Time-power two-dimensional domain adaptive interference resilient multiple access method and system

Abstract

The application discloses a time-power two-dimensional domain adaptive interference resilient multiple access method and system, and belongs to the technical field of wireless communication. The method comprises the following steps: dividing each transmission time slot into a preparation stage and a transmission stage; in the preparation stage, a signal source node performs channel estimation, divides signal sink nodes into two types of high gain and low gain according to instantaneous channel gain, and determines resource allocation parameters through two-stage gradient-interior point collaborative optimization; in the transmission stage, the signal source node performs orthogonal transmission and superposition transmission according to the resource allocation parameters: when the orthogonal transmission is performed, signals are transmitted to target nodes with full power, and corresponding nodes are directly decoded; when the superposition transmission is performed, multiple signals are power-domain superposition coded and broadcast according to a predetermined power allocation, high gain nodes are decoded by using serial interference cancellation, and low gain nodes are directly decoded. The application has robustness to non-ideal serial interference cancellation, and can maintain significant rate performance advantages under any residual interference intensity.

Description

Technical Field

[0001] This invention relates to a time-power two-dimensional adaptive interference elastic multiple access method and system, belonging to the field of wireless communication technology. Background Technology

[0002] The massive influx of IoT terminals, the explosive growth of data volume, and the increasing diversification of business models have posed unprecedented challenges to the spectrum efficiency, connection density, and energy efficiency of wireless communication systems. Non-orthogonal multiple access (NOMA), by superimposing multiple user signals on the same time-frequency resources, can theoretically significantly improve spectrum efficiency and has become one of the key candidate technologies for future mobile communication systems. However, traditional NOMA is usually designed as an independent multiple access scheme, making it difficult to achieve smooth compatibility with widely deployed orthogonal multiple access (OMA) systems, such as Time Division Multiple Access (TDMA) and Orthogonal Frequency Division Multiple Access (OFDMA), resulting in high system upgrade costs.

[0003] To balance performance gains and deployment costs, hybrid NOMA schemes have emerged, where some resources utilize NOMA transmission while the remaining resources retain OMA transmission. Most existing hybrid NOMA research assumes that the receiver can achieve ideal serial interference cancellation (SIC) and primarily focuses on uplink scenarios. However, in real-world communication systems, factors such as channel estimation errors, hardware impairments, and decoding feedback delays are unavoidable, preventing SIC from completely eliminating inter-user interference, thus creating a "non-ideal SIC" problem.

[0004] Non-ideal SiC inevitably introduces residual interference. When the residual interference is small, NOMA and hybrid NOMA can still maintain a better sum rate than OMA; however, when the residual interference exceeds a certain threshold, the actual sum rate of NOMA and hybrid NOMA may be lower than that of OMA under the same conditions. This means that there is a "performance pit" in traditional NOMA and hybrid NOMA schemes: when non-ideal SiC is severe, not only is no gain obtained, but it is actually worse than using OMA alone. This problem seriously weakens the robustness and performance advantages of NOMA and hybrid NOMA in practical deployment. In addition, downlink NOMA adopts a differentiated power allocation strategy, that is, the system allocates higher transmit power to users with poor channel conditions. In this process, the residual interference power level generated by non-ideal SiC is directly related to the high transmit power of users with poor channel conditions. In practice, severe non-ideal SiC conditions cause the power of this residual interference to be much higher than the signal power of users with better channel conditions. This makes non-ideal SiC have a significant impact on the signal-to-interference-plus-noise ratio of users with better channel conditions, thus severely restricting the performance of NOMA and hybrid NOMA in terms of spectral efficiency and system robustness. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a time-power two-dimensional adaptive interference elastic multiple access method and system. Through joint optimization in the time and power domains, the duration ratio of orthogonal transmission and superimposed transmission and the power allocation coefficient of superimposed transmission are dynamically adjusted, so that the sum rate of the system under any residual interference intensity is higher than that of traditional OMA, NOMA and hybrid NOMA methods.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] In a first aspect, the present invention provides a time-power two-dimensional adaptive interference elastic multiple access method, comprising:

[0008] In a downlink communication system that includes one source node and multiple destination nodes, each transmission time slot is divided into a preparation phase and a transmission phase.

[0009] In the preparation phase, the source node performs channel estimation, classifies each sink node into high-gain sink nodes and low-gain sink nodes according to the instantaneous channel gain, and determines the resource allocation parameters for the transmission phase through a two-stage gradient-interior point collaborative optimization method.

[0010] During the transmission phase, the source node performs at most two orthogonal transmissions and one superimposed transmission according to the resource allocation parameters;

[0011] In the orthogonal transmission, the source node sends a signal to the target destination node at full power, and the corresponding target destination node directly decodes its own signal.

[0012] In the superposition transmission, the source node performs power domain superposition encoding on the signals of multiple destination nodes and broadcasts them according to a predetermined power allocation relationship. The high-gain destination node uses serial interference cancellation technology to decode its own signal, while the low-gain destination node directly decodes its own signal.

[0013] Furthermore, the transmission phase includes three variable-duration transmission sub-time slots, namely, a first orthogonal transmission sub-time slot, a second orthogonal transmission sub-time slot, and a superimposed transmission sub-time slot; the resource allocation parameters include an optimal duration ratio and an optimal power allocation coefficient within the superimposed transmission sub-time slot, wherein the optimal duration ratio includes the duration ratio of the first orthogonal transmission sub-time slot, the second orthogonal transmission sub-time slot, and the superimposed transmission sub-time slot; the plurality of destination nodes include a first destination node and a second destination node, wherein the first destination node is the high-gain destination node, and the second destination node is the low-gain destination node;

[0014] During the first orthogonal transmission sub-time slot, the source node transmits the first destination signal to the first destination node at full power, and the first destination node receives and decodes the first destination signal.

[0015] During the second orthogonal transmission sub-slot, the source node transmits the second destination signal to the second destination node at full power, and the second destination node receives and decodes the second destination signal.

