Large-scale access energy-saving optimization method and device and electronic equipment

Abstract

The invention discloses a large-scale access energy-saving optimization method and device and electronic equipment, and the method comprises the steps: transmitting a plurality of pieces of response configuration information to a plurality of MTDs, so that each MTD transmits uplink control information according to the response configuration information; defining a clustering access definition optimization problem of the uplink control information according to each piece of uplink control information, smoothing the clustering access definition optimization problem by using a spectral radius gradient solution method, and performing strategic grouping on the plurality of MTDs according to the plurality of pieces of uplink control information to obtain a strategic grouping scheme; a clustering access optimization definition problem is converted into a characteristic value maximization problem of an incidence matrix, and a feasible solution in an augmented form is obtained by introducing augmented variable calculation through the characteristic value maximization problem so as to jointly optimize a discontinuous reception period and a timing parameter corresponding to each strategic grouping scheme. The large-scale access energy-saving optimization method is high in energy efficiency, low in power consumption, small in time delay and low in operation cost.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a large-scale access energy-saving optimization method, device and electronic equipment. Background Art

[0002] In large-scale IoT deployments, such as massive machine-type communications (mMTC) in fifth generation (5G) networks, a large number of devices need to access the network simultaneously. These devices usually have low power requirements and do not require frequent data transmission. Therefore, how to effectively manage the access of these devices while reducing power consumption and shortening wake-up latency has become an important research topic. Uplink control information is a key component of communication between the network and the terminal, especially the scheduling request, which is crucial to reducing signaling overhead and power consumption. The discontinuous reception mechanism allows the terminal to enter a dormant state during inactivity to save power, but frequent state switching may also lead to longer wake-up latency. However, existing research only focuses on uplink control information transmission or only focuses on the discontinuous reception mechanism, lacking a unified framework and joint optimization trade-off method that comprehensively considers the interaction between the two.

[0003] At present, large-scale access energy-saving optimization methods are mainly divided into two categories: resource allocation optimization methods based on control signaling mechanisms and multiple access technologies, and discontinuous reception parameter configuration methods based on model prediction. Resource allocation optimization methods based on control signaling mechanisms and multiple access technologies require compressed control information and more accurate synchronization mechanisms, which not only increase the processing complexity of the terminal, but also introduce additional error risks, affecting the quality of data transmission. Discontinuous reception parameter configuration methods based on model prediction rely on historical observation priors, which affects the effectiveness of parameter configuration and has poor robustness.

[0004] First, the resource allocation optimization method based on control signaling mechanism and multiple access technology mainly reduces the number of bits of control information through compression coding technology, and adopts more efficient multiple access technology to improve spectrum efficiency, reduce the number of resource blocks required for control information transmission, and thus reduce transmission delay and power consumption. B. Li et al. proposed an efficient and low-overhead uplink scheduling algorithm suitable for large-scale Internet of Things applications, describing a capacity outer circle subject to sampling constraints, in which only a small number of terminals are allowed to use the control channel for system status reporting at a time, and realizing the capacity outer circle by utilizing the prior information of channel state distribution and instantaneous queue length. The proposed method satisfies the sampling constraints and reduces throughput loss. A. Ramadan et al. proposed a hybrid uplink scheduler based on non-orthogonal multiple access and orthogonal multiple access, which can optimize the system under the clustering constraints of heterogeneous network targets and overload scenarios, and allows some devices to share the same resources in frequency and time, which can effectively improve the spectrum and scheduling efficiency of the network. S.Lv et al. studied energy-saving and secure packet transmission in non-orthogonal multiple access-assisted large-scale access networks, and developed a decentralized stateless multi-agent reinforcement learning algorithm, which combined with non-orthogonal multiple access relays can improve energy efficiency while reducing information exchange overhead. Y.Meng et al. studied a comprehensive learning framework for unlicensed non-orthogonal multiple access with power control schemes in large-scale access networks, aiming to minimize long-term system power consumption while meeting packet error rate constraints, which includes two interrelated sub-algorithms to optimize power levels and contention transmission unit allocation.

[0005] Secondly, the DRX parameter configuration method based on model prediction dynamically adjusts the DRX parameters according to the historical behavior of the device and the prediction model, so that the device can reduce the sleep time when the data transmission demand is high, and increase the sleep time when the data transmission demand is low, so as to adapt to the power consumption requirements in different application scenarios. N.Mysore Balasubramanya et al. proposed an improved DRX parameter configuration method, which introduced a fast sleep mode and a wake-up signal, allowing the device to quickly enter the sleep state when there is no data transmission task, and quickly wake up when needed, which can effectively improve the energy efficiency of IoT devices. HWFerng et al. proposed a DRX configuration scheme with adaptive parameters, which allows the device terminal to impose constraints on delay or power consumption. The base station can iteratively estimate the packet arrival rate and find feasible parameters for the device terminal to meet the constraints and have an acceptable convergence speed. J.Song et al. proposed a multi-agent deep reinforcement learning method with an integrated self-attention mechanism to reduce the power consumption of the device terminal, so that the model can optimize the DRX parameters independently of the scheduling algorithm of the base station, and enhance its adaptability to dynamically changing network conditions. J.Zhou et al. proposed a discontinuous reception parameter configuration mechanism based on online learning to improve the service energy efficiency of IoT devices by adapting to different traffic patterns. In the proposed mechanism, time is divided into multiple time intervals, and the discontinuous reception period is adjusted by learning the traffic statistics at the beginning of each time interval. In order to accelerate the learning process, a symmetric sampling method is adopted, which has significantly improved the delay and energy efficiency. J.Wu et al. proposed a machine learning algorithm based on an expert library to predict the discontinuous reception parameter configuration. Without making any assumptions about the underlying traffic process, only the observed packet arrival time is used to predict the arrival time of the next packet, and the predicted next packet arrival time is used to dynamically adjust the sleep interval. Since different IoT services have different requirements for energy saving and data transmission delay, a loss function is designed by combining these two performance indicators to enable the discontinuous reception parameter configuration to support a wide range of application services.

