Resource Cooperative Allocation and Optimization Method for Star-Scintillation-Based Wireless Communication Systems

Abstract

This invention discloses a resource collaborative allocation and optimization method for wireless communication systems based on star-flash technology, involving collaborative allocation of wireless resources in multi-user, multi-service scenarios. This method simultaneously considers the connection relationship between the access point and user equipment, channel resource allocation, and modulation and coding scheme selection, and integrates service quality constraints of service types for resource scheduling modeling. The method includes the following steps: iteratively optimizing the channel allocation strategy of the access point (AP) using a greedy algorithm; modeling the access control problem between the AP and user equipment (UE) as finding the optimal AP-UE pair based on matching theory, and then solving it using a delayed reception algorithm, thereby effectively reducing link SINR and improving network throughput; and implementing adaptive MCS selection based on fuzzy logic, effectively reducing computational complexity and improving system performance while ensuring optimal link MCS selection.

Description

Background Technology

[0001] With the rapid development of wireless communication technology, multi-AP, multi-UE device architecture has become a key solution for improving network capacity and service quality. In practical application scenarios such as the Industrial Internet of Things (IIoT), smart homes, and high-definition video transmission, user demands for communication speed, latency, and reliability are showing a highly heterogeneous trend. Currently, the development of the IoT and the Industrial Internet is increasingly reliant on the deep integration of technologies such as cloud computing and artificial intelligence to build entirely new infrastructure, manufacturing systems, and service models, and to spawn diverse new applications and consumption scenarios. This poses higher performance requirements and challenges to short-range wireless communication technologies.

[0002] However, existing short-range wireless communication technologies still operate at latency levels in the tens of milliseconds, especially in environments with dense multi-node deployments, making it difficult to meet the deterministic quality of service requirements while ensuring high-speed transmission. NearLink, as an emerging short-range wireless communication technology, boasts advantages such as ultra-low latency, high reliability, high speed, strong anti-interference capabilities, high security, and high-precision synchronization, demonstrating its potential to support efficient data interaction in scenarios such as smart cars, smart terminals, smart homes, and smart manufacturing.

[0003] Given its superior performance, the application of StarScan technology has demonstrated strong potential in multi-AP, multi-UE architectures. By applying StarScan to such architectures, flexible access control and resource allocation can be achieved, thereby addressing the high demands and challenges brought about by the Internet of Things (IoT) and the Industrial Internet, and propelling wireless communication technology into a new stage of development. However, this combination also faces numerous challenges. In multi-AP systems, the access relationship between APs and UEs is complex, constrained by various factors such as channel conditions, spectrum resource allocation, and mutual interference. The processes of channel allocation, AP-UE matching, and modulation / coding scheme selection are coupled, significantly impacting the overall system performance. Especially in complex environments with strong interference and multiple concurrent services, how to improve system capacity while meeting the QoS requirements of different service types has become one of the core issues that current wireless communication systems urgently need to address.

[0004] To address the aforementioned challenges, resource allocation algorithms are typically employed. However, existing algorithms for solving resource allocation problems mostly utilize heuristics, graph theory methods, or deep reinforcement learning. While these methods improve system performance to some extent, they still suffer from high computational complexity, slow convergence speed, and difficulty in meeting real-time requirements. Furthermore, most research focuses on resource optimization within existing network architectures, with less consideration given to the impact of network initialization on resource allocation efficiency and final performance. To address this deficiency, intervening in resource scheduling strategies during network initialization to achieve coordinated optimization of network configuration and resource allocation can more effectively improve overall network performance and better guarantee Quality of Service (QoS). By performing pre-optimization during network construction, complex resource allocation problems can be effectively solved in multi-AP, multi-UE systems, thereby providing a strong guarantee for efficient communication services.

[0005] In summary, designing a joint resource allocation optimization strategy with low computational complexity and adaptability to multi-service QoS requirements has significant theoretical and practical value for improving the performance and adaptability of next-generation wireless communication systems. Summary of the Invention

[0006] The present invention proposes a resource collaborative allocation and optimization method for wireless communication systems based on star flash, which can at least solve one of the technical problems in the background art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for resource collaborative allocation and optimization in a star-flash wireless communication system includes the following steps: S0. Construct a scenario for collaborative resource allocation in a multi-access-point wireless communication system; S1. Establish the AP channel allocation model in the wireless communication system; S2. Establish the AP-UE access control model in the wireless communication system; S3. Establish the MCS selection model for the AP-UE link in the wireless communication system; S4. Based on the AP channel allocation model, AP-UE access control model, and AP-UE link MCS selection model, an optimization problem is constructed. The goal is to maximize the AP upload rate of the system by jointly optimizing the AP channel allocation strategy, AP-UE access control strategy, and MCS selection strategy, while satisfying the system bit error rate and signal-to-interference-plus-noise ratio constraints. S5. Decompose the optimization problem into two sub-problems and perform iterative optimization. S51. Fixed AP-UE access control policy and MCS selection policy, using a greedy algorithm to optimize AP channel allocation strategy based on minimum co-channel interference. S52. Based on the output of S51, optimize the AP-UE access control strategy. Based on matching theory, transform the AP-UE access control problem into an AP-UE pair matching problem. Establish the AP-UE PL based on the AP's utility function, and establish the UE-AP PL based on the UE's utility function. Then, use the delayed reception algorithm to complete the AP-UE pair matching. S53. Based on the output of S52, and using the concept of fuzzy logic, design a fuzzy controller to achieve adaptive selection of the MCS.

