5G heterogeneous BSMG collaborative sleep and energy sharing method and system based on deep learning traffic prediction method and improved MOEAD algorithm

Abstract

The present invention discloses a traffic prediction method and system for 5G heterogeneous BSMG based on deep learning, including: S1, obtaining the current traffic data of each base station microgrid; S2, inputting it into the CNN-DAM-BiLSTM deep learning model, and outputting the traffic data for the next time period. Also disclosed is a 5G heterogeneous BSMG collaborative sleep and energy sharing method and system, including: T1, based on the predicted traffic data and sleep load threshold, performing a sleep operation when the base station microgrid is below the sleep load threshold; T2, obtaining the user's maximum demand rate, energy sharing amount and purchased energy, sleep and active status, inputting it into the 5G heterogeneous BSMG collaborative sleep and energy sharing model, and outputting the sleep and energy sharing strategy. The present invention can accurately predict future traffic data and can largely guarantee the user's communication quality, while improving the energy efficiency of the mobile cellular network.

Description

Technical Field

[0001] The present invention relates to the field of smart grid information engineering. More specifically, the present invention relates to a 5G heterogeneous BSMG collaborative sleep and energy sharing method and system based on a deep learning-based traffic prediction method and an improved MOEAD algorithm. Background Art

[0002] With the continuous development of information and communication technology (ICT) based on fifth-generation (5G) and more advanced mobile networks, the number of mobile users and IoT devices is expected to explode. To meet the low latency and high reliability requirements of future wireless devices, 5G heterogeneous networks will enable more efficient and flexible network management through the dense deployment of micro and macro base stations. At the same time, the energy consumption and environmental impact of large-scale base station deployment and high-load operation are becoming increasingly significant, meaning that ICT will become a major source of global energy consumption and greenhouse gas emissions in the future. Therefore, building a green 5G heterogeneous network is crucial to the development of the ICT industry.

[0003] To address these challenges, the application of sustainable renewable energy sources (such as solar and wind power) can effectively reduce base station electricity costs and carbon emissions. However, traditional power grids may not be able to fully absorb green energy, ultimately burdening the environment. Microgrids are a key component of smart grids. By integrating renewable energy into interconnected heterogeneous base station microgrid (BSMG) systems, they facilitate the bidirectional flow of information and energy and become a promising solution for future green communication networks. Base station dormancy aims to switch base stations into dormant mode at appropriate times, which can reduce excessive energy consumption. Users within their coverage area will be connected to other active base stations, potentially degrading their quality of service (QoS). However, given the randomness and intermittent nature of traffic and renewable energy in base station networks, accurate prediction of BSMG traffic demand and green energy arrival is necessary to address potential information and energy coordination issues. Summary of the Invention

[0004] An object of the present invention is to solve at least the above problems and to provide at least the advantages which will be described hereinafter.

[0005] In order to achieve these objectives and other advantages according to the present invention, a traffic prediction method for 5G heterogeneous BSMG based on deep learning is provided, comprising the following steps:

[0006] S1. Obtain current flow data of each base station microgrid, wherein the base station microgrid includes a central base station microgrid and multiple sub-base station microgrids within the coverage area of ​​the central base station microgrid;

[0007] S2. Input the current flow data into a trained flow prediction model, and output the predicted flow data of each base station microgrid in the next time period, wherein the flow prediction model is a CNN-DAM-BiLSTM deep learning model, and the method for predicting the flow data in the next time period by the flow prediction model specifically includes:

[0008] Extract the flow features of the traffic data, and use the convolutional block attention mechanism (CBAM) to perform channel feature weighting and spatial feature weighting on the flow features to obtain the CBAM-weighted flow feature representation;

[0009] The traffic feature representation weighted by CBAM is used as the input of the bidirectional long short-term memory network BiLSTM, and the multi-head attention mechanism MHAM is used to weight the output of the bidirectional long short-term memory network BiLSTM and then map the output of the traffic data for the next time period.

[0010] Preferably, the convolutional block attention mechanism CBAM is specifically as follows: first, a global average pooling operation of the channel attention features is performed on the flow features extracted by the convolution layer to obtain the channel attention weight, and then a maximum pooling operation of the spatial attention features is performed to obtain the spatial attention weight, and finally, a Sigmoid activation function and feature multiplication are used to obtain the spatial attention weight vector of each channel and the weighted feature representation, that is, the CBAM weighted flow feature representation.

[0011] A 5G heterogeneous BSMG cooperative sleep and energy sharing method based on an improved MOEA / D algorithm is provided, comprising the following steps:

[0012] T1. Based on the flow data of each base station microgrid in the next time period predicted by the flow prediction method, according to a preset sleep load threshold, a sleep operation is performed when the base station microgrid is in no-load or below the sleep load threshold, and a sleep base station microgrid set and an active base station microgrid set are obtained;

[0013] T2: Obtain the user's maximum demand rate, energy sharing amount, and energy purchased from the smart grid, as well as the dormant and active states of the base station microgrid obtained in step T1, and input them into a trained 5G heterogeneous BSMG collaborative dormancy and energy sharing model. Output is the dormant base station microgrid set, active base station microgrid set, and energy sharing strategy for the next time period.

[0014] Among them, the 5G heterogeneous BSMG collaborative sleep and energy sharing model takes the BSMG system benefit and user QoS benefit as the objective function, the user's maximum demand rate, the sleep state and active state of the base station microgrid, the energy sharing amount and the energy purchased from the smart grid as the decision variables, and the user's signal-to-noise ratio SINR threshold limit, the base station microgrid overload limit, the energy storage capacity limit, the energy sharing and the smart grid energy usage limit as the constraints. The improved MOEA / D algorithm is used to solve the energy sharing strategy, wherein the improved MOEA / D algorithm uses the quantum local search method to adaptively adjust the neighborhood value size.