[0006] First, in large-scale access scenarios, the implementation complexity and cost of resource allocation optimization methods based on control signaling mechanisms and multiple access technologies are very high. The compression and optimization of control information will dramatically increase the processing complexity of the terminal, leading to an increase in the cost of terminal equipment and possibly affecting the reliability and stability of the equipment. At the same time, more efficient multiple access technologies require new hardware or software support, which will also increase the implementation cost and difficulty. On the other hand, the compression coding of control information may introduce additional bit error risks. If the compression coding is improper, the bit error rate of the control information will increase. Non-orthogonal multiple access technology will also lead to increased interference between devices. In the case of dense deployment, more interference may occur between devices, which will affect the reliability and quality of data transmission.

[0007] Secondly, regarding large-scale access energy-saving optimization, the discontinuous reception parameter configuration method based on model prediction requires a large amount of historical data for training and prediction. Training an accurate prediction model usually requires a large amount of data and computing resources. Collecting and processing this data in a large-scale network environment is very complicated, and the quality and integrity of the data directly affect the accuracy of the prediction model. In addition, the model-based prediction method requires a certain amount of time to collect data, train models, and update discontinuous reception parameters, and different devices and networks may have different behavior patterns. As the network environment and device behavior change, the prediction model needs to be updated regularly, which greatly affects the energy efficiency and latency of the implementation.

[0008] Therefore, it becomes an important issue to provide a large-scale access energy-saving optimization method with high energy efficiency, low power consumption, low latency and low operating cost. Summary of the invention

[0009] In order to solve the above problems existing in the prior art, the present invention provides a large-scale access energy-saving optimization method, device and electronic equipment.

[0010] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0011] In a first aspect, the present invention provides a large-scale access energy-saving optimization method, the large-scale access energy-saving optimization method comprising:

[0012] Sending multiple response configuration information to multiple MTDs so that each MTD sends uplink control information according to the response configuration information; the response configuration information is determined according to the task request sent by each MTD;

[0013] A cluster access definition optimization problem of uplink control information is defined according to each uplink control information, and the cluster access optimization definition problem is smoothed by using a spectral radius gradient solution method, so as to strategically group the multiple MTDs according to the multiple uplink control information to obtain a strategic grouping scheme; the cluster access definition optimization problem takes minimizing each element in a similarity metric matrix in the strategic grouping scheme as an optimization goal; each element in the similarity metric matrix represents a difference between two observed variables in the uplink control information;

[0014] The cluster access optimization definition problem is transformed into an eigenvalue maximization problem of an association matrix, and an augmented feasible solution is substituted into the eigenvalue maximization problem to achieve large-scale access energy-saving optimization by jointly optimizing the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme; the augmented feasible solution is calculated by introducing augmented variables; each element in the association matrix is ​​determined according to the number of groups in the strategic grouping.

[0015] Optionally, using a spectral radius gradient solution method to smoothly process the cluster access optimization definition problem includes:

[0016] The cluster access optimization definition problem is smoothed by minimizing the spectral radius gradient of the association matrix in the cluster access optimization definition problem and setting a trace penalty item in the cluster access optimization definition problem; the trace penalty item is set according to the accumulated number of observations of the uplink control information.

[0017] Optionally, the cluster access definition optimization problem is:

[0018]

[0019] Where S represents the similarity measure matrix; ρ represents the trace penalty term; I represents the identity matrix; A represents the association matrix; 1 represents the all-1 column vector, A1=1 represents that the sum of each row element of A is equal to 1; <·,·> represents the inner product operation of the norm; the element in the pth row and qth column of S is defined as Represents the observed variable in the uplink control information.

[0020] Optionally, the objective function of the cluster access optimization definition problem is:

[0021]

[0022] Among them, f κ,Θ (Λ) represents the objective function; κ represents the smoothing parameter; λ j (Θ -1 / 2 ΛΘ -1 / 2 ) represents the pair Θ -1 / 2 ΛΘ -1 / 2 The jth eigenvalue obtained after eigenvalue decomposition; Λ indicates that the diagonal element is λ j (Θ -1 / 2 ΛΘ -1 / 2 ) is a diagonal matrix; Θ represents a feasible solution.

[0023] Optionally, the feasible solution of the augmented form includes Θ′ and A 0 ′:

[0024]

[0025] Among them, Θ′ represents the augmented form of Θ; A 0 ' represents the augmented form of A 0 ; Θ and A 0 are all given feasible solutions; dvec(·) represents the operation of mapping the matrix into a diagonal matrix.

[0026] In a second aspect, the present invention provides a large-scale access energy-saving optimization device, the large-scale access energy-saving optimization device comprising:

[0027] An uplink control information acquisition module, used to send multiple response configuration information to multiple MTDs, so that each MTD sends uplink control information according to the response configuration information; the response configuration information is determined according to the task request sent by each MTD;

[0028] A strategic grouping scheme acquisition module is used to define a cluster access definition optimization problem of uplink control information according to each uplink control information, and use a spectral radius gradient solution method to smooth the cluster access optimization definition problem, so as to strategically group the multiple MTDs according to multiple uplink control information to obtain a strategic grouping scheme; the cluster access definition optimization problem takes minimizing each element in a similarity metric matrix in the strategic grouping scheme as an optimization goal; each element in the similarity metric matrix represents a difference between two observed variables in the uplink control information;

[0029] A large-scale access energy-saving optimization implementation module is used to transform the cluster access optimization definition problem into an eigenvalue maximization problem, and substitute the feasible solution in augmented form into the eigenvalue maximization problem to achieve large-scale access energy-saving optimization by jointly optimizing the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme; the feasible solution in augmented form is calculated by introducing augmented variables; each element in the association matrix is ​​determined according to the number of groups in the strategic grouping.

[0030] Optionally, the strategic grouping scheme acquisition module uses a spectral radius gradient solution method to smoothly process the cluster access optimization definition problem, including:

[0031] The cluster access optimization definition problem is smoothed by minimizing the spectral radius gradient of the association matrix in the cluster access optimization definition problem and setting a trace penalty item in the cluster access optimization definition problem; the trace penalty item is set according to the accumulated number of observations of the uplink control information.

[0032] Optionally, the cluster access definition optimization problem is:

[0033]

[0034] Where S represents the similarity measure matrix; ρ represents the trace penalty term; I represents the identity matrix; A represents the association matrix; 1 represents the all-1 column vector, A1=1 represents that the sum of each row element of A is equal to 1; <·,·> represents the inner product operation of the norm; the element in the pth row and qth column of S is defined as Represents the observed variable in the uplink control information.

