Wireless communication system resource collaborative allocation and optimization method based on star flash

Abstract

The invention discloses a wireless communication system resource collaborative allocation and optimization method based on star flash, and relates to wireless resource collaborative allocation in a multi-user and multi-service scene. According to the method, a connection relationship between an access point and user equipment, channel resource allocation and modulation coding mode selection are considered at the same time, and resource scheduling modeling is carried out by fusing service quality constraints of service types, and the method comprises the following steps of: carrying out iterative optimization on a channel allocation strategy of the AP by adopting a greedy algorithm; on the basis of related knowledge of a matching theory, an access control problem between the AP and the UE is modeled into a problem for searching an optimal AP-UE pair, and then the problem is solved through a delay receiving algorithm, so that the SINR of a link in a network is effectively reduced, and the throughput of the network is improved; mCS adaptive selection is realized based on fuzzy logic, and on the premise of ensuring optimal MCS selection of a link, the calculation complexity is effectively reduced and the system performance is improved.

Description

Background Art

[0001] With the rapid development of wireless communication technology, multi-AP, multi-UE architectures have become a key solution for improving network capacity and service quality. In real-world 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 becoming highly heterogeneous. Currently, the development of the Internet of Things and the Industrial Internet is increasingly dependent on the deep integration of technologies such as cloud computing and artificial intelligence. This is building new infrastructure, manufacturing systems, and service models, and spawning a diverse range of new applications and consumer scenarios. This places higher performance requirements and challenges on wireless short-range communication technologies.

[0002] However, existing short-range wireless communication technologies still struggle with latency control in the tens of milliseconds, making it difficult to guarantee high-speed transmission while also meeting the deterministic quality of service requirements, especially in densely deployed multi-node environments. NearLink, an emerging short-range wireless communication technology, offers advantages such as ultra-low latency, high reliability, high speed, strong anti-interference capabilities, high security, and high-precision synchronization. It has the potential to support efficient data exchange in scenarios such as smart cars, smart terminals, smart homes, and smart manufacturing.

[0003] Given StarSpark's superior performance, its application in multi-AP, multi-UE architectures demonstrates strong potential. Applying StarSpark to this multi-AP, multi-UE architecture enables flexible access control and resource allocation, addressing the high demands and challenges posed by the Internet of Things and Industrial Internet, and propelling wireless communication technology into a new stage of development. However, this integration also faces numerous challenges. In a multi-AP system, the access relationship between APs and UEs is complex, constrained by factors such as channel conditions, spectrum resource allocation, and mutual interference. The coupled processes of channel allocation, AP-UE matching, and modulation and coding scheme selection critically impact overall system performance. In complex environments with strong interference and multiple concurrent services, achieving improved system capacity while meeting the QoS requirements of different service types has become a core issue that must be addressed in current wireless communication systems.

[0004] To address these challenges, resource allocation algorithms are often employed. However, existing algorithms for resource allocation mostly employ heuristics, graph theory methods, or deep reinforcement learning strategies. While these methods have improved system performance to some extent, they still face challenges such as high computational complexity, slow convergence, and difficulty meeting real-time requirements. Furthermore, most research focuses on resource optimization within existing network architectures, with limited consideration of the impact of network initialization on resource allocation efficiency and ultimate performance. To address this shortcoming, incorporating resource scheduling strategies during the network initialization phase to achieve coordinated optimization of networking and resource allocation would more effectively improve overall network performance and better guarantee quality of service (QoS). Pre-optimization during the network construction phase can effectively address the complex resource allocation issues 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 adaptable to the QoS requirements of multiple services has important theoretical value and practical significance for improving the performance and adaptability of next-generation wireless communication systems. Summary of the Invention