[0008] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0009] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0010] In summary, this invention discloses a method for collaborative allocation and optimization of wireless resources in a star-flash wireless communication system, involving collaborative allocation of wireless resources in multi-user, multi-service scenarios. This method simultaneously considers the connection relationship between the access point (AP) and user equipment (UE), channel resource allocation, and modulation and coding scheme (MCS) selection. It integrates service quality constraints of service types for resource scheduling modeling, including the following steps: Iterative optimization of the channel allocation strategy for the AP using a greedy algorithm; based on matching theory, the access control problem between the AP and UE is modeled as finding the optimal AP-UE pair, and then solved using a delayed reception algorithm, thereby effectively reducing link SINR and improving network throughput; based on the results of the above two steps, adaptive MCS selection is implemented based on fuzzy logic, effectively reducing computational complexity and improving system performance while ensuring optimal link MCS selection. The method proposed in this invention effectively solves the problem of collaborative allocation of wireless resources in multi-user, multi-service scenarios. Under conditions of multi-service concurrency, high interference density, and limited resources in wireless access systems, it achieves AP channel allocation, AP-UE access control, and efficient link MCS selection, effectively improving network throughput. Attached Figure Description

[0011] Figure 1 This is a system block diagram of an embodiment of the present invention; Figure 2 This is the Star Flash MCS table of this invention embodiment; Figure 3This is a flowchart of the delayed reception algorithm according to an embodiment of the present invention; Figure 4 This is a flowchart of the fuzzy controller according to an embodiment of the present invention; Figure 5 This is a membership function graph of an embodiment of the present invention; Figure 6 This is a performance comparison chart of the algorithms in the embodiments of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0013] This invention proposes a resource collaborative allocation and optimization method for a star-based wireless communication system, comprising a scene description module, a model building module, and a model solving module, specifically including the following steps: S1. Establish the AP channel allocation model in the wireless communication system; S2. Establish the AP-UE access control model in the wireless communication system. S3. Establish the MCS selection model for the AP-UE link in the wireless communication system; S4. Based on the AP channel allocation model, AP-UE access control model, and AP-UE link MCS selection model, an optimization problem is constructed. The goal is to maximize the AP upload rate of the system by jointly optimizing the AP channel allocation strategy, AP-UE access control strategy, and MCS selection strategy, while satisfying the system bit error rate and signal-to-interference-plus-noise ratio constraints. S5. Decompose the optimization problem into two sub-problems and perform iterative optimization. S51. Fixed AP-UE access control policy and MCS selection policy, using a greedy algorithm to optimize AP channel allocation strategy based on minimum co-channel interference. S52. Based on the output of S51, optimize the AP-UE access control strategy. The AP-UE access control problem is transformed into an AP-UE pair matching problem based on matching theory. The AP-UE utility function (PL) is established based on the AP's utility function, and the UE-AP utility function (PL) is established based on the UE's utility function. Then, a delayed reception algorithm is used to complete the AP-UE pair matching. S53. Based on the output of S52, and using the concept of fuzzy logic, design a fuzzy controller to achieve adaptive selection of the MCS. At this point, the entire algorithm flow is complete, and the problem is solved.

[0014] The following is a detailed explanation: Scene description moduleThis involves constructing scenarios for the collaborative allocation of resources in multi-access point wireless communication systems. For example... Figure 1 As shown, this invention considers a network model where multiple access points (APs) connect to a large number of different types of user equipment (UEs), using the StarFlash access technology. Nodes in the StarFlash system are divided into two categories: grant nodes (G nodes) and terminal nodes (T nodes). G nodes are the nodes that send data scheduling information at the StarFlash wireless communication system access layer, and T nodes are the nodes that receive data scheduling information at the StarFlash wireless communication system access layer. Due to the characteristics of StarFlash technology, the APs described in this invention are StarFlash G nodes, and the UEs are StarFlash T nodes. Each access point (AP) operates on its available channel resources to support user equipment (UE) connections and communications. One AP connects to multiple UEs, forming multiple AP-UE links. Assume the network has... One AP, One UE, Describes the set of APs. This represents the set of UEs. The available channels of the AP are... To indicate, Once the AP and UE are matched, use to indicate and The links between them. The available bandwidth for each access point (AP) is set to the same value, using To represent. For each link , for Assigned to The transmission bandwidth. The StarScan system supports 32 modulation and coding schemes, denoted as... It can be done Figure 2 You can find it by searching. It is a binary variable, where express choose The policy situation. If the link... Choose Then binary variables ,otherwise .

[0015] In the method designed in this invention, a reasonable AP channel allocation scheme, AP-UE access control, and link are employed. The adaptive MCS adjustment aims to achieve optimal system performance. System performance is measured using the data upload rate of the APs within the system. The ambient noise power W is expressed by the formula for additive white Gaussian noise density. The calculation yielded the result. In practical systems, data transmission inevitably contains bit errors. (Using...) Indicates link Bit error rate. Indicates link The transmission rate can be expressed as:

[0016] in, The raised cosine roll-off factor is the system's coefficient, which is typically set to 0.2~0.25 in broadband wireless access systems. The modulation coefficient, For bit rate, modulation coefficient and bitrate All by This decision reflects the direct impact of MCS on link transmission rate. express The transmission rate of each AP is the sum of the rates of all its associated links, specifically expressed as:

[0017] Where K represents the relationship with K associated links.

