization system and method based on cached popular content, compared with the prior art, has the following advantages: the present invention adopts the distributed cache mode for the first time in the Content is cached on the base station to improve energy efficiency. The invention is applied to the fifth-generation mobile communication network with explosive throughput requirements, ensures user service quality and traffic load, improves energy efficiency of the wireless mobile network, and reduces carbon dioxide emission at the same time. At the same time, the method of adopting distributed buffering of popular content in the base station is introduced in the present invention, which greatly improves the downlink transmission rate of the wireless network, the user service quality, and the energy efficiency of the wireless network.

Description of drawings

Figure 1 is a flowchart of the entire downlink energy efficiency;

Figure 2 is a structural diagram of downlink storage and distribution;

FIG. 3 is a structural diagram of a distributed cache.

Fig. 4 is a diagram of experimental results of energy efficiency and cache capacity.

Detailed ways

The present invention proposes a downlink energy efficiency optimization system and method based on caching popular content. The method uses a distributed caching method to cache content with different popularity in the base station. The entire cellular network is divided into three layers, namely: service gateway (SGW), base station (BS), mobile use (UE). The method includes the following steps: the SGW caches files that may be requested by all BSs, and each BS has a cache device that can cache _Nc contents, and is connected to the SGW through a backhaul link with a capacity of _Cbh , when the file requested by the user is cached in the local base station , the base station directly obtains the file from the cache device and transmits it to the user; otherwise, the base station obtains the file from the SGW through the backhaul link and then transmits it to the user. The popular content is the content that the user requests more times in time T ₀ . The downlink energy efficiency gain is the ratio of the number of transmitted bits to the power consumed in the entire downlink network. The distributed cache is that four adjacent base stations cache different content. When each user requests content, the nearest base station that caches the content is selected to be connected. This method reduces the redundancy of base station content and increases Cache hit rate. The local base station is that each user is connected to the nearest base station.

The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

Attachment 1

Figure 1 mainly divides the energy efficiency into two parts: the throughput of the downlink and the average power consumption of the downlink.

Throughput of the downlink: content popularity, hit ratio of user requests for content, average throughput of user request hits, average throughput of user request misses.

Average downlink power consumption: base station sleep probability, base station circuit power and transmit power in sleep or active mode, average buffer power consumption, and average backhaul power consumption.

Attachment 2

Figure 2 mainly shows the distributed cache strategy of the base station. Each of the four adjacent base stations caches different content, and each user is associated with the nearest base station. The distributed caching strategy can reduce redundancy by storing different contents in different base stations, then when each base station uses the distributed caching strategy to cache N _c contents, each user can access 4N _c contents.

Attachment 3

Figure 3 shows the network structure diagram of the system. When the file requested by the user is cached in the local base station, the base station will directly obtain the file from the cache device and transmit it to the user through the transmission link; otherwise, the base station will obtain the file from the core network (SGW) through the backhaul link and then transmit it to the user. user.

Example

The following problems are mainly considered during the design of the present invention:

1) Construct a wireless downlink cache network model with _Nb base stations for full-frequency reuse downlink multi-cell multi-user, and design the distribution of users and base stations in the network model;

2) Realize the popularity distribution of content requested by users within T time;

3) Consider whether the content requested by the user is cached in the base station, and realize the design of the cache hit rate;

4) Build a strategy for distributed caching of popular content;

5) Design the sleep mode of the base station;

6) Realize the throughput of the downlink network according to the cache hit rate and miss rate

7) According to the sleep mode of the base station, respectively realize the circuit power consumption and transmission power consumption of the base station sleep state and active state;

8) Design cache power consumption and backhaul link power consumption;

In order to solve the above problems, the entire design process is shown in Figure 1 and Figure 2. The core can be divided into the following steps:

Step 1: Estimate the number of users in each cell according to the distribution of the user's Poisson point process;

Step 2: Realize the popularity distribution according to the Zipf-link distribution;

Step 3: According to the distribution of content popularity, consider the hit rate of content requested by users;

Step 4: Design a distributed cache strategy to improve the cache hit rate.

Step 5: According to the Poisson distribution of users, design the sleep mode of each base station.

Step 6: Consider the throughput of content requested by users according to the distribution of content popularity;

Step 7: Discuss the circuit power consumption and transmit power consumption of the base station in sleep and active mode of the base station.

Step 8: According to the cache device and the backhaul device, calculate the cache power consumption and the backhaul link power consumption.

Step 9: Optimize cache capacity to achieve maximum energy efficiency with optimal cache capacity.

