Base station energy efficiency evaluation optimization method and device

Abstract

The invention provides a base station energy efficiency evaluation optimization method, which comprises the following steps of: obtaining the economic total energy consumption cost of a coding multicast system based on the placement energy consumption of the coding multicast system in a content placement stage, the delivery energy consumption of the coding multicast system in a content delivery stage and real-time electricity price; taking the ratio of the user file request load to the total cost of actual economics energy consumption as the economics energy efficiency of the coding multicast system, and taking the economics energy efficiency as an energy efficiency evaluation index of the coding multicast system; and when the delivery delay of the user request is satisfied, the content placement transmission rate and the content delivery transmission rate with the economic energy efficiency maximization are realized, and the coding multicast system is optimized. According to the method, a network peak-valley period differential pricing strategy is considered, an economics energy consumption cost model is established, a base station energy efficiency index based on economics is provided, a base station economics energy efficiency optimization mechanism based on communication delay sensitivity is provided, and the base station economics energy efficiency is maximized under the constraint of communication delay, so that the service satisfaction degree of a user is guaranteed.

Description

A method and apparatus for evaluating and optimizing base station energy efficiency Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a base station energy efficiency evaluation method and optimization mechanism for cache-enabled wireless communication systems. Background Technology

[0002] The future service demands of wireless communication will expand from single-scenario to multi-scenario, and the infrastructure of wireless communication systems will gradually expand from planar to three-dimensional, from local to global, and from low- and mid-frequency bands to higher frequency bands. Limited by ever-increasing energy consumption, wireless communication systems need to improve energy efficiency while increasing capacity. How to improve energy efficiency in wireless communication networks is one of the urgent scientific problems to be explored at this stage.

[0003] In cache-enabled wireless communication systems, the delivery of user requests is divided into two phases: content placement and content delivery. During content placement, the base station pre-caches some files in the user's local storage. During content delivery, the base station transmits files not found in the user's local storage, completing the delivery of the user request. Considering the base station energy consumption generated during content delivery, the existing traditional energy efficiency definition is the ratio of the user file request load to the energy consumption under the actual distribution load to fulfill the user file request.

[0004] Future 6G networks will inevitably be heterogeneous networks integrating communication, computing, and storage. Encoding caching is a representative technology for this convergence. Existing technologies, by creating encoding multicast opportunities between users, can reduce the communication bandwidth requirements of content delivery systems, thereby increasing system capacity. Based on traditional energy efficiency metrics, existing technologies have achieved a balance between base station energy efficiency and content delivery rate by analyzing the energy efficiency maximization problem in encoding caching systems under content delivery rate constraints, thus improving system capacity.

[0005] In practical systems, the base station energy consumption during the pre-caching process in the content placement phase is related to the user's local storage space and the number of users. Given the large number of users in real-world systems, the energy consumption during the content placement phase cannot be ignored when designing base station energy efficiency indicators. Therefore, it is necessary to jointly consider content placement and content delivery, establishing a new base station energy consumption model and energy efficiency indicators. Simultaneously, to improve the user's file request service experience, base station energy efficiency needs to be optimized while ensuring user service satisfaction. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a base station energy efficiency evaluation and optimization method, comprising: for a single-base station coded multicast system, obtaining the energy consumption during the content placement phase and the energy consumption during the content delivery phase; based on the placement energy consumption, the delivery energy consumption, and the real-time electricity price, obtaining the total economic energy cost of the coded multicast system; using the ratio of user file request load to the actual total economic energy cost as the economic energy efficiency of the coded multicast system, and using the economic energy efficiency as the energy efficiency evaluation index of the coded multicast system; the actual total economic energy cost is the total economic energy cost under the actual transmission load of satisfying user file requests; and optimizing the coded multicast system by maximizing the content placement transmission rate and content delivery transmission rate to meet user request delivery delays.

[0007] Furthermore, this economics of energy efficiency Where K is the number of users in the encoded multicast system, F is the average size of the files stored at the base station, and V is the average size of the files stored at the base station. total V represents the total energy cost in this economics. total =V c +V d V c V represents the economic energy cost of the content placement phase. c =α c E c α c For the placement electricity price parameters during this content placement phase, E c To store energy consumption, V d V represents the economic energy cost of this content delivery phase. d =α d E d α d For the delivery electricity price parameters during this content delivery phase, E d Energy consumption for delivery.

[0008] Furthermore, the minimum economic energy cost V during this content placement phase. c min Obtain the corresponding optimal placement transmission rate * R c The minimum economic energy cost V during this content delivery phase. d min Obtain the corresponding optimal delivery transmission rate *R d Based on *R c and *R d This maximizes economic efficiency while meeting user request delivery delays.

[0009] Furthermore, by solving Get V c min and *Rc By solving st Get V d min and *R d Among them, R c The placement transmission rate for this content placement phase. l c P is the communication load for pre-caching content during this content placement phase. s c Δ represents the static power consumption of the base station during the content placement phase. p Let B be the power amplifier efficiency of the base station, B be the number of sub-files into which each file stored by the base station is divided, and N be the number of files stored in the base station's file library. The minimum allowed content placement transmission rate for the base station. P represents the actual average transmit power of the base station during the content placement phase. max R is the maximum average transmit power of this base station. d For the delivery transmission rate of this content delivery phase, P s d This refers to the static power consumption of the base station during the content delivery phase. The minimum content delivery transmission rate allowed for the base station. This refers to the actual average transmit power of the base station during the content delivery phase.