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

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

[0008] In another aspect, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0009] On the other hand, the present invention further discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0010] In summary, the present invention discloses a method for coordinated resource allocation and optimization in a wireless communication system based on Star Flash, specifically for coordinated wireless resource allocation in multi-user, multi-service scenarios. This method simultaneously considers the connection relationship between access points and user equipment (APs), channel resource allocation, and modulation and coding scheme (MCS) selection, integrating service type quality of service constraints to model resource scheduling. The method includes the following steps: iteratively optimizing the AP (Access Point) channel allocation strategy using a greedy algorithm; modeling the access control problem between the AP and the UE as finding an optimal AP-UE pair based on matching theory, which is then solved using a delayed reception algorithm. This effectively reduces the link SINR in the network and improves network throughput; and, based on the results of the previous two steps, adaptive MCS selection is implemented using fuzzy logic. While ensuring optimal link MCS selection, this method effectively reduces computational complexity and improves system performance. The proposed method effectively solves the problem of coordinated wireless resource allocation in multi-user, multi-service scenarios. Under the conditions of multi-service concurrency, high interference density, and limited resources in wireless access systems, it implements AP channel allocation, AP-UE access control, and efficient link MCS selection, effectively improving network throughput. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0012] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0013] The present invention proposes a method for collaborative resource allocation and optimization of a wireless communication system based on star flash, including a scenario description module, a model building module and a model solving module, specifically including the following steps: S1. Establishing an AP channel allocation model in a wireless communication system; S2. Establishing an AP-UE access control model in a wireless communication system S3. Establish an MCS selection model for an AP-UE link in a wireless communication system; S4. An optimization problem is constructed based on the AP channel allocation model, the AP-UE access control model, and the MCS selection model for the AP-UE link. The goal is to maximize the system AP upload rate and throughput by jointly optimizing the AP channel allocation strategy, the AP-UE access control strategy, and the MCS selection strategy, while satisfying the system bit error rate and signal-to-interference-and-noise ratio constraints. S5. Decompose the optimization problem into two sub-problems for iterative optimization S51, fixed AP-UE access control strategy and MCS selection strategy, using 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's PL to the UE based on the AP's utility function, and establish the UE's PL to the AP 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 the concept of fuzzy logic, a fuzzy controller is designed to complete the adaptive selection of the MCS. At this point, the entire algorithm process ends and the problem is solved.

[0014] The following are specific instructions: Scene Description Module, and construct scenarios for resource coordination in multi-access point wireless communication systems. Figure 1 As shown, the present invention considers a network model in which multiple APs access a large number of different types of UEs, and the access technology used is Star Flash access technology. The nodes of the Star Flash system are divided into two categories: management nodes (Grant Node, G node) and terminal nodes (Terminal Node, T node). The G node is the node that the access layer of the Star Flash wireless communication system sends data scheduling information, and the T node is the node that the access layer of the Star Flash wireless communication system receives data scheduling information. Due to the characteristics of Star Flash technology, the AP described in the present invention is the G node of Star Flash, and the UE is the T node of Star Flash. Each access point (AP) operates on its available channel resources to support the connection and communication of user equipment (UE). One AP accesses multiple UEs to form multiple AP-UE links. Assume that there are APs, UEs, Represents a set of APs, Represents the set of UEs. The available channels of the AP are To express, When the AP is matched with the UE, To express and The available bandwidth of each access point (AP) is set to the same value. For each link , for Assigned to The Star Flash system supports 32 modulation and coding schemes, denoted as , can be achieved through Figure 2 To query. is a binary variable, where express choose If the link Selected , then the binary variable ,otherwise .

[0015] In the method designed by the present invention, through reasonable AP channel allocation scheme, AP-UE access control and link Adaptive MCS adjustment is designed to achieve optimal system performance. The system performance is measured by the sum of the AP upload data rates. is the additive white Gaussian noise density, and the ambient noise power W is calculated by the formula In actual systems, data transmission inevitably has bit errors. Indicates a link bit error rate. Indicates a link The transmission rate can be expressed as:

[0016] in, It is the raised cosine roll-off coefficient of the system, which is generally set to 0.2~0.25 in broadband wireless access systems. is the modulation coefficient, is the code rate, modulation order and bitrate All by This 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] Among them, K represents K associated links.