[0015] Preferably, when energy is shared, the energy flow rule is to prioritize green energy sharing between base station microgrids, and the base station microgrid with surplus green energy transmits green energy to the base station microgrid with energy shortage;

[0016] When each base station microgrid cannot meet the energy demand of the base station microgrid in an energy shortage state, energy is purchased from the smart grid to provide it with energy.

[0017] Preferably, the green energy is electric energy provided by a photovoltaic power generation system.

[0018] Provided is a traffic prediction system based on the traffic prediction method for 5G heterogeneous BSMG based on deep learning, comprising:

[0019] A data acquisition module, which is used to obtain current flow data of each base station microgrid, wherein the base station microgrid includes a central base station microgrid and multiple sub-base station microgrids within the coverage area of ​​the central base station microgrid;

[0020] The flow prediction module is used to input the current flow data into a trained flow prediction model and output the predicted flow data of each base station microgrid in the next time period. The flow prediction model is a CNN-DAM-BiLSTM deep learning model. The method of predicting the flow data of the next time period by the flow prediction model specifically includes:

[0021] Extract the flow features of the traffic data, and use the convolutional block attention mechanism (CBAM) to perform channel feature weighting and spatial feature weighting on the flow features to obtain the CBAM-weighted flow feature representation;

[0022] The traffic feature representation weighted by CBAM is used as the input of the bidirectional long short-term memory network BiLSTM, and the multi-head attention mechanism MHAM is used to weight the output of the bidirectional long short-term memory network BiLSTM and then map the output of the traffic data for the next time period.

[0023] Provided is a collaborative sleep and energy sharing system based on the 5G heterogeneous BSMG collaborative sleep and energy sharing method, comprising:

[0024] A sleep module is configured to perform a sleep operation when the base station microgrid is idle or below the sleep load threshold based on the flow data of each base station microgrid in the next time period predicted by the flow prediction method, and obtain a sleep base station microgrid set and an active base station microgrid set;

[0025] The energy sharing module is used to obtain the user's maximum demand rate, energy sharing amount, and energy purchased from the smart grid, as well as the dormant and active states of the base station microgrid obtained in step T1. These are input into a trained 5G heterogeneous BSMG collaborative dormancy and energy sharing model, and the output is the dormant base station microgrid set, active base station microgrid set, and energy sharing strategy for the next time period.

[0026] Among them, the 5G heterogeneous BSMG collaborative sleep and energy sharing model takes the BSMG system benefit and user QoS benefit as the objective function, the user's maximum demand rate, the sleep state and active state of the base station microgrid, the energy sharing amount and the energy purchased from the smart grid as the decision variables, and the user's signal-to-noise ratio SINR threshold limit, the base station microgrid overload limit, the energy storage capacity limit, the energy sharing and the smart grid energy usage limit as the constraints. The improved MOEA / D algorithm is used to solve the energy sharing strategy, wherein the improved MOEA / D algorithm uses the quantum local search method to adaptively adjust the neighborhood value size.

[0027] Preferably, each central base station microgrid is configured with a corresponding photovoltaic power generation system and a smart grid power supply system, and each sub-base station microgrid is configured with a corresponding photovoltaic power generation system.

[0028] The present invention provides an electronic device, comprising:

[0029] one or more processors;

[0030] and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the traffic prediction method or the 5G heterogeneous BSMG collaborative sleep and energy sharing method.

[0031] A computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the traffic prediction method or the 5G heterogeneous BSMG collaborative sleep and energy sharing method is implemented.

[0032] The present invention has at least the following beneficial effects:

[0033] First, a dual attention mechanism, consisting of a convolutional block attention mechanism (CBAM) and a multi-head attention mechanism (MHAM), was used to optimize the convolutional neural network (CNN) and the bidirectional long short-term memory (BiLSTM) network, respectively. A 5G heterogeneous BSMG traffic prediction model based on CNN-DAM-BiLSTM was established. This model not only increases the weight of important channel features but also learns more feature information in different spaces, ultimately improving the accuracy of traffic data prediction.

[0034] Second, the proposed 5G heterogeneous BSMG collaborative sleep and energy sharing model fully considers the user experience, maximizes the benefits of the BSMG system, and improves the network energy efficiency of the BSMG system.

[0035] Third, the MOEA / D algorithm is improved through quantum local search and adaptive mutation strategies to enhance its convergence speed and global search capability, ultimately achieving the bidirectional flow of information and energy in the 5G heterogeneous BSMG system.

[0036] Other advantages, objectives and features of the present invention will be reflected in part from the following description and will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a structural diagram of a 5G heterogeneous BSMG collaborative sleep and energy sharing method based on deep learning and an improved MOEA / D algorithm according to Example 1 of the present invention;

[0038] Figure 2 This is a flow chart of a traffic prediction model for 5G heterogeneous BSMG based on CNN-DAM-BiLSTM according to Example 2 of the present invention;

[0039] Figure 3 This is a comparison chart of flow prediction curves of different flow prediction models within 24 hours in Example 2 of the present invention;

[0040] Figure 4 This is a flow chart of the improved MOEA / D algorithm based on quantum local search and adaptive mutation strategy of the present invention;

[0041] Figure 5 A comparison chart of the average system benefits of 5G heterogeneous BSMG using different multi-objective optimization algorithms provided in Example 3 of the present invention;

[0042] Figure 6 A comparison chart of system benefits of the 5G heterogeneous BSMG system under five different energy-saving algorithms provided in Example 3 of the present invention;

[0043] Figure 7 This is a comparison chart of the network energy efficiency of the 5G heterogeneous BSMG system under five different energy-saving algorithms provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be described in further detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0045] It should be noted that the experimental methods described in the following embodiments are conventional methods unless otherwise specified, and the reagents and materials are commercially available unless otherwise specified; in the description of the present invention, the orientation or positional relationship indicated by the terms is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0046] like Figures 1 to 7 As shown, the present invention provides a traffic prediction method for 5G heterogeneous BSMG based on deep learning, comprising the following steps:

[0047] S1. Obtain current flow data of each base station microgrid, wherein the base station microgrid includes a central base station microgrid and multiple sub-base station microgrids within the coverage area of ​​the central base station microgrid;

[0048] S2. Input the current flow data into a trained flow prediction model, and output the predicted flow data of each base station microgrid in the next time period, wherein the flow prediction model is a CNN-DAM-BiLSTM deep learning model, and the method for predicting the flow data in the next time period by the flow prediction model specifically includes:

[0049] Extract the flow features of the traffic data, and use the convolutional block attention mechanism (CBAM) to perform channel feature weighting and spatial feature weighting on the flow features to obtain the CBAM-weighted flow feature representation;

[0050] The traffic feature representation weighted by CBAM is used as the input of the bidirectional long short-term memory network BiLSTM, and the multi-head attention mechanism MHAM is used to weight the output of the bidirectional long short-term memory network BiLSTM and then map the output of the traffic data for the next time period.

[0051] In the above technical solution, a W-layer 5G heterogeneous BSMG system is constructed. The first layer is the central base station microgrid (CBSMG), and the other W-1 layers are sub-base station microgrids (SBSMG). Its base station network is microcells, femtocells, etc., and the base station transmission power of different layers is different. The present invention mainly studies the downlink transmission system with W=2, and realizes the bidirectional flow of information and energy between each BSMG through a centralized control unit.

[0052] The main difference between a central base station microgrid (CBSMG) and a sub-base station microgrid (SBSMG) is that a CBSMG provides low-speed services and capacity, while an SBSMG is deployed in areas with high user traffic within the CBSMG coverage area to enhance network capacity and provide high-speed services. Each BSMG is equipped with renewable energy generation and energy storage equipment. Furthermore, the CBSMG serves as an information and energy management unit, or control unit, managing multiple SBSMGs and mobile users within its coverage area. A CBSMG is powered by its own green energy generation equipment and an external smart grid. The green energy generation equipment includes photovoltaic modules and energy storage devices. In the event of a green energy shortage, the external smart grid provides power. Meanwhile, an SBSMG, while equipped with its own green energy generation equipment, has its energy management controlled by the CBSMG. In the event of energy shortages, the external smart grid can supply power to each SBSMG through the CBSMG system to ensure user QoS. Since the CBSMG also includes communication units such as the AAU and BBU, it can also be associated with mobile users. On the other hand, when the SBSMG is heavily loaded but its own green energy is insufficient, it can fully utilize green energy by cooperating with other SBSMGs; when the SBSMG is idle or under low load, it can be put into sleep mode to save energy.

[0053] Considering the differences in BSMG traffic in different time periods, a CNN-DAM-BiLSTM deep learning model is proposed based on convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) as a traffic prediction model. The convolutional block attention mechanism and multi-head attention mechanism are added to enhance the ability to focus on and capture key information in time series.

[0054] Specifically, the structure of the convolutional neural network CNN is mainly composed of convolutional layers, pooling layers and fully connected layers. However, when processing time series prediction models, traditional convolutional neural networks CNN may face the problem of information loss or local features cannot fully reflect the overall sequence trend. The attention mechanism refers to the use of limited computing resources as fully as possible when processing large amounts of data, and using them to process more important data. Based on this, this application applies the convolutional block attention mechanism (CBAM) to the convolutional neural network CNN. By considering the information interaction at different time scales and introducing a weight distribution mechanism, the convolutional neural network CNN can more effectively improve the learning ability of the important channel feature information, thereby improving the prediction accuracy of the traffic prediction model. The convolutional block attention mechanism CBAM consists of two parts: the channel attention module (CAM) and the spatial attention module (SAM). The channel attention module (CAM) first performs global average pooling on the traffic features extracted by the convolutional layer. These features are then concatenated through a fully connected layer and then max-pooled through the spatial attention module (SAM). Finally, a sigmoid activation function and feature multiplication are used to generate a spatial attention weight vector for each channel and a weighted feature representation. The channel attention module enhances the correlation between different feature channels while eliminating redundant information within the channels, helping the traffic prediction model focus on important feature channels. The spatial attention module (SAM) performs max-pooling on the features weighted by the channel attention module (CAM) along the channel dimension, obtaining the maximum channel response at each spatial location. This strengthens important features and enhances the different spatial locations within the features, helping the traffic prediction model focus on important spatial regions. Therefore, after integrating the channel and spatial attention modules into each convolutional block of the convolutional neural network (CNN), the output features of each convolutional block first pass through the channel attention module and then the spatial attention module. This results in a CBAM-weighted traffic feature representation that prioritizes important channels and spatial locations.

[0055] Specifically, the state unit of the bidirectional long short-term memory network will update and save the historical state, so the output of each step is closely related to the current input and the historical input. If the input time series is long, it will be limited by the storage space of the state unit, which may cause the loss of information of important time nodes. Based on this, when predicting the traffic of BSMG, this application introduces the multi-head attention mechanism (MHAM) into the bidirectional long short-term memory network BiLSTM to enhance the ability to pay attention to and capture different aspects of the time series. The multi-head attention mechanism can independently focus on different aspects by calculating multiple attention heads in parallel, and adopt their own weight distribution methods to analyze the input information from different angles. The multi-head attention mechanism converts the vector output by the bidirectional long short-term memory network BiLSTM into three input matrices through different mapping operations: query q (Query), key k (Key) and value v (Value), and the corresponding weights obtained through a large amount of training are w respectively. Q 、w K 、w V , from this we can get the distribution weight of each hidden layer vector:

[0056] Q=w Q q (1)

[0057] K=w K k (2)

[0058] V=w V v (3)

[0059] The multi-head attention mechanism divides the time series into L subspaces. Each head performs self-attention calculation on the subspace, then obtains multiple heads and concatenates each head. The attention output matrix is ​​normalized by the softmax function and is:

[0060]

[0061] where d k It is the feature dimension of each key, used for weight scaling, and normalized to the [0,1] interval by Softmax.

