Statistical priority multiple access optimization method and system for coordinated power domain

Abstract

This invention discloses a cooperative power domain statistical priority multiple access optimization method and system, mainly addressing the problems of limited orthogonal access, rigid threshold settings, and single reuse dimension in existing statistical priority multiple access protocols. The scheme includes: configuring multiple priority transmission queues and initializing parameters for each node; periodically sensing channel load, statistically analyzing service performance, and broadcasting the results; dynamically selecting standard power access, power domain non-orthogonal multiple access, or backoff based on a comparison of channel load and threshold; assessing its own channel situation and calculating transmit power in access modes; and adaptively adjusting the access thresholds of each priority queue through a three-layer architecture of "fast response - steady-state optimization - cooperative coordination," achieving dynamic policy management for differentiated services. This invention can maximize overall network utility while strictly meeting service quality constraints, improve the accuracy and robustness of global resource allocation, increase transmission success rate and network throughput under service load, and can be applied to wireless ad hoc networks.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and specifically relates to a statistical priority multiple access method and system, which can be used in high-density, high-dynamic self-organizing networks such as the Internet of Things, vehicle-to-everything (V2X) networks, and drone swarms. Background Technology

[0002] Statistical Priority Multiple Access (SPMA) is a contention-based channel access mechanism. Its core idea is to control access decisions for services of different priorities based on real-time measured channel statistical load and preset thresholds for each service priority. In the SPMA protocol, nodes first classify data to be transmitted into multiple queues according to their priority. During access decision-making, the node checks the queue status starting with the highest priority. For the first queue with data to be transmitted, its real-time calculated channel statistical load C is compared with the preset threshold B corresponding to that priority. If C < B, the data of that priority is allowed to access the channel and be transmitted; otherwise, the node will perform backoff. This mechanism effectively guarantees the low latency and high reliability requirements of high-priority services, while simultaneously achieving statistical multiplexing of channel resources through dynamic threshold control, thus improving overall network efficiency. However, the standard SPMA protocol has three limitations: First, it has an orthogonal access limitation, meaning that only one node is allowed to successfully transmit in the same frequency band at any given time. This becomes a bottleneck for system capacity under high load scenarios, limiting further improvement in spectrum efficiency. Second, it has a rigid threshold setting. The threshold setting in existing methods mostly relies on preset service ratios or fixed network parameters, making it difficult to adapt to dynamically changing network environments and sudden traffic, which may lead to low channel utilization or increased collision rate. Third, it has a single multiplexing dimension, relying only on statistical multiplexing in the time domain and failing to fully utilize the multiplexing potential of signals in multiple dimensions such as the power domain and spatial domain.

[0003] Power-domain non-orthogonal multiple access (PD-NOMA) is one of the key technologies for 5G and future wireless communications. Its basic principle is that at the transmitting end, multiple users are allowed to transmit signals at different transmit powers on the same time-frequency resource block; at the receiving end, serial interference cancellation (SIC) technology is used to sequentially separate and demodulate the information of each user from the received superimposed signal. Through power-domain multiplexing, NOMA can theoretically improve spectral efficiency several times over.

[0004] Currently, research on SPMA and NOMA mainly follows two independent technical routes. Simply combining the two faces several challenges: First, the lack of a central controller in a distributed environment makes it difficult to achieve globally optimal user pairing and power allocation; second, how to maintain the advantages of SPMA's distributed access while introducing NOMA's power domain reuse without introducing complex signaling interactions and pairing negotiations; third, the original SPMA threshold adaptive mechanism is based on orthogonal access design, and after introducing NOMA, the channel access and interference models change, making the original mechanism unsuitable for direct application.

[0005] Patent document CN202510258812.4 proposes a high-density wireless LAN access optimization management method and system. It first dynamically adjusts channel allocation based on data such as channel utilization, device access density, and interference through real-time network status monitoring. Combined with a channel switching mechanism based on device priority, it ensures the connection stability of critical devices. Through adaptive transmit power control and channel-linked optimization, it reduces signal overlap and interference. Multi-level load balancing is achieved among multiple access points to optimize network resource allocation. Finally, intelligent user access optimization ensures seamless access. While this method can reduce channel conflicts and improve network stability and user experience, its efficiency is highly dependent on the accuracy of status monitoring and the quality of the decision-making algorithm. Therefore, it is only suitable for high-density device access environments. In scenarios with highly random user behavior and complex environments, it may lead to incorrect resource allocation, thus reducing efficiency.

[0006] In his master's thesis, "Research on Multiple Access Protocol of TTNT Data Link," Zhou Sai proposed a method to pre-fix thresholds based on the proportion of services of each priority level. This method sets the threshold for each priority level as a proportion of the transmission intensity of each service, given a theoretical throughput. However, the system throughput limit is unlikely to reach the theoretical limit, and it is also affected by various factors, varying even within the same network under different environments. Therefore, the thresholds set using this method cannot effectively guarantee the quality of service requirements of priority services.