[0018] Model building module The main focus is on establishing models for AP channel allocation, AP-UE access control, and MCS adaptive selection issues that arise throughout the entire process of network establishment and operation. Furthermore, based on channel resource allocation, different services, bandwidth resources, and MCS selection, the model is designed to maximize the system transmission rate.

[0019] (1) AP channel allocation model During the network initialization phase, available channels must first be allocated to all access points (APs) so that the UE can select a suitable AP for access, thus forming an AP-UE link. (Using binary variables) This indicates the AP's channel selection status. If Channel selected ,but ,on the contrary, Within the same time slot, each AP can only select one channel. Since the number of APs is usually greater than the number of available channels, it's inevitable that multiple APs will reuse the same channel, causing co-channel interference. Using binary variables... This indicates the situation where different APs multiplex channels. If and Channel reused Then there is ,otherwise, .

[0020] In the logarithmic path loss model, it is represented by the signal-to-interference ratio (SIR). Subjected to other APs multiplexing the same channel ( The interference situation of co-channel interference (SIR). The formula for calculating the SIR of co-channel interference is as follows:

[0021] in, Indicate target The transmission power, This refers to path loss. The formula for calculating path loss is as follows:

[0022] in, Indicates reference distance The path loss at the location is denoted by n, which is the path loss exponent and is typically set to 23 indoors. The shadow fading is a normal distribution.

[0023] (2) AP-UE access control model Before accessing an access point (AP), the UE scans multiple APs to obtain information such as channel number, transmit power, and identifier (ID), and then selects the optimal AP for access. Access selection is based on a binary variable. Indicates: If Choose Then there is ,otherwise .when At that time, it is considered to have formed. This one link.

[0024] Once an AP-UE link is established, the AP associated with that link will allocate bandwidth to it. To simplify the problem, assume that each link... Bandwidth used The same applies. Meanwhile, the system contains numerous AP-UE links, which inevitably interfere with each other. To measure the degree of interference between different links, the signal-to-interference-plus-noise ratio (SINR) of the link is introduced as an evaluation metric. The signal-to-interference-plus-noise ratio can be expressed as:

[0025] in, express The transmission power, Indicates no connection Other Transmit power; for the link All other APs using the same channel (such as...) The transmission power of any of these will interfere with it. Furthermore, Indicates link The link gain is calculated using a logarithmic distance path loss model.

[0026] In this invention, the following three typical service types in networks are mainly considered: sensor services, video services, and voice services, respectively using... This indicates that, to meet the needs of different services, a reasonable AP-UE matching strategy is designed through model optimization, balancing transmission efficiency and reliability to achieve optimal resource allocation and effective interference control.

[0027] (3) MCS Adaptive Selection Model Modulation control system (MCS) plays a crucial role in balancing data transmission rate and reliability under various network conditions, and the MCS value of a link directly affects system performance. Selecting an appropriate MCS based on channel conditions, signal strength, interference, and network load is essential for optimizing system performance. When hardware allows, a higher-order modulation scheme can be chosen to achieve a higher transmission rate. When signal quality is good, a higher code rate encoding scheme can be selected to reduce redundancy and increase data throughput; conversely, when signal quality is average, a lower code rate encoding scheme can be chosen to increase redundancy and ensure transmission reliability.

[0028] Different MCSs exhibit significant differences in coding rate, which directly impacts the system's bit error rate (BER). Insufficient redundancy can lead to increased BER, affecting transmission reliability; while excessive redundancy reduces the system's transmission rate. Therefore, a suitable balance needs to be found between transmission rate and BER, ensuring that the system BER remains within an acceptable range while maximizing data transmission efficiency. For link The bit error rate can be calculated using the following formula:

[0029] in, For the Q function, approximately: , and It depends on The constant. And Different formulas are selected to calculate based on different MCS values. .

[0030] In summary, based on the three models proposed above, the entire optimization problem can be expressed as follows:

[0031] Among these constraints, constraint 1 ensures that each AP can only select one channel for transmission within a time slot, but allows multiple APs to reuse the same channel simultaneously; constraint 2 limits co-channel interference between APs reusing the same channel, ensuring that the interference level between APs reusing the same channel is not too high to guarantee network communication quality; constraint 3 ensures that each UE can only select one AP for access, preventing UEs from connecting to multiple APs simultaneously; constraint 4 ensures the quality of the AP-UE link, requiring the UE to switch to another AP for reconnection when the link condition is poor; constraint 5 restricts AP bandwidth allocation, meaning the total bandwidth allocated by an AP to all its AP-UE links cannot exceed its available bandwidth; and constraint 6 imposes constraints on the bit error rate (BER) based on the performance requirements of specific services. Indicates link Transmitting specific services Bit error rate at time This indicates the maximum bit error rate that the service can tolerate. The bit error rate limit requires the selected MCS to have sufficient redundancy to ensure the reliability of system transmission.

[0032] Based on the above optimization problem, this invention proposes a resource collaborative allocation and optimization method for wireless communication systems based on star flash.