[0018] Model building module It mainly builds models for the AP channel allocation problems, AP-UE access control problems and MCS adaptive selection problems that arise in the entire process from network establishment to operation, and models them into the problem of maximizing the system transmission rate based on channel resource allocation, different services, bandwidth resources and MCS selection.

[0019] In the network initialization phase, the AP channel allocation model first needs to allocate available channels to all APs so that UEs can select appropriate APs to access and thus form AP-UE links. To indicate the channel selection of the AP. Selected channel ,but ,on the contrary, In 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 is inevitable that multiple APs will reuse the same channel, causing co-channel interference. To indicate the situation where different APs reuse channels. and Multiplexed channels , then ,otherwise, .

[0020] In the logarithmic path loss model, the signal-to-interference ratio (SIR) is used to express Affected by other APs that reuse the same channel ( The calculation formula for co-channel interference SIR is as follows:

[0021] in, Indicates the target The transmission power, is the path loss. The calculation formula of path loss is as follows:

[0022] in, Indicates reference distance The path loss at the location where n is the path loss exponent, which is usually set to 23 indoors. is the shadow fading of the normal distribution.

[0023] AP-UE access control model Before accessing an AP, the UE will scan and obtain information such as the channel number, transmit power, and ID of multiple APs, and then select the best AP for access. Access selection is achieved through binary variables. Indicates: If Selected , then ,otherwise .when When This one link.

[0024] After the AP-UE link is formed, the AP associated with the link will allocate bandwidth to it. To simplify the problem, assume that each link Bandwidth used At the same time, there are a large number of AP-UE links in the system, and these links will inevitably interfere with each other. In order to measure the degree of interference between different links, the link signal-to-interference-plus-noise ratio (SINR) is introduced as an evaluation indicator. Link The signal to interference and noise ratio can be expressed as:

[0025] in, express The transmission power, Indicates no access Other The transmission power of the link , all other APs using the same channel (such as ) will interfere with it. In addition, Indicates a link The link gain is calculated using the logarithmic distance path loss model.

[0026] In the present invention, the following three typical service types in the network are mainly considered: sensor service, video service and voice service, respectively. According to the needs of different services, a reasonable AP-UE matching strategy is designed through optimization model, taking into account transmission efficiency and reliability, achieving optimal resource allocation and effective interference control.

[0027] The MCS adaptive selection model (MCS) plays a key role in balancing data transmission rate and reliability under varying network conditions. The link MCS value directly impacts system performance. Selecting the appropriate MCS based on channel conditions, signal strength, interference, and network load is essential for optimizing system performance. If hardware conditions permit, the highest possible modulation order can be selected to achieve higher transmission rates. When signal quality is good, a higher bit rate encoding method can be selected to reduce redundancy and increase data throughput. When signal quality is average, a lower bit rate encoding method can be selected to increase redundancy and ensure transmission reliability.

[0028] Different MCSs exhibit significant differences in coding rates, which directly impact the system's bit error rate (BER). Too little redundancy can increase the BER, impacting transmission reliability; too much redundancy can reduce the system's transmission rate. Therefore, it's crucial to find a balance between transmission rate and BER, ensuring the system's BER remains within acceptable limits while maximizing data transmission efficiency. For Link The bit error rate can be calculated by the following formula:

[0029] in, is the Q function, which is approximately , and It depends on A constant. And , choose different formulas to calculate according to different MCS values .

[0030] In summary, according to the three models proposed above, the entire optimization problem can be expressed as

[0031] Among them, constraint 1 ensures that each AP can only select one channel for transmission in a time slot, but allows multiple APs to reuse the same channel at the same time; constraint 2 is used to limit the co-channel interference between APs that reuse the channel. It is necessary to ensure that the interference level between APs that reuse the same channel is not too large to ensure the communication quality of the network; constraint 3 ensures that each UE can only select one AP for access to avoid UE connecting to multiple APs at the same time; constraint 4 ensures the quality of the AP-UE link. When the link condition is too poor, the UE must switch to other APs to re-access; constraint 5 limits the bandwidth allocation of the AP, that is, the total bandwidth allocated by the AP to all its AP-UE links must not exceed its available bandwidth; constraint 6 imposes constraints on the bit error rate (BER) based on the performance requirements of specific services. Among them, Indicates a link Transmit specific business The bit error rate when Indicates the maximum bit error rate that the service can tolerate. The bit error rate limit requires that the selected MCS have sufficient redundancy to ensure system transmission reliability.