[0007] Fang Yu et al. proposed an improved access strategy for the SPMA protocol in the joint power domain in their paper "Dynamic Threshold Algorithm for Joint Power Domain SPMA Protocol". By introducing power parameters, different transmission powers are allocated to simultaneous signals at the same frequency, achieving differentiation of user signals in the power domain. This expands channel resources to the time, frequency, and power domains, reducing the probability of collisions with high-priority information while increasing the transmission probability of low-priority information, thus improving channel utilization. Furthermore, to address the issue of unnecessary backoffs and decreased channel utilization caused by low-priority tactical information arrival rates dynamically changing across priorities, a dynamic threshold algorithm based on a differential integrated moving average support vector regression model to predict channel states is designed. While this algorithm can adaptively adjust the access threshold according to the real-time changes in the arrival rates of data packets of each priority, aiming to reduce collision probability and improve network throughput, the power domain's distinguishing ability reaches its limit when the network load is extremely high and the arrival rates of data packets of each priority are large. The collision probability still increases, significantly reducing system throughput. Inaccurate channel parameter analysis can also lead to power control failure, exacerbating interference and reducing service robustness. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a statistical priority multiple access optimization method and system in the cooperative power domain. This method aims to jointly optimize spectral efficiency and transmission reliability in ad hoc wireless networks, reduce the probability of interference and collisions between nodes, ensure the quality of service and transmission success rate of high-priority services, and effectively improve the system's spectral efficiency.

[0009] Utilize efficiency to ensure strong robustness to dynamic topologies and services.

[0010] The technical approach to achieving the objectives of this invention is as follows: By constructing a unique power-access dual-closed-loop collaborative control system, the inner loop rapidly adapts to the physical channel, while the outer loop slowly optimizes network balance, enabling the system to stably converge to the optimal equilibrium state where throughput is maximized, interference is controllable, and service requirements are met; by constructing a multi-dimensional state vector encompassing channel load, service distribution, node density, and interference levels, the network operating status is comprehensively perceived, and comprehensive decision-making based on multi-dimensional information allows power and threshold adjustments to more accurately reflect the true network condition; by proposing a three-layer adjustment architecture of "rapid response layer - steady-state optimization layer - collaborative coordination layer," the rapid response layer addresses sudden overloads at the millisecond level, the steady-state optimization layer achieves multi-objective optimization of nodes at the hundred-millisecond level, and the collaborative coordination layer ensures regional consistency at the second level; by classifying services into three categories—critical services, elastic services, and best-effort services—and designing differentiated adjustment strategies for each type of service, including adjustment targets, weight settings, and step size limits, the service quality of each priority service is guaranteed.

[0011] Based on the above ideas, the technical solution of the present invention includes:

[0012] 1. A statistical priority multiple access optimization method for a cooperative power domain, characterized by comprising:

[0013] (1) Configure multiple priority transmission queues for nodes in the self-organizing network, and initialize the transmission power P and access threshold B of each priority standard mode and power domain non-orthogonal multiple access NOMA mode, where the standard mode access threshold < NOMA mode access threshold ;

[0014] (2) Each node periodically senses the channel load C, statistically records it, and statistically records the reception success rate and delay of each priority data packet, and broadcasts it to neighbor nodes;

[0015] (3) Each node compares the current channel load C with its access threshold B based on the self and neighbor channel load status obtained in step (2), and dynamically selects the transmission mode:

[0016] If , then adopt the standard power access mode;

[0017] If , then adopt the NOMA power access mode and execute (4)

[0018] If , then execute backoff; [[ID= 31]]

[0019] (4) Record the transmission success rate and backoff times of each priority data packet:

[0020] (5) The node selected to adopt the NOMA power access mode further evaluates its own channel situation, dynamically maps and calculates the power attenuation coefficient required for this transmission to determine the final NOMA transmission power ;

[0021] (6) Each node adaptively adjusts the access threshold of each priority queue based on the long-term channel load trend statistically recorded in (2) above and the transmission success rate, backoff times, and delays of each priority service recorded in (4) above through a three-layer architecture of "fast response - steady-state optimization - cooperative coordination" , and implements dynamic policies for differentiated services.

[0022] Further, in (6), the access threshold of each priority queue is adaptively adjusted through a three-layer architecture of "fast response - steady-state optimization - cooperative coordination" , and its implementation includes:

[0023] (6a) Preset the basic threshold based on the network theoretical capacity and priority level ;

[0024] (6b) Make different adjustments;

[0025] (6c) Calculate the steady-state optimization components

[0026] (6d) Calculate the coordination components

[0027] (6e) Construct the access threshold using the above parameters :

[0028] ;

[0029] (6f) Based on the calculated thresholds for each priority level As a result, the standard mode access threshold was adjusted. Adjust the NOMA mode access threshold , These are the NOMA mode weight parameters.