[0016] Within the superimposed transmission sub-slot, the source node performs power domain superposition encoding on the new first destination signal and the new second destination signal according to the optimal power allocation coefficient and broadcasts them, wherein the power allocated to the new first destination signal is lower than the power allocated to the new second destination signal; after receiving the signal, the first destination node first decodes the new second destination signal and treats its own signal as interference, then uses serial interference cancellation technology to eliminate the decoded new second destination signal, and then decodes its own signal; after receiving the signal, the second destination node directly decodes the new second destination signal and treats the new first destination signal as interference.

[0017] Furthermore, the determination of resource allocation parameters for the transmission stage using the two-stage gradient-interior point collaborative optimization method includes:

[0018] An optimization model is established with the goal of maximizing the instantaneous speed and rate of the system. The optimization model introduces the residual interference coefficient for eliminating non-ideal serial interference and the target rate constraint for each sink node. The original non-convex optimization problem is transformed into a series of convex subproblems using the continuous convex approximation. The convex subproblems are solved using a two-stage gradient-interior point collaborative optimization method. In the first stage, the gradient descent method is used for global coarse search, and in the second stage, the interior point method is used for local precise solution, thereby outputting the optimal duration ratio and the optimal power allocation coefficient.

[0019] Among them, the durations of the first orthogonal transmission sub-slot, the second orthogonal transmission sub-slot, and the superimposed transmission sub-slot are all non-negative numbers and their sum is equal to the total duration of one transmission slot.

[0020] Furthermore, the continuous convex approximation transforms the original non-convex optimization problem into a series of convex subproblems, including:

[0021] In each iteration, the non-convex objective function and constraints are expanded using a first-order Taylor expansion at the current iteration point to construct a convex approximation subproblem.

[0022] In the iterative process of the continuous convex approximation, a relaxation factor is introduced to participate in the decision vector update to suppress solution oscillation. The update rule is as follows:

[0023] ;

[0024] in, As a relaxation factor, This is the solution obtained after the (k-1)th iteration. This refers to the candidate solutions obtained during the k-th iteration. It is the solution obtained after the k-th iteration.

[0025] Furthermore, in the aforementioned two-stage gradient-interior point collaborative optimization method, the following is defined: Indicates the current iteration point relative to the original function The approximate objective function obtained after performing a first-order Taylor expansion; This indicates the approximate objective function at the current iteration point. gradient vector, The approximate objective function At the current iteration point The gradient of the i-th component at point i; in the first stage, the central difference method is used to calculate the gradient of the objective function with respect to each optimization variable. The gradient calculation formula is:

[0026] ;

[0027] in, Let be the i-th standard unit vector, where the i-th component is 1 and the other components are 0. For the difference step size, At the current iteration point A positive perturbation applied to the i-th component, The current iteration point The negative perturbation applied to the i-th component;

[0028] For the constraints, a global coarse search is performed using the projective gradient descent method, and the decision vector update rule is as follows: In each iteration, the unconstrained solution is defined as... The formula is: ,in This represents the unconstrained solution obtained during the k-th iteration; Represented as reaching the feasible region The projection operator on, the projection operator is Step length press Exponential decay occurs, where... As the attenuation factor, Let i represent the i-th component after the unconstrained solution is projected. and These are the decision vectors before projection. The lower and upper bounds of the i-th component, This is the i-th component in the unconstrained solution; This represents the step size of the k-th iteration. This represents the step size of the (k+1)th iteration.

[0029] Furthermore, in the two-stage gradient-interior point co-optimization method, the first stage uses the gradient descent method to perform the first three rounds of iteration, and the second stage uses the interior point method to accurately solve the subsequent convex subproblems after the third round of continuous convex approximation iterations.

[0030] Furthermore, the establishment of the optimization model with the objective of maximizing the instantaneous sum and rate of the system includes:

[0031] This is equivalent to aiming to minimize the negative instantaneous sum and rate, and the objective function is expressed as: ,in, For the instantaneous and rate functions of the first destination node in each transmission time slot The convex approximation objective function at point , For the instantaneous and rate functions of the second destination node in each transmission time slot The convex approximation objective function at point , For the decision variable vector, , These are the duration ratios corresponding to the first and second orthogonal transmission sub-time slots in the optimal duration ratio, respectively. The power allocation coefficients allocated to the new second destination signal within the superimposed transmission sub-slot.

[0032] Furthermore, the instantaneous time and rate of the first destination node in each transmission time slot Instantaneous and rate data of the second destination node in each transmission time slot They are represented as follows:

[0033] ; ;

[0034] in: ; ; ; ;

[0035] in, First destination node First orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal. First destination node In the third orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal. For the second destination node Second orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal. For the second destination node In the third orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal. To superimpose the transmission sub-slots according to the optimal duration ratio, The transmit power of the source node, The instantaneous channel gain from the source node to the first destination node. Let be the instantaneous channel gain from the source node to the second sink node, where the noise power at both the first and second sink nodes is 1. This is the residual interference coefficient after eliminating non-ideal serial interference.

[0036] Furthermore, the optimization model aiming to maximize the instantaneous sum and rate of the system is expressed as follows: , And st , , , , ;

[0037] in, and These are the minimum rate requirements for the first and second destination nodes, respectively.

[0038] In a second aspect, the present invention provides a time-power two-dimensional domain adaptive interference elastic multiple access system for executing the time-power two-dimensional domain adaptive interference elastic multiple access method described in any of the preceding claims, wherein the system includes a source node, a first sink node, and a second sink node;

[0039] The source node is used to perform channel estimation during the preparation phase of each transmission time slot, distinguish the first and second destination nodes as high-gain and low-gain destination nodes based on the instantaneous channel gain, and jointly determine the optimal duration ratio and optimal power allocation coefficient through a two-stage gradient-interior point collaborative optimization method; it is also used to dynamically divide the current transmission time slot according to the optimal duration ratio, transmit the first destination signal at full power in the first orthogonal transmission sub-time slot, transmit the second destination signal at full power in the second orthogonal transmission sub-time slot, and perform power domain superposition coding on the new first destination signal and the new second destination signal according to the optimal power allocation coefficient in the superimposed transmission sub-time slot and broadcast it;

[0040] The first destination node is used to decode the received first destination signal in the first orthogonal transmission sub-time slot, and to decode the new second destination signal and then decode its own signal in the superimposed transmission sub-time slot using serial interference cancellation technology.