[0033] Model Solver Module This invention primarily designs a solution method based on the optimization problem proposed by the model building module. The invention first proves the problem... This is an NP-hard problem, and a two-stage approach is proposed to solve it. Considering that AP channel allocation is only related to the first two constraints and is independent of subsequent steps, the AP channel allocation is completed in the first stage. In the second stage, based on the channel allocation results of the first stage, this invention first completes the AP-UE access control problem, selecting the most suitable AP for each UE; then, according to service constraints, appropriate bandwidth is allocated to each link, and a suitable MCS is selected based on the characteristics of the link to maximize the AP and rate of the entire system. The AP channel allocation problem and the AP-UE matching problem are, to some extent, independent. AP channel allocation mainly involves interference management between APs, while AP-UE matching involves resource allocation between UEs and APs.

[0034] Equal integer decision variables make the problem It's at least an NP-hard problem, plus the constraints... Nonlinear constraints further complicate the entire problem. This becomes a mixed-integer nonlinear programming problem (MINLP). The solution space of MINLP involves optimization of both discrete and continuous variables, and nonlinear relationships may introduce multiple local optima, making the solution process not only consider integer decision variables but also handle complex nonlinear constraints. For general MINLP problems, an exact solution cannot be found in polynomial time, therefore the problem... It is classified as an NP-hard problem.

[0035] Given that this optimization problem is a mixed-integer nonlinear programming problem, direct global optimization is highly complex, difficult to solve, and may not yield the optimal solution.

[0036] Therefore, to address this challenge, this invention proposes a resource collaborative allocation and optimization method for wireless communication systems based on star-flash technology. This method is a comprehensive solution encompassing three key algorithms: First, to address the problem of excessive co-channel interference during AP operation, a greedy algorithm-based AP channel allocation method is proposed. Second, for the access control problem between AP and UE, an AP-UE access control algorithm based on matching theory is proposed, modeling the access control problem as an AP-UE matching problem and finding the optimal AP-UE pair in the network through a delayed reception algorithm, thereby achieving the optimal access strategy. Finally, to overcome the shortcomings of traditional adaptive MCS selection algorithms in terms of computational efficiency and accuracy, an adaptive MCS selection algorithm based on fuzzy logic is proposed to improve the adaptive performance of the system. The following sections will introduce the solution to the entire problem and the algorithms for solving each subproblem in turn.

[0037] 1. AP Channel Allocation Based on Greedy Algorithm

[0038] The issue being considered is the allocation of AP channels, which is only related to constraints. and Furthermore, during the problem-solving process, it is necessary to calculate the co-channel interference SIR experienced by each AP, so that a co-channel interference matrix can be established based on the SIR.

[0039] The greedy algorithm is used to solve the channel allocation problem of the AP. The process is as follows: When allocating channels to each AP, all available channels are iterated. If a channel is not occupied, it is allocated to that AP. Then, channel allocation is performed for the next AP. If a channel is occupied, the target AP is allowed to occupy it first. Then, the SIR (Search Indicator Ratio) of the target AP and all APs occupying the channel is calculated. If it is less than a threshold, the target AP can also occupy the channel; otherwise, another channel is selected. The algorithm terminates after all channels have been allocated to all APs. The entire algorithm's execution flow is as follows:

[0040] After the algorithm finishes running, all APs have been assigned channels, and the SIR-based interference matrix has been initialized. In the initial stage of the network, simultaneously allocating appropriate channels to all APs using existing spectrum resources is not redundant; it's a crucial part of the overall early planning for subsequent operations. Without a separate AP channel allocation phase, directly performing AP-UE matching would lack a macro-level plan for the overall network channel resources. This could lead to some channels being overcrowded while others are underutilized, hindering the balanced use of system resources.

[0041] 2. AP-UE Access Control Based on Matching Theory

[0042] question This involves access control for AP-UE, and the link. Bandwidth allocation and MCS selection remain a MINLP with multiple optimization variables. In this process, AP-UE control access is the most crucial task. Only with an excellent AP-UE access strategy can subsequent links be successfully implemented. Only through proper bandwidth allocation and MCS selection can the system's transmission and rate be maximized on a good basis. In this section, based on relevant knowledge of matching theory, the invention models the AP-UE access control problem as an AP-UE pair matching problem, and uses the delayed reception algorithm in matching theory to find the optimal AP-UE pair, thereby achieving optimal AP-UE access control.

[0043] AP-UE pair matching can be viewed as a classic many-to-one matching problem in matching theory, where an AP needs to be matched with multiple UEs. Since the access capacity of an AP is limited, the number of UEs it can match is also limited; therefore, AP quotas are defined. To simplify the problem, the set All objects are divided into two categories: APs affected by co-channel interference and APs not affected by co-channel interference. Different quotas are assigned to each category. A delayed reception algorithm is used to solve the entire problem. The overall process is as follows: Figure 3 As shown.

[0044] First, an set of APs and UEs is constructed. Then, the Preference Lists (PLs) for both APs and UEs are initialized. APs can set their preferences for UEs based on interference levels and service priorities; UEs can set their preferences for APs based on signal strength, available bandwidth, and service compatibility. Each UE initiates a matching proposal to the AP according to the order in its PL. The AP then accepts some matching requests from UEs and rejects others based on its PL order and quota. Finally, it checks whether all APs and UEs have been successfully matched. If there are still unmatched UEs, the above steps are repeated. This process continues until a stable AP-UE matching scheme is output.

[0045] First, the preference relationship between the AP and UE needs to be established. Let... Describes the set of APs. This represents the set of UEs. and They are respectively All objects in The set of preference relationships between all objects in the set and all objects in the other set.

