Energy consumption optimization method and system based on multi-slot multi-user non-orthogonal multiple access

Abstract

The application relates to the field of information communication, in particular to an energy consumption optimization method and system based on multi-time-slot multi-user non-orthogonal multiple access. The method comprises the following steps: obtaining total task bits of users, constructing a first target function based on a preset time-slot initial length and initial transmission power of the users; reconstructing the first target function to obtain a second target function according to the total task bits of the users; the second target function contains user cumulative normalized bit rate; solving energy consumption of the second target function to obtain a current solution value, extracting a boundary sequence based on the user cumulative normalized bit rate; the boundary sequence is the cumulative load of a user before the end of a time slot; iteratively updating the boundary sequence and the current solution value and obtaining a global optimal solution value, adjusting parameters corresponding to transmission based on the global optimal solution value, so as to optimize energy consumption; the method is used for obtaining an optimal solution of energy consumption optimization of multi-time-slot multi-user non-orthogonal multiple access, and reducing transmission energy consumption.

Description

Technical Field

[0001] This invention relates to the field of information and communication, and more specifically, to an energy consumption optimization method and system based on multi-timeslot multi-user non-orthogonal multiple access. Background Technology

[0002] In Mobile Edge Computing (MEC), computing tasks generated by user terminals often have strict latency requirements and high computational loads. Due to the inherent limitations of user terminals in terms of computing power and battery capacity, offloading computing tasks to servers deployed at the network edge has become a key technical approach to overcome terminal performance bottlenecks, reduce overall energy consumption, and ensure service latency requirements.

[0003] To achieve efficient task offloading, various access and resource allocation methods have been proposed in the industry. Among them, Non-Orthogonal Multiple Access (NOMA) technology significantly improves spectral efficiency and user connection capacity because it supports parallel transmission by multiple user terminals on the same time-frequency resources and uses Successive Interference Cancellation (SIC) at the receiving end to separate superimposed signals. In particular, the existing hybrid NOMA scheme dynamically allocates time slots, allowing uncut users to transmit in parallel using NOMA, balancing spectral efficiency and scheduling flexibility. This framework constructs an energy-minimizing model with cutoff time and power constraints and proposes a low-complexity algorithm to jointly optimize time slots and power, achieving Pareto optimal resource allocation.

[0004] However, the aforementioned mobile edge computing schemes based on hybrid non-orthogonal multiple access still exhibit shortcomings in practical applications: First, existing schemes for obtaining Pareto optimal solutions generally rely on a strong assumption: the order of deadlines for all user task transmissions must be completely consistent with the decoding order of serial interference cancellation at the receiver. In practice, the deadlines for user task transmissions are often randomly determined by the size of their associated processing tasks and are independent of the decoding order of serial interference cancellation. This assumption is difficult to hold in most practical scenarios, leading to a significant decrease in the accuracy of the time slot length and user transmit power obtained by the aforementioned schemes, failing to guarantee their theoretical Pareto optimality, and making it even more difficult to obtain the global optimal solution. Second, when jointly optimizing resources such as time slot length and user transmit power, the established mathematical model is inherently non-convex, belonging to a typical non-convex optimization problem. This non-convexity makes it impossible to directly apply traditional convex optimization methods, and existing solution methods face the dilemma of difficulty in guaranteeing both solution quality and efficiency. Summary of the Invention

[0005] This invention provides an energy consumption optimization method and system based on multi-timeslot multi-user non-orthogonal multiple access, which is used to obtain the optimal solution for energy consumption optimization of multi-timeslot multi-user non-orthogonal multiple access and reduce transmission energy consumption.

[0006] According to a first aspect of this application, an energy consumption optimization method based on multi-slot multi-user non-orthogonal multiple access is provided, the method comprising: Get the total number of users, the deadline for each user's task, and the total number of bits for each user's task; The number of time slots is preset to be the same as the total number of users, and the initial duration of each time slot is preset based on the user task deadline; and the initial transmission power corresponding to each user in each time slot is preset; Based on the initial duration of each time slot and the initial transmit power of each user in each time slot, a first objective function is constructed; The first objective function is reconstructed based on the total number of user task bits to obtain a second objective function; wherein the second objective function includes the user's cumulative normalized bit rate. The energy consumption of the second objective function is calculated to obtain the current solution value. The boundary sequence of each time slot is extracted based on the user's cumulative normalized bit rate. The boundary sequence is the cumulative load of a user before the end of the time slot. The boundary sequence and the current solution value are iteratively updated. The global optimal solution value is obtained based on the updated boundary sequence, the updated current solution value and preset conditions. Based on the global optimal solution value, the parameters corresponding to multi-slot multi-user non-orthogonal multiple access transmission are adjusted to optimize its energy consumption.

[0007] Understandably, by equivalently reconstructing the non-convex problem of the original first objective function into a convex optimization problem of the second objective function, the existence and solvability of the global optimal solution are theoretically guaranteed. At the same time, based on the iterative solution of the boundary sequence, the global optimal solution value with the lowest energy consumption is obtained. Based on the global optimal solution value, the transmission parameters are adjusted, which effectively reduces the total transmission energy consumption of the multi-timeslot multi-user non-orthogonal multiple access framework.

[0008] Optionally, the step of constructing a first objective function based on the initial duration of each time slot and the initial transmit power of each user in each time slot includes: For each time slot, based on its initial duration and the user's initial transmit power in that time slot, the user's energy consumption in that time slot is obtained, and based on the user's energy consumption in that time slot, the total energy consumption of all users in that time slot is obtained. Based on the total energy consumption of all users in each time slot, obtain the total energy consumption of all users for all time slots; The first objective function is constructed based on a preset first constraint to minimize the total energy consumption.

[0009] Understandably, by calculating the energy consumption of each user in each time slot and summing them up to obtain the total energy consumption, a first objective function with the goal of minimizing the total energy consumption was established, laying a reliable foundation for the convex reconstruction and efficient solution of the subsequent non-convex problem.

[0010] Optionally, reconstructing the first objective function based on the total number of user task bits to obtain the second objective function includes: Obtain bandwidth parameters; Based on the total number of user task bits and the bandwidth parameters, the user's cumulative normalized bit rate is obtained; The reconstructed initial transmit power is obtained by reconstructing the initial transmit power in the first objective function based on the user's cumulative normalized bit rate. The first objective function is reconstructed based on the reconstructed initial transmit power to obtain the second objective function; The first constraint of the first objective function is reconstructed based on the total number of user task bits to obtain the second constraint of the second objective function.

[0011] Understandably, by introducing the user cumulative normalized bit rate to reconstruct the transmit power in the first objective function, the original non-convex objective function is equivalently transformed into a convex optimization form, thereby ensuring the existence and solvability of the global optimal solution.