[0041] The second destination node is used to decode the received second destination signal in the second orthogonal transmission sub-slot, and directly decode the new second destination signal in the superimposed transmission sub-slot and regard the new first destination signal as interference.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0043] 1. This invention provides a time-power two-dimensional adaptive interference elastic multiple access method and system. Through joint optimization in the time and power domains, the duration ratio of orthogonal transmission and superimposed transmission and the power allocation coefficient of superimposed transmission are dynamically adjusted, so that the sum rate of the system under any residual interference intensity is higher than that of traditional OMA, NOMA and hybrid NOMA methods.

[0044] 2. This invention employs a two-stage gradient-interior point co-optimization method. This method first utilizes the low complexity and fast convergence of gradient descent for a global coarse search, and then uses the interior point method for a local precise solution, organically integrating the complementary advantages of the two algorithms. Compared to directly using the interior point method, this invention significantly improves computational efficiency while ensuring solution accuracy, achieving a good balance between convergence speed and solution quality. Attached Figure Description

[0045] Figure 1 Here is a simplified flowchart of the time-power two-dimensional adaptive interference elastic multiple access method and system provided by the present invention;

[0046] Figure 2 This is a schematic diagram of the transmission scheme of the first orthogonal transmission sub-time slot, the second orthogonal transmission sub-time slot, and the superimposed transmission sub-time slot in the implementation plan;

[0047] Figure 3 This is a comparison of the instantaneous speed and rate of the proposed method and three traditional multiple access methods under different non-ideal SIC residual interference coefficients;

[0048] Figure 4 This is a comparison of the instantaneous speed and rate of the proposed method and three traditional multiple access methods under different transmission signal-to-noise ratios;

[0049] Figure 5 This is a comparison chart of the average sum rate of the proposed method and three traditional multiple access methods under different non-ideal SIC residual interference coefficients;

[0050] Figure 6 This is a comparison chart of the average sum rate of the method proposed in this invention and three traditional multiple access methods under different transmission signal-to-noise ratios;

[0051] Figure 7This is a schematic diagram illustrating the variation of the instantaneous optimal time slot duration ratio of the proposed method with the residual interference coefficient of non-ideal SIC.

[0052] Figure 8 This is a schematic diagram illustrating the variation of the optimal average duration ratio of time slots in the proposed method as a function of the residual interference coefficient of non-ideal SIC. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0054] The time-power two-dimensional adaptive interference elastic multiple access method provided in this embodiment includes:

[0055] A downlink two-node communication system is set up, including a source node, a first sink node, and a second sink node. Within one transmission time slot, the source node serves as both sink nodes. and The system provides services. To achieve robust adaptation to non-ideal serial interference cancellation (SIC) and effectively suppress the rate loss of traditional multiple access methods, the system divides each transmission time slot into a preparation phase and a transmission phase. The preparation phase includes one preparation sub-time slot, and the transmission phase includes three transmission sub-time slots of variable duration. The preparation sub-time slot is used only for channel estimation and optimization calculations, and its length is negligible relative to the entire time slot, thus not significantly affecting the effective transmission rate of the system. The three transmission sub-time slots are sequentially designated as the first orthogonal transmission sub-time slots. Second orthogonal transmission sub-slot and superimposed transmission sub-slots And assume that the sum of the durations of the three approximately satisfies .make and , Let represent the instantaneous channel coefficients and instantaneous channel gains from the source node to the two destination nodes, respectively. Assume the channel satisfies quasi-static Rayleigh fading characteristics, and that all channel coefficients remain constant throughout the entire transmission time slot and satisfy... Furthermore, the definition Indicates user , In the sub-time slot The achievable instantaneous rate within, Indicates user , The achievable instantaneous speed and rate within each transmission time slot.

[0056] Within the first and second orthogonal transmission sub-time slots, the transmission between the source node and the destination node employs orthogonal resource allocation, ensuring no inter-node interference exists between the first and second destination nodes. (The first orthogonal transmission sub-time slot...) In the middle, only the corresponding first destination node is represented. Provides services. First destination node. Received signal at the location for:

[0057] (1);

[0058] in, The transmit power at the source node. In sub-time slot The first destination signal sent, For the first destination node in the sub-time slot It is an additive white Gaussian noise with a mean of zero and a variance of one.

[0059] Second orthogonal transmission sub-slot In the middle, it is only the corresponding second destination node. Provides services. Second destination node. Received signal at the location for:

[0060] (2);

[0061] in, In sub-time slot The second destination signal sent, For the second destination node in the sub-time slot It is an additive white Gaussian noise with a mean of zero and a variance of one.

[0062] Superimposed transmission sub-slots In this process, the source node performs power-domain superposition encoding on the new first and second destination signals and then broadcasts them. At this time, the source node simultaneously provides services to both destination nodes. Received signal at the location for:

[0063] (3);

[0064] in, In sub-time slots The new first destination signal and the new second destination signal are sent. It is a sink node The power allocation factor, and satisfying . For sink node In the sub-time slot It is an additive white Gaussian noise with a mean of zero and a variance of one.

[0065] Example 1:

[0066] Figure 1 Here is a simplified flowchart of a time-power two-dimensional adaptive interference elastic multiple access method and system provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the transmission scheme of the first orthogonal transmission sub-time slot, the second orthogonal transmission sub-time slot, and the superimposed transmission sub-time slot provided by the present invention;

[0067] See Figure 1 and Figure 2 The method provided in this embodiment specifically includes the following steps:

[0068] Step 1: First Destination Node First orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal for:

[0069] (4);

[0070] Second Subway Node Second orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal for:

[0071] (5);

[0072] Step 2: Superimpose transmission sub-slots In the process, the power allocation coefficients within the superimposed transmission sub-slots satisfy... Considering the interference effects of non-ideal SiC in actual systems, a residual interference coefficient is introduced. First destination node In the third orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal for:

[0073] (6);

[0074] Second Subway Node After receiving the signal, the first destination node The signal is treated as interference, and its own signal is directly decoded by the second sink node. In the third orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal for:

[0075] (7);

[0076] Therefore, combining equations (4) and (6), the first destination node The instantaneous sum and rate within each transmission time slot can be expressed as:

[0077] (8);

[0078] Combining equations (5) and (7), the second destination node The instantaneous sum and rate within each transmission time slot can be expressed as:

[0079] (9);

[0080] Combining equations (8) and (9), the instantaneous sum and rate of the system within each transmission time slot are:

[0081] (10);

[0082] Step 3: This invention establishes an optimization model with the goal of maximizing the instantaneous speed and rate of the system, and introduces constraints on the sub-slot duration ratio, power magnitude, and target rate constraints for each sink node.