[0046] In consideration right When considering preference relationships, a utility function can be used. To conduct quantitative analysis, thereby establishing right set of preference relations This utility function considers the following factors: 1) UE interference level to AP: This is determined by the UE's SNR. Generally, the higher the UE's SNR, the better the link condition when the UE accesses the AP, and the higher the AP's preference for that UE. 2) Service Priority: Different services have different priorities. Among the three services considered in this invention, video services have the highest priority, and sensor services have the lowest priority. This is because video services can transmit a larger amount of data at once, and prioritizing video transmission helps increase network speed. Different priority scores are designed for different services here. This fraction will be a unitless integer.

[0047] Utility function Calculated by the following formula

[0048] in express Normalized SNR, express Business score and These represent the weight coefficients of the two data points. The utility function of the AP for each UE is calculated, and all UEs are ranked to form a set of AP's preference relationships with each UE. .

[0049] In consideration right When considering preference relationships, a utility function can be used. To conduct quantitative analysis, thereby establishing right set of preference relations This utility function considers the following factors: 1) The magnitude of co-channel interference experienced by the AP: When allocating channels for APs, due to the limited number of channels, different APs may use the same channel. APs occupying the same channel will experience co-channel interference (SIR). The greater the co-channel interference, the worse the performance of the AP. Therefore, UEs will prefer APs with less co-channel interference for pairing.

[0050] 2) Service Adaptability: Different services (such as voice, video, and sensor services) have different requirements for the AP. For example, voice services are sensitive to latency, video services have high requirements for bandwidth and transmission rate, and sensor services have strict requirements for bit error rate. Different weights can be defined according to the service type to reflect service adaptability. It is an adaptation score calculated based on the UE's service type, indicating the UE's suitability in... The adaptability score for uplink transmission service z. This function is primarily built around the UE's requirements for communication quality and service adaptability. Different service types will have different adaptability scores in this function to reflect their different requirements for the AP. 3) Available bandwidth: The larger the available bandwidth of the AP, the better it can meet the needs of the UE and the higher the preference.

[0051] Utility function Calculated by the following formula

[0052] in express Co-channel interference, express Business score and These represent the weight coefficients of the two data points. The utility function of the AP for each UE is calculated, and all UEs are ranked to form a set of AP's preference relationships with each UE. .

[0053] Once the PLs of both the AP and UE are established, the AP-UE pair matching can be completed according to the delayed reception algorithm. The algorithm flow is as follows:

[0054] After the algorithm process is completed, a stable network AP-UE matching scheme can be obtained. Next, by assigning each AP-UE link... The optimal MCS is allocated to maximize the upload speed and data rate of the system APs.

[0055] 3. Adaptive MCS Selection Based on Fuzzy Logic MCS selection is also an issue. The problems that need to be solved. After completing the access control of the AP-UE, by assigning each AP-UE link The optimal MCS is allocated to maximize the upload speed and data rate of the system APs.

[0056] In wireless communication systems, there has been extensive research on MCS selection algorithms, but some traditional MCS selection algorithms currently in use have certain shortcomings. For example, the Outer Ring Link Adaptation (OLLA) algorithm mainly optimizes MCS selection by adjusting the Target Block Error Rate (BLER). Adaptive Rate Algorithm (ADA) also improves link reliability, but its adjustment process is slow, it cannot adapt to rapidly changing channels, and it relies on feedback, resulting in high latency. Exhaustive Search is another commonly used algorithm for solving the MCS selection problem. Since the MCS table has finite solutions, by iterating from largest to smallest while satisfying the minimum bit error rate of the link, the best MCS can always be selected for each link. However, the time complexity of this traversal is too high; even in small-scale networks, it requires a long time to traverse, and for slightly larger networks, it becomes almost impossible to solve within a finite time.

[0057] The adaptive MCS selection algorithm based on fuzzy logic can overcome the shortcomings of the traditional algorithms mentioned above. It can adapt to channel changes and does not rely on real-time feedback; it can determine the quality of current parameters through a pre-defined expert knowledge base. Compared to traditional MCS selection algorithms, it has a faster response, higher accuracy, and stronger robustness, making it suitable for complex and ever-changing wireless environments.

[0058] All links in the model of this invention The MCS selection problem will be solved by a fuzzy logic controller, which consists of fuzzification, fuzzy inference, and defuzzification. The entire controller's workflow is as follows: Figure 4As shown, in the fuzzification stage, a predefined membership function is used as the input for fuzzification processing. Then, the fuzzy set is input into the fuzzy rule base for fuzzy inference, generating a fuzzy output set. Finally, the fuzzy output set is defuzzified to obtain the final output result. The link signal-to-interference-plus-noise ratio (SINR) and the maximum bit error rate (BER) that the transmitted service can tolerate are selected as two important parameters as performance indicators for MCS selection. The output is the most suitable MCS for the link under the current conditions.

[0059] (1) Fuzzification: Fuzzification is the process of converting uncertain concepts or values ​​into fuzzy sets, which can map a specific actual value to one or more fuzzy sets. These fuzzy sets are defined by a set of membership functions. Each membership function describes the rule that the membership degree of the fuzzy set changes with the input value. The value of the membership function can be used to describe the membership degree of a value x to the fuzzy set. The closer the value of the membership function is to 1, the higher the degree to which x belongs to the fuzzy set; conversely, if the value of the membership function is closer to 0, the lower the degree to which x belongs to the fuzzy set. The curve shape of the membership function will affect the performance of the fuzzy logic controller. Here, the trapezoidal function is selected as the membership function, and the expression is as follows:

[0060] Define the two input variables and the fuzzy set of the output as... , representing low, medium, and high values ​​for the input and output values, respectively. The membership function graphs for the input and output are shown below. Figure 5 As shown.