[0010] This invention also proposes a base station energy efficiency evaluation and optimization device, comprising: an energy consumption acquisition module for acquiring, for example, the placement energy consumption during the content placement phase and the delivery energy consumption during the content delivery phase of a single base station's coded multicast system; an evaluation module for acquiring energy efficiency evaluation indicators of the coded multicast system, including: acquiring the total economic energy cost of the coded multicast system based on the placement energy consumption, the delivery energy consumption, and the real-time electricity price; using the ratio of user file request load to the actual total economic energy cost as the economic energy efficiency of the coded multicast system, and using the economic energy efficiency as the energy efficiency evaluation indicator; the actual total economic energy cost being the total economic energy cost under the actual transmission load of satisfying user file requests; and an optimization module for optimizing the coded multicast system by maximizing the content placement transmission rate and the content delivery transmission rate to meet user request delivery delays.

[0011] Furthermore, in this assessment module, the economic energy efficiency... Where K is the number of users in the encoded multicast system, F is the average size of the base station's stored files, and V total V represents the total energy cost in this economics. total =V c +V d V cV represents the economic energy cost of the content placement phase. c =α c E c α c For the placement electricity price parameters during this content placement phase, E c To store energy consumption, V d V represents the economic energy cost of this content delivery phase. d =α d E d α d For the delivery electricity price parameters during this content delivery phase, E d Energy consumption for delivery.

[0012] Furthermore, in this optimization module, the minimum economic energy cost V during the content placement phase is determined. c min Obtain the corresponding optimal placement transmission rate * R c The minimum economic energy cost V during this content delivery phase. d min Obtain the corresponding optimal delivery transmission rate *R d Based on *R c and *R d This maximizes economic efficiency while meeting user request delivery delays.

[0013] Furthermore, the optimization module includes: a placement phase optimization module, used to solve... st Get V c min and the corresponding *R c The delivery phase optimization module is used to solve... st Get V d min and the corresponding *R d Among them, R c The placement transmission rate for this content placement phase. l c P is the communication load for pre-caching content during this content placement phase. s c Δ represents the static power consumption of the base station during the content placement phase. p Let B be the power amplifier efficiency of the base station, B be the number of sub-files into which each file stored by the base station is divided, and N be the number of files stored in the base station's file library. The minimum allowed content placement transmission rate for the base station. P represents the actual average transmit power of the base station during the content placement phase. max R is the maximum average transmit power of this base station. dFor the delivery transmission rate of this content delivery phase, P s d This refers to the static power consumption of the base station during the content delivery phase. The minimum content delivery transmission rate allowed for the base station. This refers to the actual average transmit power of the base station during the content delivery phase.

[0014] The present invention also proposes an electronic device, including the base station energy efficiency assessment and optimization device as described above.

[0015] The present invention also proposes a computer-readable storage medium storing computer-executable instructions, characterized in that, when the computer-executable instructions are executed, the base station energy efficiency evaluation and optimization method described above is implemented.

[0016] The present invention also proposes a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the base station energy efficiency evaluation and optimization method as described above.

[0017] This invention is aimed at coding caching systems, taking into account differentiated pricing strategies during peak and off-peak periods of the network. By establishing an economic energy consumption cost model, it proposes an economic base station energy efficiency index and a base station economic energy efficiency optimization mechanism based on communication latency sensitivity. Under the constraint of communication latency, it maximizes the economic energy efficiency of the base station, thereby ensuring user service satisfaction. Attached Figure Description

[0018] Figure 1 is a schematic diagram of the coded multicast system of the present invention.

[0019] Figure 2 is a flowchart of the base station energy efficiency evaluation and optimization method of the present invention.

[0020] Figure 3 is a schematic diagram of the base station energy efficiency evaluation and optimization device of the present invention.

[0021] Figure 4 is a schematic diagram of an electronic device according to the present invention.

[0022] Figure 5 is a schematic diagram of the hardware structure of an electronic device according to the present invention.

[0023] Figure 6 is a simulation diagram of the base station economic energy efficiency of the present invention with respect to the peak-valley pricing ratio ρ.

[0024] Figure 7 is a simulation diagram of the base station economic energy efficiency of the present invention with respect to user storage space M.

[0025] The attached figures are labeled as follows:

[0026] 100: Electronic devices; 10: Base station energy efficiency assessment and optimization device

[0027] 11: Energy consumption acquisition module 12: Evaluation module

[0028] 13: Optimization Module

[0029] S1, S2, S3, S4, S5, S6: Steps Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0031] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0032] Base station energy efficiency indicators need to consider the energy consumption of both the content placement and content delivery phases. However, a simple energy efficiency analysis that adds the energy consumption of the two phases together is not suitable for real-world systems. This is because real-world systems employ a differentiated pricing strategy based on network peak and off-peak hours. The electricity price during the content placement phase, which occurs during network off-peak hours, is lower than that during the content delivery phase, which occurs during network peak hours. Therefore, the unit energy cost during the content placement phase is lower than that during the content delivery phase. When designing base station energy efficiency, a differentiated analysis of the energy costs of the two phases is necessary. Therefore, based on the differentiated pricing strategy during network peak and off-peak hours, an economically based energy efficiency indicator can be designed to establish an economic energy efficiency assessment of energy consumption costs. Then, to improve the user experience of file request services, user service satisfaction must be guaranteed during base station energy efficiency optimization, establishing a base station energy efficiency optimization mechanism constrained by user service satisfaction.

[0033] To address the problems existing in the prior art, the inventors have proposed an economic-based base station energy efficiency evaluation method and a communication latency-sensitive base station economic energy efficiency optimization mechanism for coding buffer systems.