[0012] Optionally, the first The user in the first User cumulative normalized bit rate per time slot for: in, Indicates the first The user in the first The initial number of sub-task bits per time slot; It is obtained based on the total number of user task bits; Indicates the total number of time slots; This indicates the bandwidth parameter. This represents the total number of users. Indicates the first The initial duration of each time slot.

[0013] Understandably, by defining the user cumulative normalized bit rate, the bit allocation of multiple users and multiple time slots is effectively decoupled from the relationship between channel gain and energy consumption, providing a key variable basis for subsequently transforming the original non-convex problem into an equivalent convex optimization problem and ensuring the solvability of the global optimal solution.

[0014] Optionally, the second objective function is: The users are arranged in an ascending order based on the channel gain corresponding to each user. This represents the total energy consumption corresponding to the second objective function. This represents the total number of time slots. This represents the total number of users. Indicates the first One user, Indicates the first Each time slot Indicates the first The initial duration of each time slot, Indicates the first Preset coefficients for each user Indicates the first The user in the first User cumulative normalized bit rate per time slot; Indicates noise power; This represents the channel gain corresponding to the user with the weakest channel gain among all users; the channel gain corresponding to the user and the noise power are obtained synchronously when acquiring the total number of users, the user task deadline, and the total number of user task bits. Among them, the Preset coefficients for individual users for: in, Indicates the first Channel gain for each user Indicates the first Channel gain corresponding to each user; This represents the channel gain of the user with the strongest channel gain among all users.

[0015] Understandably, by arranging users in ascending order of channel gain and defining preset coefficients, the energy consumption of multi-user non-orthogonal multiple access is expressed as a convex function of the cumulative normalized bit rate, thereby ensuring the solvability of the global optimal solution and the accurate characterization of the total energy consumption.

[0016] Optionally, the iterative update of the boundary sequence and the current solution value, and the acquisition of the global optimal solution value based on the updated boundary sequence, the updated current solution value, and preset conditions, includes: The total number of task bits for each user is evenly distributed across all time slots to obtain the initial number of task bits for each user in each time slot; Based on the initial total energy consumption and the initial number of task bits for the first user in each time slot, the difference in partial derivatives between two adjacent time slots for the first user is obtained; wherein, the initial total energy consumption is the initial total energy consumption corresponding to the second objective function, and the first user is the user with the weakest channel gain among all users; Determine whether the partial derivative difference between all two adjacent time slots and / or the boundary sequence corresponding to the first time slot in the adjacent time slots satisfy a preset condition. If not, update the boundary sequence corresponding to the first time slot in the adjacent time slots and the current solution value until the partial derivative difference between all two adjacent time slots and / or the boundary sequence corresponding to the first time slot in the adjacent time slots satisfy the preset condition. If yes, then use the current solution value as the global optimal solution value. The preset condition is: the difference between the partial derivatives of the two adjacent time slots is equal to zero; or, the difference between the partial derivatives of the two adjacent time slots is greater than or equal to zero and the boundary sequence of the first time slot is equal to zero, wherein the two adjacent time slots include the first time slot and the second time slot, and the first time slot is earlier than the second time slot.

[0017] Understandably, by uniformly distributing the initial number of task bits and iteratively judging based on the difference of partial derivatives between adjacent time slots and the boundary sequence, we can ensure rapid convergence to a global optimal solution that meets the preset conditions, thereby reducing computational complexity while ensuring solution accuracy.

[0018] Optionally, updating the boundary sequence corresponding to the first time slot in the adjacent time slots and the current solution value includes: Two adjacent time slots that do not meet the preset conditions are obtained, and corresponding adjustment values ​​are obtained based on the boundary sequences and preset coefficients corresponding to the two adjacent time slots. The first boundary sequence is obtained by updating the boundary sequence corresponding to the first time slot based on the adjustment value. If the first boundary sequence is greater than or equal to zero, the first boundary sequence is used as the updated boundary sequence of the first time slot; if the first boundary sequence is less than zero, the boundary sequence of the first time slot is set to zero. The updated current solution value is obtained by updating the boundary sequence after the first time slot.

[0019] Understandably, by calculating adjustment values ​​for adjacent time slots that do not meet the preset conditions and updating the boundary sequence while ensuring non-negativity, the current solution value is gradually corrected, thereby ensuring that the iterative process converges stably to the optimal solution in the domain.

[0020] Optionally, the first The time slot and the first The partial derivative of the next time slot adjacent to each time slot is: The users are arranged in an ascending order based on their corresponding channel gains; This represents the total energy consumption corresponding to the second objective function. Indicates the first user in the _th Number of task bits per time slot Indicates the first user in the _th Number of task bits per time slot Indicates bandwidth parameter, This represents the total number of users. Indicates the first Preset coefficients for each user Indicates the first The user in the first The boundary sequence of each time slot, Indicates the first The user in the first The boundary sequence of each time slot; the channel gain and bandwidth parameters corresponding to the user are obtained simultaneously when acquiring the total number of users, the user task deadline, and the total number of user task bits; And / or, the adjustment value for: in: and All are preset parameters. Indicates the first One user, Indicates the first Each time slot Indicates the first For users whose time slot tasks have expired, Indicates the first The user in the first The boundary sequence of time slots, Indicates the first The user in the first The boundary sequence of each time slot, Indicates the first The user in the first The boundary sequence of each time slot; For the first Preset coefficients for each user Indicates the first The user in the first Boundary sequence of time slots.

[0021] Understandably, by using the analytical expression of the difference between the partial derivatives of adjacent time slots and the corresponding adjustment values, a fast iterative update based on gradient equalization is achieved, which can accurately converge to the global optimal solution within a finite number of iterations, significantly reducing computational complexity.

[0022] Optionally, the current solution value includes one or more of the following: the current duration of each time slot, the current number of sub-task bits for each user in each time slot, and the current transmit power for each user in each time slot; And / or, the global optimal solution value includes one or more of the following: the optimized duration of each time slot, the optimized number of sub-task bits per user per time slot, and the optimized transmit power per user per time slot.

[0023] Understandably, by iteratively updating the optimized duration of each time slot, the number of user-assigned task bits, and the transmit power, the global optimal configuration corresponding to the lowest total energy consumption is obtained.