[0061] (2) Fuzzy Rules: The fuzzy rule base is created based on domain standards and some experience. The fuzzy rule base created in this paper is shown in the table below, consisting of 9 rules, as shown in Table 1. For the StarFlash access method, there are 32 available MCSs in its MCS index table, with indices ranging from 0 to 31. During fuzzification, indices 0-10 are defined as Low, 11-20 as Medium, and 21-31 as High. For example, when both SINR and BER are High, the output MCS selection result is also High, which means that the MCS traversal should start from the highest interval.

[0062] Table 1

[0063] (3) Fuzzy Inference Engine and Defuzzification: This chapter uses the Mamdani fuzzy inference engine. The Mamdani method is a classic fuzzy logic inference method, which is based on the membership function of fuzzy sets and fuzzy rules for inference. The fuzzy implication relation of the Mamdani method adopts the minimum operation rule. In this invention, after determining the fuzzy values ​​of SINR and BER, the conventional IF-THEN rule is used to infer the result and output the fuzzy value of MCS. The defuzzification method uses the maximum membership method, that is, in the fuzzy set of the inference result, the element with the largest membership degree is taken and output.

[0064] After obtaining the final MCS output, the selection of the MCS can still be obtained by traversal. However, since there are already preceding labels, the range to be traversed is greatly reduced, and the complexity of traversal is also greatly reduced.

[0065] like Figure 6 As shown in the experimental results, the proposed star-flash-based resource collaborative allocation and optimization algorithm for wireless communication systems exhibits superior performance compared to the two traditional algorithms. This advantage stems from the algorithm's comprehensive consideration of various network factors during its design, enabling it to flexibly adapt to different network environments. Furthermore, as the number of links increases, leading to intensified resource contention and increased network interference, this algorithm demonstrates stronger adaptability than traditional resource allocation algorithms. Therefore, the proposed algorithm can maintain system speed and excellent performance even in dynamic and uncertain network environments.

[0066] The proposed star-flash-based resource collaborative allocation and optimization algorithm for wireless communication systems is a joint optimization method for multi-AP wireless communication systems. It aims to solve resource allocation problems in multi-user, multi-service scenarios by collaboratively optimizing channel allocation and MCS selection. This algorithm addresses the requirements of low latency, high reliability, and high throughput under conditions of high-density interference and limited resources. It integrates greedy algorithms, matching theory, and fuzzy logic techniques to achieve optimal system performance.

[0067] In the initial stage, a greedy algorithm is used to allocate channels for the AP. By traversing available channels and based on the signal-to-interference ratio (SINR) threshold, the channel allocation scheme that minimizes co-channel interference is iteratively selected, laying the foundation for subsequent access matching and resource scheduling. Then, the delayed reception algorithm in matching theory is used to complete the access control between the AP and UE. The specific process is as follows: the UE and AP construct a preference list based on signal quality, interference level, and service priority. The UE initiates a matching request to the AP, and the AP accepts or rejects the request based on its own capacity and preferences, ultimately forming a stable AP-UE pairing. For each AP-UE link, this invention uses an adaptive MCS selection method based on fuzzy logic. By analyzing the link's signal-to-interference-plus-noise ratio (SINR) and the service's bit error rate (BER) requirements, a fuzzy logic controller dynamically selects the optimal MCS, achieving a balance between transmission rate and reliability while maintaining low computational complexity.

[0068] The algorithm's final output is an efficient resource allocation strategy, including an optimized AP channel allocation scheme, AP-UE intervention strategy, and link MCS selection. This strategy can dynamically adjust resource allocation based on real-time network conditions, significantly improving system throughput, reducing interference, and meeting the QoS requirements of various services. Through phased optimization and intelligent decision-making, the algorithm gradually approaches the optimal or near-optimal resource management scheme, providing an adaptive and efficient solution for high-density dynamic wireless communication environments.

[0069] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0070] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0071] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the mobile source emission prediction methods based on time-series feature migration described in the above embodiments.

[0072] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0073] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0074] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0075] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0076] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for collaborative resource allocation and optimization in a wireless communication system based on star flash, characterized in that, Includes the following steps, S0. Construct a scenario for collaborative resource allocation in a multi-access-point wireless communication system; S1. Establish the AP channel allocation model in the wireless communication system; S2. Establish the AP-UE access control model in the wireless communication system; S3. Establish the MCS selection model for the AP-UE link in the wireless communication system; S4. Based on the AP channel allocation model, AP-UE access control model, and AP-UE link MCS selection model, an optimization problem is constructed. The goal is to maximize the AP upload rate of the system by jointly optimizing the AP channel allocation strategy, AP-UE access control strategy, and MCS selection strategy, while satisfying the system bit error rate and signal-to-interference-plus-noise ratio constraints. S5. Decompose the optimization problem into two sub-problems and perform iterative optimization. S51. Fixed AP-UE access control policy and MCS selection policy, using a greedy algorithm to optimize AP channel allocation strategy based on minimum co-channel interference. S52. Based on the output of S51, optimize the AP-UE access control strategy. Based on matching theory, transform the AP-UE access control problem into an AP-UE pair matching problem. Establish the AP-UE PL based on the AP's utility function, and establish the UE-AP PL based on the UE's utility function. Then, use the delayed reception algorithm to complete the AP-UE pair matching. S53. Based on the output of S52, and using the concept of fuzzy logic, design a fuzzy controller to achieve adaptive selection of the MCS.