[0035] Optionally, the objective function of the cluster access optimization definition problem is:

[0036]

[0037] Among them, f κ,Θ (Λ) represents the objective function; κ represents the smoothing parameter; λ j (Θ -1 / 2 ΛΘ -1 / 2 ) represents the pair Θ -1 / 2 ΛΘ -1 / 2 The jth eigenvalue obtained after eigenvalue decomposition; Λ indicates that the diagonal element is λ j (Θ -1 / 2 ΛΘ -1 / 2 ) is a diagonal matrix; Θ represents a feasible solution.

[0038] Optionally, the feasible solution of the augmented form includes Θ′ and A 0 ′:

[0039]

[0040] Among them, Θ′ represents the augmented form of Θ; A 0 ' represents the augmented form of A 0 ; Θ and A 0 are all given feasible solutions; dvec(·) represents the operation of mapping the matrix into a diagonal matrix.

[0041] In a third aspect, the present invention provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0042] Memory, used to store computer programs;

[0043] The processor is used to implement the method steps described in any of the above-mentioned large-scale access energy-saving optimization methods when executing the program stored in the memory.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any of the above-mentioned large-scale access energy-saving optimization methods are implemented.

[0045] The present invention provides a large-scale access energy-saving optimization method, which converts the cluster access optimization definition problem of uplink control information into the eigenvalue maximization problem of the association matrix in which each element is determined according to the number of groups in the strategic grouping, and substitutes the feasible solution in augmented form into the eigenvalue maximization problem to jointly optimize the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme, thereby reducing unnecessary signaling interactions in the case of large-scale MTD access, thereby reducing system complexity and operating costs. In view of the fact that a large number of devices in application scenarios such as the Internet of Things are in a low-activity state for a long time, by defining a cluster access optimization definition problem with the optimization goal of minimizing each element in the similarity metric matrix in the strategic grouping scheme, the discontinuous reception period is reasonably configured, and unnecessary monitoring and transmission operations of the device are reduced, thereby significantly reducing the energy consumption of the terminal device and extending the battery life. While ensuring low power consumption, jointly optimizing the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme can also minimize the transition time of the device from the sleep state to the active state, that is, shortening the wake-up delay.

[0046] The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of a large-scale access energy-saving optimization method provided by an embodiment of the present invention;

[0048] Figure 2 It is a schematic diagram of an mMTC network model of typical uplink and downlink signaling and data interaction provided by an embodiment of the present invention;

[0049] Figure 3(a) and Figure 3(b) are schematic diagrams of energy saving ratio and average wake-up delay under different numbers of MTDs;

[0050] Figure 4(a) and Figure 4(b) are schematic diagrams of energy saving ratio and average wake-up delay under different cumulative observation times;

[0051] Figure 5 It is a structural schematic diagram of a large-scale access energy-saving optimization device provided by an embodiment of the present invention;

[0052] Figure 6 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0054] In order to solve the technical problems of low energy efficiency, high power consumption, large delay and high operating cost in the large-scale access energy-saving optimization method in the prior art, the embodiment of the present invention provides a large-scale access energy-saving optimization method, see Figure 1 , Figure 1 1 is a flow chart of a large-scale access energy-saving optimization method provided by an embodiment of the present invention, which specifically includes the following steps:

[0055] Step S101 : sending a plurality of response configuration information to a plurality of MTDs, so that each MTD sends uplink control information according to the response configuration information; the response configuration information is determined according to the task request sent by each MTD.

[0056] In the embodiment of the present invention, MTD (Machine Type Device) includes sensor devices, smart metering devices, industrial Internet of Things devices, smart city devices, etc. In the 5G large-scale access scenario, these devices are collectively referred to as MTD.

[0057] See also Figure 2 , Figure 2 It is a schematic diagram of the mMTC network model of typical uplink and downlink signaling and data interaction provided by an embodiment of the present invention. The base station performs resource scheduling of machine type devices according to the uplink control information transmitted by the physical uplink control channel (Physical Uplink Control Channel, PUCCH), and then transmits downlink shared data to the activated MTD through the downlink control channel (Physical Downlink Control Channel, PDCCH). An activated MTD refers to an MTD whose sent task request is responded to by the base station. Before the MTD transmits uplink control information, it is necessary to send a task request to the base station first. The base station will determine the response configuration information of the physical resource element allocation of the uplink control information for the MTD according to the task request, and finally send the response configuration information containing the above allocation information to indicate the activation status of the MTD. MTD includes activated devices, i.e., activated MTDs, and silent devices, i.e., unactivated MTDs. The silent state refers to the state in which the MTD is not communicating, and the transmission state refers to the state in which the MTD is communicating and transmitting.

[0058] In the embodiment of the present invention, after receiving the response configuration information, the MTD sends uplink control information to the base station. The specific process is as follows:

[0059] The uplink control information is first converted into a symbol vector b = [b 1 ,b 2 ,...,b E], where E is the total number of bits after modulation, and each modulation symbol carries 2 bits of information. Within the allocated resource block, specific physical resource elements are further allocated, and each resource element corresponds to the position of a subcarrier on an orthogonal frequency division multiplexing (OFDM) symbol. The modulated symbol vector b = [b 1 ,b 2 ,...,b E ] is mapped to the allocated physical resource elements to form a signal vector u = [u 1 ,u 2 ,...,u M ], M is the number of resource elements allocated to uplink control information, ensuring that each symbol is correctly placed on the specified resource element. Next, the symbols are mapped in the frequency domain and time domain. In the frequency domain, the symbols are assigned to specific subcarriers, and in the time domain, the symbols are assigned to specific OFDM symbols, ensuring that the uplink control information is distributed in time and frequency to maximize the diversity and reliability of transmission.