[0024] According to a second aspect of this application, an energy consumption optimization system based on multi-timeslot multi-user non-orthogonal multiple access is provided, the system comprising: The acquisition module is used to obtain the total number of users, the deadline for user tasks, and the total number of bits for user tasks; The preset module is used to preset the number of time slots, which is the same as the total number of users, and to preset the initial duration of each time slot based on the user task deadline; and to preset the initial transmission power of each user in each time slot. The first objective function construction module is used to construct a first objective function based on the initial duration of each time slot and the initial transmit power of each user in each time slot; The reconstruction module is used to reconstruct the first objective function based on the total number of user task bits to obtain a second objective function; wherein the second objective function includes the user's cumulative normalized bit rate; The optimization module is used to solve the second objective function by energy consumption to obtain the current solution value, and to extract the boundary sequence of each time slot based on the user's cumulative normalized bit rate; the boundary sequence is the cumulative load of the user before the end of the time slot; the boundary sequence and the current solution value are iteratively updated, and the global optimal solution value is obtained according to the updated boundary sequence, the updated current solution value and preset conditions; The adjustment module is used to adjust the parameters corresponding to multi-slot multi-user non-orthogonal multiple access transmission based on the global optimal solution value, so as to optimize its energy consumption.

[0025] Based on any of the above aspects, the energy consumption optimization method and system based on multi-timeslot multi-user non-orthogonal multiple access provided in this application embodiment can achieve the following technical effects: By reconstructing the first objective function, the original non-convex problem is equivalently transformed into a convex optimization problem, theoretically guaranteeing the existence and solvability of the global optimal solution of the objective function. This addresses the challenges of hybrid non-orthogonal multiple access... Mobile edge computing transmission framework (HNOMA-MEC, Hybrid Non- In the Orthogonal Multiple Access Mobile Edge Computing (OECC) energy minimization problem, the non-convexity caused by power and time slot coupling is addressed by introducing the number of bits uploaded by users in each time slot as an auxiliary variable. This reconstructs the original problem into an equivalent convex optimization form, thus eliminating the interference of local optima on the optimization results. This reconstruction not only ensures that the solution process converges to the global optimum but also provides a solid theoretical foundation for the design of subsequent efficient algorithms, avoiding the shortcomings of traditional non-convex optimizations, such as reliance on initial values ​​and susceptibility to local optima.

[0026] Optimizing the second objective function based on the boundary sequence yields the globally optimal solution with the lowest energy consumption, significantly reducing total transmission energy consumption. By defining the cumulative load of users before the end of each time slot as the boundary sequence and constructing a convergence criterion for the difference in partial derivatives between adjacent time slots, the boundary sequence and the current solution value are iteratively updated to ultimately obtain the globally optimal solution that satisfies the preset conditions. The process converges to the global optimum within a finite number of iterations, exhibiting low computational complexity. Based on this optimal solution, the overall total transmission energy consumption in a multi-timeslot, multi-user non-orthogonal multiple access framework can be effectively reduced. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This diagram illustrates an application scenario of an energy consumption optimization method based on multi-timeslot multi-user non-orthogonal multiple access provided in this embodiment.

[0029] Figure 2 This is a flowchart illustrating an energy consumption optimization method based on multi-timeslot multi-user non-orthogonal multiple access provided in this embodiment.

[0030] Figure 3 The flowchart for constructing the first objective function is provided for this embodiment.

[0031] Figure 4 The flowchart for constructing the second objective function is provided for this embodiment.

[0032] Figure 5 This is a schematic diagram illustrating the allocation of three user tasks in this embodiment.

[0033] Figure 6 This is a flowchart for obtaining the global optimal solution value provided in this embodiment.

[0034] Figure 7 This embodiment provides a flowchart for updating the current solution value and the boundary sequence.

[0035] Figure 8 The diagram showing the relationship between total energy consumption and total number of user task bits, optimized using four methods, is provided for this embodiment.

[0036] Figure 9 The diagram showing the relationship between total energy consumption and network coverage area based on four optimization methods provided in this embodiment. Figure 10 The graph showing the relationship between total energy consumption and total number of users based on four optimization methods provided in this embodiment.

[0037] Figure 11 The diagram showing the relationship between total energy consumption and network coverage area based on four optimization methods provided in this embodiment.

[0038] Figure 12 This is a schematic diagram of the functional modules of an energy consumption optimization system based on multi-time-slot multi-user non-orthogonal multiple access provided in this embodiment. Detailed Implementation

[0039] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this application. To better illustrate the following embodiments, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product; it is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0040] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0041] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0042] Existing hybrid nonorthogonal multiple access mobile edge computing methods have two technical problems: First, the strong assumption that the cutoff time order is consistent with the serial interference cancellation decoding order is difficult to hold in practice, leading to a decrease in solution accuracy and making it impossible to guarantee Pareto optimal and global optimal solutions; Second, the established mathematical model is inherently non-convex, and traditional convex optimization methods cannot solve it directly, making it difficult to guarantee both optimization quality and efficiency.

[0043] This embodiment provides a technical solution that can solve the above problems. The specific implementation of this application will be described in detail below with reference to the accompanying drawings.

[0044] An exemplary diagram illustrates an application scenario of an energy consumption optimization method based on multi-slot multi-user non-orthogonal multiple access, provided in an embodiment of this application. For example... Figure 1 As shown, the application scenario includes at least a server 100 and a terminal 200 that can communicate with the server 100.

[0045] Understandably, the server 100 can be an independent electronic device or a cluster of multiple electronic devices; the terminal 200 can be a smartphone terminal, personal computer, tablet computer, vehicle terminal, etc., but is not limited to these.

[0046] In one feasible implementation, server 100 and terminal 200 may respectively execute the energy consumption optimization method based on multi-timeslot multi-user non-orthogonal multiple access provided in the embodiments of this application. Alternatively, the energy consumption optimization method based on multi-timeslot multi-user non-orthogonal multiple access provided in the embodiments of this application may be partially executed in server 100 and partially executed in terminal 200.

[0047] like Figure 2 As shown, this embodiment provides an energy consumption optimization method based on multi-slot multi-user non-orthogonal multiple access, which can be further divided into the following steps: S100: Obtain the total number of users, the deadline for user tasks, and the total number of bits for user tasks; Understandably, in the transmission framework of hybrid non-orthogonal multiple access mobile edge computing, multiple user terminals transmit in parallel on the same channel to achieve channel multiplexing and improve transmission efficiency. Therefore, it is necessary to first obtain the total number of users participating in the transmission to provide a data basis for subsequent time slot number preset and resource allocation, thereby obtaining the globally optimal solution to the overall total energy consumption minimization problem while improving transmission rate and ensuring reliability.

[0048] In this embodiment, the time when the user completes transmission on the channel is determined based on indicators such as data volume and latency requirements of the task to be processed. This time is the task deadline. The task deadline reflects the final point in time when the user occupies the channel. After this time, the user stops sending data to the channel and is considered to have completed the transmission. Simultaneously, the data volume of the task to be processed by the user can determine the total number of bits for the user's task, providing a crucial data foundation for subsequent reconstruction of the objective function and solving for the global optimal solution.

