Multi-dimensional resource allocation method and system of mobile antenna assisted mixed NOMA (Non-Orthogonal Multiple Access)

Abstract

The invention discloses a multi-dimensional resource allocation method and system of a mobile antenna assisted mixed NOMA (Non-Orthogonal Multiple Access). The method comprises the following steps: a base station takes the total unloading energy consumption of all users as a target function to construct an optimization problem of energy consumption minimization, and minimizes the total unloading energy consumption of all users by optimizing user unloading power, unloading time slots and mobile antenna positions of the users; setting an unloading time slot, user unloading power and an initial value of a movable antenna position of a user, and entering an iterative loop: giving the movable antenna position of the user, and jointly optimizing the unloading time slot and the user unloading power; giving an unloading time slot and user unloading power, and optimizing the position of a movable antenna of a user; and iteration is carried out until convergence, and optimal values of the unloading time slot, the user unloading power and the mobile antenna position of the user are obtained. According to the method, the unloading time slot, the user unloading power and the mobile antenna position of the user are jointly optimized, the unloading flexibility of the user is improved, and the unloading energy consumption of the user is reduced.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to a movable antenna assisted hybrid NOMA multi-dimensional resource allocation method and system. Background Art

[0002] With the explosive growth of computing-intensive applications such as virtual reality (VR), augmented reality (AR) and autonomous driving in the wireless Internet of Things, mobile edge computing (MEC) technology has emerged as an important enabling technology for the next generation of mobile communication networks. The core goal of MEC is to offload computing tasks from resource-constrained mobile devices (such as smartphones, sensors, etc.) to infrastructure at the edge of the network (such as MEC servers deployed at base stations), and use the powerful computing power of edge servers to achieve efficient task processing. Compared with local computing, MEC technology can not only shorten task processing latency through the high-performance computing units of edge servers, but also mobile devices can avoid continuous high-load operation of local processors through computing offload, thereby effectively extending device battery life and reducing energy consumption.

[0003] However, the large-scale application of MEC faces the following challenges: on the one hand, the limited computing / storage resources of edge servers cannot meet the dynamic needs of multiple users and multiple tasks, and an efficient resource allocation mechanism is required; on the other hand, the scarcity of wireless spectrum resources and the energy limitation of terminals make computing offloading and resource allocation under limited resources a complex optimization problem. In this context, non-orthogonal multiple access (NOMA), as an important emerging technology for next-generation wireless networks, has become the key to breaking through the performance bottleneck of MEC with its spectrum efficiency advantage. NOMA allows multiple users to share the same time-frequency resource block for signal transmission, and achieves signal separation with the help of successive interference cancellation (SIC) technology. In the MEC scenario, NOMA supports multiple users to concurrently transmit offload requests through power domain multiplexing, effectively improving system throughput.

[0004] Although NOMA-assisted MEC has shown significant advantages in improving offload transmission efficiency and computing offload capabilities, its practical applications still face challenges such as insufficient dynamic resource adaptation and a single resource allocation dimension. Specifically, the performance of NOMA transmission depends on the channel conditions of mobile devices, and the heterogeneous task loads of mobile terminals (such as the coexistence of computing-intensive tasks and low-latency sensitive tasks), differentiated terminal offload urgency, and solidified channel conditions will lead to low offload transmission efficiency and limitations in device fairness, thereby reducing the performance of the overall system. Therefore, it is urgent to develop a new type of transmission technology to enhance the dynamic adaptability and robustness of the system to meet the needs of future ultra-dense, low-latency, and highly reliable mobile edge computing scenarios. Summary of the invention

[0005] In order to solve at least one of the problems existing in the prior art, the present invention provides a movable antenna assisted hybrid NOMA (non-orthogonal multiple access) multi-dimensional resource allocation method and system, which can actively regulate channel gain to reduce the unloading transmission energy consumption of mobile devices in a mobile edge computing network.