[0062] MultiHead(Q,K,V)=Concat(head1,...,head l )w l (5)

[0063] In formula (5), w l is the weight of the linear transformation, head l Represents the L-th head of the multi-head attention module, and Concat represents the concatenation operation.

[0064] Specifically, the CBAM-weighted traffic feature representation obtained by the output of the convolutional neural network (CNN) is used as the input of the bidirectional long short-term memory (BiLSTM) network. The output of the BiLSTM network is first weighted by the multi-head attention mechanism, and then the comprehensive feature information is passed to the fully connected layer to predict the traffic in the future.

[0065] The present invention combines the convolutional block attention mechanism (CBAM) and the multi-head attention mechanism (MHAM) to optimize the convolutional neural network (CNN) and the bidirectional long short-term memory (BiLSTM) network, respectively, and establishes a traffic prediction model for the base station microgrid (BSMG) based on CNN-DAM-BiLSTM. In the feature extraction stage, by introducing the CBAM mechanism into CNN, the channel attention module and the spatial attention module are integrated after each convolution block of CNN. The output feature map of each convolution block first passes through the channel attention module, and then passes through the spatial attention module to increase the weight of important channel features, and finally passes through the pooling layer and the fully connected layer for processing. Since BiLSTM has the problem of longitudinal time series information loss, in order to better capture the correlation between long time series data, multi-head attention is added to the BiLSTM model to form the MHAM-BiLSTM model. The BiLSTM network learns to extract features with long-term correlation characteristics from the traffic feature vectors extracted by the optimized convolutional neural network (CNN). Based on this, combined with the multi-head attention mechanism, the output of the BiLSTM network is weighted, so that more feature information can be learned in different spaces. The weighted comprehensive feature information is then passed to the fully connected layer to predict traffic for a period of time in the future.

[0066] The present invention also provides a 5G heterogeneous BSMG collaborative sleep and energy sharing method based on the traffic prediction method and the improved MOEA / D algorithm, comprising the following steps:

[0067] T1, based on the flow data of each base station microgrid in the next time period predicted by the flow prediction method, according to a preset sleep load threshold, perform a sleep operation when the base station microgrid is in no-load or below the sleep load threshold, and obtain a sleep base station microgrid set and an active base station microgrid set;

[0068] T2: Obtain the user's maximum demand rate, energy sharing amount, and energy purchased from the smart grid, as well as the dormant and active states of the base station microgrid obtained in step T1, and input them into a trained 5G heterogeneous BSMG collaborative dormancy and energy sharing model. Output is the dormant base station microgrid set, active base station microgrid set, and energy sharing strategy for the next time period.

[0069] Among them, the 5G heterogeneous BSMG collaborative sleep and energy sharing model takes the BSMG system benefit and user QoS benefit as the objective function, the user's maximum demand rate, the sleep state and active state of the base station microgrid, the energy sharing amount and the energy purchased from the smart grid as the decision variables, and the user's signal-to-noise ratio SINR threshold limit, the base station microgrid overload limit, the energy storage capacity limit, the energy sharing and the smart grid energy usage limit as the constraints. The improved MOEA / D algorithm is used to solve the energy sharing strategy, wherein the improved MOEA / D algorithm uses the quantum local search method to adaptively adjust the neighborhood value size.

[0070] Specifically, in the above technical solution, first define set B n = {0, 1, 2, ..., N} to represent a central base station microgrid CBSMG and N sub-base station microgrids SBSMG in the 5G heterogeneous BSMG system, let U m ={0,1,2,...,M} is the set of users of the 5G heterogeneous BSMG system. The clusters do not interfere with each other. The state of the 5G heterogeneous BSMG system changes with the traffic load and energy of the base station within the time slot length ΔT.

[0071] For the n User j∈U m , its signal-to-noise ratio (SINR) is:

[0072]

[0073] Where W i and τ i are the transmit power and channel gain of BSMGi, W s and τ s is the transmit power and channel gain of the sth interference source. Finally, is the power of the noise.

[0074] According to Shannon's formula, the average service rate available to user j on BSMGi at time slot t can be calculated as follows:

[0075] c i,j (t) = μ0(t)log2(1+SINR i,j ) (7)

[0076] Wherein, μ0(t) is the bandwidth provided by a resource block of BSMGi at time t.

[0077] Assume that a user is associated with only one BSMG during time slot t, and the current set of users associated with BSMGi is U i,j , and the maximum demand rate when user j is served is r i,j(t), the normalized load of BSMGi in time slot t is obtained as:

[0078]

[0079] Among them, ψ i is the number of resource blocks that can be allocated to BSMGi. For simplicity, it is assumed that BSMG will not operate in an overloaded state, and the BSMG operating state is divided into active state and dormant state. CBSMG is always in active state, while SBSMG can choose to dormant operation due to its high flexibility. Based on the above background, the energy consumption of CBSMG and SBSMG can be calculated as:

[0080] P c =P0+ρ i (θP max +P f ) (9)

[0081]

[0082] Energy consumption of CBSMG c It includes static energy consumption and dynamic energy consumption. P0 is the fixed energy consumption at static state, while the dynamic energy consumption depends on the real-time load state of BSMG. max and P f are the maximum transmission power and circuit power of CBSMG, and θ is the power loss coefficient caused by hardware loss. In SBSMG, as the service load changes, its energy consumption remains basically unchanged, so P A and P SL To express the energy consumption of SBSMG in active and dormant states. According to formulas (9) and (10), the total energy consumption of 5G heterogeneous BSMG system is:

[0083]

[0084] The calculation method for network energy efficiency is as follows:

[0085]

[0086] Specifically, based on the traffic data predicted by the traffic prediction model, when SBSMG is idle or ρ i When (t) is small, the BSMG can be selectively placed in sleep mode to save grid energy or renewable energy. i (t) determines the state of SBSMG λ(t). When the small cell becomes idle (ρ i (t)=0), they will switch to the dormant state and remain in the dormant state while the traffic load continues to be lower than the preset load threshold (ρi (t)<ρ th ).