[0049] S200: Preset the number of time slots to be the same as the total number of users, and preset the initial duration of each time slot based on the user task deadline; preset the initial transmission power of each user in each time slot; Understandably, in this embodiment, the end of each time slot is set to be triggered by the arrival of the task deadline of at least one user. The deadlines of multiple users will naturally divide the transmission process into time slots equal to the total number of users. At the same time, the above setting can establish a definite mapping relationship between users and time slots, so as to uniformly optimize resource allocation.

[0050] In this embodiment, the initial duration of each time slot is preset based on the user task deadline, and the association between the user task deadline data and the time slot is established. This ensures that the time slot division of the entire transmission process naturally satisfies the deadline constraints of all users, eliminating the need to deal with the deadline timeout problem in subsequent optimization. At the same time, this also transforms the time slot length from a variable to be optimized into a known constant, reducing the complexity of the optimization problem.

[0051] Specifically, to reduce computational complexity, the users are pre-sorted in non-descending order based on their corresponding channel gains, resulting in a user hierarchy. The channel gains of each user are obtained simultaneously when acquiring the total number of users, the user task deadline, and the total number of bits for each user task.

[0052] For example, there exists The user, the first individual users Channel gain ( ),satisfy ,in To determine the channel gain for the first user after the arrangement process is complete. After completing the arrangement work, the first Channel gain corresponding to each user.

[0053] The number of time slots is preset to be the same as the total number of users. For example, the preset number of time slots is: ,and .

[0054] Specifically, when setting the initial duration of each time slot, it is necessary to first preprocess the user based on the user's task deadline: Sort the user task deadlines in non-decreasing order, and obtain the time slot-user sorting array based on the non-decreasing order. ,in, , define the first After the first time slot ends, the... Each user completes the transmission of the task. For the slot-user permutation array... ,satisfy: For example, there exists Each user has a corresponding task deadline of [time]. ,and Then, sort the user task deadlines in non-descending order to obtain the following sorting results: Then the time slot-user permutation array is obtained. ,Right now .

[0055] Array sorted by time Get the deadline for each user's task. Initial duration of each time slot : In this embodiment, the transmit power of each user in each time slot is one of the global optimal solutions that need to be obtained. By setting a reasonable initial transmit power as the starting point for optimization, redundant calculations that iterate from zero can be avoided, thereby improving the solution efficiency.

[0056] S300. Based on the initial duration of each time slot and the initial transmit power of each user in each time slot, a first objective function is constructed; In this embodiment, based on the premise of meeting the deadline and transmission volume requirements of each user task, the overall total energy consumption is minimized, thereby constructing the first objective function.

[0057] Specifically, such as Figure 3As shown, the construction of the first objective function based on the initial duration of each time slot and the initial transmit power of each user in each time slot includes the following steps: S310. For each time slot, based on its initial duration and the user's initial transmission power in that time slot, obtain the user's energy consumption in that time slot, and based on the user's energy consumption in that time slot, obtain the total energy consumption of all users in that time slot. S320. Based on the total energy consumption of all users in each time slot, obtain the total energy consumption of all users corresponding to all time slots; S330. Construct the first objective function by minimizing the total energy consumption based on the preset first constraint.

[0058] In this embodiment, the first objective function is: in, This represents the total energy consumption corresponding to the first objective function. No. The user in the first The initial transmit power of each time slot.

[0059] Specifically, the first constraint includes a first user task deadline constraint, a first task quantity constraint, and a first transmission deadline constraint; First task deadline constraint for: in, Indicates the first The deadline for each user's task; Indicates the first One time slot; Indicates the first The initial duration of each time slot; For the first The cutoff time slot number for each user; Understandably, arrays sorted by time , define the first The cutoff time slot number for each user is: That is, the first Individual users in time slots The transmission of the task ends upon completion, thereby obtaining the user-timeslot sorted array. . Slot-User Sort Array The inverse mapping yields: in, Indicates the first The total number of time slots sent by the i-th user task, which represents the i-th The time slot number at which each user task is due.

[0060] First task constraint for: in, Indicates the first The user in the first The transmission rate of each time slot; where Based on the The user in the first The initial transmit power and corresponding channel gain of each time slot are obtained; Indicates the first The initial duration of each time slot; Understandably, in each time slot Within this system, all users utilize non-orthogonal multiple access for uplink transmission. The decoding order of the uplink devices, based on serial interference cancellation, is determined by the channel gain, with users having higher channel gains being decoded first. Therefore, based on the above principle and the first... The user in the first The initial transmit power and corresponding channel gain of the first time slot can be used to calculate the second time slot. The user in the first Transmission rate per time slot : in, For broadband parameters, For the first Channel gain for each user For the first The user in the first Channel gain achieved by each user For noise power; where broadband parameters are... and noise power When obtaining the total number of users, the deadline for user tasks, and the total number of bits for user tasks, obtain them simultaneously.

[0061] First transmission cutoff constraint If the current time slot exceeds the total number of time slots for user task transmission, the user's transmit power is set to zero in the current time slot and all subsequent time slots.

[0062] Specifically, the first transmission cutoff constraint It can be represented as: S400. Reconstruct the first objective function based on the total number of user task bits to obtain a second objective function; wherein the second objective function includes the user's cumulative normalized bit rate. In this embodiment, to overcome the non-convexity of the first objective function, this application introduces the total number of user task bits to reconstruct the first objective function, thereby obtaining a second objective function that is convex. Solving the second objective function is a convex optimization problem, and theoretically there exists a global optimal solution.

[0063] Specifically, such as Figure 4 As shown, the process of reconstructing the first objective function based on the total number of user task bits to obtain the second objective function includes the following steps: S410, Obtain bandwidth parameters; In this embodiment, the bandwidth parameter is a configuration parameter of the channel provided to the user for data transmission. It reflects the data transmission capability of the channel and can be obtained simultaneously when acquiring the total number of users, the deadline of user tasks, and the total number of bits of user tasks, so as to provide a data basis for subsequent reconstruction of the first objective function.

[0064] S420. Based on the total number of user task bits and the bandwidth parameters, obtain the user's cumulative normalized bit rate; Specifically, no. The user in the first User cumulative normalized bit rate per time slot for: in, Indicates the first The user in the first The initial number of sub-task bits per time slot, The total number of task bits for each user is obtained based on the total number of task bits for each user. Specifically, during the initialization phase, the total number of task bits for each user can be evenly distributed across all time slots to obtain the initial number of task bits for each user in each time slot.