[0083] Therefore, the problem of maximizing the instantaneous sum and rate of this system can be defined as:

[0084] (P1): (11a);

[0085] st (11b);

[0086] (11c);

[0087] (11d);

[0088] (11e);

[0089] (11f);

[0090] in, Indicates the destination node The quality of user service is used to ensure the minimum rate requirements of each destination node.

[0091] Step 4: This optimization problem is a single-objective non-convex optimization problem under multi-dimensional decision variable constraints. The vector formed by the decision variables of the optimization problem is... .because , can be obtained Therefore, the simplified decision vector is as follows: To clarify the above rate expression For decision variable vectors The dependency relationship is denoted as . Indicates the current iteration point relative to the original function The approximate function is obtained after performing a first-order Taylor expansion. Convex optimization algorithms have the characteristic that a local optimum is also a global optimum. However, since the optimization problem described above is non-convex, and the set of its constraints is also non-convex, it is difficult to obtain the optimal solution directly using traditional convex optimization algorithms. Therefore, this invention first uses the Continuous Convex Approximation (SCA) algorithm to transform the non-convex problem and non-convex set into a series of solvable convex subproblems and convex sets. The optimal solution of the original problem is then gradually approximated by iteratively solving the convex subproblems.

[0092] The core idea of ​​the SCA algorithm is to construct a series of convex problems and iteratively solve them to gradually approximate the optimal solution of the original non-convex problem. Specifically, in each iteration, the non-convex components in the objective function and constraints are replaced with convex approximation functions constructed based on the current iteration point, forming a solvable convex subproblem. By successively solving this series of convex subproblems, the solution sequence gradually converges to the optimal solution of the original non-convex problem.

[0093] The optimization problem studied in this invention is a non-convex optimization problem, and its non-convexity mainly stems from the superposition of transmission sub-slots. First destination node instantaneous rate function Second Destination Node instantaneous rate function The introduction of residual disturbance coefficients from non-ideal SiC leads to fractional coupling between power allocation coefficient variables in the instantaneous rate function. Simultaneously, coupling also exists between the duration ratio variable and the power allocation coefficient variable, causing the objective function and constraints in the problem studied in this invention to fail to satisfy the properties of convex functions. Therefore, it is difficult to directly obtain the global optimal solution using convex optimization algorithms.

[0094] Step 5: To transform the non-convex problem into a convex problem, this invention first uses the SCA algorithm to construct a convex approximation function from the original non-convex function. In the k-th iteration, the instantaneous rate function... With instantaneous rate function exist Perform a first-order Taylor expansion at the point, thus forming the convex subproblem. Wherein, This represents the current iteration point in the k-th iteration. , , , They represent the variables after the (k-1)th iteration. , , The values ​​obtained by each solution.

[0095] According to equation (6), the instantaneous rate function can be obtained. exist The first-order Taylor expansion function at ... as follows:

[0096] (12);

[0097] According to equation (7), the instantaneous rate function can be obtained. exist The first-order Taylor expansion function at ... as follows:

[0098] (13);

[0099] Therefore, combining equations (4) and (12), the instantaneous sum and rate functions of the first destination node in each transmission time slot are... Convex approximate objective function at It can be represented as:

[0100] (14);

[0101] For the user service quality constraints of the first destination node, we can obtain the following from equation (14):

[0102] (15);

[0103] Similarly, combining equations (5) and (13), the instantaneous sum and rate functions of the second destination node in each transmission time slot are... Convex approximate objective function at It can be represented as:

[0104] (16);

[0105] Regarding the user service quality constraints for the second destination node, we can obtain the following from equation (16):

[0106] (17);

[0107] In the process of updating iteration points in the SCA algorithm, to avoid iterative oscillations that may occur when the obtained candidate solutions become iteration points in the next round, and to improve convergence stability, this invention introduces a relaxation factor to participate in the solution update process, namely:

[0108] (18);

[0109] in, As a relaxation factor, This is the solution obtained after the (k-1)th iteration. This refers to the candidate solutions obtained during the k-th iteration. It is the solution obtained after the k-th iteration.

[0110] This invention decomposes the original non-convex problem into a series of convex subproblems using the SCA algorithm. To balance convergence rate and accuracy during the solution process, this invention further employs a two-stage gradient-interior point co-optimization method. This method fully leverages the complementary advantages of gradient descent and interior point methods. Gradient descent offers advantages such as low computational complexity and fast convergence rate, making it suitable for rapid coarse searching of the feasible region in the early stages of SCA iterations. Meanwhile, the interior point method can accurately solve constrained convex optimization problems, making it suitable for refined solutions in the later stages of SCA iterations, thus ensuring the accuracy of the final solution.

[0111] Step 6: First, considering that the interior-point method of the constrained nonlinear function minimization solver (fmincon function) is suitable for solving constrained nonlinear minimization problems, and the gradient descent method is mainly used to solve the minimum value problem of the function, while the original goal of this invention is to maximize the instantaneous sum and rate of the system. Therefore, at the beginning of the algorithm design, the original problem is first transformed into an equivalent transformation. In other words, the optimization model with the goal of maximizing the instantaneous sum and rate of the system is first equivalently transformed into one with the goal of minimizing the negative instantaneous sum and rate, and then solved using the two-stage gradient-interior-point co-optimization method. At this time, the objective function can be transformed according to equations (14) and (16). Represented as:

[0112] (19);

[0113] The present invention will now employ a two-stage optimization method to solve the convex subproblem in each iteration. This method consists of two stages: the first three SCA iterations constitute the first stage, which uses gradient descent. The core of gradient descent is to iteratively update the variables along the opposite direction of the objective function's gradient, thereby gradually approaching the minimum point of the objective function. Since the optimization objective of this invention has been equivalently transformed into minimizing the negative instantaneous sum and rate, the transformed objective function can be directly used in the gradient descent stage. .