[0087] The sets of active BSMG and dormant BSMG are defined respectively.

[0088] is Γ active (t) = {i∈BSMG n ∩i≠0λ(t)=1} and Γ sleep (t) = {i∈BSMG n ∩i≠0λ(t)=0}, let R i =maxr i,j (t),j∈U j It represents the maximum required rate set of users during the low-load period, and then introduces the following constraints for active and dormant SBSMGs, so that BSMGs can guarantee user QoS through rate indicators and do not allow any BSMG to be overloaded.

[0089]

[0090] Among them, SINR th The traffic data is obtained based on the aforementioned traffic prediction algorithm, and thus the candidate sleep set of BSMGs is obtained. Specifically, the energy consumption of a single BSMG i can be expressed as:

[0091]

[0092] Furthermore, considering the user experience, the network energy efficiency obtained based on the maximum required rate defines the user QoS gain function as follows:

[0093]

[0094] Then, we continue to determine the dormancy and energy sharing scheme through energy sharing between BSMGs. Specifically,

[0095] When renewable energy is insufficient, BSMG will also purchase energy from the smart grid to ensure the user's Qos. For simplicity, solar energy is used as the only source of renewable energy for BSMG. At time slot t, BSMG's renewable energy generation can be expressed as g(t). The renewable energy generation can be predicted based on historical photovoltaic power generation data and statistical methods. The predicted value is:

[0096]

[0097] Where: g p (t) is the power generation of the photovoltaic power generation system; Under standard test conditions (G STC =1000W / m2 , T STC =25℃); G is the light radiation intensity; ET is the ambient temperature; χ is the power coefficient, assuming that the energy consumption of renewable energy power generation is 0.

[0098] Each BSMG is equipped with energy storage equipment, which can not only cope with future high load demands, but also reduce the dependence on smart grid energy, thereby fully utilizing renewable energy. Assuming that the initial energy storage is 0, the energy storage value of BSMG during time slot t is expressed as e s (t), which is subject to the maximum energy storage capacity E s Energy sharing between BSMGs is based on the difference in energy consumption due to temporal and spatial variations in traffic loads, as well as the difference in renewable energy reserves due to geographical diversity. Based on the current load and renewable energy harvesting status of BSMGi, the energy available for energy sharing can be expressed as:

[0099]

[0100] Since the actual distance between each BSMG is small, assuming that the energy sharing transmission loss can be ignored, it means that BSMGi has surplus energy to share with other BSMGs with energy shortage. Therefore, at time slot t, the two BSMGi and i * The actual energy sharing strategy between when i=i * represents the energy added by BSMGi from the smart grid, where u max is the maximum value of energy sharing and satisfies the following constraints:

[0101] 0≤u i (t)≤u max (18)

[0102] To encourage energy sharing between heterogeneous BSMGs, the cost of energy sharing between BSMGs is set to be higher than the cost of energy purchased from the smart grid. * Compared with the method of relying entirely on traditional power grid for power supply, the benefits of BSMG system after adopting cooperative sleep and energy sharing can be obtained as follows:

[0103]

[0104] Then the BSMG sleep strategy Γ active (t) and Γ sleep (t), Energy u purchased from the smart grid ii (t), the energy shared between BSMGs BSMG loads are jointly optimized to minimize the cost of purchasing energy from the external smart grid while considering user QoS, ultimately achieving the best energy saving effect. The optimization problem can be expressed as:

[0105]

[0106] sqFt i ≥log2(1+SINR th )

[0107] 0≤ρ i (t)≤1

[0108] 0≤u i (t)≤u max

[0109] e s (t)≤E s (20)

[0110] Analysis of this optimization problem reveals that it is a typical NP-hard problem, with the objective function being to maximize the BSMG system revenue and user QoS benefit functions. Decision variables include the user's maximum demand rate, the active and dormant states of the BSMG, the amount of energy shared, and the amount of energy purchased from the smart grid. Constraints include the user's SINR threshold, the BSMG overload limit, the energy storage capacity limit, and the energy sharing and smart grid energy usage limits. Based on this, the MOEA / D algorithm (a decomposition-based multi-objective evolutionary algorithm) is used to solve the BSMG collaborative dormancy and energy sharing strategy that maximizes the BSMG system revenue and user QoS benefit functions.

[0111] Specifically, the MOEA / D algorithm first generates a set of uniform weight vectors, divides the multi-objective problem into many single-objective sub-problems, assigns corresponding weights and the neighborhood of related population points to each sub-problem, and generates new solutions for each sub-problem through cross-mutation. Finally, the Chebyshev method is used to aggregate the multi-objective problem into a single-objective problem, and the parent population is updated. Each sub-problem is solved by optimizing the solution of the corresponding neighborhood, and the above steps are repeated until the termination condition is reached. The MOEA / D algorithm has good performance in dealing with continuous multi-objective problems, but its neighborhood value is fixed, which will slow down the convergence speed, and because it only uses a fixed mutation probability, it will lead to weak global search capabilities. Based on this, the present application proposes a method for adaptively adjusting the size of neighborhood values ​​based on quantum local search.