[0065] S430. Reconstruct the initial transmit power in the first objective function based on the user's cumulative normalized bit rate to obtain the reconstructed initial transmit power; In this embodiment, through derivation, the initial transmit power can be expressed as: in, Represented as the first The user in the first The cumulative normalized bit rate of users in each time slot.

[0066] S440. Based on the reconstructed initial transmit power, the first objective function is reconstructed to obtain the second objective function; Specifically, the second objective function is: This represents the total energy consumption corresponding to the second objective function. Indicates the first Preset coefficients for each user This represents the channel gain corresponding to the user with the weakest channel gain among all users. Among them, the Preset coefficients for individual users for: in, Indicates the first Channel gain for each user Indicates the first Channel gain corresponding to each user; This represents the channel gain of the user with the highest channel gain among all users. S450. Based on the total number of user task bits, the first constraint of the first objective function is reconstructed to obtain the second constraint of the second objective function.

[0067] Specifically, the second constraint may include a second task deadline constraint, a second task quantity constraint, and a second transmission deadline constraint.

[0068] Among them, the second task deadline constraint and the first task deadline constraint You can refer to the deadline constraint for the first task. The settings will not be elaborated here.

[0069] Second task constraint for: Indicates the first The user in the first Number of task bits per time slot Indicates the first Total number of task bits per user.

[0070] Second transmission cutoff constraint If the current time slot exceeds the total number of time slots for user task transmission, the number of sub-task bits for the user in the current time slot and all subsequent time slots is set to zero, i.e.: S500: Solve the energy consumption of the second objective function to obtain the current solution value; extract the boundary sequence of each time slot based on the user's cumulative normalized bit rate; the boundary sequence is the cumulative load of the user before the end of the time slot; iteratively update the boundary sequence and the current solution value; obtain the global optimal solution value based on the updated boundary sequence, the updated current solution value and preset conditions. In this embodiment, based on the aforementioned convex optimization reconstruction of the first objective function, an equivalent convex problem is obtained: the optimization of the energy consumption of the second objective function. Although the second objective function can theoretically be solved using classical convex optimization methods, the singularity of the second objective function when the slot length approaches zero and the large size of the variables make the solution inefficient when directly applying general solution methods or solvers. Therefore, this application proposes to use a newly defined boundary sequence to solve the second objective function, and defines this solution method as the Convex Optimization Based Algorithm for HNOMA-MEC (COBAHN).

[0071] In this embodiment, the boundary sequence is the cumulative load of the user before the end of the time slot. The boundary sequence for each time slot is extracted based on the user's cumulative normalized bit rate. That is, for each time slot... Boundary sequences are extracted from the user cumulative normalized bit rate calculated in the time slot. As the initial value. Note that when hour, Defined as 0.

[0072] Understandably, in this application, a defined boundary sequence is used. Parameter estimation The user in the first Number of task bits per time slot The calculation process for this estimate follows these principles: If a user In two adjacent time slots and There is a relationship: the user-normalized bit rate corresponding to the time slot. Then there must be: (1) or ,Right now Constrained by the user or user At the cumulative load level.

[0073] (2) .Right now Constrained by the user With users Between the cumulative load levels.

[0074] Combine the above principles with the second task constraint in the second objective function. By combining them, we can obtain the first... The user in the first Number of task bits per time slot .like Figure 5 As shown, Figure 5 The deadline for users in China is met. (Right now ).user All tasks are allocated to time slots This determines the first boundary cumulative load. Subsequently, the user and Resource allocation, while satisfying its own second task constraint. and lower boundary sequence Under constraints, the boundary interval formed in the previous stage (e.g.) Strictly defined. This process clearly demonstrates that the boundary sequence... It can uniquely determine the first The user in the first Number of task bits per time slot .

[0075] Specifically, such as Figure 6 As shown, the iterative update of the boundary sequence and the current solution value, and the acquisition of the global optimal solution value based on the updated boundary sequence, the updated current solution value, and preset conditions, may include the following steps: S510. Distribute the total number of task bits for each user evenly to all time slots to obtain the initial number of task bits for each user in each time slot. Specifically, the initial number of bits for each task can be: for , .

[0076] S520. Based on the initial total energy consumption and the initial number of task bits of the first user in each time slot, obtain the difference of the partial derivative of the first user in two adjacent time slots; wherein, the initial total energy consumption is the initial total energy consumption corresponding to the second objective function, and the first user is the user with the weakest channel gain among all users; In this embodiment, through derivation, for time slots and the time slot The next adjacent time slot Calculate the initial total energy consumption For users In the time slot Time slot The difference in partial derivatives of the number of bits in each task.

[0077] Specifically, no. The time slot and the first The partial derivative of the next time slot adjacent to each time slot is: in, This represents the total energy consumption corresponding to the second objective function. Indicates the first user in the _th Number of task bits per time slot Indicates the first user in the _th Number of task bits per time slot Indicates the first Preset coefficients for each user Indicates the first The user in the first The boundary sequence of each time slot, Indicates the first The user in the first Boundary sequence of time slots.

[0078] S530. Determine whether the partial derivative difference between all two adjacent time slots and / or the boundary sequence corresponding to the first time slot in the adjacent time slots satisfy a preset condition. If not, update the boundary sequence corresponding to the first time slot in the adjacent time slots and the current solution value until the partial derivative difference between all two adjacent time slots and / or the boundary sequence corresponding to the first time slot in the adjacent time slots satisfy the preset condition. If yes, use the current solution value as the global optimal solution value. The preset condition is: the partial derivative difference between the two adjacent time slots is equal to zero; or, the partial derivative difference between the two adjacent time slots is greater than or equal to zero and the boundary sequence of the first time slot is equal to zero. The two adjacent time slots include the first time slot and the second time slot, and the first time slot is earlier than the second time slot.

[0079] In this embodiment, except for the first time slot, since the calculation of the partial derivative difference requires data from two adjacent time slots, the last time slot must have only completed the calculation of the partial derivative difference once. Considering that subsequent adjustments only need to be made for the earlier of the two adjacent time slots, i.e. the first time slot, the last time slot does not need to perform the partial derivative difference calculation twice as required by other time slots.

[0080] Understandably, for a given calculated current solution value, when it is in any time slot... All time slots meet the preset conditions. The difference of the partial derivative with its next adjacent time slot At the same time, or simultaneously satisfying the time slot. The difference of the partial derivative with its next adjacent time slot With the time slot Corresponding boundary sequence If the current solution value meets the preset conditions, it is determined that the current solution value can be regarded as the global optimal solution value and output. If the current solution value does not meet the preset conditions, the boundary sequence corresponding to the first time slot in the adjacent time slot and the current solution value need to be updated until the preset conditions are met.