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

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

[0034] The problem of equal integer decision variables makes At least it is an NP-hard problem, plus the constraints Nonlinear constraints such as It becomes a mixed integer nonlinear programming problem (MINLP). The solution space of MINLP involves the optimization of discrete and continuous variables, and nonlinear relationships may introduce multiple local optimal solutions, so the solution process not only needs to consider integer decision variables, but also needs to deal with complex nonlinear constraints. For general MINLP problems, it is impossible to find an exact solution in polynomial time, so the problem It is classified as an NP-hard problem.

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

[0036] Therefore, to address this challenge, the present invention proposes a method for collaborative resource allocation and optimization of wireless communication systems based on Star Flash. This method is a comprehensive solution that covers the following three key algorithms: To address the problem of excessive co-channel interference when the AP is working, a greedy algorithm-based AP channel allocation method is proposed; secondly, to address the access control problem between the AP and the UE, an AP-UE access control algorithm based on matching theory is proposed, which models the access control problem as an AP-UE matching problem and uses a delayed reception algorithm to find the optimal AP-UE pair in the network, thereby achieving the optimal access strategy; finally, to address the shortcomings of the traditional adaptive MCS selection algorithm in computational efficiency and accuracy, a fuzzy logic-based adaptive MCS selection algorithm is proposed to improve the adaptive performance of the system. Next, the solution to the entire problem and the algorithm for solving each sub-problem will be introduced in turn.

[0037] AP channel allocation based on greedy algorithm

[0038] The problem of AP channel allocation is considered, which is only related to the constraint and In the process of problem solving, the SIR of the same-channel interference suffered by each AP should be calculated, so that the same-channel interference matrix can be established based on the SIR.

[0039] Use the greedy algorithm to solve the AP channel allocation problem. The process is as follows: When assigning a channel to each AP, all available channels are traversed. If a channel is not occupied, it is assigned to that AP, and then the channel is assigned to the next AP. If the channel is occupied, the target AP is allowed to occupy the channel first. The SIR of 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, another channel is selected. After all APs have been assigned channels, the algorithm ends. The entire algorithm operation process is as follows:

[0040] When the algorithm completes, all APs have been assigned channels, and the SIR-based interference matrix has been initialized. In the initial stages of a network, utilizing existing spectrum resources to assign appropriate channels to all APs is not redundant; it is a crucial step in the overall planning for subsequent work. Without separate AP channel allocation steps, directly performing AP-UE matching lacks a comprehensive plan for the overall network channel resources. This can lead to overcrowding in some channels and underutilization in others, hindering the balanced use of system resources.

[0041] AP-UE access control based on matching theory

[0042] question Involving AP-UE access control, link The bandwidth allocation and MCS selection is still a MINLP with multiple optimization variables. AP-UE access control is the most important task. Only when an excellent AP-UE access strategy is obtained, the subsequent links Only through accurate bandwidth allocation and MCS selection can we achieve maximum system transmission and data rate on a sound basis. In this section, based on the relevant knowledge of matching theory, the present invention models the AP-UE access control problem as an AP-UE pair matching problem. The delayed reception algorithm in matching theory is used to find the optimal AP-UE pair, thereby achieving optimal AP-UE access control.

[0043] The matching of AP-UE pairs can be regarded as the classic many-to-one matching problem in matching theory, that is, one AP needs to match multiple UEs. Due to the limited access capacity of the AP, the number of UEs that can be matched is limited, so the AP quota is set. In order to simplify the problem, the set All objects in the system are divided into two categories: APs that are affected by co-channel interference and APs that are not affected by co-channel interference. Different quotas are set for each of them. The delayed acceptance algorithm is used to solve the entire problem. The overall process is as follows Figure 3 shown.