[0060] After the uplink control information is configured through the allocated uplink resource blocks, it is sent back to the base station by the MTD. Figure 2 As shown in (a) of FIG, the base station adjusts the resource allocation strategy based on the received uplink control information to optimize the network performance. Considering the influence of multipath propagation and fading, the uplink control information y received at frequency f and time t f,t It can be expressed as:

[0061]

[0062] Among them, α i p t represents the probabilistic activation state of the i-th MTD at time t, where if the i-th MTD is in the activated state, then α i (t)=1, otherwise represents the channel frequency response of the i-th MTD at frequency f; N f is the total number of frequency points corresponding to the discrete Fourier transform size; represents the frequency domain representation of the uplink control information u sent by the i-th MTD; f,t represents the Gaussian white noise at the base station receiving side; i=1,2,...,N p ; N p represents the total number of MTDs; l = 1, 2, ..., L; L represents the length of the channel pulse; j' represents the imaginary unit.

[0063] Step S102, defining a cluster access definition optimization problem for uplink control information according to each uplink control information, and using a spectral radius gradient solution method to smooth the cluster access optimization definition problem, so as to strategically group multiple MTDs according to multiple uplink control information to obtain a strategic grouping scheme; the cluster access definition optimization problem takes minimizing each element in a similarity metric matrix in the strategic grouping scheme as an optimization goal; each element in the similarity metric matrix represents the difference between two observed variables in the uplink control information.

[0064] In the embodiment of the present invention, after receiving the uplink control information, the base station side selects a specific scheduling time slot T using PUCCH format 2 in the typical time slot structure of the uplink control information including the scheduling request. SR The uplink control information is collected and converted into an equivalent vector, called the uplink control information vector, denoted as y f =(y f,1 ,...y f,TSR ) T , T represents the transpose of the vector. Inspired by the semidefinite programming clustering method, such as Figure 2 As shown in (a), the present invention uses the uplink control information vector y f Reconstruct the clustering quantity criterion in and transform it into an equivalent real variable That is, the observed variables, so that the embodiment of the present invention can dynamically adjust Figure 2 Parameter configuration of the discontinuous reception cycle in (b).

[0065] In one implementation, the cluster access definition optimization problem takes minimizing each element in the similarity metric matrix in the strategic grouping scheme as the optimization goal. The cluster access definition optimization problem of defining uplink control information according to each uplink control information in mMTC is:

[0066]

[0067] Where S represents the similarity measure matrix; ρ represents the trace penalty term; I represents the identity matrix; A represents the association matrix; 1 represents the all-1 matrix; <·, ·> represents the inner product operation of the norm, that is, Tr((-S-ρI) T A), Tr(·) is the trace of the matrix; S represents the similarity measure matrix, where each element of S is defined as

[0068] In the actual mMTD scenario, the observed variables can be assigned to K independent and unidentified cluster sets, i.e., strategic groupings, where C = {C 1 ,...,C k ,...,C KThe cluster resource identifier allocation for indicating the discontinuous reception timer configuration can be represented by an association matrix A, where p,q∈C k Since the number of clusters K is unknown, we need to calculate the number of clusters based on the cumulative number of observations N. c , that is, the discontinuous reception period parameter adjusted by the number of frequency point f changes, to set the trace penalty term ρ. Among them, the correlation matrix is ​​an optimization variable that can be used to adaptively select the number of clusters and find the best cluster at the same time.

[0069] In the embodiment of the present invention, based on the cluster access of uplink control information, the discontinuous reception management is further adjusted, such as Figure 2 As shown in (b) in the figure. The duration of the discontinuous reception cycle includes a long cycle and a short cycle, which is crucial for energy saving. The uplink control information enables the length of the discontinuous reception cycle to be dynamically adjusted by providing timely scheduling prompts. The uplink control information can implicitly affect the setting of discontinuous reception related timers according to the association matrix, such as the duration of the timer, inactivity timer and discontinuous reception retransmission timer. By coordinating these timers with the traffic pattern inferred from the uplink control information, the mMTC network can further optimize power consumption without sacrificing service quality. Among them, the traffic pattern can be inferred by analyzing the uplink control information. The uplink control information contains a lot of information, such as feedback, scheduling requests, channel quality indication, etc. With this information, the base station can understand the activities and needs of each connected device; periodic traffic patterns are characterized by data transmission at fixed or nearly fixed intervals, such as sensors regularly sending environmental monitoring data or smart meters uploading power consumption information; bursty traffic patterns are irregular data transmissions triggered by specific events, such as fire alarm systems that only send alarms when fire is detected, and smart home devices that perform operations based on user instructions; continuous traffic patterns refer to long-term continuous data streams, which may be accompanied by short intervals, such as video surveillance cameras continuously uploading video streams or real-time status feedback on industrial automation production lines; hybrid traffic patterns combine the characteristics of two or more of the above patterns, such as vehicle location tracking in intelligent transportation systems, which has both periodic location updates and bursty data transmissions caused by special events such as traffic accidents.

[0070] In addition, the Medium Access Control Element (MAC CE) carries instructions for starting a discontinuous reception period based on the type of uplink control information received, thereby ensuring that the device enters the discontinuous reception state at the best time according to the instructions of the network control plane.

[0071] Based on (P 1), the discontinuous reception period parameters can be further optimized by the smoothed eigenvalue maximization method with trace penalty ρ. The uplink control information with scheduling request is the driving signaling for adaptive discontinuous reception management, which can enhance the overall performance of the mMTC network by balancing energy efficiency and communication response.

[0072] In one implementation, using the spectral radius gradient solution method to smoothly process the cluster access optimization definition problem includes:

[0073] The cluster access optimization definition problem is smoothed by minimizing the spectral radius gradient of the association matrix in the cluster access optimization definition problem and setting the trace penalty term in the cluster access optimization definition problem.

[0074] First, in order to solve the problem of inconsistent clustering of uplink control information in the high-dimensional environment of mMTC, by minimizing the spectral radius gradient of the association matrix A, the embodiment of the present invention is aimed at (P 1 ) proposes a specific gradient solution. When the number of clusters K is unknown, the trace penalty term ρ is determined by a data-driven tuning parameter. By replacing the trace constraint in the optimization problem with a properly selected trace penalty term ρ based on the dual certificate construction (the dual certificate construction is the dual solution construction of the semidefinite programming relaxation), a smooth approximation can be applied to configure the timer of the MAC CE to start the discontinuous reception activation, thereby converting (P 1 ) is transformed into a standard semidefinite rule program:

[0075]

[0076] Among them, Φ t is a (N c N f )×(N c N f ) is a symmetric matrix, φ t Represents an element in a set of real numbers, whose index t ranges from 1 to T SR When applying the smoothing approximation, one must specify the problem (P 2 ) takes as input a strictly feasible solution Θ. Given Θ, its projection on the boundary of the positive semidefinite cone It can be defined as:

[0077]

[0078] Among them, λ min (Θ -1 / 2 AΘ -1 / 2 ) means to convert Θ -1 / 2 AΘ -1 / 2 Mapped to its minimum eigenvalue, corresponds to the intersection of the projection normal from θ onto A with the positive semidefinite cone.