[0034] To adapt to the future development trend of wireless communication networks, wireless communication systems need to improve energy efficiency while increasing capacity. Considering that coding caching systems can effectively improve system capacity, this invention designs and optimizes new base station energy efficiency indicators for coding caching systems. In coding caching systems, the power resource scarcity differs between the content placement and content delivery phases. A differentiated pricing strategy based on network peak and off-peak periods indicates that the electricity price during the content placement phase (off-peak) is lower than that during the content delivery phase (peak). Because energy consumption is closely related to electricity price, the higher the unit electricity price, the higher the economic cost per unit of energy consumption, and vice versa. Therefore, this invention introduces a differentiated pricing strategy for content placement during off-peak periods and content delivery during peak periods, establishes an economic energy cost model, and proposes an economically based base station energy efficiency indicator. The proposed economic energy efficiency is defined as the ratio of the user file request load to the economic energy cost under the actual transmission load required to satisfy user file requests in the content delivery system. Next, based on the proposed economic energy efficiency index, this invention proposes an economic energy efficiency optimization mechanism for base stations with communication latency sensitivity in coding caching systems. The goal is to maximize economic energy efficiency under the constraint of user request communication latency, thereby achieving a balance between economic energy efficiency and user request communication latency while improving system capacity. The proposed economic energy efficiency maximization problem can be solved using a greedy algorithm. Simulations show that combining coding caching with the proposed economic energy efficiency index can effectively improve base station energy efficiency. The base station energy efficiency evaluation and optimization method of this invention includes:

[0035] 1. Based on a single-base-station, multi-user encoded multicast system, during the content placement phase, the base station pre-caches some files to the user's local storage. During the content delivery phase, users synchronously request content, and the base station delivers the user's request via encoded multicast.

[0036] 2. Due to the different network conditions during the content placement and delivery phases, the video service transmission rates are different during the content placement and delivery phases, and the average base station transmit power is given.

[0037] 3. Provide energy consumption models for the content placement and content delivery phases.

[0038] 4. Introducing economic theory, this paper proposes the energy efficiency indicator "economic energy efficiency" to achieve economic analysis of energy efficiency across multiple phases through differentiated pricing for content placement during off-peak periods and content delivery during peak periods. The energy cost during the content placement phase, when electricity prices are lower, is defined as V. c =α c E c , where α c It is a fixed parameter related to the electricity price during the content placement phase, 0≤α c ≤α max α maxThis represents the maximum pricing parameter value allowed by the system. The energy cost for content delivery during periods with higher electricity prices is defined as V. d =α d E d , where α d It is a fixed parameter related to the electricity price during the content delivery phase, 0≤α d ≤α max α c ≤α d .definition Let V be the peak-valley electricity price ratio for the content placement and delivery phases. Then, the total economic energy cost V of the content delivery system can be obtained. total =V c +V d =α c E c +α d E d =(ρE c +E d )α d Based on differentiated pricing during peak and off-peak periods, an economic energy efficiency index E is defined. 3 This is the ratio of the user file request load to the economic energy cost of fulfilling the actual transmission load in a content delivery system.

[0039] 5. To ensure user service satisfaction, a communication latency-sensitive base station economic energy efficiency optimization mechanism is proposed, aiming to maximize economic energy efficiency under the constraint of user request communication latency. Considering the different transmission rates in the content placement and delivery phases of actual communication scenarios, it is assumed that different communication latency constraints exist for these phases. Given the number of users and file size, the system assumes a constant user file request load. The proposed economic energy efficiency maximization problem can be equivalently transformed into an economic energy cost minimization problem.

[0040] 6. The problem of minimizing economic energy consumption costs can be decomposed into two sub-problems: the economic energy consumption cost in the content placement stage and the economic energy consumption cost in the content delivery stage.

[0041] Among them: the economic energy consumption cost V during the content placement stage of the solution process. c The process of the subproblem includes:

[0042] 1) Initialization

[0043] 2) Given a step size R c from With step size Step by step to According to (11), R is obtained c The corresponding Vc (R c );

[0044] 3) Based on Compare V c (R c ) and the current V c (R c ).like renew Record R c For the new optimal * R c ,otherwise and * R c Remain unchanged;

[0045] 4) Repeat steps 2) and 3) until you get the result. and its corresponding optimal * R c .

[0046] Solve for the economic energy cost V during the content delivery phase. d The process of minimizing the problem includes:

[0047] 1) Initialization

[0048] 2) Given a step size R d from With step size Step by step to According to (12), R is obtained. d The corresponding V d (R d );

[0049] 3) Based on Compare V d (R d ) and the current V d (R d ).like renew Record R d For the new optimal * R d ,otherwise and * R d Remain unchanged;

[0050] 4) Repeat steps 2) and 3) until you get the result. and its corresponding optimal * R d .

[0051] Finally, based on the results and Find E 3 maximum value * E 3 .

[0052] In other words, the base station energy efficiency evaluation and optimization method of the present invention includes two parts: one is an economic base station energy efficiency evaluation method, and the other is a base station economic energy efficiency optimization mechanism for communication latency sensitive to coding buffer systems.

[0053] This economic-based base station energy efficiency assessment method addresses the technical issue that traditional base station energy efficiency indicators only consider the energy consumption of base stations during the content delivery phase, while the energy consumption during the content placement phase in actual systems cannot be ignored (due to network peak-valley pricing strategies, the unit energy consumption cost during the content placement phase is lower than that during the content delivery phase). By establishing an economic energy consumption cost model, an economic-based base station energy efficiency indicator is designed, defining economic energy efficiency as the ratio of the user file request load in the content delivery system to the economic energy consumption cost under the actual transmission load to meet the user file request.