[0044] First, a set of APs and UEs is constructed, and then the preference lists (PLs) for each AP and UE are initialized. APs can set preferences for UEs based on their interference level and service priority; UEs can set preferences for APs based on their signal strength, available bandwidth, and service suitability. UEs initiate matching proposals to APs based on the order in their PLs. Based on the order and quota in their PLs, the APs accept matching requests from some UEs and reject others. Finally, a check is performed to ensure that all APs and UEs have been successfully matched. If any UEs remain unmatched, the above steps are repeated until a stable AP-UE matching solution is output.

[0045] First, the preference relationship between AP and UE must be established. Represents a set of APs, Represents a set of UEs. and They are All objects and The set of preference relations of all objects in the set to all objects in the other set.

[0046] Considering right When the preference relationship is To quantitatively analyze and establish right Preference relation set The factors considered in this utility function are: 1) UE interference level to the AP: This is determined by the UE's signal-to-noise ratio (SNR). Generally speaking, the higher the UE's SNR, the better the link quality when the UE accesses the AP, and the higher the AP's preference for the UE. 2) Service priority: Different services have different priorities. Among the three services considered in this invention, video service has the highest priority and sensor service has the lowest priority. The reason for this setting is that video service can transmit a large amount of data at one time. Giving priority to video service is beneficial to increasing the network speed. Different priority scores are designed for different services. , the fraction will be a whole number without units.

[0047] Utility function Calculated by the following formula

[0048] in express The normalized SNR of express Business score, and Represents the weight coefficient of the two data respectively. Calculate the value of the utility function of AP for each UE and sort all UEs to form the preference relationship set of AP for UE .

[0049] Considering right When the preference relationship is To quantitatively analyze and establish right Preference relation set The factors considered in this utility function are: 1) The extent of co-channel interference (SIR) to the AP: When assigning AP channels, due to the limited number of channels, different APs may use the same channel. APs occupying the same channel are subject to SIR (Simultaneous Interference). The greater the SIR, the worse the AP's performance. Therefore, UEs prefer pairing with APs with minimal SIR.

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

[0051] Utility function Calculated by the following formula

[0052] in express The same-frequency interference express Business score, and Represents the weight coefficient of the two data respectively. Calculate the value of the utility function of AP for each UE and sort all UEs to form the preference relationship set of AP for UE .

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

[0054] When the algorithm process is completed, a stable network AP-UE matching solution can be obtained. Allocate the optimal MCS to maximize system AP upload and speed.

[0055] Adaptive MCS selection based on fuzzy logic MCS selection is also a problem After completing the access control of AP-UE, each AP-UE link Allocate the optimal MCS to maximize system AP upload and speed.

[0056] In wireless communication systems, extensive research has been conducted on MCS selection algorithms, but some traditional MCS selection algorithms currently in use have limitations. For example, the Outer Loop Link Adaptation (OLLA) algorithm optimizes MCS selection by adjusting the target block error rate (BLER). The Adaptive Rate Algorithm (ADA) improves link reliability, but the adjustment process is slow and cannot adapt to rapidly changing channels. Furthermore, it relies on feedback and has high latency. Exhaustive search is also a commonly used algorithm to solve the MCS selection problem. The MCS table has a limited number of options. While meeting the minimum link bit error rate, traversing the table from highest to lowest can always select the optimal MCS for each link. However, this traversal is time-consuming, requiring a significant amount of time even in small networks. For larger networks, it becomes nearly impossible to find the optimal MCS within a limited timeframe.

[0057] An adaptive MCS selection algorithm based on fuzzy logic overcomes the shortcomings of these traditional algorithms. It adapts to channel variations and does not rely on real-time information feedback. Instead, it uses a pre-defined expert knowledge base to determine the quality of current parameters. Compared to traditional MCS selection algorithms, it offers faster response, higher accuracy, and greater robustness, making it suitable for complex and changing wireless environments.