[0114] Step 7: When calculating the gradient, although the convex approximation function theoretically possesses an analytical gradient, in practical applications, the derivation and programming implementation of the analytical gradient are prone to errors due to the complex calculations involved in the objective function expression in this invention. To ensure solution accuracy while avoiding the complexity of analytical differentiation, this invention employs a numerical approximation method. Specifically, considering the truncation error of the central difference method... It is significantly better than the truncation error of forward or backward difference methods. Therefore, this invention uses the central difference method to approximate the gradient of the objective function, which improves the algorithm's versatility and robustness, and avoids deriving the gradient separately for each problem. Definition Indicates the current iteration point relative to the original function The approximate function obtained after performing a first-order Taylor expansion. This indicates the approximate objective function at the current iteration point. gradient vector, The approximate objective function At the current iteration point The gradient of the i-th component at point i. The gradient calculation formula is expressed as:

[0115] (20);

[0116] in, Let be the i-th standard unit vector, where the i-th component is 1 and the other components are 0. For the difference step size, At the current iteration point Similarly, a positive perturbation applied to the i-th component... This represents a negative perturbation, while the other components remain unchanged.

[0117] Regarding the handling of constraints, since gradient descent is an optimization method used to solve unconstrained minimization problems, and projective gradient descent is a variation of gradient descent widely used in constrained optimization problems, this invention employs projective gradient descent to map the updated variables to the feasible region, ensuring that the variables satisfy the boundary constraints during iteration. For the user service quality constraints in the original optimization problem, this invention will handle them uniformly in the interior-point method of the second stage.

[0118] For the boundary constraints of the duration ratio variables of orthogonal transmission and superimposed transmission, and the power allocation coefficient variables of superimposed transmission, the variables are projected into the feasible region after each gradient update using a boundary projection method. This invention defines... and They represent decision vectors respectively. The lower and upper bounds of the decision vector for the optimization problem. The feasible domain is ,in, and These are the decision vectors before projection. The lower and upper bounds of the i-th component, Represents the decision vector The i-th component, ε is a minimal positive number. In each iteration, the unconstrained solution is defined as... The formula is:

[0119] (twenty one);

[0120] This formula represents moving one step from the current iteration point along the negative gradient direction, i.e., the direction in which the objective function decreases the fastest. The obtained trial points. Among them... This represents the unconstrained solution obtained during the k-th iteration. This represents the step size of the k-th iteration, with an initial value of In gradient descent, the gradient represents the direction in which the objective function grows fastest at the current point. Therefore, updating the decision vector in the opposite direction of the gradient allows the objective function value to decrease the fastest, i.e., to approach the current minimum point of the objective function as quickly as possible. This minimum point corresponds to the maximum point of the original objective function. The decision vector update rule can then be expressed as:

[0121] (twenty two);

[0122] Represented as reaching the feasible region The projection operator on the surface, which achieves independent component-wise correction through element-wise constraints, is expressed as:

[0123] (twenty three);

[0124] in, This represents the i-th component after projecting the unconstrained solution, i.e., the i-th component of the candidate solution obtained during the k-th iteration. Let be the i-th component in the unconstrained solution. To balance the convergence speed and accuracy of the gradient descent method, the step size is reduced by a decay factor after each iteration. Exponential decay is calculated using the following formula:

[0125] (twenty four);

[0126] in, This represents the step size of the k-th iteration. This represents the step size of the (k+1)th iteration.

[0127] Step 8: Based on the results obtained in the first stage using gradient descent, the second stage employs the interior-point method to further refine the solution of the convex subproblem generated after the third iteration. Specifically, the `fmincon` function from the Matrix Labs (MATLAB) optimization toolbox is called, and the interior-point method is used to directly solve the constrained convex optimization problem, incorporating the convexized objective function and constraints into the solution process. This method can reliably solve the convex subproblem formed in each iteration while strictly satisfying all constraints, and obtain the global optimum.

[0128] In the k-th iteration, the convex optimization problem to be solved can be expressed as:

[0129] (P2): (25a);

[0130] st (25b);

[0131] (25c);

[0132] (25d);

[0133] (25e);

[0134] (25f);

[0135] Algorithm 1, based on SCA and a two-stage gradient-interior point co-optimization method, has the following specific steps:

[0136] Initialization: Iteration index SCA convergence threshold Maximum number of SCA iterations relaxation factor Convergence sign ;

[0137] when and Based on the current situation Construct a convex approximate objective function and constraints;

[0138] if Candidate solutions are obtained by calling the projected gradient descent method. ;

[0139] Otherwise, the interior-point method using the fmincon function is called to obtain candidate solutions. ;

[0140] if ;calculate and ;

[0141] if & ; Output the optimal solution ;

[0142] Otherwise, update the iteration point. Update the iterative index .

[0143] To verify the beneficial effects of the present invention, the following steps will be implemented:

[0144] In Example 1, it is assumed that the transmit signal-to-noise ratio of the source node is 30 dB. The minimum data rate requirement for the first sink node is set to 1 bps / Hz, and the minimum data rate requirement for the second sink node is set to 1.3 bps / Hz. The channel gain is fixed. , The channel gains are randomly generated from a complex Gaussian distribution with a mean of 0 and variances of both real and imaginary parts of 1 / 2. The SCA convergence threshold is set to... The maximum number of iterations is 280, and the relaxation factor is 0.8 to balance the convergence speed and stability of SCA. The first three iterations use gradient descent for a fast coarse search, with an initial step size of 0.15, a step size decay factor of 0.8, and a difference step size of [missing value]. The interior-point method solver options are set to a maximum number of function evaluations. The maximum number of iterations is All tolerances are An optimization model aimed at maximizing the instantaneous sum and rate of the system was established using a two-stage gradient-interior point co-optimization method, and the instantaneous time slot duration ratio and power allocation coefficient were obtained. Considering the non-ideal SiC characteristics in the actual system, the residual interference coefficient of the non-ideal SiC was set to vary from 0 to 0.02 in a step size of 0.002.