[0030] Furthermore, as stated in (6b) Various adjustments were made, including:

[0031] (6b1) Continuously monitor composite metrics within 5 windows, including instantaneous channel load. Success rate of reception at each priority level p When one or more of these indicators show an abnormal combination, execute (6h);

[0032] (6b2) Implement a tiered response based on the severity of the event:

[0033] For mild congestion, only the medium-to-low priority thresholds are applied. Perform gentle shrinkage to 80%-90% of the original value;

[0034] For moderate congestion, apply all priority thresholds. Compress to 60%-80% of the original value and hold for 20-50ms;

[0035] For severe congestion or strong interference, all priority thresholds should be applied. The signal is compressed to 40%-60% of its original value, and all services that are currently backing up are reset for 50-100ms to reduce channel contention pressure as quickly as possible.

[0036] 2. A statistical priority multiple access optimization system in the cooperative power domain, characterized in that it comprises:

[0037] The channel sensing and statistics module is used to periodically sense and calculate the channel load, count and record the reception success rate of data packets of each priority, and assemble status broadcast messages.

[0038] The multi-priority queue management module is used to maintain and manage multiple sending queues according to business priorities;

[0039] The access mode decision module is used to compare the current channel load with the preset access threshold and decide whether to use standard power access, NOMA power access, or a backoff mechanism.

[0040] The NOMA power control module is used to assess the node's own channel situation, calculate the power attenuation coefficient, and determine the final transmit power when the decision is to enable NOMA access.

[0041] The multi-dimensional status recording module is used to record the success rate, backoff count, and service latency of data packets of various priorities under different access modes;

[0042] The adaptive strategy optimization module is used to dynamically adjust the access threshold and access strategy of each priority queue based on long-term channel load trends and multi-dimensional state records, through a three-layer architecture of "fast response-steady-state optimization-coordinated coordination".

[0043] The neighbor status maintenance module is used to process received neighbor status broadcast messages and maintain the channel load, reception success rate, and policy information of neighbor nodes.

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] Firstly, by utilizing historical operational data to guide the direction of threshold parameter updates, this invention dynamically optimizes resource allocation within a period of hundreds of milliseconds. This allows for maximizing the overall network efficiency while strictly meeting hard constraints on service quality, including packet loss rate and latency limits.

[0046] Secondly, by utilizing the threshold information messages received from neighboring nodes, all nodes in this invention calculate the collaborative coordination component, causing their local thresholds to converge towards highly reliable neighboring nodes. This improves the accuracy and robustness of global resource allocation and avoids the "local optimum, global suboptimal" problem caused by nodes relying solely on local information for independent optimization.

[0047] Third, this invention combines power domain non-orthogonal multiple access technology with the SPMA protocol and proposes a cooperative power domain SPMA protocol access model framework. By adopting a distributed power adjustment strategy based on channel state and service priority, it can significantly improve the transmission success rate and throughput of the network under high service load and reduce the overall latency of the network. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating the implementation of the statistical priority multiple access optimization method in the cooperative power domain of the present invention.

[0049] Figure 2 This is a schematic diagram illustrating the decision-making process for data transmission at different priorities in the method of this invention;

[0050] Figure 3 This is a sub-flowchart for calculating the NOMA transmit power in the method of this invention;

[0051] Figure 4 This is a block diagram of the statistical priority multiple access optimization system in the cooperative power domain of the present invention;

[0052] Figure 5 This is a graph showing the change in the probability of successful transmission of the highest priority frame as a function of network traffic volume in the present invention and existing methods.

[0053] Figure 6 This is a comparison curve of network throughput versus network traffic volume in the present invention and existing methods. Detailed Implementation

[0054] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0055] Example 1: Statistical Priority Multiple Access Optimization Method in Cooperative Power Domain.

[0056] This embodiment implements and simulates a cooperative power domain statistical priority multiple access optimization method in a dynamically competitive wireless communication network environment. The wireless communication network environment is a distributed network composed of multiple nodes. Each node has wireless transceiver, power control, and local computing capabilities. Each node carries multiple priority services with different quality of service requirements. Without centralized scheduling and global control, the method achieves efficient and reliable access of multiple priority services in a shared channel by sensing channel load, statistically analyzing service success rates, deciding on access modes, and adaptively adjusting strategies.

[0057] Reference Figure 1 The implementation steps of this example include:

[0058] Step 1: The node establishes multiple sending scheduling queues with different priorities.

[0059] 1.1) The node divides the service data packets according to their urgency and reliability requirements. Priorities, and for each

[0060] Priority is maintained by maintaining an independent first-in-first-out (FIFO) transmission queue;

[0061] 1.2) Each priority Initialize two threshold values: standard power access threshold. and NOMA power

[0062] Access threshold .

[0063] This example assumes, but is not limited to, the following: =0.368, ;

[0064] Step 2: All nodes continuously sense the channel, calculate the channel statistical load C, and periodically broadcast the statistical information to neighboring nodes.