[0058] All links in the model of the present invention The MCS selection problem will be solved by a fuzzy logic controller, which consists of fuzzification, fuzzy reasoning and defuzzification. The workflow of the entire controller is as follows: Figure 4As shown in the figure, during the fuzzification phase, a predefined membership function is used to fuzzify the input. The fuzzy set is then input into a fuzzy rule base for fuzzy inference, generating a fuzzy output set. Finally, the fuzzy output set is defuzzified to obtain the final output. Two key parameters, the link signal-to-interference-and-noise ratio (SINR) and the maximum bit error rate (BER) that the transmitted service can tolerate, are selected as performance indicators for MCS selection. The output is the most appropriate 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 realize the mapping of 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 law of how the membership of the fuzzy set changes with the input value. The value of the membership function can be used to describe the membership 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 shape of the membership function curve 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] The two input variables and the output fuzzy set are defined as , representing low, medium and high input and output values respectively. The membership function diagram of input and output is as follows Figure 5 shown.

[0061] (2) Fuzzy rules: The fuzzy rule base is created based on the standards and experience in the field. The fuzzy rule base created in this paper is shown in the following table. It consists of 9 rules, as shown in Table 1. For the access method of Star Flash, there are 32 available MCSs in its MCS index table, with indexes from 0 to 31. When fuzzifying, indexes 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 that performs inference based on the membership function of fuzzy sets and fuzzy rules. The fuzzy implication relationship of the Mamdani method uses 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 results 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 is output.

[0064] After obtaining the final MCS output result, the MCS selection can still be obtained through traversal. However, because there are already pre-placed labels, the scope of traversal is greatly reduced, and the complexity of traversal is also greatly reduced.

[0065] like Figure 6 As shown in the figure, experimental results demonstrate that the proposed StarFlash-based wireless communication system resource collaborative allocation and optimization algorithm exhibits superior performance compared to two traditional algorithms. This advantage stems from the fact that the algorithm comprehensively considers multiple network factors during its design, enabling it to flexibly adapt to diverse network environments. Furthermore, as the number of links increases, leading to intensified resource competition and increased network interference, this algorithm demonstrates greater adaptability than traditional resource allocation algorithms. This demonstrates that the proposed algorithm can maintain system summation rate and excellent performance even in dynamic and uncertain network environments.

[0066] The proposed Star Flash-based collaborative resource 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, designed to meet the requirements of low latency, high reliability, and high throughput under conditions of high-density interference and limited resources, 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 to the AP. By traversing available channels and based on a signal-to-interference ratio threshold, a channel allocation scheme that minimizes co-channel interference is iteratively selected, laying the foundation for subsequent access matching and resource scheduling. The delayed reception algorithm from matching theory is then used to complete AP and UE access control. 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 it based on its own capacity and preferences, ultimately forming a stable AP-UE pairing. For each AP-UE link, the present 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 is used to dynamically select the optimal MCS, achieving a balance between transmission rate and reliability while maintaining low computational complexity.

[0068] The algorithm ultimately outputs an efficient resource allocation strategy, including optimized AP channel allocation, AP-UE intervention strategy, and link MCS selection. This strategy dynamically adjusts resource allocation based on real-time network status, 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 optimal or near-optimal resource management solutions, providing an adaptive and efficient solution for high-density dynamic wireless communication environments.

[0069] In another aspect, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0070] On the other hand, the present invention further discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0071] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute any of the mobile source emission prediction methods based on time series feature migration in the above embodiments.

[0072] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts of the above methods.

[0073] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of 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 the present 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 a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0074] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0075] Each embodiment in this specification is described in a related manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so the description is relatively simple. For related parts, refer to the description of the method embodiment.