[0079] Then, for s 0 Satisfaction 0 <<S,Θ> , semidefinite programming problem (P 2 ) can be formulated as a specific eigenvalue optimization problem, where the constraints are only linear equations. * Is the problem (P 3 )’s optimal solution:

[0080]

[0081] in,<S,Λ> represents the inner product operation between S and the characteristic diagonal matrix Λ to be optimized; s 0 represents the inner product threshold between S and the feasible solution; Indicates that it contains<S,Λ> =s 0 The constraint set of

[0082] but Is the problem (P 2 ). In addition, if A * The problem (P 2 ), then the problem (P 3 ) can be expressed as:

[0083]

[0084] Among them, s * express<S,A> The minimum value of .

[0085] In one implementation, in order to improve the convergence speed, the problem (P 3 ), the objective function of the final cluster access optimization definition problem includes:

[0086]

[0087] Among them, f κ,Θ (Λ) represents the objective function; κ represents the smoothing parameter; λ j (Θ -1 / 2 ΛΘ -1 / 2 ) represents the matrix Θ -1 / 2 ΛΘ -1 / 2 The jth eigenvalue obtained after eigenvalue decomposition; Λ indicates that the diagonal element is the eigenvalue λ j (Θ -1 / 2 ΛΘ -1 / 2 ) is a diagonal matrix; Θ represents a feasible solution.

[0088] Next, we use projected gradient descent to solve The number of repetitions is represented by τ and satisfies the following conditions:

[0089]

[0090] Where R represents the radius of the Euclidean sphere, in order to achieve a sufficiently small tolerance level ε>0 within the range of R, that is, the association matrix in the uplink control information resource allocation after smooth approximation clustering is expressed as A τ It can be expressed as:

[0091]

[0092] In addition, in order to improve the gradient Computational efficiency, projecting the gradient into space The above processing is expressed as The space By all satisfying <Φ t ,A>=0 and<S,A> = 0. The above projection matrix expansion can be used to handle the clustering situation of unknown number K. The mMTC network can strategically group MTDs with similar uplink control information transmission modes.

[0093] Step S103, converting the cluster access optimization definition problem into the eigenvalue maximization problem of the association matrix, and substituting the feasible solution in augmented form into the eigenvalue maximization problem to achieve large-scale access energy-saving optimization by jointly optimizing the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme; the feasible solution in augmented form is calculated by introducing augmented variables; each element in the association matrix is ​​determined according to the number of groups in the strategic grouping.

[0094] In the embodiment of the present invention, according to the strategic grouping scheme, the smoothing problem (P 3 ) can be further extended to the original problem (P 1 ), that is, without knowing K in advance, while keeping the cluster separation rate that can be achieved when K is known. On this basis, in order to optimize the discontinuous reception period and timing parameters accordingly, the embodiment of the present invention introduces the following augmented variables, given (P 1 ) is a strictly feasible solution Θ and A 0 , so that <-S,Θ><<-S,A 0 >=-s 0 :

[0095]

[0096] Among them, Θ′ represents the augmented form of Θ; A 0 ' represents the augmented form of A 0 ; dvec(·) represents the operation of mapping the matrix into a diagonal matrix, whose diagonal elements are the vectorized form of the original matrix.

[0097] By taking the pair θ and A 0 The embodiment of the present invention can define Λ s exist The projection of is the solution to the following minimization problem:

[0098] In space Solve for Λ c Make Minimum, among which represents the F-norm square of the matrix; Λ c Representation Space The characteristic diagonal matrix of S is matched on the top; Λ s Represents the projection space The upper characteristic diagonal matrix Λ c Therefore, discontinuous reception management is equivalent to solving the eigenvalue maximization problem by applying a smooth approximation, where To determine The closed-form solution of the present invention is based on Θ′ and A 0 ' s The specific form of satisfies the KARUSH-KUHN-TUCKER optimality condition (which refers to the necessary conditions for the optimal solution of nonlinear programming), where Λ s Project to The frame (F 1 ) indicates that the framework consists of the following equivalent equations and unknowns ω and ψ:

[0099]

[0100] in, Tr(·) represents the matrix trace operation; ψ represents the implicit parameter of the smooth approximation; ω p Represents the explicit parameter of the smooth approximation.

[0101] By solving the framework (F 1 ), we can get the projection matrix This matrix shows how to transform the problem into an eigenvalue maximization problem, thereby reducing the effective dimensionality of the problem. In parallel with the known K scenario, the following augmented variables are needed to derive Projection on:

[0102]

[0103] Among them, S′ represents S in I′ represents the projection of I on Projection on ; diag(·) represents the operation of constructing a diagonal matrix; e p and e qis a unit vector that is non-zero at the i-th and j-th positions and zero at all other positions. p,q is defined as a (N c T SR ) 2 dimensional zero vector, but in the ((p-1)(N c T SR )+q) and the ((q-1)(N c T SR )+p) positions are 1.

[0104] Given Solving the KARUSH-KUHN-TUCKER condition yields a framework containing the following constraint projections (F 2 ):

[0105]

[0106] in,

[0107] The projection matrix is ​​obtained by solving the frame (F 2 ) can be expressed as:

[0108]

[0109] in, For unknown K, and the correlation matrix A are located in the orthogonal space, indicating that through the dual solution construction, there is a feasible solution that is optimal under the corresponding cluster separation rate. Based on this, the discontinuous reception timing parameters are in A p,q A dual solution is constructed at the position, and the interior point method can be used to solve the relaxation problem of the semidefinite programming, ensuring that the timer parameters related to the trace penalty term ρ remain feasible and do not depend on any specific clustering prior information. At this point, the mMTC network can strategically group MTDs with similar uplink control information transmission modes and optimize the discontinuous reception timer and cycle parameters accordingly, ultimately achieving efficient resource utilization and energy-saving optimization.