[0054] This invention presents a communication latency-sensitive base station economic energy efficiency optimization mechanism for encoding caching systems. Based on the proposed economic energy efficiency index, and considering that encoding caching systems can effectively improve system capacity, this invention optimizes base station economic energy efficiency for encoding caching systems. Taking into account the different communication latency constraints between the content placement and content delivery stages, a communication latency-sensitive base station economic energy efficiency optimization mechanism for encoding caching systems is proposed. The aim is to maximize base station economic energy efficiency under the user request communication latency constraint, thereby improving user service satisfaction.

[0055] The base station energy efficiency evaluation and optimization method of the present invention will be described in detail below with reference to the accompanying drawings.

[0056] Figure 1 is a schematic diagram of the coded multicast system of the present invention. As shown in Figure 1, the following description is based on a coded multicast system consisting of a single base station and K users. A given base station's file library stores N files, each file being F bits in size. Each user's local storage capacity is M·F bits, where M ≤ N. It should be understood that a multi-base station coded multicast system can be considered as a combination of multiple single-base station coded multicast systems; therefore, the base station energy efficiency evaluation and optimization method of the present invention is also applicable to multi-base station coded multicast systems.

[0057] Figure 2 is a flowchart of the base station energy efficiency evaluation and optimization method of the present invention. As shown in Figure 2, in the first embodiment of the present invention, a base station energy efficiency evaluation and optimization method is proposed, specifically including:

[0058] Step S1: The process of this encoded multicast system delivering user requests can be divided into two stages: the content placement stage and the content delivery stage. The content placement stage occurs during network off-peak hours, where each file in the file library is evenly divided into B sub-files, each sub-file being [size missing]. Bit. Define W i,j Represents file W i The j-th subfile, i∈[1,N], j∈[1,B]. Here is given... W is a positive integer. Each user has an independent random cache file. i L sub-files. Define l c The communication load for content pre-caching during the content placement phase is easily known. c =KMF. The content delivery phase occurs during peak network periods, with users simultaneously initiating requests and transmitting them via an encoded multicast mechanism. Definition l d The average content delivery load of the base station during the content delivery phase can be obtained.

[0059] Step S2: Considering the different network conditions during the content placement and delivery phases, this invention assumes that the video service transmission rates can be different during these phases. The transmission rate at which the base station pre-caches a portion of the files to the user's local storage during the content placement phase is defined as R. c At this time, the actual average transmit power of the base station is Duration of base station pre-caching process during content placement phase Define the transmission rate at which the base station completes the delivery of user requests during the content delivery phase as R. d At this time, the actual average transmit power of the base station is The duration of content delivery by the base station during the content delivery phase

[0060] Step S3: Assume that each user in the system makes a synchronous request, and each user submits a file request once. Analyze the overall average energy consumption and average energy efficiency for one content placement and one content delivery process. Consider that the energy consumption of the proposed wireless communication system includes two parts: energy consumption during the content placement phase and energy consumption during the content delivery phase.

[0061] First, we introduce the 5G base station power consumption model. The main wireless equipment of a base station mainly consists of two parts: a baseband unit (BBU) and a radio remote unit (RRU). From the perspectives of static and dynamic power consumption, the power consumption calculation formula for a base station can be expressed as follows:

[0062] P total =P s +P r (1)

[0063] Among them, P s This represents the static power consumption of the base station, including the power consumption of the base station air conditioning, supporting equipment, BBU, and RRU static components; P r P represents the dynamic power consumption of the base station. r =Δ p P t , where P t Δ represents the actual transmit power of the base station (related to communication load or transmission rate). p This indicates the power amplifier efficiency (output power / power supplied by the power supply).

[0064] According to formula (1), the power consumption P during the content placement stage is... c Including base station static power consumption P s c and its dynamic power consumption during content caching and transfer. in This represents the actual average transmit power of the base station during the content placement phase. Therefore, the power consumption during the content placement phase is...

[0065]

[0066] Energy consumption E during content placement phase c =P c T c .

[0067] Similarly, the power consumption P during the content delivery phase d This mainly includes the static energy consumption P of the base station. s d (=P s c and the dynamic power consumption generated by it in completing the delivery of user-requested content. in This represents the actual average transmit power of the base station during the content delivery phase. Therefore, the power consumption during the content delivery phase is...

[0068]

[0069] Therefore, the energy consumption E during the content delivery phase d =P d T d .

[0070] Step S4: In a content delivery system, considering the different levels of power scarcity between the content placement and delivery phases, power resources are generally abundant during the placement phase and scarce during the delivery phase. Based on peak-valley pricing strategies, the electricity price during the content placement phase (when network activity is low) is lower than that during the content delivery phase (when network activity is high). Furthermore, energy consumption is closely related to electricity price; the higher the unit electricity price, the higher the economic cost per unit of energy consumption, and vice versa. Therefore, in wireless communication systems, the energy consumption of the content placement and delivery phases cannot be simply summed for energy efficiency analysis, as this fails to reflect the distinct energy consumption characteristics of the two phases.

[0071] This invention introduces economic theory and proposes the energy efficiency indicator of "economic energy efficiency." Its aim is to achieve an economic analysis of energy efficiency across multiple stages by differentiating pricing for content placement during off-peak periods and content delivery during peak periods. First, this invention considers energy cost as an economic representation of energy consumption at different stages. The energy cost for content placement during the lower electricity price stage is defined as V. c =α c E c , where α c It is a fixed parameter related to the electricity price during the content placement phase, 0≤α c ≤α max α max This represents the maximum pricing parameter value allowed by the system. The energy cost for content delivery during periods with higher electricity prices is defined as V. d =α d E d , where α d It is a fixed parameter related to the electricity price during the content delivery phase, 0≤α d ≤α max α c ≤α d .definition Peak-valley electricity price ratio for the content placement and content delivery phases.