[0145] Figure 3 The diagram shows a comparison of the instantaneous speed and rate of the proposed method and three traditional multiple access methods under different non-ideal SiC residual interference coefficients. Figure 3It can be seen that, under the condition of a transmit signal-to-noise ratio of 30 dB, with the elastic change of residual interference from non-ideal SiC, the instantaneous sum and rate of the method proposed in this embodiment remain at approximately 10.14 bps / Hz, which has a significant advantage over the instantaneous sum and rate of other methods. With the elastic change from ideal SiC to non-ideal SiC, the instantaneous sum and rate of the traditional NOMA method continuously decrease from approximately 10.38 bps / Hz to approximately 6.34 bps / Hz. The traditional OMA method is not affected by residual interference from non-ideal SiC, and its instantaneous sum and rate remain constant at approximately 9.55 bps / Hz. However, with the elastic change from ideal SiC to non-ideal SiC, the traditional hybrid NOMA method encounters the influence of residual interference from non-ideal SiC, and its instantaneous sum and rate first drops sharply from 10.38 bps / Hz to approximately 9.54 bps / Hz, then rises to 9.55 bps / Hz and remains almost unchanged, with a trend almost identical to that of the instantaneous sum and rate of the traditional OMA. This is because, in order to meet the minimum rate requirements of the second sink node, the traditional hybrid NOMA method allocates most of the power in the shared time slot to the second sink node.

[0146] In Example 2, the residual interference coefficient of the non-ideal SIC is fixed at 0.012, and the transmit signal-to-noise ratio of the source node varies from 25 dB to 45 dB in 5 dB increments. All other system parameters remain the same as in Example 1.

[0147] Figure 4 The diagram shows a comparison of the instantaneous speed and rate of the proposed method and three conventional multiple access methods under different transmit signal-to-noise ratios. Figure 4 As can be seen, with the increase of the transmission signal-to-noise ratio, the instantaneous sum and rate of the method proposed in this embodiment increases from approximately 8.42 bps / Hz to approximately 15.21 bps / Hz, maintaining a significant advantage over other methods. The instantaneous sum and rate of the conventional NOMA method slowly increases from approximately 6.73 bps / Hz to approximately 7.15 bps / Hz. The instantaneous sum and rate of both the conventional OMA and conventional hybrid NOMA methods increase from approximately 7.90 bps / Hz to approximately 14.54 bps / Hz.

[0148] In Example 3, it is assumed that the transmit signal-to-noise ratio of the source node is 30 dB. The minimum data rate requirement for the first sink node is set to 1 bps / Hz, and the minimum data rate requirement for the second sink node is 1.3 bps / Hz. 10,000 Monte Carlo simulations are then performed. In each Monte Carlo simulation, channel estimation is performed first. Specifically, two independent Rayleigh fading channel gain values ​​are generated. In the preparation sub-slot, two complex Gaussian random variables with a mean of 0 and variances of 1 / 2 for both real and imaginary parts are generated, and their squared moduli are taken to obtain the pre-allocated channel gain value following a complex Gaussian distribution. and Then, according to the above... and Determine the two actual channel gains and The larger of the two pre-allocated channel gain values ​​is used as the channel gain of the first sink node. The smaller of the two pre-allocated channel gains is compared with a preset lower limit of 0.001, and the larger of the two values ​​is taken as the channel gain of the second sink node. Set the SCA convergence threshold to... The maximum number of iterations is 280, and the relaxation factor is 0.8 to balance the convergence speed and stability of SCA. The first three iterations use gradient descent for a fast coarse search, with an initial step size of 0.15, a step size decay factor of 0.8, and a difference step size of [missing value]. The interior-point method solver options are set to a maximum number of function evaluations. The maximum number of iterations is All tolerances are An optimization model aimed at maximizing the instantaneous sum and rate of the system was established using a two-stage gradient-interior point co-optimization method, and the instantaneous time slot duration ratio and power allocation coefficient were obtained. Considering the non-ideal SiC characteristics of the actual system, the residual interference coefficient of the non-ideal SiC was set to vary from 0 to 0.02 in a step size of 0.002. Then, the effective results of the maximum instantaneous sum and rate and the optimal instantaneous time slot duration ratio were averaged from 10,000 Monte Carlo simulations to obtain the average sum and rate and the average time slot duration ratio.

[0149] Figure 5 The diagram shows a comparison of the average sum rate of the proposed method and three traditional multiple access methods under different non-ideal SIC residual interference coefficients. Figure 5 It can be seen that, under the condition of a transmit signal-to-noise ratio of 30 dB, with the elastic change of residual interference from non-ideal SiC, the average sum rate of the method proposed in this embodiment remains at approximately 10 bps / Hz, which is significantly superior to the average sum rate of other methods. With the elastic change from ideal SiC to non-ideal SiC, the average sum rate of the traditional NOMA method continuously decreases from approximately 10.09 bps / Hz to approximately 6.26 bps / Hz. The traditional OMA method is unaffected, and its average sum rate remains constant at approximately 9.17 bps / Hz. However, with the elastic change from ideal SiC to non-ideal SiC, the traditional hybrid NOMA method first drops sharply from approximately 10.11 bps / Hz to approximately 9.45 bps / Hz, and then slowly decreases to approximately 9.21 bps / Hz, with its decreasing trend gradually approaching the average sum rate of the traditional OMA.

[0150] In Example 4, the residual interference coefficient of the non-ideal SIC is fixed at 0.012, and the transmit signal-to-noise ratio of the source node varies from 25 dB to 45 dB in 5 dB increments. The remaining system parameters are consistent with those in Example 3.

[0151] Figure 6 The diagram shows a comparison of the average sum and rate of the proposed method and three traditional multiple access methods under different transmit signal-to-noise ratios. Figure 6 As can be seen, with the increase of the transmission signal-to-noise ratio, the average sum rate of the method proposed in this embodiment increases from approximately 8.24 bps / Hz to approximately 14.93 bps / Hz. Compared with other methods, the average sum rate of this method always maintains a significant advantage. The average sum rate of the conventional NOMA method slowly increases from approximately 6.52 bps / Hz to approximately 7.15 bps / Hz. The average sum rate of the conventional OMA increases from approximately 7.59 bps / Hz to approximately 14.11 bps / Hz, while the conventional hybrid NOMA method increases from approximately 7.74 bps / Hz to approximately 14.11 bps / Hz. Notably, when the transmission signal-to-noise ratio starts from 40 dB, its average sum rate trend is exactly the same as that of the conventional OMA.