Among them, the detailed description of each step is as follows:

Step 1: Construct a wireless downlink buffer network model with N _b base stations for full-frequency reuse downlink multiple cells and multiple users, and design the distribution of users and base stations in the network model to follow the Poisson point distribution process, and estimate each The number of users in the community. The network model of the entire downlink is shown in Figure 2. Assume a full-frequency reuse downlink multi-cell multi-user wireless buffer network with N _b base stations. Each base station has N _t transmit antennas and a cache device for caching N _c contents, and each base station is connected to the core network (SGW) through a backhaul link with a backhaul capacity of _Cbh . Due to the limited number of transmitting antennas at the base station, if there are many users sending requests that are greater than the number of antennas, the base station cannot serve all users at the same time. At this time, round-robin scheduling is used to serve multiple users. When the user density in the cell is high, the round-robin scheduling method is used to select N _t users, and the probability that base station b serves user ui is

There are several users in each cell, and the spatial distribution of users is modeled as a uniform Poisson point process. Assuming that the density in the entire network is λ _u , the probability of u users in each cell is:

Due to the adoption of full-frequency reuse, Inter-cell Interference (ICI) is one of the main bottlenecks limiting the throughput at this time. In order to achieve energy efficiency gain, we assume that each base station knows the ideal channel information, and simultaneously serve multiple scheduled users through Zero-Forcing Beamforming (ZFBF). This method is a widely used precoder, the purpose is Eliminates multi-user interference and provides equal power distribution among multiple users. Since the network energy efficiency is linked to the throughput of the downlink, it depends heavily on the interference level. In order 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 measures such as coordinated beamforming and serial interference suppression are adopted. Then the introduction SINR of the uth user in cell i can be expressed as:

Among them, P is the transmission power, r _ub and h _ub represent the distance and channel vector from base station i to u scheduled users respectively. α is the path loss index, σ ² is the variance of Gaussian white noise, β∈[0,1] reflects the proportion of residual interference value after ICI can pass some interference management technology to the total interference without interference management, for example, β= 0 reflects an optimistic situation, and all ICIs are completely eliminated, and β=1 reflects a pessimistic situation, and the interference coordination between base stations is not considered.

When the content requested by the user is not cached on the base station, the base station needs to obtain it from the core network through the backhaul link, and the backhaul traffic load is constrained by the backhaul capacity. The instantaneous downlink throughput of all i-th cells can be expressed as:

Where B is the bandwidth of the downlink, C _bh is the backhaul capacity, N _c is the size of the content cached in the base station, and the min{x,y} function returns the smallest value between x and y.

Step 2: Realize the popularity distribution according to the Zipf-link distribution.

Classify the content according to the popularity of the content, that is, analyze the popularity of the content according to the frequency of user requests for the content, the number of visits and the time when the content is updated, and divide it into different popularity levels. The content popularity distribution changes with time, and the popular content is regarded as static in the present invention, so the energy consumption of refreshing cached content can be ignored. In this invention, we consider a static directory containing F _f content, ranked according to popularity from most to least popular. In research, Zipf-link distributions are widely used to represent many real-world problems. Assuming that each user requests one content from the directory, the probability that a user requests the fth content is:

Where F _f ={1,...,F _f } represents the entire content directory, and the value of δ is between 0.5-1.0, which determines the steepness of the popularity distribution curve, which depends on user behavior and base station deployment density.

Step 3: According to the content popularity distribution, consider the hit rate of the content requested by the user.

According to the popularity distribution of Zipf-link in step 2, the hit rate of the content requested by the user is completed, and the specific operation is as follows:

When the content requested by the user is cached in the local base station (each user is connected to the nearest base station), the base station directly obtains the content from the cache device and transmits it to the user through a wireless link, which is called a cache hit user; if the user requests If the content is not cached in the local base station, the base station will obtain the file from the SGW through the backhaul link and then transmit it to the base station, and then transmit it to the user through the wireless link, which is called a cache miss user. According to the distribution model of content popularity, the cache hit rate is defined as:

in Indicates the cache capacity. N _c is the size of each base station buffer.

According to the cache hit rate and user distribution density, we deduce that when the i-th base station serves ui users, the probability that u _ic users are cache hit users is:

Step 4: Design a distributed cache strategy to improve the cache hit rate.

The distributed caching strategy is to cache the popular content of the 4th, 8th, and 4N _c in the base station marked with "a", and the popular content of the 3rd, 7th, and 4N _c -1 in the base station marked with "b". Content, the base station marked with "c" caches the popular content of the 2nd, 6th, and 4N _c -2, and the base station marked with "d" caches the popular content of the 1st, 5th, and 4N _c -3, among which each other Each adjacent base station caches different content, which reduces the redundancy of the base station cache, and at the same time increases the hit rate of user requests by 4 times. The model diagram of the distributed cache strategy is shown in Figure 3. The distributed cache adopted by the present invention is that four adjacent base stations cache different content. When each user requests content, the nearest base station that caches the content is selected to be connected. This method reduces the redundancy of base station content and increases Cache hit rate.