[0072] Based on this, the total economic energy cost of the content delivery system can be calculated as follows:

[0073] V total =V c +V d =α c E c +α d E d =(ρE c +E d )α d (4)

[0074] Based on differentiated pricing during peak and off-peak periods, an economic energy efficiency indicator E is defined. 3This is the ratio of the user file request load to the economic energy cost of fulfilling the actual transmission load in a content delivery system.

[0075]

[0076] Furthermore, we can obtain

[0077]

[0078] As can be seen from formula (6), the economic energy efficiency E 3 Subject to the content placement stage transmission rate R c and content delivery phase transmission rate R d The effect can be seen by adjusting R c and R d Optimization to achieve economic energy efficiency E 3 Maximize. According to During the content delivery phase, when R d A reduced transmission rate increases the latency of user request delivery (i.e., delivery transmission time), thereby decreasing user service satisfaction. Therefore, it is necessary to achieve economic efficiency (E) while ensuring user service satisfaction. 3 maximize.

[0079] Step S5: To ensure user service satisfaction, this invention proposes a communication latency-sensitive base station economic energy efficiency optimization mechanism, aiming to maximize economic energy efficiency under user request communication latency constraints. Considering the different transmission rates in the content placement and content delivery stages in actual communication scenarios, this invention assumes different communication latency constraints for the content placement and content delivery stages. To simplify the problem, this invention assumes that all users have the same communication latency constraints.

[0080] During the content placement phase, users do not issue file requests, so their tolerance for communication latency is relatively high. The base station only needs to pre-cache all files before the content delivery phase begins. (Definition) The maximum latency for pre-caching transmission during the content placement phase. (Definition) This represents the maximum tolerable delivery delay for users during the content delivery phase. To ensure user service satisfaction, it must meet the following requirements. So, the actual transmission rate R during the base station content placement phase c Must meet in Minimum pre-buffered transmission rate allowed for the base station; actual transmission rate R during the base station content delivery phase. d Must meet in Given the minimum content delivery transmission rate allowed by the base station, the economic energy efficiency optimization problem under the user-requested communication latency constraint can be expressed as follows:

[0081] P 1:

[0082]

[0083] Formula (7a) represents the pre-buffered transmission rate constraint that satisfies the communication delay during the content placement phase, formula (7b) represents the content delivery transmission rate constraint that satisfies the communication delay during the content delivery phase, and formula (7c) represents the maximum average transmit power constraint of the base station. max This represents the maximum average transmit power of the base station.

[0084] Given the number of users K and the file size F, and assuming a constant file request load in the system, the above economic energy efficiency maximization problem P1 can be equivalent to the economic energy cost V. total Minimize problem P2, specifically

[0085] P 2:

[0086]

[0087] Formula (8a) represents the pre-buffered transmission rate constraint that satisfies the communication delay during the content placement phase, Formula (8b) represents the content delivery transmission rate constraint that satisfies the communication delay during the content delivery phase, and Formula (8c) represents the maximum average transmit power constraint of the base station.

[0088] Step S6: V in P2 total The minimization problem can be decomposed into the economic energy cost V during the content placement phase. c Sub-problem P3(a) and the economic energy cost V of the content delivery stage d Subproblem P3(b) is as follows.

[0089] Step S61: Economic Energy Cost V of Content Placement Stage c Solving subproblem P3(a)

[0090] P 3(a):

[0091]

[0092] Formula (9a) represents the pre-buffered transmission rate constraint that satisfies the communication delay during the content placement phase, and formula (9b) represents the maximum average transmit power constraint of the base station. For P3(a), given the maximum average transmit power P of the base station... max The maximum allowable transfer rate during the content placement phase can be obtained. Then, in subproblem P3(a)

[0093] Because subproblem P3(a) is about R c a monotonic function, Therefore, it can be solved using a greedy algorithm. The specific solution steps are as follows:

[0094] 1) Initialize according to formula (9)

[0095] 2) Given a step size R c from With step size Step by step to According to (9), R is obtained. c The corresponding V c (R c );

[0096] 3) Based on Compare V c (R c ) and the current V c (R c ).like ,renew Record R c For the new optimal * R c ,otherwise and * R c Remain unchanged;

[0097] 4) Repeat steps 2) and 3) until you get the result. and its corresponding optimal * R c .

[0098] Step S62: Economic Energy Cost V of Content Delivery Phase d Minimize problem P3(b)

[0099] P 3(b):

[0100]

[0101] Formula (10a) represents the content delivery transmission rate constraint that satisfies the communication delay during the content delivery phase, and formula (10b) represents the maximum average transmit power constraint of the base station. Similar to P3(a), given the maximum average transmit power P of the base station... max The maximum allowable transfer rate during the content delivery phase can be obtained. Then, in subproblem P3(b)

[0102] Similar to P3(a), subproblem P3(b) is about R. d a monotonic function, Therefore, it can be solved using a greedy algorithm. The specific solution steps are as follows:

[0103] 1) Initialize according to formula (10)

[0104] 2) Given a step size R d from With step size Step by step to According to (10), R is obtained. d The corresponding V d (R d );

[0105] 3) Based on Compare V d (R d ) and the current V d (R d ).like renew Record R d For the new optimal * R d ,otherwise and * R d Remain unchanged;

[0106] 4) Repeat steps 2) and 3) until you get the result. and its corresponding optimal * R d .