[0081] Specifically, such as Figure 7 As shown, updating the boundary sequence corresponding to the first time slot in the adjacent time slots and the current solution value may include the following steps: S531. Obtain two adjacent time slots that do not meet the preset conditions, and obtain the corresponding adjustment values ​​according to the boundary sequences and preset coefficients corresponding to the two adjacent time slots. In this embodiment, when the partial derivative difference between two adjacent time slots fails to meet the preset conditions, the boundary sequence of the earlier time slot, i.e. the first time slot, in the adjacent time slot needs to be adjusted based on the calculated adjustment value. This will affect the boundary sequences of all time slots and the partial derivative difference calculated for all adjacent time slots, so that the adjusted partial derivative difference between the two adjacent time slots and / or the boundary sequence corresponding to the first time slot in the adjacent time slots can meet the preset conditions.

[0082] Specifically, the adjustment value for: in: and All are preset parameters. Indicates the first For users whose time slot tasks have expired, Indicates the first The user in the first The boundary sequence of each time slot, Indicates the first The user in the first The boundary sequence of each time slot; Indicates the first The user in the first Boundary sequence of time slots.

[0083] S532. Based on the adjustment value, update the boundary sequence corresponding to the first time slot to obtain the first boundary sequence. If the first boundary sequence is greater than or equal to zero, then use the first boundary sequence as the updated boundary sequence of the first time slot; if the first boundary sequence is less than zero, then set the boundary sequence of the first time slot to zero. Specifically, in this embodiment, the first boundary sequence can be obtained by adding the boundary sequence corresponding to the first time slot to the adjustment value. Furthermore, cases where the first boundary sequence is less than 0 need to be truncated.

[0084] S533. The current solution value is updated based on the boundary sequence updated in the first time slot to obtain the updated current solution value.

[0085] In this embodiment, based on the updated boundary sequence of the first time slot, the boundary sequences of all time slots and the partial derivative differences of all adjacent time slots will change. These changes will synchronously affect the calculated current solution value. Therefore, the second objective function needs to be re-solved to obtain the updated current solution value.

[0086] Specifically, the current solution value includes one or more of the following: the current duration of each time slot, the current number of sub-task bits for each user in each time slot, and the current transmit power for each user in each time slot; And / or, the global optimal solution value includes one or more of the following: the optimized duration of each time slot, the optimized number of sub-task bits per user per time slot, and the optimized transmit power per user per time slot.

[0087] Preferably, in this embodiment, after obtaining the global optimal solution value, the optimized duration of each time slot, the optimized number of sub-task bits for each user in each time slot, and the optimized transmit power for each user in each time slot can be output based on the global optimal solution value, specifically: Optimized duration for each time slot: ; Optimized task bits per user per time slot: ; Optimized transmit power for each user in each time slot: ; The three optimal variables mentioned above together constitute a complete resource allocation scheme, which precisely specifies the transmission power and task bits of each user in each time slot, enabling the reduction of transmission energy consumption when multiple users transmit through the channel based on the obtained three optimal variables.

[0088] S600. Based on the global optimal solution value, adjust the parameters corresponding to the multi-slot multi-user non-orthogonal multiple access transmission to optimize its energy consumption.

[0089] In this embodiment, the duration of each time slot, the number of sub-task bits for each user in each time slot, and the transmit power are adjusted to the corresponding global optimal values, thereby minimizing the energy consumption of multi-time slot multi-user non-orthogonal multiple access transmission and optimizing the energy consumption of transmission.

[0090] For example, to verify the effectiveness of this application, the method of this application (hereinafter referred to as COBAHN) is compared with three benchmark methods: the traditional Orthogonal Multiple Access (OMA) method, the Non-Orthogonal Multiple Access (NOMA) method, and the Pareto optimization algorithm. Meanwhile, to comprehensively evaluate the performance of the method under different device configurations, various simulation scenarios were set up in the experiment, including changes in task load, number of users, network coverage, and user deadline urgency. The main simulation parameters are shown in Table 1.

[0091] Table 1 like Figure 8 Reflects total energy consumption as a function of the total number of user task bits. The results show that COBAHN achieves the lowest energy consumption under all task loads, and its advantage becomes more significant as the total number of user task bits increases. At N=50 Mbits, COBAHN reduces energy consumption by 59%, 23%, and 10% compared to NOMA, OMA, and Pareto algorithms, respectively; when N=100 Mbits, it reduces energy consumption by 43% and 22% compared to OMA and Pareto, respectively. This indicates that COBAHN is particularly suitable for computationally intensive scenarios.

[0092] like Figure 9 Reflects total energy consumption as the number of users The relationship between the changes is as follows. As the total number of users increases, COBAHN consistently maintains the lowest energy consumption, and its advantages become even more pronounced, demonstrating excellent scalability.

[0093] like Figure 10 This reflects the change in total power consumption as the network coverage area expands. A larger coverage area increases the average distance to users, leading to deteriorated channel conditions. COBAHN maintains the lowest power consumption across all coverage areas, demonstrating its robustness to varying coverage sizes.

[0094] like Figure 11 Reflecting total energy consumption as the length of the first time slot The changes, including the length of the first time slot. This reflects the urgency of user deadlines. COBAHN in all COBAHN maintains global optimality across all values, while OMA and NOMA only perform well under specific conditions, which verifies the universal superiority of COBAHN.

[0095] In summary, the method proposed in this application can significantly reduce the total energy consumption of multi-time-slot multi-user non-orthogonal multiple access in various typical scenarios. Compared with existing methods, it can achieve a 5% to 24% reduction in energy consumption, and the energy-saving effect can reach 10% and 22% under medium and heavy workloads, respectively.

[0096] like Figure 12 As shown in the illustration, this application also provides an energy consumption optimization system based on multi-timeslot multi-user non-orthogonal multiple access. Optionally, the system includes: The module includes: acquisition module 611, preset module 612, first objective function construction module 613, reconstruction module 614, optimization module 615, and adjustment module 616, wherein: Module 611 is used to obtain the total number of users, the deadline for user tasks, and the total number of bits for user tasks; In this embodiment, the acquisition module 611 can be used to perform... Figure 2 For a detailed description of the acquisition module 611, please refer to the description of step S100 shown.

[0097] The preset module 612 is used to preset the number of time slots, which is the same as the total number of users, and to preset the initial duration of each time slot based on the user task deadline; and to preset the initial transmit power of each user in each time slot. In this embodiment, the preset module 612 can be used to perform... Figure 2 For a detailed description of the preset module 612, please refer to the description of step S200 shown.

[0098] The first objective function construction module 613 is used to construct a first objective function based on the initial duration of each time slot and the initial transmit power of each user in each time slot; In this embodiment, the first objective function construction module 613 can be used to execute Figure 2 For a detailed description of step S300 shown, and the first objective function construction module 613, please refer to the description of step S300.