[0006] A multi-dimensional resource allocation method and system for hybrid non-orthogonal multiple access assisted by a movable antenna is provided for a mobile edge computing (MEC) network. In the present application, a MEC network is considered to include a single-antenna base station with a MEC server deployed and multiple users equipped with a single movable antenna, each of which needs to calculate a computationally intensive and delay-critical task and offload it to the base station. The offloading amount and maximum offloading time of each user are different during the offloading process. In the present application, the architecture of the movable antenna can be to adjust the position of the antenna through a mechanical structure, or to achieve dynamic adjustment of the antenna by using the morphological changes of fluid materials such as liquid metals and electrolytes. The present application adopts a MEC strategy of hybrid NOMA transmission, and the offloading of users is arranged according to the urgency of their tasks. Unlike the MEC strategy of orthogonal transmission, when a user with a more urgent task is offloading, other users with less urgent tasks can still offload. Unlike the traditional NOMA transmission MEC strategy that forces all users to start and complete offloading at the same time, the hybrid NOMA transmission MEC strategy divides the transmission cycle into multiple offloading time slots. Users can offload task data in different offloading time slots in segments, and the task data in the same offloading time slot uses NOMA transmission. This strategy improves offloading efficiency and provides users with more flexible opportunities to offload tasks.

[0007] In the method proposed in the present application, the base station estimates the channel state information (CSI) of each user's communication link through channel estimation and compressed sensing methods, including the response, transmission angle and arrival angle of each transmission path. The base station divides the entire transmission unloading cycle into unloading time slots equal to the number of users. Users unload in different time slots according to the urgency of their own tasks and the amount of unloaded data. Different users use NOMA transmission to unload task data in the same unloading time slot. The user's unloading completion time will constrain the user to complete the unloading in a specific unloading time slot, and the unloading time slot will also be subject to the unloading completion time of each user. For example, the user with the shortest unloading completion time, that is, the user with the highest task urgency, must complete the unloading in the first unloading time slot, and the duration of the first unloading time slot shall not be greater than the unloading completion time of the user with the shortest unloading completion time. The user with the second shortest unloading completion time can unload task data in the first two time slots, but must also complete the unloading in the first two time slots, and the sum of the durations of the first two unloading time slots shall not be greater than the unloading completion time of the user with the second shortest unloading completion time. Similarly, the user with the longest unloading completion time can unload task data in all unloading time slots, and the total duration of all unloading time slots shall not be greater than the unloading completion time of the user with the longest unloading completion time.

[0008] In the method proposed in this application, the unloading time slot, user unloading power and movable antenna position are jointly optimized as follows: (1) The base station will construct an energy-minimizing optimization problem based on the obtained channel response information, the user's offloading urgency, and the user's task offloading amount, with the goal of minimizing the user's total offloading energy consumption, and design the offloading time slot, user offloading power, and the position of the user's movable antenna. In each offloading time slot, the movable antennas equipped by all users will adjust their positions, actively causing the channel time-varying characteristics on the user link to increase additional time diversity gain, further reducing the user offloading energy consumption of the network.

[0009] (2) The unloading time slot, user unloading power and the position of the movable antenna are calculated through alternating iterations to obtain the optimal value. At the initial stage of the iteration, the initial unloading time slot, user unloading power and the position of the movable antenna are set. In each iteration, given the position of the movable antenna in the previous iteration, the unloading time slot and user unloading power are jointly designed; given the unloading time slot and user unloading power in this iteration, the movable antenna position of all users in the unloading time slot is jointly designed until convergence or the maximum number of iterations is reached. The optimal unloading time slot, the optimal user unloading power and the optimal user movable antenna position are obtained. After the base station calculates the above parameters, they are sent to each user through the downlink control channel.