[0065] 2.1) All nodes continuously sense the channel and calculate the channel statistical load C;

[0066] 2.1.1) The node listens to the channel within the predetermined sensing time slot and counts the total duration during which the channel is in a busy state within the time window T. ;

[0067] 2.1.2) Calculate the current channel load based on the statistical results. and for Record the value;

[0068] 2.1.3) Use the Exponentially Weighted Moving Average (EWMA) algorithm to analyze the channel load over multiple consecutive periods N. Smoothing is performed to obtain channel load values ​​that reflect long-term trends. As the channel load at the current moment :

[0069] ,

[0070] in, This represents the step size of the sliding window. The channel occupancy rate at time t. For the window number, Let be the weight value of the corresponding window, and we have: , .

[0071] 2.2) All nodes statistically analyze the success rate and latency of data packets of each priority level, and periodically broadcast this information to their neighboring nodes.

[0072] 2.2.1) Each node maintains a receive counter and a successful receive counter, which are used to record the total number of data packets of each priority and the number of data packets that pass the cyclic redundancy check (CRC) respectively, and to count the delay.

[0073] 2.2.2) At the end of each statistical period, calculate the reception success rate for each priority k.

[0074] ,

[0075] in To successfully receive a number of data packets, The total number of data packets received.

[0076] ;

[0077] 2.2.3) Calculate the reception success rate for each priority level. And the time delay is encapsulated into a status broadcast message, which is periodically broadcast to neighboring nodes.

[0078] Step 3: All nodes generate priority k data packets, compare the channel load C with the threshold B, and select a transmit or backoff strategy.

[0079] Reference Figure 2 The implementation of this step includes the following:

[0080] 3.1) Upon arrival of a service from the application layer, it is inserted into the corresponding priority queue according to its priority and awaits scheduling.

[0081] 3.2) During scheduling, the system traverses the queues from high to low priority. Each queue follows the first-in-first-out principle to obtain one service to be sent. Once a service is successfully obtained, the traversal stops.

[0082] 3.3) Determine if the service has expired:

[0083] If it expires, remove the service from the queue and return to step 3.2).

[0084] Otherwise, proceed to step 3.4).

[0085] 3.4) Compare the priority threshold B corresponding to the service with the channel load statistics C to dynamically select the transmission mode:

[0086] like In this case, the standard power access mode is adopted;

[0087] like If so, the NOMA power access mode is adopted, and step 4 is executed.

[0088] like If the backoff occurs, the system will wait for the next scheduling moment to return to step 3.2.

[0089] Step 4: Select nodes using NOMA power access mode to further assess their channel situation, dynamically map and calculate the power attenuation factor required for this transmission, and determine the final NOMA transmit power. .

[0090] Reference Figure 3 The implementation of this step includes the following:

[0091] 4.1) The node reads the channel load status of its primary neighbors to further assess its own channel situation:

[0092] 4.1.1) A node reads its own stored list of n primary neighbor nodes from the most recent broadcast. Channel load status ;

[0093] 4.1.2) Based on channel load status Calculate its own relative channel congestion index :

[0094] ,

[0095] in, The current channel load as perceived by the node itself. The weights are assigned based on the distance to neighboring nodes or historical signal strength, and ;

[0096] 4.1.3) Based on relative congestion Assess your own channel contention position in the local network: The higher the value, the more crowded the channel environment of this node is relative to its main interfering neighbors;

[0097] 4.2) Dynamically map nodes and calculate the power attenuation factor required for this launch to determine the final NOMA launch power. :

[0098] 4.2.1) Based on relative congestion Calculate the normalized channel index :

[0099] ,

[0100] in, and These are the maximum and minimum reference value boundaries preset according to the network deployment environment;

[0101] 4.2.2) A non-uniform partitioning strategy is adopted to divide the normalized channel index. Mapped to A discrete power level is obtained to obtain the power level. :

[0102] ;

[0103] in, This is the minimum effective threshold used to filter out measurement noise and outliers; It is a non-uniformity factor;

[0104] 4.2.3) Based on priority k and relative congestion Calculate the power level Basic attenuation coefficient :

[0105] ,

[0106] in The minimum power attenuation coefficient, The weights are for priority k.

[0107] 4.2.4) Applying a boundary protection mechanism, the temporary coefficients are constrained within the effective range to obtain the final attenuation coefficient. :

[0108] ,

[0109] in, Under the same priority, The larger, The smaller; in the same The higher the priority, the better. The larger;

[0110] 4.2.5) Based on the attenuation coefficient and the initial NOMA mode reference power Calculate the final NOMA transmit power. :

[0111] .

[0112] Step 5: The node calculates the success rate and latency of receiving data packets of each priority level.

[0113] 5.1) Each node maintains a receive counter and a successful receive counter, which are used to record the total number of data packets of each priority and the number of data packets that pass the cyclic redundancy check (CRC) respectively, and to count the delay.

[0114] 5.2) At the end of each statistical period, calculate the reception success rate for each priority k. ,in To successfully receive a number of data packets, The total number of data packets received. ;

[0115] 5.3) Calculate the reception success rate for each priority level. And the time delay is encapsulated into a status broadcast message, which is periodically broadcast to neighboring nodes.

[0116] Step 6: The node adaptively adjusts the access threshold of each priority queue through a three-layer architecture of "fast response - steady-state optimization - collaborative coordination". .