[0076] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

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

2. The method for collaborative resource allocation and optimization of a wireless communication system based on star flash according to claim 1, characterized in that: Step S0 includes, Consider a network model where multiple APs access a large number of different types of UEs, using the Star Flash access technology. The nodes of the Star Flash system are divided into two categories: management nodes or G nodes and terminal nodes or T nodes. G nodes are nodes in the access layer of the Star Flash wireless communication system that send data scheduling information, and T nodes are nodes in the access layer of the Star Flash wireless communication system that receive data scheduling information. Among them, AP is the G node of Xingshan, and UE is the T node of Xingshan; Each access point (AP) operates on its available channel resources to support the connection and communication of user equipment (UE). One AP connects to multiple UEs, forming multiple AP-UE links. Assume that there is APs, UEs, Represents a set of APs, Represents the set of UEs, and the available channels of AP are To express, ; When the UE successfully accesses the AP, To express and The available bandwidth of each access point AP is set to the same value. To represent; for each link , for Assigned to The transmission bandwidth of the Star Flash system supports 32 modulation and coding schemes, denoted as ; is a binary variable, where express choose Strategy situation; if the link Selected , then the binary variable ,otherwise ; By setting AP channel allocation scheme, AP-UE access control and link Adaptive MCS adjustment to achieve optimal system performance; Use the rate at which APs in the system upload data to measure the performance of the system. is the additive white Gaussian noise density, and the ambient noise power W is calculated by the formula Calculated; use Indicates a link bit error rate; Indicates a link The transmission rate is expressed as: in, is the raised cosine roll-off coefficient of the system, is the modulation coefficient, is the code rate, modulation order 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: Among them, K represents K associated links.

3. The method for collaborative resource allocation and optimization of a wireless communication system based on star flash according to claim 2, characterized in that: Step S1 includes, Using binary variables To indicate the channel selection of the AP, if Selected channel ,but ,on the contrary, ;In the same time slot, each AP can only select one channel; Using binary variables To indicate the situation where different APs multiplex channels; if and Multiplexed channels , then ,otherwise, ; Under the logarithmic path loss model, the signal-to-interference ratio SIR is used to express The APs that use the same channel are The calculation formula of co-channel interference SIR is as follows: in, Indicates the target The transmission power, is the path loss; the calculation formula of path loss is as follows: in, Indicates reference distance The path loss at the location where n is the path loss exponent, which is usually set to 23 indoors. is the shadow fading of the normal distribution.

4. The method for collaborative resource allocation and optimization of a wireless communication system based on star flash according to claim 3, characterized in that: Step S2 includes, Before accessing an AP, the UE scans multiple APs to obtain their channel numbers, transmit powers, and IDs, and then selects the optimal AP for access. Access selection through binary variables Indicates: If Selected , then ,otherwise ;when When This one link; After an AP-UE link is formed, the AP associated with the link allocates bandwidth to it; Assume that each link Bandwidth used Same; At the same time, there are a large number of AP-UE links in the system, and there is mutual interference between these links; In order to measure the degree of interference between different links, the link signal to interference and noise ratio SINR is introduced as an evaluation indicator; Link The signal-to-interference-noise ratio is expressed as: in, express The transmission power, Indicates no access Other The transmission power of the link , all other APs using the same channel The transmission power will interfere with it; also, Indicates a link The link gain is calculated using the logarithmic distance path loss model.

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

6. The method for collaborative resource allocation and optimization of a wireless communication system based on star flash according to claim 5, characterized in that: Step S4 includes the MCS selection model based on the AP channel allocation model, the AP-UE access control model, and the AP-UE link. The entire optimization problem is expressed as Among them, constraint 1, namely C1, ensures that each AP can only select one channel for transmission in a time slot, but allows multiple APs to reuse the same channel at the same time; constraint 2, namely C2, is used to limit the co-channel interference between APs that reuse the channel, ensuring that the interference level between APs that reuse the same channel is not too large to ensure the communication quality of the network; constraint 3, namely C3, ensures that each UE can only select one AP for access, avoiding the UE from connecting to multiple APs at the same time; constraint 4, namely C4, ensures the quality of the AP-UE link. When the link condition is too poor, the UE must switch to other APs to re-access; constraint 5, namely C5, limits the bandwidth allocation of the AP, that is, the total bandwidth allocated by the AP to all its AP-UE links must not exceed its available bandwidth; constraint 6, namely C6, imposes constraints on the bit error rate (BER) based on the performance requirements of specific services; among them, Indicates a link Transmit specific business The bit error rate when Indicates the maximum bit error rate that the service can tolerate.