[0010] In order to implement the proposed method, a multi-dimensional resource allocation system for hybrid NOMA assisted by movable antennas is constructed. The system includes a channel reconstruction module, a multi-dimensional resource collaborative calculation module, a multi-dimensional resource execution module, and a wireless transmission / reception transmission module. The channel reconstruction module is used to obtain the multipath response parameters of the wireless channel from the base station to the mobile user, including the transmission angle, arrival angle and complex response value of each transmission path, and generate a channel state information matrix; the multi-dimensional resource collaborative calculation module is mainly used to jointly calculate the unloading time slot, the user unloading power and the position of the user's movable antenna; the multi-dimensional resource execution module is used to write the unloading time slot into the scheduler of the base station, and configure the user's RF front end and the movable antenna position according to the unloading time slot, the user unloading power and the movable antenna; the wireless transmission / reception transmission module is used for the reception and transmission of base station and mobile user signals. Before the mobile user task is unloaded, the channel reconstruction module extracts the multipath response parameters through the sparse signal recovery algorithm, and the parameters are used as the input environment state of the multi-dimensional resource collaborative calculation module.

[0011] Compared with the prior art, the present invention can at least achieve the following beneficial effects: The present invention can be applied in mobile edge computing network scenarios. By introducing movable antennas in the hybrid non-orthogonal multiple access offloading transmission scheme, the time diversity of the computing network is increased, and the offloading time slots, user offloading power and the position of the user's movable antenna in the network are jointly designed to obtain the optimal value, which can improve the offloading flexibility of users in the network and reduce the user's offloading energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flow chart of a multi-dimensional resource allocation method for movable antenna-assisted hybrid NOMA provided in an embodiment of the present application.

[0013] Figure 2 A schematic diagram of an implementation environment provided for an embodiment of the present application.

[0014] Figure 3 A hybrid NOMA-assisted MEC transmission strategy diagram provided for an embodiment of the present application.

[0015] Figure 4 A flowchart for the joint design optimization of the unloading time slot, user unloading power and user movable antenna position provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the present application clearer, the implementation mode of the present application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] The core of the embodiment of the present invention is to provide a multi-dimensional resource allocation method and system for movable antenna-assisted hybrid non-orthogonal multiple access (NOMA) to reduce the unloading energy consumption of users in mobile edge computing (MEC) scenarios.

[0018] The embodiment of the present invention provides a multi-dimensional resource allocation method for a movable antenna assisted hybrid NOMA, which is applied to a mobile edge computing network, such as Figure 2 The mobile edge computing network consists of a single antenna and a base station with a MEC server deployed. K There are users equipped with a single movable antenna, where the user index and the user index set are represented as k and Each user needs to calculate a computationally intensive and latency-critical task and offload it to the base station. During the offloading process, the offloading amount and maximum offloading time of each user are different. k The amount of unloaded data and the maximum unloading time are expressed as and In each offloading cycle, the user uses the hybrid NOMA transmission method to offload the task data. First, the base station divides the entire transmission offloading cycle into offloading time slots equal to the number of users, where i The unloading time slot is represented as . Users use different unloading powers to unload tasks in different unloading time slots according to the urgency of unloading their own tasks and the amount of data required for unloading tasks. Different users use NOMA transmission mode to unload task data in the same unloading time slot. The user's unloading completion time will constrain the user to complete the unloading in a specific unloading time slot. At the same time, the unloading time slot will also be subject to the unloading completion time limit of each user. The urgency of unloading the user's task is judged according to the user's unloading completion time. In one of the embodiments of the present invention, the unloading urgency of the user's task is set to be strongly correlated with the user's unloading completion time, that is, the shortest user unloading completion time corresponds to the highest unloading urgency, and vice versa. In order to better describe the method proposed in the embodiment of the present invention, it is assumed that the relationship between the user's unloading completion time is as follows , which is the uninstall completion time of user 1 The shortest,users who are considered to have the highest urgency for offloading tasks,users K Uninstallation completion time The longest is considered as the user with the lowest offloading urgency of the task. According to the hybrid NOMA criterion, user 1 must be in the first offloading time slot. The uninstallation is complete, and It must not be greater than the uninstall completion time of the user with the shortest uninstall completion time, that is To satisfy ; User 2 can unload task data in the first two unloading time slots, and the sum of the durations of the first two unloading time slots must not be greater than the maximum unloading time of user 2, that is, the following constraints are met ; and so on, k The user can be in k Unload task data before the unloading time slot, and its unloading time slot meets , Indicates k The total duration of all unloading time slots shall not be greater than the unloading completion time of the user with the longest unloading completion time. Figure 3 As shown in In the unloading time slot, only the user i To User K Task offloading can be performed because user 1 to user i -1 has completed uninstallation, in In the unloading time slot, only the user K In this embodiment, a slow fading far-field response channel is considered, in which the moving area of ​​each user's movable antenna is significantly smaller than the signal propagation distance, and the user k The feasible area of ​​the movable antenna in one dimension Therefore, it is reasonable to assume that the transmission angle (Angle of Departure, AoD), the angle of arrival (Angle of Arrival, AoA) and the amplitude of the complex path coefficient remain unchanged on each transmission path, and the change of the movable antenna position will only affect the change of the complex path coefficient. In one embodiment of the present invention, respectively represent the transmission angle from the base station to the user k The received field response vector and from user k The transmission field response vector to the base station The expression is: ; in represents an imaginary unit; Indicates the radiation wavelength of the signal; Indicates the location of the base station fixed antenna; Indicates base station to user k In the The angle of arrival of the root receiving path, , Indicates the diameter number of the receiving diameter; Indicates user k To the base station The emission angle of the root emission path, , Indicates the diameter of the launch path, Indicates user k The movable antenna is i The location of the unloading time slot.