2. The resource collaborative allocation and optimization method for a wireless communication system based on star flash as described in claim 1, characterized in that: Step S0 includes, Consider a network model where multiple access points (APs) connect to a large number of different types of UEs, and the access technology used is StarFlash access technology. The nodes of the StarSpark system are divided into two categories: management nodes, namely G nodes, and terminal nodes, namely T nodes. G nodes are the nodes that send data scheduling information at the access layer of the StarSpark wireless communication system, and T nodes are the nodes that receive data scheduling information at the access layer of the StarSpark wireless communication system. Among them, AP is the G node of StarShine, and UE is the T node of StarShine; Each access point (AP) operates on its available channel resources to support the connection and communication of user equipment (UE). One AP can access multiple UEs, forming multiple AP-UE links. Suppose there is a network One AP, One UE, Describes the set of APs. This represents the set of UEs, and the available channels of the AP are used. To indicate, ; After the UE successfully connects to the AP, use to indicate and The available bandwidth for each access point (AP) in the link between them is set to the same value. To represent; for each link , for Assigned to The transmission bandwidth; the StarScan system supports 32 modulation and coding schemes, denoted as... ; It is a binary variable, where express choose The policy situation; if the link Choose Then binary variables ,otherwise ; By configuring the AP channel allocation scheme, AP-UE access control, and link... Adaptive MCS adjustment to achieve optimal system performance; The system's performance is measured by the rate at which data is uploaded by the access points (APs) in the system. The ambient noise power W is expressed by the formula for additive white Gaussian noise density. Calculated; use Indicates link Bit error rate; Indicates link The transmission rate is expressed as: in, The raised cosine roll-off coefficient of the system. The modulation coefficient, For bit rate, modulation coefficient and bitrate All by Decide, express The transmission rate of each AP is the sum of the rates of all its associated links, specifically expressed as: Where K represents the relationship with K associated links.

3. The resource collaborative allocation and optimization method for a wireless communication system based on star flash as described in claim 2, characterized in that: Step S1 includes, Using binary variables To indicate the channel selection status of the AP, if Channel selected ,but ,on the contrary, Within the same time slot, each AP can only select one channel. Using binary variables To represent the situation where different APs multiplex channels; if and Channel reused Then there is ,otherwise, ; In the logarithmic path loss model, the signal-to-interference ratio (SIR) is used to represent it. Subject to other APs that reuse the same channel The interference situation; the formula for calculating the SIR of co-channel interference is as follows: in, Indicate target The transmission power, This refers to path loss; the formula for calculating path loss is as follows: in, Indicates reference distance The path loss at the location is given by n, which is the path loss exponent and set to 23 indoors. The shadow fading is a normal distribution.

4. The resource collaborative allocation and optimization method for a wireless communication system based on star flash as described in claim 3, characterized in that: Step S2 includes, Before accessing an AP, the UE will scan to obtain information such as the channel number, transmit power, and identification ID of multiple APs, and then select the optimal AP for access. Access selection via binary variables Indicates: If Choose Then there is ,otherwise ;when At that time, it is considered to have formed. This one link; Once an AP-UE link is established, the AP associated with that link allocates bandwidth to it. Assuming each link Bandwidth used The same applies; simultaneously, there are numerous AP-UE links in the system, and these links interfere with each other; to measure the degree of interference between different links, the signal-to-interference-plus-noise ratio (SINR) of the links is introduced as an evaluation metric; The signal-to-interference-plus-noise ratio is expressed as: in, express The transmission power, Indicates no connection Other Transmit power; for the link All other APs using the same channel The transmission power of any device will interfere with it. also, Indicates link The link gain is calculated using a logarithmic distance path loss model.

5. The resource collaborative allocation and optimization method for a wireless communication system based on star flash as described in claim 4, characterized in that: Step S3 includes, For link The bit error rate is calculated using the following formula: in, For the Q function, approximately: , and It depends on The constant; and Different formulas are selected to calculate based on different MCS values. .

6. The resource collaborative allocation and optimization method for a wireless communication system based on star flash as described in claim 5, characterized in that: Step S4 includes the optimization problem formulated based on the AP channel allocation model, AP-UE access control model, and AP-UE link MCS selection model. Among these constraints, constraint 1 (C1) ensures that each AP can only select one channel for transmission within a time slot, but allows multiple APs to reuse the same channel simultaneously; constraint 2 (C2) limits co-channel interference between APs reusing channels, ensuring that the interference level between APs reusing the same channel is not too high to guarantee network communication quality; constraint 3 (C3) ensures that each UE can only select one AP for access, preventing UEs from connecting to multiple APs simultaneously; constraint 4 (C4) ensures the quality of the AP-UE link, requiring the UE to switch to another AP to reconnect when the link condition is poor; constraint 5 (C5) restricts AP bandwidth allocation, meaning the total bandwidth allocated by an AP to all its AP-UE links cannot exceed its available bandwidth; and constraint 6 (C6) imposes constraints on the bit error rate (BER) based on the performance requirements of specific services. Indicates link Transmitting specific services Bit error rate at time This indicates the maximum bit error rate that the service can tolerate.