[0099] Specifically, the first objective function construction module 613 is further configured to, for each time slot, obtain the energy consumption of the user in that time slot based on its initial duration and the initial transmission power of the user in that time slot, and obtain the total energy consumption of all users in that time slot based on the energy consumption of the user in that time slot; obtain the total energy consumption of all users corresponding to all time slots based on the total energy consumption of all users in each time slot; and construct the first objective function by minimizing the total energy consumption based on a preset first constraint.

[0100] In this embodiment, the first objective function construction module 613 can also be used to execute Figure 3 For a detailed description of the first objective function construction module 613, see steps S310-S330 shown. For a detailed description of steps S310-S330, please refer to the description of steps S310-S330.

[0101] Reconstruction module 614 is used to reconstruct the first objective function according to the total number of user task bits to obtain a second objective function; wherein the second objective function includes the user's cumulative normalized bit rate; In this embodiment, the reconstruction module 614 can be used to perform... Figure 2 For a detailed description of the reconstruction module 614, please refer to the description of step S400 shown.

[0102] Specifically, the reconstruction module 614 is further configured to: obtain bandwidth parameters; obtain the user's cumulative normalized bit rate based on the total number of user task bits and the bandwidth parameters; reconstruct the initial transmit power in the first objective function according to the user's cumulative normalized bit rate to obtain the reconstructed initial transmit power; reconstruct the first objective function based on the reconstructed initial transmit power to obtain the second objective function; and reconstruct the first constraint of the first objective function based on the total number of user task bits to obtain the second constraint of the second objective function.

[0103] In this embodiment, the reconstruction module 614 can also be used to perform... Figure 4 For a detailed description of the reconstruction module 614, see steps S410-S450 shown below. Further details regarding steps S410-S450 can be found in the description of steps S410-S450.

[0104] Optimization module 615 is used to solve the second objective function by energy consumption to obtain the current solution value, extract the boundary sequence of each time slot based on the user's cumulative normalized bit rate; the boundary sequence is the cumulative load of a user before the end of the time slot; iteratively update the boundary sequence and the current solution value, and obtain the global optimal solution value according to the updated boundary sequence, the updated current solution value and preset conditions; In this embodiment, the optimization module 615 can be used to perform... Figure 2 For a detailed description of the optimization module 615, please refer to the description of step S500 shown.

[0105] Specifically, the optimization module 615 is further configured to evenly distribute the total task bits of each user across all time slots to obtain the initial sub-task bits of each user in each time slot; based on the initial total energy consumption and the initial sub-task bits of the first user in each time slot, obtain the partial derivative difference between two adjacent time slots for the first user; wherein, the initial total energy consumption is the initial total energy consumption obtained by the second objective function, and the first user is the user with the weakest channel gain among all users; and determine whether the partial derivative difference between all two adjacent time slots and / or the boundary sequence corresponding to the first time slot in the adjacent time slots satisfy the pre- If a condition is set, the boundary sequence corresponding to the first time slot in the adjacent time slots and the current solution value are updated, until the partial derivative difference values ​​corresponding to all two adjacent time slots and / or the boundary sequence corresponding to the first time slot in the adjacent time slots satisfy the preset condition; if so, the current solution value is taken as the global optimal solution value; wherein, the preset condition is: the partial derivative difference value of the two adjacent time slots is equal to zero; or, the partial derivative difference value of the two adjacent time slots is greater than or equal to zero and the boundary sequence of the first time slot is equal to zero, wherein the two adjacent time slots include the first time slot and the second time slot, and the first time slot is earlier than the second time slot.

[0106] In this embodiment, the optimization module 615 can also be used to perform... Figure 6 For a detailed description of the optimization module 615, please refer to the description of steps S510-S530 shown.

[0107] Specifically, the optimization module 615 is further configured to obtain two adjacent time slots that do not meet the preset conditions, obtain corresponding adjustment values ​​based on the boundary sequences corresponding to the two adjacent time slots and preset coefficients; update the boundary sequence corresponding to the first time slot based on the adjustment values ​​to obtain a first boundary sequence; if the first boundary sequence is greater than or equal to zero, then the first boundary sequence is used as the updated boundary sequence of the first time slot; if the first boundary sequence is less than zero, then the boundary sequence of the first time slot is set to zero; update the current solution value based on the updated boundary sequence of the first time slot to obtain the updated current solution value.

[0108] In this embodiment, the optimization module 615 can also be used to perform... Figure 7 For a detailed description of the optimization module 615, please refer to the description of steps S531-S533 shown in the figure.

[0109] The adjustment module 616 is used to adjust the parameters corresponding to the multi-slot multi-user non-orthogonal multiple access transmission based on the global optimal solution value, so as to optimize its energy consumption.

[0110] In this embodiment, the adjustment module 616 can be used to perform... Figure 2 For a detailed description of the optimization module 616, please refer to the description of step S600 shown.

[0111] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. An energy consumption optimization method based on multi-timeslot multi-user non-orthogonal multiple access, characterized in that, The method includes: Get the total number of users, the deadline for each user's task, and the total number of bits for each user's task; The number of time slots is preset to be the same as the total number of users, and the initial duration of each time slot is preset based on the user task deadline; and the initial transmission power corresponding to each user in each time slot is preset; Based on the initial duration of each time slot and the initial transmit power of each user in each time slot, a first objective function is constructed; The first objective function is reconstructed based on the total number of user task bits to obtain a second objective function; wherein the second objective function includes the user's cumulative normalized bit rate. The energy consumption of the second objective function is calculated to obtain the current solution value. The boundary sequence of each time slot is extracted based on the user's cumulative normalized bit rate. The boundary sequence is the cumulative load of a user before the end of the time slot. The boundary sequence and the current solution value are iteratively updated. The global optimal solution value is obtained based on the updated boundary sequence, the updated current solution value and preset conditions. Based on the global optimal solution value, the parameters corresponding to multi-slot multi-user non-orthogonal multiple access transmission are adjusted to optimize its energy consumption.

2. The energy consumption optimization method according to claim 1, characterized in that, The first objective function is constructed based on the initial duration of each time slot and the initial transmit power of each user in each time slot, including: For each time slot, based on its initial duration and the user's initial transmit power in that time slot, the user's energy consumption in that time slot is obtained, and based on the user's energy consumption in that time slot, the total energy consumption of all users in that time slot is obtained. Based on the total energy consumption of all users in each time slot, obtain the total energy consumption of all users for all time slots; The first objective function is constructed by minimizing the total energy consumption based on a preset first constraint.