[0019] The method steps provided by the embodiment of the present invention are as follows: Step 1: The base station divides an offloading transmission cycle into multiple variable offloading time slots, and constructs an energy consumption minimization optimization problem based on the channel response information, the urgency of the user's offloading task, and the amount of data required for the user's offloading task, with the total offloading energy consumption of all users as the objective function. , by optimizing user offload power , Unloading time slot and the user's removable antenna position To minimize the total offloading energy consumption of all users, the offloading time slot, user offloading power and the position of the user's movable antenna are jointly designed, where Indicates user k The collection of unloaded power, Indicates user k The unloading power in the first unloading time slot is, Indicates user k In the i The unloading power of unloading time slots, Indicates user k In the k Unloading power of unloading time slot; Indicates user k A collection of movable antenna positions, The movable antenna of user 1 is i The location of the unloading time slot, Indicates user k The movable antenna is i The location of the unloading time slot, Indicates user k The movable antenna is k The position of the unloading time slot. Set the initial iteration value , set the initial unloading time slot , initial user unloading power and initial user-movable antenna position .

[0020] Step 2: Given the user's movable antenna position for the current iteration , according to the coupling relationship between the unloading time slot and the user unloading power, the optimization variable is defined and , Indicates i The optimization variables of the unloading time slots are Indicates user k In the i The energy consumption optimization variable for unloading time slots is Indicates user k In the i The unloading power of the unloading time slot is and Substitute into the optimization problem of minimizing energy consumption , to minimize the energy consumption Convert to convex programming problem , by using an iterative concave-convex programming method to solve the convex difference programming problem , get the movable antenna position for a given user The optimal unloading time slot and the optimal user unloading power under the condition of .

[0021] Convex programming problem The expression is as follows:

[0022] in Represents the number of users; the corresponding user index set is represented as ; Indicates user m In the i Energy consumption optimization variables for unloading time slots; represents the power of Gaussian white noise; Indicates user m The movable antenna is i The channel response coefficient in the offloading time slot; constraint C1 ensures that the user's offloading throughput meets the Nate amount required by the offloading task, where Indicates user k The amount of Knights required for the offloading task; Constraint C2 represents the restriction on the offloading time slot optimization variable under the hybrid NOMA criterion, Constraint C3 ensures that the offloading time slot optimization variable is non-negative, and Constraint C4 ensures that the energy consumption optimization variable is non-negative; where , represents the sub-item of the user offload throughput expression.

[0023] Solving the Convex Programming Problem via Iterative Concave-Convex Programming Get the best and Then, the optimal unloading time slot is calculated for the given user's movable antenna position. and user unloading power .