[0107] Step S63: Finally, based on the results obtained in step S61... and the result obtained in step S62 E can be obtained from formulas (4) and (5). 3 maximum value * E 3 Problem P1 has been solved.

[0108] It should be noted that, in various embodiments of the present invention, the order of the steps does not imply the order of execution. The execution order of each step should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0109] The following are system embodiments corresponding to the above method embodiments. This embodiment can be implemented in conjunction with the above embodiments. The relevant technical details mentioned in the above embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above embodiments.

[0110] Figure 3 is a schematic diagram of the base station energy efficiency evaluation and optimization device of the present invention. As shown in Figure 3, in the second embodiment of the present invention, a base station energy efficiency evaluation and optimization device 10 is provided, comprising:

[0111] Energy consumption acquisition module 11 is used to acquire the total economic energy cost of the coded multicast system; wherein,

[0112] The process of delivering user requests in this encoded multicast system can be divided into two phases: the content placement phase and the content delivery phase. The content placement phase occurs during off-peak network periods, and each file in the file library is evenly divided into B sub-files. The communication load during the content placement phase, which involves content pre-caching, is l. c =KMF, Average content delivery load of base stations during the content delivery phase

[0113] Considering the different network conditions during the content placement and delivery phases, the video service transmission rates may differ between these phases. During the content placement phase, the base station pre-caches some files to the user's local storage at a transmission rate of R. c At this time, the actual average transmit power of the base station is The duration of the base station pre-caching process during the content placement phase. During the content delivery phase, the base station completes the transmission rate of user request delivery at a rate of R. d At this time, the actual average transmit power of the base station is The duration of content delivery by the base station during the content delivery phase.

[0114] From the base station static power P s and base station dynamic power consumption P r From the perspective of data analysis, the power consumption of the base station during the content placement phase Energy consumption E during content placement phase c =P c T c Base station power consumption during content delivery phase Energy consumption E during content delivery phased =P d T d .

[0115] Evaluation module 12 is used to obtain the energy efficiency evaluation index of the encoded multicast system; it introduces economic theory and proposes the energy efficiency index of economic energy efficiency, with the aim of realizing economic analysis of energy efficiency in multiple stages by differentiating the pricing of content placement during off-peak periods and content delivery during peak periods.

[0116] Energy consumption cost V during content placement phase c =α c E c Energy consumption cost V during content delivery d =α d E d The total economic energy cost V in the content delivery system total =V c +V d =α c E c +α d E d =(ρE c +E d )α d .

[0117] Based on differentiated pricing during peak and off-peak periods, an economic energy efficiency indicator E is defined. 3 E is the ratio of the user file request load to the economic energy cost of fulfilling the actual transmission load in a content delivery system. 3 =KF / V total It can be seen that E 3 The larger the value, the closer the total economic energy cost of the encoded multicast system is to the user file request load, i.e., the lower it is. Therefore, it can be expressed as the economic energy efficiency index E. 3 Energy efficiency evaluation indicators for coding multicast systems.

[0118] The optimization module 13 is used to optimize the coded multicast system by maximizing the content placement transmission rate and content delivery transmission rate to meet the user request delivery delay.

[0119] Economic Energy Efficiency (E) 3 Subject to the content placement stage transmission rate R c and content delivery phase transmission rate R d The effect can be seen by adjusting R c and R d Optimization to achieve economic energy efficiency E 3 Maximize, that is

[0120]

[0121] To ensure user service satisfaction, this invention proposes a base station economics and energy efficiency optimization mechanism sensitive to communication latency. Considering the different transmission rates during the content placement and delivery phases in actual communication scenarios, this invention assumes different communication latency constraints for the content placement and delivery phases. To simplify the problem, this invention assumes all users have the same communication latency constraints. Definitions The maximum latency for pre-caching transmission during the content placement phase. (Definition) This represents the maximum tolerable delivery delay for users during the content delivery phase. To ensure user service satisfaction, it must meet the following requirements. So, the actual transmission rate R during the content placement phase c Must meet The actual transmission rate R during the base station content delivery phase d Must meet The economic energy efficiency optimization problem P1 under the constraint of user request communication delay can be expressed as follows:

[0122] P 1:

[0123]

[0124] Given the number of users K and the file size F, and assuming a constant file request load in the system, the above economic energy efficiency maximization problem P1 can be equivalent to the economic energy cost V. total Minimize problem P2, specifically

[0125] P 2:

[0126]

[0127] Furthermore, in P2, V total The minimization problem can be decomposed into the economic energy cost V during the content placement phase. c Sub-problem P3(a) and the economic energy cost V of the content delivery stage d Subproblem P3(b), i.e.

[0128] P 3(a):

[0129]

[0130] P 3(b):

[0131]

[0132] Because subproblems P3(a) and P3(b) are respectively about R c and R d Since it is a monotonic function, the subproblems P3(a) and P3(b) can be solved using a greedy algorithm to obtain the minimum economic energy cost V during the content placement stage. c min and the corresponding optimal placement transmission rate *R c And the minimum economic energy cost V during the content delivery phase. d min and the corresponding optimal delivery transmission rate *R d .