[0112] First calculate the adaptive expansion coefficient:

[0113]

[0114] It max is the maximum number of iterations, γmax and γ min is a preset fixed value, and then the attraction point S is generated based on the individual's historical optimal position and the group's historical optimal position:

[0115]

[0116] where φ is a random matrix uniformly distributed between [0,1]. Then, based on the δ-potential well, assuming that the neighborhood position vector has quantum behavior, the wave function φ is used to describe the state of the vector. By solving the Schrödinger equation for the one-dimensional δ-potential well, the probability density of the vector appearing at a certain point in space is obtained:

[0117]

[0118] Where, is the new neighborhood position vector, L is the position vector after the individual expansion coefficient changes, and finally the new position vector equation is obtained through Monte Carlo random simulation:

[0119]

[0120] In addition, considering the information of individuals in the population, an adaptive mutation probability υ(t+1) is generated based on the number of iterations and Gaussian mutation to enhance the ability of global search, as shown in formula (25):

[0121]

[0122] in, is the rate adjustment factor, and Ga(0,1) is the standard normal distribution, which has better global search capability without reducing the convergence speed.

[0123] In this technical solution, by building a 5G heterogeneous BSMG collaborative sleep and energy sharing model, fully considering the user experience, the system's network energy efficiency is improved while maximizing its benefits. Furthermore, by improving the MOEA / D algorithm through quantum local search and adaptive mutation strategies, its convergence speed and global search capabilities are enhanced, ultimately achieving bidirectional information and energy flow in the 5G heterogeneous BSMG system.

[0124] Based on the same inventive concept, the present invention also provides a traffic prediction system based on the traffic prediction method of 5G heterogeneous BSMG based on deep learning, comprising:

[0125] A data acquisition module, which is used to obtain current flow data of each base station microgrid, wherein the base station microgrid includes a central base station microgrid and multiple sub-base station microgrids within the coverage area of ​​the central base station microgrid;

[0126] The flow prediction module is used to input the current flow data into a trained flow prediction model and output the predicted flow data of each base station microgrid in the next time period. The flow prediction model is a CNN-DAM-BiLSTM deep learning model. The method of predicting the flow data of the next time period by the flow prediction model specifically includes:

[0127] Extract the flow features of the traffic data, and use the convolutional block attention mechanism (CBAM) to perform channel feature weighting and spatial feature weighting on the flow features to obtain the CBAM-weighted flow feature representation;

[0128] The traffic feature representation weighted by CBAM is used as the input of the bidirectional long short-term memory network BiLSTM, and the multi-head attention mechanism MHAM is used to weight the output of the bidirectional long short-term memory network BiLSTM and then map the output of the traffic data for the next time period.

[0129] In this technical solution, a dual attention mechanism, consisting of a convolutional block attention mechanism (CBAM) and a multi-head attention mechanism (MHAM), is used to optimize the convolutional neural network (CNN) and the bidirectional long short-term memory (BiLSTM) network, respectively. This allows for the development of a 5G heterogeneous BSMG traffic prediction model based on CNN-DAM-BiLSTM. This model not only increases the weight of important channel features but also learns more feature information across different spatial domains, ultimately improving the accuracy of traffic data prediction.

[0130] Based on the same inventive concept, the present invention also provides a collaborative sleep and energy sharing system based on the 5G heterogeneous BSMG collaborative sleep and energy sharing method, comprising:

[0131] A sleep module is configured to perform a sleep operation when the base station microgrid is idle or below the sleep load threshold based on the flow data of each base station microgrid in the next time period predicted by the flow prediction method, and obtain a sleep base station microgrid set and an active base station microgrid set;

[0132] The energy sharing module is used to obtain the user's maximum demand rate, energy sharing amount, and energy purchased from the smart grid, as well as the dormant and active states of the base station microgrid obtained in step T1. These are input into a trained 5G heterogeneous BSMG collaborative dormancy and energy sharing model, and the output is the dormant base station microgrid set, active base station microgrid set, and energy sharing strategy for the next time period.

[0133] Among them, the 5G heterogeneous BSMG collaborative sleep and energy sharing model takes the BSMG system benefit and user QoS benefit as the objective function, the user's maximum demand rate, the sleep state and active state of the base station microgrid, the energy sharing amount and the energy purchased from the smart grid as the decision variables, and the user's signal-to-noise ratio SINR threshold limit, the base station microgrid overload limit, the energy storage capacity limit, the energy sharing and the smart grid energy usage limit as the constraints. The improved MOEA / D algorithm is used to solve the energy sharing strategy, wherein the improved MOEA / D algorithm uses the quantum local search method to adaptively adjust the neighborhood value size.

[0134] The proposed 5G heterogeneous BSMG collaborative sleep and energy sharing model takes user experience into account, maximizing system benefits while improving network energy efficiency. Furthermore, the MOEA / D algorithm is improved through quantum local search and adaptive mutation strategies to enhance its convergence speed and global search capabilities, ultimately enabling bidirectional information and energy flow within the 5G heterogeneous BSMG system.

[0135] The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present invention may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0136] The present invention further provides an electronic device, comprising:

[0137] one or more processors;

[0138] and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to execute the traffic prediction method or the 5G heterogeneous BSMG collaborative sleep and energy sharing method. The electronic device may be any terminal device including a mobile phone, a laptop computer, a desktop computer, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales), an in-vehicle computer, or the like.

[0139] The present invention also provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the traffic prediction method or the 5G heterogeneous BSMG collaborative sleep and energy sharing method.

[0140] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware, and of course can also be implemented by means of dedicated hardware including application-specific integrated circuits, dedicated CPUs, dedicated memories, dedicated components, etc. In general, all functions performed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structures used to implement the same function can also be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for the present invention, software program implementation is a better implementation method in most cases. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0141] <Example 1>

[0142] like Figure 1 As shown, a 5G heterogeneous BSMG cooperative sleep and energy sharing method based on deep learning and improved MOEA / D algorithm includes the following steps:

[0143] Step 1: Build a 5G heterogeneous BSMG system consisting of a central base station microgrid and sub-base station microgrids as a simulation scenario, and enable bidirectional flow of information and energy between each BSMG through a centralized control unit;

[0144] Step 2: Build a deep learning model based on CNN-DAM-BiLSTM and train it using historical traffic data to obtain a trained deep learning model, namely the CNN-CBAM-BiLSTM traffic prediction model.