[0024] In one embodiment of the present invention, the convex programming problem The steps to solve are as follows: (1) Set the initial iteration value , initialize the optimization variables , , express In the n The value of the iteration, express In the n The value of the iteration, and Respectively and In the q The value of the iteration, Indicates user m In the i The unloading power of an unloading time slot.

[0025] (2) At the point The opposite style Perform a first-order Taylor expansion and obtain the convex upper bound approximation . Instead of convex programming problem Chinese , get the problem .

[0026] (3) Solve the problem using the interior point method , get the problem The optimal solution , and . , and Represent the problem separately Optimizing variables , and The optimal value of .

[0027] (4) Order , , , .

[0028] (5) (2), (3) and (4) until convergence, the output is q The movable antenna position of a given user in iteration In the case of i The optimal value of unloading time slots and users k In the i The optimal value of unloading power in each time slot .according to and Get in q The movable antenna position of a given user in iteration The optimal unloading time slot under the condition of and optimal user unloading power .

[0029] Step 3: Assign an unloading time slot and user unloading power , introduce auxiliary variables and The base station takes improving the user throughput in each unloading time slot as the criterion and constructs , and Optimization problem of movable antenna position design based on ; Then introduce auxiliary variables , the problem is solved by using the quadratic transformation method Equivalently converted to the problem That is, the convex approximate expression. The auxiliary variables are updated sequentially through the alternating iterative criteria and the user's removable antenna position , and finally converges to a given unloading time slot and user unloading power Optimal user-movable antenna position for the situation .

[0030] Auxiliary variables and the user's removable antenna position The updated rules are as follows: (1) Position of movable antenna for a given user In the case of The update rules are as follows: ; in Indicates that in a given In the case of auxiliary variables The optimal value of , From base station to userk The received field response vector, Indicates user k The path response matrix, whose elements Indicates user k The transmission link between the base station and p Root emission diameter to l Response of the root receiving path; , From base station to user m The received field response vector, Indicates user m The path response matrix, whose elements Indicates user m The transmission link between the base station and p Root emission diameter to l Response of the root receiving path; Indicates that from the user k To the base station at the movable antenna position The emission field response vector of Indicates that from the user m To the base station at the movable antenna position The emission field response vector of Indicates user k In the i The unloading power of an unloading time slot.

[0031] (2) Given auxiliary variables In the case of , the problem is solved by an iterative continuous convex approximation method To update the user's movable antenna position , the specific steps are as follows: a. Define users k The movable antenna is i The location optimization variable for the unloading time slot is , define the function , ,in Indicates that the base station receives user k The movable antenna is in position Time i The real energy gain function of the unloading time slot; Indicated in auxiliary variables Value given to the user m The movable antenna is in position Time i The channel gain function of the unloaded time slot; It means taking the real part of the complex value in brackets; Indicates the conjugate transpose operation of the scalar / vector in the brackets; Indicates that from the user k The movable antenna is located at i The unloading time slot is The transmission field response vector to the base station at time ; Indicates that from the user m The movable antenna is located at i The unloading time slot is The transmission field response vector to the base station at time . and Substitution Problem , get the problem .

[0032] b. Initialize the iteration value , ,in Represents the position optimization variable In the u The value of the iteration, express In the q The number of iterations.

[0033] c. Calculate the functions separately using Taylor's principle and At the point and The convex lower and upper bounds at are given by:

[0034] in, and Respectively represent functions and At the point and The convex lower and upper bounds of and Respectively represent functions and At the point and The gradient at It means that the iterative continuous convex approximation method is used In the u The value of the iteration, It means that the iterative continuous convex approximation method is used In the u The value of the iteration, and represents the regularization parameter, respectively, by calculating and The upper bound of the Hessian value at each iteration is obtained.

[0035] d. By and Replacement Problem of and , get the problem In the u Iterative convex approximation problem ,as follows:

[0036] in, Indicates user k The unloading time slot index set corresponding to the unloading data. The interior point method is used to solve the problem , get the problem Optimizing variables The optimal solution , Indicates user k The movable antenna is in the feasible area in one dimension.