Step 5: According to the Poisson distribution of users, design the sleep mode of each base station.

The energy consumption of the base station depends on the type of the base station and the working state of the base station. When the base station is in working state, power amplifiers, signal processing units, antennas, etc. will all generate energy consumption. When the base station is in sleep state, some units will be turned off, so as to save some energy consumption. In order to reduce energy consumption and avoid interference between base stations, the present invention considers that the BS dormancy ranges from a very short time (less than 1 ms) to a longer time (100 ms). Once there is no user service, the BS becomes a dormant mode, otherwise the BS will be active Mode operation, according to the distribution of users, the BS sleep rate is:

Step 6: Consider the throughput when users request content according to the content popularity distribution

According to step 1 and step 3, we assume that (u _i1 … u _ic ) the content requested by the user is cached in the i-th base station, and (u _ic+1 … u _i ) the content requested by the user is not cached in the i-th base station , we can conclude that the average throughput of the i-th cell is

Step 7: Discuss the circuit power consumption and transmit power consumption of the base station in sleep and active mode of the base station.

The energy consumption of the base station depends on the type of the base station and the working state of the base station. When the base station is in working state, power amplifiers, signal processing units, antennas, etc. will all generate energy consumption. When the base station is in sleep state, some units will be turned off, so as to save some energy consumption.

According to the sleep mode of the base station in step 5, the circuit power consumption and transmission power consumption are derived.

The average circuit power consumption of the base station in sleep or active state is:

Where ζ _BS =1 represents the active state of the base station, ζ _BS =0 represents the sleep state of the base station, P ₁ is the power supply of the base station in the active mode, and P ₂ is the circuit power of the base station in the sleep mode.

The average circuit power consumption of the base station in sleep or active state is:

Among them, P represents the transmit power in the active mode of the base station, and ρ is a factor affecting the power amplifier.

Step 8: According to the cache device and the backhaul device, calculate the cache power consumption and the backhaul link power consumption.

The present invention uses DRAM as cache hardware, and when caching content in each base station, the cache hardware will generate some power consumption. The average cache power consumption of BS can be expressed as:

Wherein ω ₁ is the power coefficient of the cache hardware, and the unit is (watt/bit). In the present invention, in order to calculate the energy efficiency more conveniently, we consider that each content is F bits of the same size.

The present invention uses optical fiber as the backhaul link (with a capacity of 1 Gbps). When we download content through the backhaul link, the backhaul link will generate power consumption, and the average backhaul power consumption of BS can be expressed as:

where _ω2 is the power coefficient of the backhaul hardware and η is the cache capacity.

Step 9: Optimize cache capacity to achieve maximum energy efficiency with optimal cache capacity.

From the average downlink throughput and base station power consumption in the above steps, we derive the energy efficiency of the downlink network in the base station buffer. In the present invention, the energy efficiency gain of the downlink network is defined as the bit rate sent by the downlink to the user when the user requests content in the unit power consumption of the entire downlink network, that is, the average of the number of transmitted bits and the consumption The ratio of 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:

The aim of the present invention is to maximize the energy efficiency of downlink wireless networks by finding the best buffer content in the local base station. Mathematically, we have the following optimization problem,

The process of finding the maximum energy efficiency is:

1) First, derivate the energy efficiency EE within the interval range, and set the derivative to 0, you can get:

where Q and M are constants.

2) command function And to equation f (η) derivation, get:

When η>0, f'(η)<0, so the equation f(η) is a monotonically decreasing function. Because when η=η ₀ , the equation f(η)=0, we can get,

Since f(η) decreases with the increase of η, so when η<η ₀ , the energy efficiency EE is a monotonically increasing function, and when η>η ₀ , the energy efficiency EE is a monotonically decreasing function, so it is the maximum of EE at η ₀ value. Thus it can be obtained that when the buffer capacity is η ₀ , the energy benefit is the greatest.

Experimental results

Figure 4 shows the relationship between energy efficiency and cache capacity, where energy efficiency first increases with cache capacity and then decreases. When the cache capacity is equal to 0.17, the energy efficiency EE reaches the maximum. This also means that optimizing the caching strategy and caching capacity can maximize energy efficiency.

The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications are also possible. It should be regarded as the protection scope of the present invention.

Claims (10)

1. A cellular downlink network energy efficiency optimization system based on caching popular content, characterized by: in the cellular downlink network, contents are classified according to the popularity degree of the contents, and different contents are stored in 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 N _c A buffer device for individual contents, with a throughput of C _bh The 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 popularity of the content, namely, the popularity of the content is analyzed according to the request frequency, the access times and the content updating time of the mobile user, and the content is divided into different popularity grades.