[0117] 6.1) Nodes are preset with basic thresholds based on the network's theoretical capacity and priority level. ;

[0118] 6.2) Adjust the rapid adjustment component according to the severity of the event. Make different adjustments;

[0119] 6.2.1) Continuously monitor composite indicators within 5 windows, including instantaneous channel load. Success rate of reception at each priority level p When one or more of these indicators show an abnormal combination, proceed to step 5.2.2).

[0120] 6.2.2) Implement a tiered response based on the severity of the event:

[0121] For mild congestion, only the medium-to-low priority thresholds are applied. Perform gentle shrinkage to 80%-90% of the original value;

[0122] For moderate congestion, apply all priority thresholds. Compress to 60%-80% of the original value and hold for 20-50ms;

[0123] For severe congestion or strong interference, all priority thresholds should be applied. The signal is compressed to 40%-60% of its original value, and all services that are currently backing up are reset for 50-100ms to reduce channel contention pressure as quickly as possible.

[0124] 6.3) Calculate the steady-state optimization components ;

[0125] 6.3.1) The core task of the steady-state optimization layer is formalized into the following multi-objective optimization problem:

[0126] ;

[0127] in: This is the network threshold vector, representing the set of all priority thresholds;

[0128] It is the overall network utility function, which consists of weighted throughput gains ( Weighted packet loss penalty ) and weighted delay penalty ( It is composed of three linearly combined parts;

[0129] Priority Service throughput The weight parameters, Priority Packet loss rate The weight parameters, Priority Business latency Weight parameters;

[0130] 6.3.2) Cache the most recent M optimization cycles threshold vector and their corresponding utility values Mark the historical best parameters ;

[0131] 6.3.3) Calculate steady-state optimization components using historical reference and projection gradient methods. :

[0132] First, set historical reference parameters. : ,in, These are historical reference parameters selected for the current period, typically using the historically optimal parameters. ;

[0133] Secondly, based on the set historical reference parameters The network threshold vector at the current time step is calculated using the projection gradient method. :

[0134] ,

[0135] in, Towards the feasible region The projection operator is used to ensure that the updated parameters always satisfy the hard constraints. It is the learning rate in the nth iteration;

[0136] Finally, based on the network threshold vector at the current time... and the network threshold vector at the previous time step Calculate the steady-state optimization components: ;

[0137] 6.4) Calculate the coordination components :

[0138] 6.4.1) The node receives the threshold information field from its neighboring nodes and denotes the threshold vector of the neighboring nodes as follows: ;

[0139] 6.4.2) Abstract the network topology into an undirected graph. and nodes The set of neighbors, that is, the set of nodes All directly connected nodes are denoted as , where the node set Corresponding network nodes, edge sets Corresponding communication link;

[0140] 6.4.3) Based on the current threshold vector of neighboring nodes obtained in step 6.4.1) and the nodes obtained in step 6.4.2) Neighbor set and their weights Calculate the collaborative and coordinated components :

[0141] ,

[0142] in, To coordinate step size; For nodes The set of neighbors; For a moment node For nodes The weights; For nodes At any moment The threshold vector;

[0143] 6.5) Construct the access threshold using the parameters obtained in steps 6.1) to 6.4) above. :

[0144] ;

[0145] 6.6) Based on the calculated thresholds for each priority level As a result, the standard mode access threshold was adjusted. and NOMA mode access threshold :

[0146] ,

[0147] ,in These are the NOMA mode weight parameters.

[0148] This completes the optimization of statistical priority multiple access.

[0149] It should be noted that the step numbers in the above examples are only for the purpose of clearly describing the present invention and facilitating understanding, and the order of the numbering is not limited.

[0150] Example 2: Statistical Priority Multiple Access Optimization System in Cooperative Power Domain.

[0151] Reference Figure 4 This system includes: a channel sensing and statistics module 1, a multi-priority queue management module 2, an access mode decision module 3, a NOMA power control module 4, a multi-dimensional state recording module 5, a neighbor state maintenance module 6, and an adaptive policy optimization module 7. The channel sensing and statistics module 1 includes: a channel sensing submodule 11, a data statistics submodule 12, and a broadcast message submodule 13; the adaptive policy optimization module 7 includes: a fast response submodule 71, a steady-state optimization submodule 72, and a cooperative coordination submodule 73.

[0152] The working principle of the entire system is as follows:

[0153] The channel awareness and statistics module 1 is used to periodically sense the channel status and statistically analyze service performance in the network, providing a basis for access decisions and policy optimization. Specifically, the channel awareness submodule 11 periodically monitors the physical channel, statistically analyzes channel occupancy per unit time, calculates the current channel load C, and outputs the calculated channel load to the access mode decision module 3. The data statistics submodule 12 statistically analyzes and calculates the historical reception success rate and latency of data packets of each priority according to service priority, and outputs the statistical results to the broadcast message submodule 13, the multi-dimensional status recording module 5, and the adaptive policy optimization module 7. The broadcast message submodule 13 encapsulates the reception success rate information of each priority data packet obtained by this node into a status broadcast message, which is periodically broadcast to neighboring nodes. After receiving the broadcast, the neighboring nodes output the statistical results to the neighbor status maintenance module 6.