[0037] e. Order , Repeat steps c, d, and e until convergence, and output the given auxiliary variable Optimal user movable antenna position in case of .

[0038] (3) Repeat steps (1) and (2) until convergence, and output q given unloading time slot in iterations and user unloading power Optimal user-movable antenna position for the situation .

[0039] Step 4: Calculate the total energy consumption of the iterated users based on the optimal unloading time slot and the user unloading power. Determine whether the convergence condition is met by the total energy consumption value of the users. Update the iterated variables That is: according to and Calculate the The total energy consumption of the user in iterations ,according to and Calculate the The total energy consumption of the user in iterations , and Respectively expressed in The movable antenna position of a given user in iteration The optimal unloading time slot and the optimal user unloading power under the condition of , represents the convergence error parameter.

[0040] Step 5: If the condition of step 4 is not met, repeat steps 2 and 3.

[0041] Step 6: If the conditions in step 4 are met, , , Output the optimal unloading time slot , optimal user unloading power and optimal user-removable antenna position .

[0042] In one of the embodiments of the present invention, in order to implement the method proposed in the aforementioned embodiment, a transmission system based on a movable antenna assisted hybrid NOMA is proposed, including a channel reconstruction module, a multi-dimensional resource collaborative computing module, a multi-dimensional resource execution module, a wireless transmission module and a wireless reception transmission module. The channel reconstruction module is constructed based on a heterogeneous computing platform (CPU+GPU), and sparsely recovers the received signal through a sparse signal recovery algorithm based on compressed sensing, extracts the parameters of several transmission paths with higher energy, calculates the complex response value of each extracted path, and generates a channel state information matrix. The parameters of the transmission path and the channel state information matrix are transmitted to the multi-dimensional resource collaborative computing module through an optical fiber link; the multi-dimensional resource collaborative computing module is also constructed based on a heterogeneous computing platform, by constructing an optimization model (optimization problem of minimizing energy consumption). ), jointly calculate the unloading time slot, user unloading power and the position of the user's movable antenna; the output optimal unloading time slot is written into the scheduler of the base station through the multi-dimensional resource execution module, and the user RF front end and the movable antenna position are configured according to the unloading time slot, user unloading power and the user's movable antenna; the optimal user unloading power and the user's movable antenna position are sent to all mobile users through the wireless transmission module. The mobile user receives and analyzes the unloading power and the movable antenna position through the wireless receiving transmission module.

[0043] The aforementioned embodiment of the present invention is based on the multi-dimensional resource allocation of time, power and space domain coordination of hybrid NOMA assisted by movable antennas, and the energy consumption of mobile users in the mobile edge computing network system is reduced by coordinating and optimizing the three-dimensional resources of time, power and space domains. The base station implements time domain resource scheduling by allocating the unloading time slot of the user task according to the urgency of the mobile user's unloading task, the amount of unloading tasks and the obtained channel response information; the power domain resource allocation is realized by regulating the unloading power of the mobile user through the hybrid NOMA criterion; the mobile user actively regulates the channel gain within the unloading cycle by changing the spatial position of each unloading time slot equipped with a movable antenna, bringing additional time diversity gain and realizing spatial resource allocation; according to the relationship between the user's unloading completion time and the unloading time slot, the superposition interference limit of hybrid NOMA and the spatial movement range of the movable antenna, a multi-dimensional resource joint optimization problem is constructed to jointly optimize the unloading time slot, unloading power and the position of the user's movable antenna. It is applied to a mobile edge computing network assisted by a movable antenna, which has a base station where a mobile edge computing server is deployed and a mobile user who needs to offload tasks. In the method described, a movable antenna is equipped on a mobile user, and the mobile user unloads its own tasks to a mobile edge computing server through a hybrid non-orthogonal multiple access transmission mode. By constructing a multidimensional resource allocation problem in the time, power and space domains with the goal of minimizing user energy consumption, the initial unloading time slot, the initial user unloading power and the initial position of the user's movable antenna are set; then an iterative loop is entered, given the user's movable antenna position of the current iteration, the problem is converted into a convex difference programming form by defining new optimization variables, and then the unloading time slot and user unloading power are solved by an iterative convex programming method; in this iteration, by giving the unloading time slot and user unloading power, the base station constructs a position design optimization problem for the user's movable antenna with the criterion of enhancing the user's unloading throughput, and then calculates the convex approximation expression of the relevant throughput expression and solves the user's movable antenna position through an iterative continuous convex approximation method; repeat the above two steps until convergence, and the optimal unloading time slot, optimal user unloading power and optimal user's movable antenna position are obtained.