[0110] In an embodiment of the present invention, the cluster access optimization definition problem of uplink control information is transformed into an eigenvalue maximization problem of an association matrix in which each element is determined according to the number of groups in the strategic grouping, and the feasible solution in augmented form is substituted into the eigenvalue maximization problem to jointly optimize the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme, thereby reducing unnecessary signaling interactions in the case of large-scale MTD access, thereby reducing system complexity and operating costs. In view of the fact that a large number of devices in application scenarios such as the Internet of Things are in a low-activity state for a long time, the cluster access optimization definition problem is defined with the minimization of each element in the similarity metric matrix in the strategic grouping scheme as the optimization target to reasonably configure the discontinuous reception period, reduce unnecessary monitoring and transmission operations of the device, thereby significantly reducing the energy consumption of the terminal device and extending the battery life. While ensuring low power consumption, jointly optimizing the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme can also minimize the transition time of the device from the sleep state to the active state, that is, shorten the wake-up delay.

[0111] A simulation experiment of a large-scale access energy-saving optimization method provided by an embodiment of the present invention is as follows:

[0112] In the simulation, uplink control information transmission and discontinuous reception configuration parameters that comply with the 5G standard are used. The subcarrier spacing is set to 15kHz, and the time slot length is 14 symbols, where the scheduling request is mapped to the first symbol of each time slot. Therefore, the following settings are used for PUCCH format 2: E = 24, M = 128, N f =12 and T SR =20. The present invention adopts a discontinuous reception long period of 2048 milliseconds and a discontinuous reception short period of 32 milliseconds, which is consistent with the expected idle period of a typical MTD. In addition, a 20MHz channel with a noise floor of -101dBm and a channel impulse response length of 12 is considered in the experiment.

[0113] 1) By using the Poisson process to calculate the MTD flow α i (t)p t The event-driven and payload exchange traffic patterns are evaluated. The two traffic patterns follow Poisson distribution and the average packet arrival rates (denoted by η) are 1 / 300 and 1 / 10 respectively.

[0114] 2) Based on the discrete-time semi-Markov process model, the present invention compares the performance of the proposed energy-saving optimization scheme with the typical discontinuous reception mechanism in terms of energy-saving ratio and average wake-up delay.

[0115] See Figure 3(a) and Figure 3(b). Figure 3(a) and Figure 3(b) are schematic diagrams comparing the energy saving ratio and average wake-up delay under different numbers of MTDs. In this simulation experiment, the proposed energy-saving optimization scheme is evaluated under event-driven and payload exchange traffic modes with different numbers of MTDs N. p The energy saving ratio and average wake-up delay under different MTD numbers are shown in FIG3 (a). As shown in FIG3 (a), which is the energy saving ratio under different MTD numbers, as the total number of MTDs increases, the energy saving performance of the large-scale access energy saving optimization method provided by the embodiment of the present invention gradually improves and tends to be stable, and is better than the benchmark solution under different data packet arrival rates. As the number of data packets arriving per unit time decreases, that is, as η decreases, the MTD will remain in sleep mode instead of switching to an active state to receive data, thereby saving more energy. In addition, since the focus of optimizing discontinuous reception management is to improve the MTD activation state operation, improving the energy saving rate will lead to more sleep switching, which will inevitably cause an increase in the average wake-up delay. It has always been a difficult point to find a balance between energy saving and delay reduction between discontinuous reception cycle switching and selection. As shown in FIG3 (b), which is the average wake-up delay under different MTD numbers, this simulation experiment shows that the proposed large-scale access energy saving optimization method under different η values ​​can effectively control the wake-up delay while saving energy. Therefore, it can be seen that as the number of MTDs increases, the improvement of clustering accuracy can improve the adaptive adjustment capability of the discontinuous reception configuration, and to a certain extent, it can also compensate for the increase in delay.

[0116] Referring to FIG. 4(a) and FIG. 4(b), FIG. 4(a) and FIG. 4(b) are schematic diagrams of energy saving ratio and average wake-up delay under different cumulative observation times. The present invention plots energy saving ratio and average wake-up delay versus cumulative observation times N. c . Figure 4(a) is the energy saving ratio under different cumulative observation times, which shows that the energy saving ratio of the proposed scheme under different η values ​​is better than the baseline scheme as a whole. At the same time, the increase in the number of observations does not necessarily improve the energy saving efficiency. For a given number of MTDs, a small increase in the number of observations has a positive impact on the improvement of energy saving performance. However, too many observations will increase the complexity of resource allocation after uplink control information clustering, and the optimality of discontinuous reception management configuration is difficult to guarantee, resulting in increased energy consumption. Figure 4(b) is the average wake-up delay under different cumulative observation times, which shows that the average wake-up delay of the proposed scheme under different η values ​​can maintain a comparable level with the baseline scheme, which can greatly compensate for the delay increase caused by energy saving improvement. The accumulation of observation times will lead to an increase in average delay, and the reason why too many observations lead to a small decrease in average wake-up delay is consistent with Figure 4(a). It is also because too many observations cannot guarantee the optimality of discontinuous reception management configuration, which increases the duration conflict of the retransmission timer, but increases the MTD silence time and reduces the overall average wake-up delay of mMTC.

[0117] In the embodiment of the present invention, the uplink control information transmission resource scheduling and the adaptive adjustment of the discontinuous reception cycle and timer in the large-scale access network are jointly considered, and a new solution is proposed to reduce network power consumption and ensure fast response time at the same time. In order to solve the joint optimization based on the uplink control information transmission resource scheduling and the discontinuous reception cycle configuration, a special gradient type solution method is developed through the spectral radius of the similarity metric matrix. By utilizing the projection matrix expansion of the clustered uplink control information, the discontinuous reception cycle timer and cycle parameters can be closely aligned with the true optimal value in the original space with a high probability.