[0133] In a third embodiment of the present invention, a computer-readable storage medium is provided. The base station energy efficiency evaluation and optimization device of the present invention, if its functions are implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. Therefore, in the third embodiment of the present invention, a computer-readable storage medium is provided for storing a computer program that executes a base station energy efficiency evaluation and optimization method. It should be understood that the computer-readable storage medium in the embodiments of the present invention may be volatile memory and / or non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0134] Figure 4 is a schematic diagram of an electronic device according to the present invention. As shown in Figure 4, in the fourth embodiment of the present invention, an electronic device 100 is proposed, including the base station energy efficiency evaluation and optimization device as described above. Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware (e.g., processor, FPGA, ASIC, etc.). All or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module in the above embodiments can be implemented in hardware, for example, by implementing its corresponding function through an integrated circuit, or it can be implemented in the form of a software functional module, for example, by a processor executing a program / instruction stored in memory to implement its corresponding function. The embodiments of the present invention are not limited to any particular combination of hardware and software.

[0135] It should be noted that the structure of the electronic device shown in the accompanying drawings of this invention does not constitute a limitation thereof. The actual knowledge structure recognition device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0136] The electronic device of the present invention can be any device with data processing capabilities, such as a computer or other similar device. The device embodiment can be implemented through software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of the device with data processing capabilities loading the corresponding computer program instructions from the non-volatile memory into memory for execution. Figure 5 is a schematic diagram of the hardware structure of an electronic device according to the present invention. As shown in Figure 5, from a hardware perspective, this is a hardware structure diagram of any device with data processing capabilities where the base station energy efficiency evaluation and optimization device of the present invention is located. In addition to the processor, memory, network interface, and non-volatile memory shown in Figure 5, the device with data processing capabilities in the embodiment may also include other hardware depending on the actual function of the device, which will not be elaborated further.

[0137] When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0138] For cache-enabled wireless communication systems, the inventors conducted simulation experiments to verify the proposed base station economic efficiency optimization mechanism sensitive to communication latency. The experimental parameters were: N = 50 files in the base station's file library in the cache-enabled content delivery system, and F = 3 × 10⁻⁶ files per file. 7 Given bits and K = 20 users, consider a multicast shared channel between the base station and users with zero mean and variance σ. 2 An additive white Gaussian noise channel with a channel bandwidth of B = 30MHz and σ 2 =1, Given the base station content transmission rate R c The actual average transmit power during the base station content placement phase is Given the base station content transmission rate R d The actual average transmit power during the base station content placement phase is Base station static power consumption P s c =P s d =10.16W, power amplifier efficiency Δ p =15.13, Electricity price α during content delivery phase d = 1.2 yuan / kWh. Constraint: t c =500s,t d =10s, P max =20W. Step size of the greedy algorithm.

[0139] Considering both synchronous coded caching systems and non-coded caching systems, this invention will combine traditional energy-efficient coded caching systems (EE-based coded caching) and economically energy-efficient non-coded caching systems (E-based coded caching). 3 The system performance of the proposed economic energy efficiency was verified by using two simulation comparison mechanisms: energy-based uncoded caching and energy-based uncoded caching (EE-based uncoded caching).

[0140] Figure 6 shows a simulation of the base station's economic energy efficiency with respect to the peak-valley pricing ratio ρ, where M = 10. It can be seen that the economic energy efficiency of all systems decreases as ρ increases. This is because an increase in ρ leads to an increase in the economic energy cost during the content placement phase. The economic energy efficiency of the coded caching system is higher than that of the non-coded caching system. Similarly, the traditional energy efficiency of the coded caching system is also higher than that of the non-coded caching system. This is because the coded caching system can reduce the content delivery load during the content delivery phase through coded multicast, thereby reducing the economic energy cost. Moreover, the base station's economic energy efficiency based on the proposed economic energy efficiency for both the coded and non-coded caching systems is higher than that based on the traditional energy efficiency. This is because the proposed economic energy efficiency considers the unit energy cost during the content placement phase to be lower than the unit energy cost during the content delivery phase based on peak-valley differentiated pricing, while the traditional base station energy efficiency considers the unit economic energy consumption to be equal in both phases. Therefore, the economic energy cost of the traditional energy efficiency is higher than that of the proposed economic energy efficiency. When ρ = 1, the economic cost per unit of energy consumption in the content placement stage is equal to the economic cost per unit of energy consumption in the content delivery stage. At this time, the economic energy consumption is equivalent to the traditional energy efficiency index. Specifically, considering that ρ = 0.3 in the actual system, for the encoding caching system, the proposed economic energy efficiency can be improved by more than 2 times compared with the traditional energy efficiency; for the non-encoding caching system, the proposed economic energy efficiency can be improved by more than 2.5 times compared with the traditional energy efficiency.

[0141] Figure 7 shows the simulation of base station economic energy efficiency with respect to user storage space M, where ρ = 0.3. It can be seen that the economic energy efficiency of all systems decreases with increasing M. This is because increasing M increases the pre-caching load during the content placement stage, thus increasing the economic energy consumption cost during content placement. The economic energy efficiency of the coded caching system is higher than that of the non-coded caching system. Similarly, the traditional energy efficiency of the coded caching system is also higher than that of the non-coded caching system. This is because the coded caching system can reduce the content delivery load during the content delivery stage by encoding multicast, thereby reducing the economic energy consumption cost. The base station economic energy efficiency obtained based on the proposed economic energy efficiency of the coded caching system and the non-coded caching system is higher than that of the communication system based on traditional energy efficiency. The reason is the same as the explanation of the performance in Figure 1. Specifically, for the coded caching system, the proposed economic energy efficiency is on average more than 1.8 times higher than the traditional energy efficiency; for the non-coded caching system, the proposed economic energy efficiency is on average more than 1.5 times higher than the traditional energy efficiency.

[0142] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the present invention, and the patent protection scope of the present invention should be defined by the claims.