2. The method for a cellular downlink network energy efficiency optimization system based on caching popular content according to claim 1, wherein: 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, which is called a cache miss user; 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 F _f Cache hit rate H of user request content, average throughput R of user request content, and base station dormancy rate P based on user distribution _s Power consumption P generated by backhaul link ₄ Power consumption P for caching popular content ₃ Average circuit power consumption of base station in sleep and active modes is P _t Average transmission power P of base station in dormant and active modes _c (ii) a Introducing a buffer capacity eta = N _c /F _f The 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: a distributed cache strategy is designed, and the cache hit rate H is improved;

and 5: designing a sleep mode of each base station according to the Poisson distribution of users;

and 6: according to the content popularity distribution, considering the average throughput R of the content requested by the user;

and 7: discussion of average Circuit Power consumption P of base stations in sleep and active modes _t Average transmission power P of base station in sleep and active modes _c ；

And step 8: calculating power consumption P for caching popular content according to the caching device and the backhaul device ₃ And power consumption P generated by backhaul link ₄ ；

And step 9: optimizing the cache capacity to achieve maximum energy efficiency under the optimal cache capacity, with average total power consumption ofThe downlink energy efficiency gain is equivalent to the average throughput R of the network and the average total power consumption of the base stationThe ratio of (a):

solving and optimizing problem max _η∈(0,1) EE (η), η represents the cache capacity; when eta = eta ₀ The energy efficiency is maximal.

3. The method for a cellular downlink network energy efficiency optimization system based on caching popular content according to claim 2, wherein: the specific method of the step 2 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:

wherein, F _f ＝{1,…,F _f The 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:

whereinIndicating the buffer capacity, N _c Is the size of each base station buffer, F _f ＝{1,…,F _f Denotes the entire content directory, f denotes the user request for the f-th content, and δ has a value between 0.5 and 1.0.

4. The method for cellular downlink network energy efficiency optimization system based on caching popular content according to claim 2, wherein: in step 4, the distributed caching strategy is in a cellular downlinkIn the road 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 the 4 th, 8 th and 4 th N _c Class b base stations buffer class 3, 7, 4N _c -1 popular content, class c base station buffer 2, 6, 4N _c -2 popular content, class d base station buffer 1, 5, 4N _c -3 popular content, N _c Is the size of each base station buffer.

5. The method for cellular downlink network energy efficiency optimization system based on caching popular content according to claim 2, wherein: the specific method of the step 5 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:

wherein λ is _u Indicating the density of the users in a Poisson distribution, with N in the cell _b Number of base stations.

6. The method for a cellular downlink network energy efficiency optimization system based on caching popular content according to claim 2, wherein: the specific method of the step 6 comprises the following steps: when the base station does not cache the content requested by the user, the average sending rate of the user request, that is, the average throughput of the network needs to be obtained through the backhaul link, and the backhaul traffic load is constrained by the backhaul capacity, so the average instantaneous downlink throughput of the b-th cell is expressed as:

whereinIs the instantaneous received signal-to-interference ratio, C _bh Is the backhaul capacity of the backhaul link, u _b Total number of users, u _c Indicating the number of users whose requested content is cached at the base station.

7. The method for a cellular downlink network energy efficiency optimization system based on caching popular content according to claim 2, wherein: in step 7, the average power consumption of the circuit when the base station is in the sleep or active state is:

wherein ζ _BS =1 represents the active state of the base station, ζ _BS =0 representing the sleep state of the base station, P ₁ Is the base station power supply power in active mode, P ₂ Is 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:

where P represents the transmit power in the base station active mode and P is a factor affecting the power amplifier.

8. The method for a cellular downlink network energy efficiency optimization system based on caching popular content according to claim 2, wherein: the backhaul link power consumption P in step 8 ₄ The 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 generated ₄ Comprises the following steps:

wherein, ω is ₂ Is the power factor of the backhaul device,representing the average rate of cache misses.

9. The method for a cellular downlink network energy efficiency optimization system based on caching popular content according to claim 2, wherein: the cache power consumption P in the step 8 ₃ The 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 P ₃ Comprises the following steps:

P ₃ ＝ω ₁ ηFF _f

where F is the size of each content, mbyte, ω ₁ Is the power parameter of the cache, and η represents the cache capacity.

10. The method for a cellular downlink network energy efficiency optimization system based on caching popular content according to claim 2, wherein: in step 9, solving and optimizing the problem specifically includes:

where η represents the cache capacity, F _f ＝{1,…,F _f Represents 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, C _bh Is the backhaul capacity, N, of the backhaul link _b Represents the number of base stations in the cell, the value of delta is between 0.5 and 1.0, N _c Is the size of each base station buffer, ω ₁ Representing the power coefficient, ω, of a caching hardware device ₂ Representing the power coefficient of the backhaul device.