7. The resource collaborative allocation and optimization method for a wireless communication system based on star flash as described in claim 6, characterized in that: Step S51 specifically includes, The greedy algorithm is used to solve the channel allocation problem of the AP. The process is as follows: When allocating channels to each AP, all available channels are traversed. If a channel is not occupied, it is allocated to that AP. Then, channel allocation is performed for the next AP. If the channel is occupied, the target AP is allowed to occupy the channel first. Then, the SIR between the target AP and all APs occupying the channel is calculated. If it is less than the threshold, the target AP can also occupy the channel. Otherwise, other channels are selected. After all channels have been allocated to all APs, the algorithm ends.

8. The resource collaborative allocation and optimization method for a wireless communication system based on star flash as described in claim 7, characterized in that: Step S52 specifically includes, set All objects are divided into two categories: APs affected by co-channel interference and APs not affected by co-channel interference. Different quotas are assigned to each category. A delayed reception algorithm is used to solve the entire problem. Details are as follows: First, establish the preference relationship between the AP and UE; assuming... Describes the set of APs. Represents the set of UEs; and They are respectively All objects and The set of preference relationships between all objects in the set and all objects in the other set; In consideration right When determining preference relationships, use a utility function. To conduct quantitative analysis, thereby establishing right set of preference relations ; This utility function considers the following factors: 1) UE interference level to AP: This is determined by the UE's SNR. The higher the UE's SNR, the better the link condition when the UE accesses the AP, and the higher the AP's preference for that UE. 2) Business Priority: Different businesses have different priorities, and different priority scores are designed for different businesses here. This fraction will be a unitless integer; Utility function Calculated by the following formula in express Normalized SNR, express Business score and These represent the weight coefficients of the two data points; the utility function of the AP for each UE is calculated, and all UEs are ranked to form a set of AP's preference relationships with UEs. ; In consideration right When determining preference relationships, use a utility function. To conduct quantitative analysis, thereby establishing right set of preference relations The factors considered in this utility function are: 1) The magnitude of co-channel interference experienced by APs: When allocating channels for APs, due to the limited number of channels, different APs may use the same channel; APs occupying the same channel will be subject to co-channel interference (SIR). The greater the co-channel interference, the worse the performance of the AP. Therefore, UEs will prefer APs with less co-channel interference for pairing. 2) Business adaptability: Different businesses have different requirements for AP. Different weights are defined according to the business type to reflect the business adaptability. It is an adaptation score calculated based on the UE's service type, indicating the UE's suitability in... The adaptability of the transmission service z; This function is built around the UE's requirements for communication quality and service adaptability; different service types will have different adaptability scores in this function to reflect different requirements for the AP; 3) Available bandwidth: The larger the available bandwidth of the AP, the better it can meet the needs of the UE, and the higher the preference. Utility function Calculated by the following formula in express Co-channel interference, express Business score and These represent the weight coefficients of the two data points; the utility function of the AP for each UE is calculated, and all UEs are ranked to form a set of AP's preference relationships with UEs. ; Once the PLs of both the AP and UE are established, the AP-UE pair matching is completed according to the delayed reception algorithm.

9. The resource collaborative allocation and optimization method for a wireless communication system based on star flash as described in claim 8, characterized in that: Step S53 specifically includes, After completing the access control of the AP-UE, by assigning each AP-UE link Allocate the optimal MCS to maximize the upload speed and data rate of the system APs; All links The MCS selection problem will be solved by a fuzzy logic controller, which consists of fuzzification, fuzzy inference and defuzzification. In the fuzzification stage, a predefined membership function is used as the input for fuzzification processing. Then, the fuzzy set is input into the fuzzy rule base for fuzzy inference to generate a fuzzy output set. Finally, the fuzzy output set is defuzzified to obtain the final output result. The link signal-to-interference-plus-noise ratio (SINR) and the maximum bit error rate (BER) that the transmitted service can tolerate are selected as the two important parameters for MCS selection.

10. The resource collaborative allocation and optimization method for a wireless communication system based on star flash as described in claim 9, characterized in that: Step S53 also includes, (1) Fuzzification: Fuzzification is the process of converting uncertain concepts or values ​​into fuzzy sets. It can map a specific actual value to one or more fuzzy sets. These fuzzy sets are defined by a set of membership functions. Each membership function describes the rule that the membership degree of the fuzzy set changes with the input value. The value of the membership function can be used to describe the membership degree of a value x to the fuzzy set. The closer the value of the membership function is to 1, the higher the degree to which x belongs to the fuzzy set. Conversely, if the value of the membership function is closer to 0, the lower the degree to which x belongs to the fuzzy set. The curve shape of the membership function will affect the performance of the fuzzy logic controller. (2) Fuzzy rules: The fuzzy rule library created is shown in Table 1. For the Star Flash access method, there are 32 available MCS in its MCS index table, with indices from 0 to 31. During fuzzification, indices 0-10 are defined as Low, 11-20 as Medium, and 21-31 as High. The traversal of MCS should start from the highest interval. Table 1 (3) Fuzzy inference engine and defuzzification: The Mamdani fuzzy inference engine is adopted. The fuzzy implication relation of the Mamdani method adopts the minimum operation rule. After determining the fuzzy values ​​of SINR and BER, the conventional IF-THEN rule is used to infer the result and output the fuzzy value of MCS. The defuzzification method uses the maximum membership method, that is, in the fuzzy set of the inference result, the element with the largest membership degree is taken and output.

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