[0154] The multi-priority queue management module 2 is used to maintain and manage multiple priority data packet transmission queues according to the Quality of Service (QoS) requirements of different services. It classifies and queues arriving data packets according to service priority, and when a service transmission permission is obtained, it retrieves the data packet from the queue of the corresponding priority and outputs it to the access mode decision module 3.

[0155] The access mode decision module 3 is used to determine the access behavior mode of the node based on real-time channel conditions. It compares the current channel load value C obtained by the channel sensing and statistics module 1 with the preset access threshold B for each priority service.

[0156] like In this case, the standard power access mode is adopted;

[0157] like If so, the NOMA power access mode is adopted, and the NOMA power control module 4 is triggered;

[0158] like If so, then a retreat will be executed;

[0159] The access mode identifier and result are then output to the multi-dimensional status recording module 5.

[0160] The NOMA power control module 4 is used to calculate the appropriate transmit power for the node when a NOMA access decision is made. It first assesses the node's own channel situation, determining the relative channel conditions of the node in potential NOMA pairings based on information such as historical reception success rates; then, it calculates a power attenuation coefficient based on the assessment results; finally, it combines the base transmit power and the attenuation coefficient to determine the actual transmit power used for this NOMA access.

[0161] The multi-dimensional state recording module 5 is used to persistently store key performance indicators of the system operation, serving as the data foundation for policy optimization. It records the historical transmission success rate, cumulative backoff count, and average service latency of data packets of each priority under different access modes, receives real-time results from the access mode decision module 3 for updates, and outputs its stored historical statistical data to the adaptive policy optimization module 7.

[0162] The neighbor state maintenance module 6 is used to maintain the network state view of neighbor nodes. Based on the success rate of each priority reception of neighbor nodes obtained by the broadcast message submodule 13 and the threshold B adopted, it constructs a local neighbor node state table and outputs the state table to the adaptive policy optimization module 7.

[0163] The adaptive strategy optimization module 7 is used to dynamically optimize the access strategy parameters of each priority service based on long-term network status and multi-dimensional historical records, employing a three-layer architecture of "fast response-steady-state optimization-coordinated coordination" to continuously improve system performance. Specifically, the fast response submodule 71 dynamically adjusts the access thresholds of each priority service based on real-time success or failure results fed back by the access mode decision module 3, quickly adapting to sudden traffic changes; the steady-state optimization submodule 72 calculates and iteratively updates the steady-state optimal access thresholds for each priority service using a preset optimization algorithm based on long-term historical statistical data provided by the multi-dimensional status recording module 5; and the coordinated coordination submodule 73 evaluates the matching degree between the local threshold settings and neighbors based on neighbor node status information provided by the neighbor status maintenance module 6, fine-tuning the local access thresholds to optimize the overall performance of the local network. The module ultimately outputs updated access thresholds for each priority service. This will be fed back to the access mode decision module 3 to complete the strategy closed loop.

[0164] It should be noted that the above functional modules can be implemented, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, they can be implemented, in whole or in part, as program instruction products. A program instruction product includes one or a set of program instructions. When the program instructions are loaded and executed on a computer, the described process or function is generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The program instructions can be stored in a computer-readable and writable storage medium, or transferred from one computer's readable and writable storage medium to another.

[0165] In this embodiment, the direct coupling or communication connection between the modules can be achieved through indirect coupling or communication connection via interfaces, devices, or modules. The functional modules and sub-modules in this embodiment can dynamically reside within a single processing unit, or each module can exist physically independently, or two or more modules can dynamically reside within a single processing unit. When these dynamic components are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable and writable storage medium. This storage medium can be a memory, disk, or optical disc, etc.

[0166] The effects of this invention can be further illustrated by the following simulation experiments.

[0167] I. Simulation Conditions

[0168] The simulation experiment platform is: a 32-core AMD Ryzen Threadripper PRO 5975WX 64-bit CPU with a main frequency of 3.6GHz and 128GB of memory.

[0169] The software platform for the simulation experiment is: Windows 10 operating system and EXATA.

[0170] In the simulation scenario, 30 nodes are distributed in a 10km*10km area and move randomly at a speed of 10m / s. All nodes are reachable by a single hop. The channel transmission rate is 2Mbps and the network traffic range is 0-15Mbps.

[0171] The services are divided into eight priority levels, from 0 to 7, with lower priority numbers indicating higher priority. Two different service intensities, Service I and Service II, are set, as detailed in Table 1. The arrival interval for each priority service follows a Poisson distribution, the payload length in each frame is 1024 bits, and the destination address is randomly selected.

[0172] Table 1

[0173]

[0174] II. Simulation Content

[0175] Simulation 1: Under the above conditions, simulations of priority multiple access optimization were performed using the present invention and the existing fixed threshold statistical priority multiple access (SPMA) method under different service modes. The success probability of the highest priority service frame was statistically analyzed for each method. The results are as follows: Figure 5 As shown.