[0118] In the embodiment of the present invention, a large-scale access energy-saving optimization method based on uplink control information clustering and discontinuous reception management is used to analyze the uplink control information transmission process and discontinuous reception configuration in a large-scale access network in detail. Compared with the resource allocation optimization method based on the control signaling mechanism and multiple access technology, the proposed method tailors a comprehensive uplink and downlink interaction analysis process according to the characteristics of a small number of device activations and sporadic burst transmission of data in a large-scale access network, which will not increase the processing complexity of the terminal and introduce additional error risks, and can effectively reduce the wake-up delay while reducing power consumption. In addition, compared with the discontinuous reception parameter configuration method based on model prediction, the present invention processes the uplink control information through a clustering algorithm, which can effectively identify different types of device behavior patterns, and make corresponding discontinuous reception configuration adjustments accordingly. This method based on signaling interaction driving not only improves the flexibility of the system, but also enhances its adaptability to changes in the external environment, such as changes in device behavior, fluctuations in network load, etc. Therefore, even in a complex and changeable large-scale access network environment, the large-scale access energy-saving optimization method provided by the embodiment of the present invention can perform well and has strong robustness and stability.

[0119] Based on the same inventive concept, the embodiment of the present invention also provides a structural diagram of a large-scale access energy-saving optimization device, see Figure 5 , Figure 5 : is a structural diagram of a large-scale access energy-saving optimization device provided by an embodiment of the present invention, and the large-scale access energy-saving optimization device includes:

[0120] An uplink control information acquisition module 501 is used to send multiple response configuration information to multiple MTDs, so that each MTD sends uplink control information according to the response configuration information; the response configuration information is determined according to the task request sent by each MTD;

[0121] The strategic grouping scheme acquisition module 502 is used to define a cluster access definition optimization problem of uplink control information according to each uplink control information, and use a spectral radius gradient solution method to smooth the cluster access optimization definition problem, so as to strategically group the multiple MTDs according to the multiple uplink control information to obtain a strategic grouping scheme; the cluster access definition optimization problem takes minimizing each element in a similarity metric matrix in the strategic grouping scheme as an optimization goal; each element in the similarity metric matrix represents a difference between two observed variables in the uplink control information;

[0122] The large-scale access energy-saving optimization implementation module 503 is used to transform the cluster access optimization definition problem into an eigenvalue maximization problem, and substitute the feasible solution in augmented form into the eigenvalue maximization problem to achieve large-scale access energy-saving optimization by jointly optimizing the non-continuous reception period and timing parameters corresponding to each strategic grouping scheme; the feasible solution in augmented form is calculated by introducing augmented variables; each element in the association matrix is ​​determined according to the number of groups in the strategic grouping.

[0123] In an embodiment of the present invention, the cluster access optimization definition problem of uplink control information is transformed into an eigenvalue maximization problem of an association matrix in which each element is determined according to the number of groups in the strategic grouping, and the feasible solution in augmented form is substituted into the eigenvalue maximization problem to jointly optimize the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme, thereby reducing unnecessary signaling interactions in the case of large-scale MTD access, thereby reducing system complexity and operating costs. In view of the fact that a large number of devices in application scenarios such as the Internet of Things are in a low-activity state for a long time, the cluster access optimization definition problem is defined with the minimization of each element in the similarity metric matrix in the strategic grouping scheme as the optimization target to reasonably configure the discontinuous reception period, reduce unnecessary monitoring and transmission operations of the device, thereby significantly reducing the energy consumption of the terminal device and extending the battery life. While ensuring low power consumption, jointly optimizing the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme can also minimize the transition time of the device from the sleep state to the active state, that is, shorten the wake-up delay.

[0124] Optionally, the strategic grouping scheme acquisition module uses a spectral radius gradient solution method to smoothly process the cluster access optimization definition problem, including:

[0125] The cluster access optimization definition problem is smoothed by minimizing the spectral radius gradient of the association matrix in the cluster access optimization definition problem and setting a trace penalty item in the cluster access optimization definition problem; the trace penalty item is set according to the accumulated number of observations of the uplink control information.

[0126] Optionally, the cluster access definition optimization problem is:

[0127]

[0128] Where S is the similarity measure matrix; ρ is the trace penalty term; I is the identity matrix; A is the association matrix; 1 is the all-1 column vector, A1=1 means that the sum of each row of A is equal to 1; <·, ·> represents the inner product operation of the norm; the element in the pth row and qth column of S is defined as Represents the observed variable in the uplink control information.

[0129] Optionally, the objective function of the cluster access optimization definition problem is:

[0130]

[0131] Among them, f κ,Θ (Λ) represents the objective function; κ represents the smoothing parameter; λ j (Θ -1 / 2 ΛΘ -1 / 2 ) represents the pair Θ -1 / 2 ΛΘ -1 / 2 The jth eigenvalue obtained after eigenvalue decomposition; Λ indicates that the diagonal element is λ j (Θ -1 / 2 ΛΘ -1 / 2 ) is a diagonal matrix; Θ represents a feasible solution.

[0132] Optionally, the feasible solution of the augmented form includes Θ′ and A 0 ′:

[0133]

[0134] Among them, Θ′ represents the augmented form of Θ; A 0 ' represents the augmented form of A 0 ; Θ and A 0 are all given feasible solutions; dvec(·) represents the operation of mapping the matrix into a diagonal matrix.

[0135] The embodiment of the present invention further provides an electronic device, such as Figure 6 As shown, it includes a processor 601, a communication interface 602, a memory 603 and a communication bus 604, wherein the processor 601, the communication interface 602, and the memory 603 communicate with each other through the communication bus 604.

[0136] Memory 603, used for storing computer programs;

[0137] The processor 601 is configured to implement the method steps described in any of the above-mentioned large-scale access energy-saving optimization methods when executing the program stored in the memory 603.

[0138] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not represent that there is only one bus or one type of bus.

[0139] The communication interface is used for communication between the above electronic device and other devices.

[0140] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0141] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0142] The present invention also provides a computer-readable storage medium in which a computer program is stored, and when the computer program is executed by a processor, the method steps described in any of the above large-scale access energy-saving optimization methods are implemented.

[0143] Optionally, the computer-readable storage medium may be a non-volatile memory (NVM), such as at least one disk memory.

[0144] Optionally, the computer-readable memory may also be at least one storage device located away from the aforementioned processor.