[0145] Then, the traffic data of each BSMG in the current 5G heterogeneous BSMG system is input into the trained deep learning model, and the predicted traffic data of each BSMG in the future is output;

[0146] Step 3: According to the sleep load threshold ρ th and predicted traffic data of each BSMG in the future period, and performs sleep operation when the BSMG is in no-load or low-load state;

[0147] Step 4: Construct two objective functions: BSMG system benefit and user QoS benefit. Take the user's maximum demand rate, the active and dormant states of the BSMG, the energy sharing amount, and the energy purchased from the smart grid as decision variables. Take the user's SINR threshold limit, the BSMG overload limit, the energy storage capacity limit, the energy sharing, and the smart grid energy usage limit as constraints. Solve them using the improved MOEA / D algorithm, and ultimately achieve energy sharing in the 5G heterogeneous BSMG system.

[0148] The energy flow rule is to prioritize energy sharing between BSMGs. BSMGs with surplus energy will transmit energy to BSMGs with energy shortages. If each BSMG is unable to meet the needs of a BSMG with energy shortages, the external power grid will provide energy for it.

[0149] <Example 2>

[0150] like Figure 2 and 3 As shown, building and comparing the deep learning model based on CNN-DAM-BiLSTM mentioned in Example 1 includes the following steps:

[0151] Step 1: Obtain the dataset required to train the deep learning model. The dataset contains cellular traffic values ​​and base station coordinates recorded at one-hour intervals for a city in China.

[0152] Step 2: Build four different traffic prediction models, including CNN-BiLSTM, CNN-BiGRU, CNN-SA-BiLSTM, and the CNN-DAM-BiLSTM model of this application. Use the data from the dataset in step 1 to train the four traffic prediction models and output their respective prediction results. Among them, BiGRU represents a bidirectional gated recurrent unit (Bidirectional Gated Recurrent Unit) and SA-BiLSTM represents a self-attention bidirectional long short-term memory network (Self-Attention Bidirectional Long Short-Term Memory).

[0153] Step 3: Compare the predicted values ​​of traffic data by the four traffic prediction models with the actual values.

[0154] The results show that CNN-DAM-BiLSTM has the best prediction performance. Therefore, when performing BSMG sleep operation, based on the comparison results, the CNN-DAM-BiLSTM model is selected for traffic prediction.

[0155] <Example 3>

[0156] The improved MOEA / D algorithm proposed in this invention is simulated in simulation software, and its performance is compared with multi-objective optimization algorithms such as MOSSA, NSGA-Ⅱ, and MOPSO. Figure 5 The figures show a comparison of the average system benefits of the improved MOEA / D algorithm and the other three algorithms in the optimization problem involved in Example 1 when the number of iterations is set to 400. It can be seen from the figure that the improved MOEA / D algorithm converges to the optimal solution faster than the other algorithms, and has a stronger ability to escape the local optimum after introducing the mutation operation. The optimal solution obtained is closer to the theoretical optimum than other algorithms.

[0157] like Figure 6 、 7 As shown, the cooperative sleep and energy sharing method (IMOEA / D-CSESA) proposed in Example 1 is compared with energy-saving algorithms in other documents. The energy-saving algorithms of different comparative documents are introduced as follows:

[0158] Comparative Algorithm 1: Randomly select active SBSMGs in the network to sleep based on the distance between CBSMGs;

[0159] Comparative Algorithm 2: Without considering user QoS, it relies on deep learning traffic prediction results to perform base station hibernation.

[0160] Comparative Algorithm 3: Solve the optimization problem involved in Example 1 using reinforcement learning methods such as Q learning;

[0161] Comparative Algorithm 4: Solve the BSMG collaborative sleep and energy sharing problem involved in Example 1 through dynamic evolutionary game.

[0162] Figure 6 、 7 The comparison of the strategies solved by the above five algorithms in terms of system benefits and network energy efficiency within one day is shown. It can be seen that the IMOEA / D-CSESA strategy proposed in this paper excels in both aspects, effectively reducing cellular network energy consumption while ensuring user QoS.

[0163] The significance of this invention lies in its ability to largely guarantee communication quality for users in the system while adapting to the current explosive growth in user traffic, while also improving the energy efficiency of mobile cellular networks. This technology plays a crucial role in enabling the bidirectional flow of information and energy in BSMGs, reducing carbon emissions, achieving sustainable development, and building a low-carbon, intelligent cellular network.

[0164] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A traffic prediction method for 5G heterogeneous BSMG based on deep learning, characterized by: The following steps are involved: S1. Obtain current flow data of each base station microgrid, wherein the base station microgrid includes a central base station microgrid and multiple sub-base station microgrids within the coverage area of ​​the central base station microgrid; S2. Input the current flow data into a trained flow prediction model, and output the predicted flow data of each base station microgrid in the next time period, wherein the flow prediction model is a CNN-DAM-BiLSTM deep learning model, and the method for predicting the flow data in the next time period by the flow prediction model specifically includes: Extract the flow features of the traffic data, and use the convolutional block attention mechanism (CBAM) to perform channel feature weighting and spatial feature weighting on the flow features to obtain the CBAM-weighted flow feature representation; The traffic feature representation weighted by CBAM is used as the input of the bidirectional long short-term memory network BiLSTM, and the multi-head attention mechanism MHAM is used to weight the output of the bidirectional long short-term memory network BiLSTM and then map the output of the traffic data for the next time period.