[0176] from Figure 5 It is evident that in existing methods, when network traffic exceeds 7 Mbps, the success rate of transmission of the highest priority service frames in both Service Mode I and Service Mode II drops sharply, falling to 95% at 15 Mbps, which can no longer guarantee 99% reliability. However, in the method of this invention, as network traffic increases, both Service I and Service II can guarantee 99% reliability for the highest priority service when network traffic is between 0 and 15 Mbps.

[0177] Simulation 2: Under the above conditions, simulations of priority multiple access optimization were conducted using the present invention and the existing fixed threshold statistical priority multiple access (SPMA) method under different service modes. The network throughput of each method was statistically analyzed, and the results are as follows: Figure 6 As shown.

[0178] from Figure 6 As can be seen, in existing methods, when the traffic volume is below approximately 8 Mbps, the throughput of service mode I and service mode II increases synchronously with the traffic volume; when the traffic volume reaches 8 Mbps or above, the throughput basically stabilizes; when the traffic volume is 15 Mbps, the throughput is approximately 8 Mbps, and cannot be further improved. However, in the present invention, within the range of traffic volume from 0 to 15 Mbps, the throughput of service mode I and service mode II consistently increases with the increase of traffic volume, and at the same traffic volume, the throughput is significantly higher than that of existing methods. When the traffic volume is 15 Mbps, the throughput of service mode II is close to 10.2 Mbps, and the throughput of service mode I is close to 9.5 Mbps, indicating that the present invention can utilize channel resources more efficiently and adapt to higher traffic volume demands.

[0179] The above description is merely two specific examples of the present invention and does not constitute any limitation on the present invention. Obviously, those skilled in the art, after understanding the content and principles of the present invention, may make various modifications and changes in form and details without departing from the principles and structure of the present invention. However, these modifications and changes based on the ideas of the present invention are still within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing statistical priority multiple access in a coordinated power domain, the method comprising: include: (1) configuring a plurality of priority sending queues for a node in a self-organizing network, and initializing a transmission power P and an access threshold B of each priority standard mode and a power domain non-orthogonal multiple access (NOMA) mode, wherein the standard mode access threshold NOMA mode access threshold ; (2) Each node periodically senses the channel load C, performs statistics and records it, and calculates the success rate and delay of receiving data packets of each priority, and broadcasts it to neighboring nodes; (3) Based on the channel load status of itself and its neighbors obtained in step (2), each node compares its current channel load C with its access threshold B and dynamically selects the transmission mode: If then the standard power access mode is employed; If then NOMA power access mode is adopted, and (4) is performed If then perform backoff; (4) Record the success rate and backoff count of data packets of each priority: (5) The node selecting the NOMA power access mode further evaluates its own channel situation, dynamically maps and calculates the power attenuation coefficient required for this transmission to determine the final NOMA transmission power ; (6) Each node, based on the long-term channel load trend statistics in (2) above and the multi-dimensional network status of the mode transmission success rate, backoff times and the delay of each priority service recorded in (4) above, adjusts the access threshold of each priority queue through a three-layer architecture of "fast response - steady-state optimization - coordinated coordination" Implement dynamic strategy for differentiated services.

2. The method of claim 1, wherein, In (1), configuring a multi-priority sending queue for nodes in an ad hoc network involves dividing service data packets into multiple priorities according to their urgency and reliability requirements, and maintaining an independent first-in-first-out sending queue for each priority.

3. The method of claim 1, wherein, The implementation of (2) whereby each node periodically senses the channel load C, performs statistics and records it, includes: (2a) The node listens to the channel in a predetermined sensing time slot, counting the total time duration that the channel is busy within a time window T ; (2b) Calculate the current channel load based on the statistical results. ,Will Record the value; (2c) Use the exponentially weighted moving average (EWMA) algorithm to analyze the channel load over multiple consecutive periods N. Smoothing is performed to obtain channel load values ​​that reflect long-term trends. As the channel load at the current moment : , in, This represents the step size of the sliding window. The channel occupancy rate at time t. For the window number, Let be the weight value of the corresponding window, and we have: , .

4. The method according to claim 1, characterized in that, The implementation of (2) for calculating the success rate and latency of data packets of each priority includes: (2d) The node maintains a receive counter and a successful receive counter, which are used to record the total number of data packets of each priority and the number of data packets that pass the cyclic redundancy check (CRC) respectively, and to count the delay. (2e) At the end of each statistical period, calculate the reception success rate for each priority k. ,in To successfully receive a number of data packets, The total number of data packets received. ; (2f) Calculate the reception success rate for each priority level. And the time delay is encapsulated into a status broadcast message, which is periodically broadcast to neighboring nodes.

5. The method according to claim 1, characterized in that, The node selected in (5) to use the NOMA power access mode further evaluates its own channel situation, which includes: (5a) The node reads the channel load status stored in its own memory from the n most recent broadcast neighbor nodes. ; (5b) Based on channel load status Calculate its own relative channel congestion index : ， in, The current channel load as perceived by the node itself. The weights are assigned based on the distance to neighboring nodes or historical signal strength, and ; (5c) Based on relative congestion Assess your own channel contention position in the local network: The higher the value, the more crowded the channel environment of this node is relative to its main interfering neighbors.