[0152] Figure 7 This diagram illustrates the variation of the instantaneous optimal time slot duration ratio of the proposed method with the residual interference coefficient of the non-ideal SIC. Figure 7 It can be seen that when the interference coefficient The value is 0 (i.e., ideal SIC), and the sub-slot is... The instantaneous duration is approximately 0.0224%, sub-slot. The instantaneous duration is approximately 99.9776%. This is due to the residual interference coefficient of the non-ideal SiC. Increasing from 0.002 to 0.02, sub-slot The instantaneous duration increased from approximately 85.1015% to approximately 85.1016% and then remained unchanged; sub-slot The instantaneous duration remained at approximately 14.8983%, sub-slot. The instantaneous duration decreased from approximately 0.0002% to approximately 0.0001% and then remained unchanged.

[0153] Figure 8 This diagram illustrates the variation of the optimal average time slot duration ratio of the proposed method with the residual interference coefficient of non-ideal SIC. Figure 8 It can be seen that when the interference coefficient The value is 0 (i.e., ideal SIC), and the sub-slot is... The average duration is approximately 0.0712%, sub-slot. The average duration is approximately 99.9288%. This is due to the residual interference coefficient of the non-ideal SIC. Increasing from 0.002 to 0.02, sub-slot The average duration slowly increased from approximately 84.0747% to approximately 85.337%. Sub-slots The average duration generally showed an upward trend, from approximately 14.4085% to approximately 14.5827%, initially increasing and then fluctuating slightly in between. Sub-slots The average duration decreased from approximately 1.5168% to approximately 0.0803%.

[0154] This is because the presence of residual interference from non-ideal SIC weakens the instantaneous rate gain in the superimposed transmission sub-slots. When As the size increases, superimposed transmission sub-slots are added. The signal-to-interference-plus-noise ratio (SIR) of the first destination node decreases as the instantaneous rate decreases. It also gets smaller and smaller. Therefore, the system will adaptively adjust the sub-slot duration ratio. With the non-ideal SIC residual interference coefficient... As the number of superimposed transmission sub-slots increases, the system will gradually reduce the number of these slots. The ratio, and simultaneously increase the first orthogonal transmission sub-slot. With the second orthogonal transmission sub-slot .

[0155] Example 5: This example provides a time-power two-dimensional domain adaptive interference elastic multiple access system for executing the time-power two-dimensional domain adaptive interference elastic multiple access method described in any example 1. The system includes a source node, a first sink node, and a second sink node.

[0156] The source node is used to perform channel estimation during the preparation phase of each transmission time slot, distinguish the first and second destination nodes as high-gain and low-gain destination nodes based on the instantaneous channel gain, and jointly determine the optimal duration ratio and optimal power allocation coefficient through a two-stage gradient-interior point collaborative optimization method; it is also used to dynamically divide the current transmission time slot according to the optimal duration ratio, transmit the first destination signal at full power in the first orthogonal transmission sub-time slot, transmit the second destination signal at full power in the second orthogonal transmission sub-time slot, and perform power domain superposition coding on the new first destination signal and the new second destination signal according to the optimal power allocation coefficient in the superimposed transmission sub-time slot and broadcast it;

[0157] The first destination node is used to decode the received first destination signal in the first orthogonal transmission sub-time slot, and to decode the new second destination signal and then decode its own signal in the superimposed transmission sub-time slot using serial interference cancellation technology.

[0158] The second destination node is used to decode the received second destination signal in the second orthogonal transmission sub-slot, and directly decode the new second destination signal in the superimposed transmission sub-slot and regard the new first destination signal as interference.

[0159] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0160] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0161] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0162] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0163] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.

Claims

1. A time-power two-dimensional adaptive interference elastic multiple access method, characterized in that, include: In a downlink communication system that includes one source node and multiple destination nodes, each transmission time slot is divided into a preparation phase and a transmission phase. In the preparation phase, the source node performs channel estimation, classifies each sink node into high-gain sink nodes and low-gain sink nodes according to the instantaneous channel gain, and determines the resource allocation parameters for the transmission phase through a two-stage gradient-interior point collaborative optimization method. During the transmission phase, the source node performs at most two orthogonal transmissions and one superimposed transmission according to the resource allocation parameters; In the orthogonal transmission, the source node sends a signal to the target destination node at full power, and the corresponding target destination node directly decodes its own signal. In the superposition transmission, the source node performs power domain superposition encoding on the signals of multiple destination nodes and broadcasts them according to a predetermined power allocation relationship. The high-gain destination node uses serial interference cancellation technology to decode its own signal, and the low-gain destination node directly decodes its own signal. The transmission phase includes three variable-duration transmission sub-time slots, namely, a first orthogonal transmission sub-time slot, a second orthogonal transmission sub-time slot, and a superimposed transmission sub-time slot; the resource allocation parameters include an optimal duration ratio and an optimal power allocation coefficient within the superimposed transmission sub-time slot, wherein the optimal duration ratio includes the duration ratio of the first orthogonal transmission sub-time slot, the second orthogonal transmission sub-time slot, and the superimposed transmission sub-time slot; the multiple destination nodes include a first destination node and a second destination node, wherein the first destination node is the high-gain destination node, and the second destination node is the low-gain destination node; During the first orthogonal transmission sub-time slot, the source node transmits the first destination signal to the first destination node at full power, and the first destination node receives and decodes the first destination signal. During the second orthogonal transmission sub-slot, the source node transmits the second destination signal to the second destination node at full power, and the second destination node receives and decodes the second destination signal. Within the superimposed transmission sub-slot, the source node performs power domain superposition encoding on the new first destination signal and the new second destination signal according to the optimal power allocation coefficient and broadcasts them, wherein the power allocated to the new first destination signal is lower than the power allocated to the new second destination signal; after receiving the signal, the first destination node first decodes the new second destination signal and treats its own signal as interference, then uses serial interference cancellation technology to eliminate the decoded new second destination signal, and then decodes its own signal; after receiving the signal, the second destination node directly decodes the new second destination signal and treats the new first destination signal as interference.