[0044] In summary, this embodiment discloses a multi-dimensional resource allocation method and system for movable antenna assisted hybrid NOMA, which realizes efficient utilization of multi-dimensional resources in time, power and space domains by jointly designing the unloading time slots of the mobile edge computing network, the user unloading power and the position of the movable antenna, introduces a new time diversity gain, and reduces the user unloading energy consumption of the mobile edge computing network.

[0045] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principles of the present invention shall be equivalent replacement modes and shall be included in the protection scope of the present invention.

Claims

1. A multi-dimensional resource allocation method for hybrid NOMA assisted by movable antennas, characterized in that: The method is applied to a mobile edge computing network, where users of the mobile edge computing network are equipped with movable antennas, and tasks are offloaded to a base station where a MEC server is deployed through a hybrid NOMA criterion, including the following steps: The base station divides an offloading transmission cycle into multiple variable offloading time slots, and the base station takes the total offloading energy consumption of all users as the objective function based on the channel response information, the urgency of the user's offloading task and the amount of data required for the user's offloading task, and constructs an energy consumption minimization optimization problem. The purpose is to minimize the total offloading energy consumption of all users by optimizing the offloading time slots, the user's offloading power and the user's movable antenna position; An initial unloading time slot, an initial user unloading power and an initial position of the user's movable antenna are set, and an iterative loop is entered: given the user's movable antenna position, the unloading time slot and the user's unloading power are jointly optimized; given the unloading time slot and the user's unloading power, the user's movable antenna position is optimized; the iteration is repeated until convergence to obtain the optimal unloading time slot, the optimal user unloading power and the optimal user's movable antenna position.

2. The multi-dimensional resource allocation method for movable antenna assisted hybrid NOMA according to claim 1, characterized in that: In each unloading time slot, the movable antennas equipped by all users will adjust their positions, actively causing the channel time-varying characteristics on the user link to increase additional time diversity gain.

3. The multi-dimensional resource allocation method for movable antenna assisted hybrid NOMA according to claim 1, characterized in that: According to the hybrid NOMA principle, the base station divides an offloading cycle into offloading time slots equal to the number of users. Users use different offloading powers to offload tasks in different offloading time slots according to the urgency of the offloading task and the amount of data required for the offloading task. In the same unloading time slot, users use NOMA transmission to unload task data. The base station sorts the urgency of users' task unloading. The unloading time slot in which users are allowed to unload tasks depends on the urgency of their task unloading.

4. The multi-dimensional resource allocation method for movable antenna assisted hybrid NOMA according to claim 3 is characterized in that: The urgency of the user's task uninstallation is determined according to the user's uninstallation completion time; the user's uninstallation completion time constrains the user to complete the uninstallation within a specific uninstallation time slot, and the uninstallation time slot is also limited by each user's uninstallation completion time.

5. The multi-dimensional resource allocation method for movable antenna assisted hybrid NOMA according to claim 4, characterized in that: The user with the shortest uninstallation completion time is regarded as the user with the highest task urgency. This user must complete the uninstallation in the first uninstallation time slot, and the duration of the first uninstallation time slot must not be greater than the uninstallation completion time of this user. The user with the second shortest unloading completion time can unload task data in the first two time slots, and must also complete the unloading in the first two unloading time slots, and the total duration of the first two unloading time slots shall not be greater than the unloading completion time of the user with the second shortest unloading completion time; the user with the longest unloading completion time can unload task data in all unloading time slots, and the total duration of all unloading time slots shall not be greater than the unloading completion time of the user with the longest unloading completion time.