2. The method for predicting traffic of 5G heterogeneous BSMG based on deep learning according to claim 1, characterized in that: The convolutional block attention mechanism (CBAM) is specifically as follows: first, a global average pooling operation of the channel attention features is performed on the traffic features extracted by the convolutional layer to obtain the channel attention weights, and then a maximum pooling operation of the spatial attention features is performed to obtain the spatial attention weights. Finally, the Sigmoid activation function and feature multiplication are used to obtain the spatial attention weight vector of each channel and the weighted feature representation, i.e., the CBAM weighted traffic feature representation.

3. 5G heterogeneous BSMG cooperative sleep and energy sharing method based on improved MOEA / D algorithm, characterized by: The following steps are involved: T1. Based on the flow data of each base station microgrid in the next time period predicted by the flow prediction method according to claim 1, according to a preset sleep load threshold, a sleep operation is performed when the base station microgrid is in no-load or below the sleep load threshold, and a sleep base station microgrid set and an active base station microgrid set are obtained; T2: Obtain the user's maximum demand rate, energy sharing amount, and energy purchased from the smart grid, as well as the dormant and active states of the base station microgrid obtained in step T1, and input them into a trained 5G heterogeneous BSMG collaborative dormancy and energy sharing model. Output is the dormant base station microgrid set, active base station microgrid set, and energy sharing strategy for the next time period. Among them, the 5G heterogeneous BSMG collaborative sleep and energy sharing model takes the BSMG system benefit and user QoS benefit as the objective function, the user's maximum demand rate, the sleep state and active state of the base station microgrid, the energy sharing amount and the energy purchased from the smart grid as the decision variables, and the user's signal-to-noise ratio SINR threshold limit, the base station microgrid overload limit, the energy storage capacity limit, the energy sharing and the smart grid energy usage limit as the constraints. The improved MOEA / D algorithm is used to solve the energy sharing strategy, wherein the improved MOEA / D algorithm uses the quantum local search method to adaptively adjust the neighborhood value size.

4. The 5G heterogeneous BSMG cooperative sleep and energy sharing method based on the improved MOEA / D algorithm according to claim 3 is characterized in that: During energy sharing, the energy flow rule is to prioritize green energy sharing between base station microgrids, and the base station microgrid with surplus green energy will transmit green energy to the base station microgrid with energy shortage; When each base station microgrid cannot meet the energy demand of the base station microgrid in an energy shortage state, energy is purchased from the smart grid to provide it with energy.

5. The 5G heterogeneous BSMG cooperative sleep and energy sharing method based on the improved MOEA / D algorithm according to claim 4 is characterized in that: The green energy is the electric energy provided by the photovoltaic power generation system.

6. A traffic prediction system based on the traffic prediction method for 5G heterogeneous BSMG based on deep learning according to claim 1 or 2, characterized in that: include: A data acquisition module, which is used to obtain current flow data of each base station microgrid, wherein the base station microgrid includes a central base station microgrid and multiple sub-base station microgrids within the coverage area of ​​the central base station microgrid; The flow prediction module is used to input the current flow data into a trained flow prediction model and output the predicted flow data of each base station microgrid in the next time period. The flow prediction model is a CNN-DAM-BiLSTM deep learning model. The method of predicting the flow data of the next time period by the flow prediction model specifically includes: Extract the flow features of the traffic data, and use the convolutional block attention mechanism (CBAM) to perform channel feature weighting and spatial feature weighting on the flow features to obtain the CBAM-weighted flow feature representation; The traffic feature representation weighted by CBAM is used as the input of the bidirectional long short-term memory network BiLSTM, and the multi-head attention mechanism MHAM is used to weight the output of the bidirectional long short-term memory network BiLSTM and then map the output of the traffic data for the next time period.

7. A collaborative sleep and energy sharing system based on the 5G heterogeneous BSMG collaborative sleep and energy sharing method according to any one of claims 3 to 5, characterized in that: include: A sleep module is configured to perform a sleep operation when the base station microgrid is idle or below the sleep load threshold based on the flow data of each base station microgrid in the next time period predicted by the flow prediction method, and obtain a sleep base station microgrid set and an active base station microgrid set; The energy sharing module is used to obtain the user's maximum demand rate, energy sharing amount, and energy purchased from the smart grid, as well as the dormant and active states of the base station microgrid obtained in step T1. These are input into a trained 5G heterogeneous BSMG collaborative dormancy and energy sharing model, and the output is the dormant base station microgrid set, active base station microgrid set, and energy sharing strategy for the next time period. Among them, the 5G heterogeneous BSMG collaborative sleep and energy sharing model takes the BSMG system benefit and user QoS benefit as the objective function, the user's maximum demand rate, the sleep state and active state of the base station microgrid, the energy sharing amount and the energy purchased from the smart grid as the decision variables, and the user's signal-to-noise ratio SINR threshold limit, the base station microgrid overload limit, the energy storage capacity limit, the energy sharing and the smart grid energy usage limit as the constraints. The improved MOEA / D algorithm is used to solve the energy sharing strategy, wherein the improved MOEA / D algorithm uses the quantum local search method to adaptively adjust the neighborhood value size.

8. The cooperative sleep and energy sharing system according to claim 7, wherein: Each central base station microgrid is equipped with a corresponding photovoltaic power generation system and a smart grid power supply system, and each sub-base station microgrid is equipped with a corresponding photovoltaic power generation system.

9. An electronic device, characterized in that include: one or more processors; And a memory communicatively connected to at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the traffic prediction method described in claim 1 or 2 or the 5G heterogeneous BSMG collaborative sleep and energy sharing method described in any one of claims 3 to 5.

10. A computer-readable storage medium, characterized in that A computer program is stored on the storage medium, and when the program is executed by the processor, it implements the traffic prediction method described in claim 1 or 2 or the 5G heterogeneous BSMG collaborative sleep and energy sharing method described in any one of claims 3 to 5.

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