Claims

1. A method for evaluating and optimizing the energy efficiency of a base station, characterized in that, include: For a single-base station coded multicast system, obtain the energy consumption of content placement during the content placement phase and the energy consumption of content delivery during the content delivery phase. Based on the placement energy consumption, the delivery energy consumption, and the real-time electricity price, obtain the total economic energy cost of the coded multicast system; The economic energy efficiency of the encoded multicast system is defined as the ratio of user file request load to the actual economic total energy cost, and this economic energy efficiency is used as the energy efficiency evaluation index of the encoded multicast system. The actual economic total energy cost is the economic total energy cost under the actual transmission load of user file requests. The encoded multicast system is optimized by maximizing the content placement transmission rate and content delivery transmission rate to meet the user request delivery delay.

2. The base station energy efficiency evaluation and optimization method as described in claim 1, characterized in that, This economic energy efficiency Where K is the number of users in the encoded multicast system, F is the average size of the files stored at the base station, and V is the average size of the files stored at the base station. total V represents the total energy cost in this economics. total =V c +V d V c V represents the economic energy cost of the content placement phase. c =α c E c α c For the placement electricity price parameters during this content placement phase, E c To store energy consumption, V d V represents the economic energy cost of this content delivery phase. d =α d E d α d For the delivery electricity price parameters during this content delivery phase, E d Energy consumption for delivery.

3. The base station energy efficiency evaluation and optimization method as described in claim 2, characterized in that, The minimum economic energy cost V of this content placement phase. c min Obtain the corresponding optimal placement transmission rate *R c The minimum economic energy cost V during this content delivery phase. d min Obtain the corresponding optimal delivery transmission rate *R d Based on *R c and *R d This maximizes economic efficiency while meeting user request delivery delays.

4. The base station energy efficiency evaluation and optimization method as described in claim 3, characterized in that: By solving s.t. Get V c min and *R c By solving s.t. Get V d min and *R d Among them, R c The placement transmission rate for this content placement phase. l c P is the communication load for pre-caching content during this content placement phase. s c Δ represents the static power consumption of the base station during the content placement phase. p Let B be the power amplifier efficiency of the base station, B be the number of sub-files into which each file stored by the base station is divided, and N be the number of files stored in the base station's file library. The minimum content placement transmission rate allowed by the base station. P represents the actual average transmit power of the base station during the content placement phase. max R is the maximum average transmit power of this base station. d For the delivery transmission rate of this content delivery phase, P s d This refers to the static power consumption of the base station during the content delivery phase. The minimum content delivery transmission rate allowed for the base station. This refers to the actual average transmit power of the base station during the content delivery phase.

5. A base station energy efficiency evaluation and optimization device, characterized in that, include: The energy consumption acquisition module is used to acquire the placement energy consumption during the content placement phase and the delivery energy consumption during the content delivery phase for a single base station encoding multicast system. An evaluation module is used to obtain energy efficiency evaluation indicators for the encoded multicast system; including: obtaining the total economic energy cost of the encoded multicast system based on the placement energy consumption, the delivery energy consumption, and the real-time electricity price; using the ratio of user file request load to the actual total economic energy cost as the economic energy efficiency of the encoded multicast system, and using the economic energy efficiency as the energy efficiency evaluation indicator; the actual total economic energy cost is the total economic energy cost under the actual transmission load to meet user file requests; an optimization module is used to optimize the encoded multicast system by maximizing the content placement transmission rate and content delivery transmission rate to meet user request delivery latency.

6. The base station energy efficiency evaluation and optimization device as described in claim 5, characterized in that, In this assessment module, the economic energy efficiency Where K is the number of users in the encoded multicast system, F is the average size of the base station's stored files, and V total V represents the total energy cost in this economics. total =V c +V d V c V represents the economic energy cost of the content placement phase. c =α c E c α c For the placement electricity price parameters during this content placement phase, E c To store energy consumption, V d V represents the economic energy cost of this content delivery phase. d =α d E d α d For the delivery electricity price parameters during this content delivery phase, E d Energy consumption for delivery.

7. The base station energy efficiency evaluation and optimization device as described in claim 6, characterized in that, In this optimization module, the minimum economic energy cost V during the content placement phase is determined. c min Obtain the corresponding optimal placement transmission rate *R c The minimum economic energy cost V during this content delivery phase. d min Obtain the corresponding optimal delivery transmission rate *R d Based on *R c and *R d This maximizes economic efficiency while meeting user request delivery delays.

8. The base station energy efficiency evaluation and optimization device as described in claim 7, characterized in that, The optimization module includes: a placement phase optimization module, used to solve... s.t. Get V c min and *R c The delivery phase optimization module is used to solve... s.t. Get V d min and *R d Among them, R c The placement transmission rate for this content placement phase. l c P is the communication load for pre-caching content during this content placement phase. s c Δ represents the static power consumption of the base station during the content placement phase. p Let B be the power amplifier efficiency of the base station, B be the number of sub-files into which each file stored by the base station is divided, and N be the number of files stored in the base station's file library. The minimum content placement transmission rate allowed by the base station. P represents the actual average transmit power of the base station during the content placement phase. max R is the maximum average transmit power of this base station. d For the delivery transmission rate of this content delivery phase, P s d This refers to the static power consumption of the base station during the content delivery phase. The minimum content delivery transmission rate allowed for the base station. This refers to the actual average transmit power of the base station during the content delivery phase.

9. An electronic device comprising the base station energy efficiency assessment and optimization apparatus as described in claim 8.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed, the base station energy efficiency evaluation and optimization method as described in any one of claims 1 to 4 is implemented.

11. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the base station energy efficiency evaluation and optimization method according to any one of claims 1-4.

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