3. The energy consumption optimization method according to claim 1, characterized in that, The step of reconstructing the first objective function based on the total number of user task bits to obtain the second objective function includes: Obtain bandwidth parameters; Based on the total number of user task bits and the bandwidth parameters, the user's cumulative normalized bit rate is obtained; The reconstructed initial transmit power is obtained by reconstructing the initial transmit power in the first objective function based on the user's cumulative normalized bit rate. The first objective function is reconstructed based on the reconstructed initial transmit power to obtain the second objective function; The first constraint of the first objective function is reconstructed based on the total number of user task bits to obtain the second constraint of the second objective function.

4. The energy consumption optimization method according to claim 3, characterized in that, No. The user in the first User cumulative normalized bit rate per time slot for: in, Indicates the first The user in the first The initial number of sub-task bits per time slot, It is obtained based on the total number of user task bits; Indicates the total number of time slots; This indicates the bandwidth parameter. This represents the total number of users. Indicates the first The initial duration of each time slot.

5. The energy consumption optimization method according to claim 1, characterized in that, The second objective function is: The users are arranged in an ascending order based on the channel gain corresponding to each user. This represents the total energy consumption corresponding to the second objective function. This represents the total number of time slots. This represents the total number of users. Indicates the first One user, Indicates the first Each time slot Indicates the first The initial duration of each time slot, Indicates the first Preset coefficients for each user Indicates the first The user in the first User cumulative normalized bit rate per time slot; Indicates noise power; This represents the channel gain corresponding to the user with the weakest channel gain among all users; the channel gain corresponding to the user and the noise power are obtained synchronously when acquiring the total number of users, the user task deadline, and the total number of user task bits. Among them, the Preset coefficients for individual users for: in, Indicates the first Channel gain for each user Indicates the first Channel gain corresponding to each user; This represents the channel gain of the user with the strongest channel gain among all users.

6. The energy consumption optimization method according to claim 1, characterized in that, The iterative update of the boundary sequence and the current solution value, and the acquisition of the global optimal solution value based on the updated boundary sequence, the updated current solution value, and preset conditions, includes: The total number of task bits for each user is evenly distributed across all time slots to obtain the initial number of task bits for each user in each time slot; Based on the initial total energy consumption and the initial number of task bits for the first user in each time slot, the difference in partial derivatives between two adjacent time slots for the first user is obtained; wherein, the initial total energy consumption is the initial total energy consumption corresponding to the second objective function, and the first user is the user with the weakest channel gain among all users; Determine whether the partial derivative difference between all two adjacent time slots and / or the boundary sequence corresponding to the first time slot in the adjacent time slots satisfy a preset condition. If not, update the boundary sequence corresponding to the first time slot in the adjacent time slots and the current solution value until the partial derivative difference between all two adjacent time slots and / or the boundary sequence corresponding to the first time slot in the adjacent time slots satisfy the preset condition. If yes, then use the current solution value as the global optimal solution value. The preset condition is: the difference between the partial derivatives of the two adjacent time slots is equal to zero; or, the difference between the partial derivatives of the two adjacent time slots is greater than or equal to zero and the boundary sequence of the first time slot is equal to zero, wherein the two adjacent time slots include the first time slot and the second time slot, and the first time slot is earlier than the second time slot.

7. The energy consumption optimization method according to claim 6, characterized in that, Updating the boundary sequence corresponding to the first time slot in the adjacent time slots and the current solution value includes: Two adjacent time slots that do not meet the preset conditions are obtained, and corresponding adjustment values ​​are obtained based on the boundary sequences and preset coefficients corresponding to the two adjacent time slots. The first boundary sequence is obtained by updating the boundary sequence corresponding to the first time slot based on the adjustment value. If the first boundary sequence is greater than or equal to zero, the first boundary sequence is used as the updated boundary sequence of the first time slot; if the first boundary sequence is less than zero, the boundary sequence of the first time slot is set to zero. The updated current solution value is obtained by updating the boundary sequence after the first time slot.

8. The energy consumption optimization method according to claim 6, characterized in that, No. The time slot and the first The partial derivative of the next time slot adjacent to each time slot is: The users are arranged in an ascending order based on their corresponding channel gains; This represents the total energy consumption corresponding to the second objective function. Indicates the first user in the _th Number of task bits per time slot Indicates the first user in the _th Number of task bits per time slot Indicates bandwidth parameter, This represents the total number of users. Indicates the first Preset coefficients for each user Indicates the first The user in the first The boundary sequence of each time slot, Indicates the first The user in the first The boundary sequence of each time slot; the channel gain and bandwidth parameters corresponding to the user are obtained simultaneously when acquiring the total number of users, the user task deadline, and the total number of user task bits; And / or, the adjustment value for: in: and All are preset parameters. Indicates the first One user, Indicates the first Each time slot Indicates the first For users whose time slot tasks have expired, Indicates the first The user in the first The boundary sequence of each time slot, Indicates the first The user in the first The boundary sequence of each time slot, Indicates the first The user in the first The boundary sequence of each time slot; For the first Preset coefficients for each user Indicates the first The user in the first Boundary sequence of time slots.

9. The energy consumption optimization method according to claim 1, characterized in that, The current solution value includes one or more of the following: the current duration of each time slot, the current number of sub-task bits for each user in each time slot, and the current transmit power for each user in each time slot; And / or, the global optimal solution value includes one or more of the following: the optimized duration of each time slot, the optimized number of sub-task bits per user per time slot, and the optimized transmit power per user per time slot.

10. An energy consumption optimization system based on multi-time-slot multi-user non-orthogonal multiple access, characterized in that, The system includes: The acquisition module is used to obtain the total number of users, the deadline for user tasks, and the total number of bits for user tasks; The preset module is used to preset the number of time slots, which is the same as the total number of users, and to preset the initial duration of each time slot based on the user task deadline; and to preset the initial transmission power of each user in each time slot. The first objective function construction module is used to construct a first objective function based on the initial duration of each time slot and the initial transmit power of each user in each time slot; The reconstruction module is used to reconstruct the first objective function based on the total number of user task bits to obtain a second objective function; wherein the second objective function includes the user's cumulative normalized bit rate; The optimization module is used to solve the second objective function by energy consumption to obtain the current solution value, and to extract the boundary sequence of each time slot based on the user's cumulative normalized bit rate; the boundary sequence is the cumulative load of the user before the end of the time slot; the boundary sequence and the current solution value are iteratively updated, and the global optimal solution value is obtained according to the updated boundary sequence, the updated current solution value and preset conditions; The adjustment module is used to adjust the parameters corresponding to multi-slot multi-user non-orthogonal multiple access transmission based on the global optimal solution value, so as to optimize its energy consumption.

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