2. The time-power two-dimensional adaptive interference elastic multiple access method according to claim 1, characterized in that, The method of determining resource allocation parameters for the transmission phase using a two-stage gradient-interior point collaborative optimization method includes: An optimization model is established with the goal of maximizing the instantaneous power and rate of the system. The optimization model introduces the residual interference coefficient for eliminating non-ideal serial interference and the target rate constraint for each sink node. The original non-convex optimization problem is transformed into a series of convex subproblems using the continuous convex approximation. The convex subproblems are solved using a two-stage gradient-interior point collaborative optimization method. In the first stage, the gradient descent method is used for global search, and in the second stage, the interior point method is used for local solution, thereby outputting the optimal duration ratio and the optimal power allocation coefficient. Among them, the durations of the first orthogonal transmission sub-slot, the second orthogonal transmission sub-slot, and the superimposed transmission sub-slot are all non-negative numbers and their sum is equal to the total duration of one transmission slot.

3. The time-power two-dimensional adaptive interference elastic multiple access method according to claim 2, characterized in that, The method employs a continuous convex approximation to transform the original non-convex optimization problem into a series of convex subproblems, including: In each iteration, the non-convex objective function and constraints are expanded using a first-order Taylor expansion at the current iteration point to construct a convex approximation subproblem. In the iterative process of the continuous convex approximation, a relaxation factor is introduced to participate in the decision vector update to suppress solution oscillation. The update rule is as follows: ； in, As a relaxation factor, This is the solution obtained after the (k-1)th iteration. This refers to the candidate solutions obtained during the k-th iteration. It is the solution obtained after the k-th iteration.

4. The time-power two-dimensional adaptive interference elastic multiple access method according to claim 2, characterized in that, In the aforementioned two-stage gradient-interior point collaborative optimization method, the definition is... Indicates the current iteration point relative to the original function The approximate objective function obtained after performing a first-order Taylor expansion; This indicates the approximate objective function at the current iteration point. gradient vector, The approximate objective function At the current iteration point The gradient of the i-th component at point i; in the first stage, the central difference method is used to calculate the gradient of the objective function with respect to each optimization variable. The gradient calculation formula is: ； in, Let be the i-th standard unit vector, where the i-th component is 1 and the other components are 0. For the difference step size, At the current iteration point A positive perturbation applied to the i-th component, The current iteration point The negative perturbation applied to the i-th component; For the constraints, a global coarse search is performed using the projective gradient descent method, and the decision vector update rule is as follows: In each iteration, the unconstrained solution is defined as... The formula is: ,in This represents the unconstrained solution obtained during the k-th iteration; Represented as reaching the feasible region The projection operator on, the projection operator is Step length press Exponential decay occurs, where... As the attenuation factor, Let i represent the i-th component after the unconstrained solution is projected. and These are the decision vectors before projection. The lower and upper bounds of the i-th component, This is the i-th component in the unconstrained solution; This represents the step size of the k-th iteration. This represents the step size of the (k+1)th iteration.

5. The time-power two-dimensional adaptive interference elastic multiple access method according to claim 2, characterized in that, In the proposed two-stage gradient-interior point co-optimization method, the first stage uses the gradient descent method to perform the first three iterations, and the second stage uses the interior point method to solve the subsequent convex subproblems after the third round of continuous convex approximation iterations.

6. The time-power two-dimensional adaptive interference elastic multiple access method according to claim 4, characterized in that, The establishment of an optimization model aimed at maximizing the instantaneous sum and rate of the system includes: This is equivalent to aiming to minimize the negative instantaneous sum and rate, and the objective function is expressed as: ,in, For the instantaneous and rate functions of the first destination node in each transmission time slot The convex approximation objective function at point , For the instantaneous and rate functions of the second destination node in each transmission time slot The convex approximation objective function at point , For the decision variable vector, , These are the duration ratios corresponding to the first and second orthogonal transmission sub-time slots in the optimal duration ratio, respectively. The power allocation coefficients allocated to the new second destination signal within the superimposed transmission sub-slot.

7. The time-power two-dimensional adaptive interference elastic multiple access method according to claim 6, characterized in that, The instantaneous speed and rate of the first destination node in each transmission time slot Instantaneous and rate data of the second destination node in each transmission time slot They are represented as follows: ； ； in: ; ; ; ; in, First destination node First orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal. First destination node In the third orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal. For the second destination node Second orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal. For the second destination node In the third orthogonal transmission sub-slot The instantaneous rate that can be achieved when decoding its own signal. To superimpose the transmission sub-slots according to the optimal duration ratio, The transmit power of the source node, The instantaneous channel gain from the source node to the first destination node. Let be the instantaneous channel gain from the source node to the second sink node, where the noise power at both the first and second sink nodes is 1. This is the residual interference coefficient after eliminating non-ideal serial interference.

8. The time-power two-dimensional adaptive interference elastic multiple access method according to claim 7, characterized in that, The optimization model that aims to maximize the instantaneous sum and rate of the system is expressed as: , ; And st , , , , ; in, and These are the minimum rate requirements for the first and second destination nodes, respectively.

9. A time-power two-dimensional adaptive interference elastic multiple access system, characterized in that, For performing the time-power two-dimensional domain adaptive interference elastic multiple access method according to any one of claims 1 to 8, the system includes a source node, a first sink node, and a second sink node; The source node is used to perform channel estimation during the preparation phase of each transmission time slot, distinguish the first and second destination nodes as high-gain and low-gain destination nodes based on the instantaneous channel gain, and jointly determine the optimal duration ratio and optimal power allocation coefficient through a two-stage gradient-interior point collaborative optimization method; it is also used to dynamically divide the current transmission time slot according to the optimal duration ratio, transmit the first destination signal at full power in the first orthogonal transmission sub-time slot, transmit the second destination signal at full power in the second orthogonal transmission sub-time slot, and perform power domain superposition coding on the new first destination signal and the new second destination signal according to the optimal power allocation coefficient in the superimposed transmission sub-time slot and broadcast it; The first destination node is used to decode the received first destination signal in the first orthogonal transmission sub-time slot, and to decode the new second destination signal and then decode its own signal in the superimposed transmission sub-time slot using serial interference cancellation technology. The second destination node is used to decode the received second destination signal in the second orthogonal transmission sub-slot, and directly decode the new second destination signal in the superimposed transmission sub-slot and regard the new first destination signal as interference.

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