[0145] In another embodiment of the present invention, a computer program product including instructions is provided, which, when executed on a computer, enables the computer to execute the method steps described in any of the above large-scale access energy-saving optimization methods.

[0146] It should be noted that the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0147] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0148] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other changes to the disclosed embodiments by viewing the drawings and the disclosed content. In the description of the present invention, the term "comprising" does not exclude other components or steps, "one" or "an" does not exclude multiple situations, and "multiple" means two or more, unless otherwise clearly and specifically limited. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0149] The method provided in the embodiment of the present invention can be applied to electronic devices. Specifically, the electronic device can be: a desktop computer, a portable computer, an intelligent mobile terminal, a server, etc. This is not limited here, and any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0150] As for the device / electronic device / storage medium embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0151] It should be noted that the device, electronic device and storage medium of the embodiments of the present invention are respectively a device, electronic device and storage medium applying the above-mentioned large-scale access energy-saving optimization method. All embodiments of the above-mentioned large-scale access energy-saving optimization method are applicable to the device, electronic device and storage medium, and can achieve the same or similar beneficial effects.

[0152] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A large-scale access energy saving optimization method, characterized in that: The large-scale access energy-saving optimization method comprises: Sending multiple response configuration information to multiple MTDs so that each MTD sends uplink control information according to the response configuration information; the response configuration information is determined according to the task request sent by each MTD; A cluster access definition optimization problem of uplink control information is defined according to each uplink control information, and the cluster access optimization definition problem is smoothed by using a spectral radius gradient solution method, so as to strategically group the multiple MTDs according to the multiple uplink control information to obtain a strategic grouping scheme; the cluster access definition optimization problem takes minimizing each element in a similarity metric matrix in the strategic grouping scheme as an optimization goal; each element in the similarity metric matrix represents a difference between two observed variables in the uplink control information; The cluster access optimization definition problem is transformed into an eigenvalue maximization problem of an association matrix, and an augmented feasible solution is substituted into the eigenvalue maximization problem to achieve large-scale access energy-saving optimization by jointly optimizing the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme; the augmented feasible solution is calculated by introducing augmented variables; each element in the association matrix is ​​determined according to the number of groups in the strategic grouping.

2. The large-scale access energy saving optimization method according to claim 1, characterized in that: The cluster access optimization definition problem is smoothly processed using the spectral radius gradient solution method, including: The cluster access optimization definition problem is smoothed by minimizing the spectral radius gradient of the association matrix in the cluster access optimization definition problem and setting a trace penalty item in the cluster access optimization definition problem; the trace penalty item is set according to the accumulated number of observations of the uplink control information.

3. The large-scale access energy saving optimization method according to claim 1, characterized in that: The cluster access definition optimization problem is: Where S represents the similarity measure matrix; ρ represents the trace penalty term; I represents the identity matrix; A represents the association matrix; 1 represents the all-1 column vector, A1=1 represents that the sum of each row element of A is equal to 1; <·,·> represents the inner product operation of the norm; the element in the pth row and qth column of S is defined as Represents the observed variable in the uplink control information.

4. The large-scale access energy saving optimization method according to claim 1, characterized in that: The objective function of the cluster access optimization definition problem is: Among them, f κ,Θ (Λ) represents the objective function; κ represents the smoothing parameter; λ j (Θ -1 / 2 ΛΘ -1 / 2 ) represents the pair Θ -1 / 2 ΛΘ -1 / 2 The jth eigenvalue obtained after eigenvalue decomposition; Λ indicates that the diagonal element is λ j (Θ -1 / 2 ΛΘ -1 / 2 ) is a diagonal matrix; Θ represents a feasible solution.

5. The large-scale access energy saving optimization method according to claim 1, characterized in that: The feasible solutions of the augmented form include Θ′ and A0′: Where Θ′ represents the augmented form of Θ; A0′ represents the augmented form of A0; Θ and A0 are both given feasible solutions; dvec(·) represents the operation of mapping the matrix into a diagonal matrix.

6. A large-scale access energy-saving optimization device, characterized in that: The large-scale access energy-saving optimization device comprises: An uplink control information acquisition module, used to send multiple response configuration information to multiple MTDs, so that each MTD sends uplink control information according to the response configuration information; the response configuration information is determined according to the task request sent by each MTD; A strategic grouping scheme acquisition module is used to define a cluster access definition optimization problem of uplink control information according to each uplink control information, and use a spectral radius gradient solution method to smooth the cluster access optimization definition problem, so as to strategically group the multiple MTDs according to multiple uplink control information to obtain a strategic grouping scheme; the cluster access definition optimization problem takes minimizing each element in a similarity metric matrix in the strategic grouping scheme as an optimization goal; each element in the similarity metric matrix represents a difference between two observed variables in the uplink control information; A large-scale access energy-saving optimization implementation module is used to transform the cluster access optimization definition problem into an eigenvalue maximization problem, and substitute the feasible solution in augmented form into the eigenvalue maximization problem to achieve large-scale access energy-saving optimization by jointly optimizing the discontinuous reception period and timing parameters corresponding to each strategic grouping scheme; the feasible solution in augmented form is calculated by introducing augmented variables; each element in the association matrix is ​​determined according to the number of groups in the strategic grouping.

7. The large-scale access energy-saving optimization device according to claim 6, characterized in that: The strategic grouping scheme acquisition module uses a spectral radius gradient solution method to smoothly process the cluster access optimization definition problem, including: The cluster access optimization definition problem is smoothed by minimizing the spectral radius gradient of the association matrix in the cluster access optimization definition problem and setting a trace penalty item in the cluster access optimization definition problem; the trace penalty item is set according to the accumulated number of observations of the uplink control information.

8. The large-scale access energy-saving optimization device according to claim 6, characterized in that: The cluster access definition optimization problem is: Where S represents the similarity measure matrix; ρ represents the trace penalty term; I represents the identity matrix; A represents the association matrix; 1 represents the all-1 column vector, A1=1 represents that the sum of each row element of A is equal to 1; <·,·> represents the inner product operation of the norm; the element in the pth row and qth column of S is defined as Represents the observed variable in the uplink control information.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the large-scale access energy-saving optimization method described in any one of claims 1-5 when executing the program stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the large-scale access energy-saving optimization method according to any one of claims 1 to 5 is implemented.

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