7. The method for collaborative resource allocation and optimization of a wireless communication system based on star flash according to claim 6, characterized in that: Step S51 specifically includes: Use the greedy algorithm to solve the AP channel allocation problem. The process is as follows: When assigning a channel to each AP, all available channels are traversed. If the channel is not occupied, it is assigned to the AP, and then the channel is assigned to the next AP. If the channel is occupied, the target AP is allowed to occupy the channel first, and then the SIR of 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 APs are assigned channels, the algorithm ends.

8. The method for collaborative resource allocation and optimization of a wireless communication system based on star flash according to claim 7, characterized in that: Step S52 specifically includes: will be collected All objects in the system are divided into two categories: APs that are subject to co-channel interference and APs that are not subject to co-channel interference. Different quotas are set for each of them. The delayed acceptance algorithm is used to solve the entire problem. The details are as follows: First, establish the preference relationship between AP and UE; Represents a set of APs, Represents a set of UEs; and They are All objects and The set of preference relations of all objects in the set to all objects in the other set; Considering right When the preference relationship is To quantitatively analyze and establish right Preference relation set ; The factors considered in this utility function are: 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 the UE. 2) Business priority: Different businesses have different priorities. Different priority scores are designed for different businesses. , the fraction will be a whole number without units; Utility function Calculated by the following formula in express The normalized SNR of express Business score, and Represent the weight coefficients of the two data respectively; calculate the value of the utility function of AP for each UE and sort all UEs, thereby forming a set of preference relationships between AP and UE ; Considering right When the preference relationship is To quantitatively analyze and establish right The preference relation set ; The factors considered in this utility function are: 1) The extent of co-channel interference (SIR) to which the AP is exposed: When assigning AP channels, due to the limited number of channels, different APs may use the same channel. APs occupying the same channel are subject to SIR (Simultaneous Channel Interference). The greater the SIR, the worse the AP's performance. Therefore, UEs prefer pairing with APs with less SIR. 2) Service adaptability: Different services have different requirements for APs. Different weights are defined based on the service type to reflect the service adaptability. It is the fitness score calculated based on the UE's service type, indicating the UE's Adaptability when transmitting service z; This function is built around the UE's requirements for communication quality and service adaptability. Different service types will reflect different requirements for APs through different adaptability scores in this function. 3) Available bandwidth: The larger the AP's available bandwidth, the better it can meet UE needs and the higher its preference; Utility function Calculated by the following formula in express The same-frequency interference express Business score, and Represent the weight coefficients of the two data respectively; calculate the value of the utility function of AP for each UE and sort all UEs to form the preference relationship set of AP for UE ; After the PLs of both the AP and the UE are established, the AP-UE pair matching is completed according to the delayed reception algorithm.

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

10. The method for collaborative resource allocation and optimization of a wireless communication system based on star flash according to 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 realize the mapping of 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 law of the membership of the fuzzy set changing with the input value. The value of the membership function can be used to describe the membership 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 shape of the membership function curve will affect the performance of the fuzzy logic controller. The trapezoidal function is selected as the membership function, and the expression is as follows: The two input variables and the output fuzzy set are defined as , representing low, medium, and high input and output values respectively; (2) Fuzzy rules: The created fuzzy rule base is shown in Table 1. For the access mode of Star Flash, there are 32 available MCSs in its MCS index table, with indexes from 0 to 31. When fuzzifying, the indexes 0-10 are defined as Low, 11-20 as Medium, and 21-31 as High. The traversal of the MCS should start from the highest interval. Table 1 (3) Fuzzy inference engine and defuzzification: The Mamdani fuzzy inference engine is used, and the fuzzy implication relationship 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 results 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 is output.

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