6. The multi-dimensional resource allocation method for movable antenna assisted hybrid NOMA according to claim 1, characterized in that: Given the user's movable antenna position, when jointly optimizing the unloading time slot and the user's unloading power, the optimization problem of minimizing energy consumption is converted into a convex difference programming problem and solved, so as to obtain the optimal unloading time slot and the optimal user unloading power under the given user's movable antenna position.

7. The multi-dimensional resource allocation method for movable antenna assisted hybrid NOMA according to claim 6, characterized in that: The specific steps of converting the optimization problem of minimizing energy consumption into a convex programming problem and solving it to obtain the optimal unloading time slot and the optimal user unloading power under the movable antenna position of a given user include: Given the user's movable antenna position, define the optimization variable and , Indicates i The optimization variables of the unloading time slots are Indicates user k In the i The energy consumption optimization variable for unloading time slots is Indicates user k In the i The unloading power of the unloading time slot is and Substitute the energy minimization optimization problem into the one that can convert it into a convex difference programming problem. The convex difference programming problem is solved by an iterative convex-concave programming method to obtain the optimal unloading time slot and the optimal user unloading power given the user's movable antenna position.

8. The multi-dimensional resource allocation method for movable antenna assisted hybrid NOMA according to any one of claims 1 to 7, characterized in that: The method of optimizing the user's movable antenna position for a given unloading time slot and user unloading power comprises the steps of: Given unloading time slot and user unloading power , introduce auxiliary variables and The base station is based on enhancing the user offload throughput and building , and Design optimization problem of movable antenna position; Introducing auxiliary variables , the problem is solved by using the quadratic transformation method Equivalently converted into a convex approximate expression, the auxiliary variables are updated in sequence through the alternating iterative criterion and the user's removable antenna position , and finally converges to the optimal user movable antenna position under given unloading time slot and user unloading power.

9. The multi-dimensional resource allocation method for movable antenna assisted hybrid NOMA according to claim 8, characterized in that: Auxiliary variables and the user's removable antenna position The updated rules are: The movable antenna position of a given user , for the auxiliary variable The update criteria are: ； In the formula, Indicates that in a given Auxiliary variables in the case The optimal value of , From base station to user k The received field response vector, Indicates user k The path response matrix, whose elements Indicates user k The transmission link between the base station and p Root emission diameter to l The root receiving path response, Indicates the number of receiving paths. Indicates the diameter number of the launch diameter; , From base station to user m The received field response vector, Indicates the conjugate transpose operation of the scalar / vector in the brackets. Indicates user m The path response matrix, whose elements Indicates user m The transmission link between the base station and p Root emission diameter to l Response of the root receiving path; Indicates that from the user k To the base station at the movable antenna position The emission field response vector of Indicates that from the user m To the base station at the movable antenna position The emission field response vector of Indicates user k In the i The unloading power of unloading time slots, Indicates user m In the i Unloading power of unloading time slot; Given auxiliary variables In the case of , the convex approximation expression is solved by an iterative continuous convex approximation method to update the user's movable antenna position ; Repeat for auxiliary variables and the user's removable antenna position The update is performed until convergence, and the optimal user's movable antenna position is output under a given unloading time slot and user unloading power.

10. A multi-dimensional resource allocation system for mobile antenna-assisted hybrid NOMA, characterized in that: The system includes the following modules: The channel reconstruction module is used to obtain the multipath response parameters of the wireless channel from the base station to the mobile user, including the transmission angle, arrival angle and complex response value of each transmission path, and generate a channel state information matrix; Multi-dimensional resource collaborative computing module, used to jointly optimize the offloading time slot, user offloading power, and the position of the user's movable antenna; A multi-dimensional resource execution module, used for writing the unloading time slot into the scheduler of the base station, and configuring the user radio frequency front end and the movable antenna position according to the unloading time slot, the user unloading power and the user's movable antenna; Wireless receiving and transmitting module, used for receiving base station and mobile user signals; Wireless transmission module, used for receiving and transmitting base station and mobile user signals.

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