6. The method according to claim 1, characterized in that, In step (5), the power attenuation coefficient required for this launch is dynamically mapped and calculated to determine the final NOMA launch power. Its implementation includes: (5d) Based on relative congestion Calculate the normalized channel index : ， in, and These are the maximum and minimum reference value boundaries preset according to the network deployment environment; (5e) A non-uniform partitioning strategy is adopted to normalize the channel index. Mapped to A discrete power level is obtained to obtain the power level. : ， in, This is the minimum effective threshold used to filter out measurement noise and outliers; It is a non-uniformity factor; (5f) Based on priority k and relative congestion Calculate the power level Basic attenuation coefficient : ， in The minimum power attenuation coefficient, The weights are for priority k. (5g) By applying a boundary protection mechanism, the temporary coefficient is constrained within the effective range to obtain the final attenuation coefficient. : ， The mapping relationship satisfies: Under the same priority, The larger, The smaller; in the same The higher the priority, the better. The larger; (5h) Based on the attenuation coefficient and the initial NOMA mode reference power Calculate the final NOMA transmit power. : 。 7. The method according to claim 1, characterized in that, In step (6), a three-layer architecture of "fast response - steady-state optimization - collaborative coordination" is used to adaptively adjust the access threshold of each priority queue. Its implementation includes: (6a) Set basic thresholds based on network theoretical capacity and priority levels. ; (6b) Adjust the rapid adjustment component according to the severity of the event. Make different adjustments; (6c) Calculate the steady-state optimization components ,in, This is the network threshold vector, representing the set of all priority thresholds; These are historical reference parameters selected for the current period, typically using the historically optimal parameters. ; Towards the feasible region The projection operator ensures that the updated parameters always satisfy the hard constraints. It is the learning rate in the nth iteration; (6d) Calculate the coordination components ,in, To coordinate step size; For nodes The set of neighbors; For a moment node For nodes The weights; For nodes At any moment The threshold vector; (6e) Construct the access threshold using the above parameters : ； (6f) Based on the calculated thresholds for each priority level As a result, the standard mode access threshold was adjusted. Adjust the NOMA mode access threshold , These are the NOMA mode weight parameters.

8. The method according to claim 6, characterized in that, The above (6b) Various adjustments were made, including: (6b1) Continuously monitor composite metrics within 5 windows, including instantaneous channel load. Success rate of reception at each priority level p When one or more of these indicators show an abnormal combination, execute (6h); (6b2) Implement a tiered response based on the severity of the event: For mild congestion, only the medium-to-low priority thresholds are applied. Perform gentle shrinkage to 80%-90% of the original value; For moderate congestion, apply all priority thresholds. Compress to 60%-80% of the original value and hold for 20-50ms; For severe congestion or strong interference, all priority thresholds should be adjusted. The signal is compressed to 40%-60% of its original value, and all services that are currently backing up are reset for 50-100ms to reduce channel contention pressure as quickly as possible.

9. A statistical priority multiple access optimization system in the cooperative power domain, characterized in that, include: The channel sensing and statistics module is used to periodically sense and calculate the channel load, count and record the reception success rate of data packets of each priority, and assemble status broadcast messages. The multi-priority queue management module is used to maintain and manage multiple sending queues according to business priorities; The access mode decision module is used to compare the current channel load with the preset access threshold and decide whether to use standard power access, NOMA power access, or backoff mechanism. The NOMA power control module is used to assess the node's own channel situation, calculate the power attenuation coefficient, and determine the final transmit power when the decision is to enable NOMA access. The multi-dimensional status recording module is used to record the success rate, backoff count, and service latency of data packets of various priorities under different access modes; The neighbor status maintenance module is used to process received neighbor status broadcast messages and maintain the channel load, reception success rate, and policy information of neighbor nodes; The adaptive strategy optimization module is used to dynamically adjust the access threshold and access strategy of each priority queue based on long-term channel load trends and multi-dimensional state records, through a three-layer architecture of "fast response-steady-state optimization-coordinated collaboration".

10. The system according to claim 8, characterized in that, The channel sensing and statistics module includes: The channel awareness submodule is used to periodically sense the data packet reception status of the physical layer and calculate the channel load; The data statistics submodule is used to calculate and statistically analyze the reception success rate of data packets of different priorities. The broadcast message submodule is used to assemble the success rate information of the received data packets of each priority into broadcast messages and send them periodically. The adaptive strategy optimization module includes: The fast response submodule is used to quickly and dynamically adjust the access thresholds of services of different priorities based on real-time transmission results. The steady-state optimization submodule is used to calculate and update the steady-state optimal access threshold for each priority service based on historical statistical data and optimization algorithms. The coordination submodule is used to coordinate and adjust the local access threshold based on the status information of neighboring nodes in order to optimize local network performance.

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