With the exponential growth of mobile terminals and the widespread adoption of Internet of Things(IoT)technologies,an increasing number of devices rely on wireless local area networks(WLAN)for data transmission.To add...With the exponential growth of mobile terminals and the widespread adoption of Internet of Things(IoT)technologies,an increasing number of devices rely on wireless local area networks(WLAN)for data transmission.To address this demand,deploying more access points(APs)has become an inevitable trend.While this approach enhances network coverage and capacity,it also exacerbates co-channel interference(CCI).The multi-AP cooperation introduced in IEEE 802.11be(Wi-Fi 7)represents a paradigm shift from conventional single-AP architectures,offering a novel solution to CCI through joint resource scheduling across APs.However,designing efficient cooperation mechanisms and achieving optimal resource allocation in dense AP environment remain critical research challenges.To mitigate CCI in high-density WLANs,this paper proposes a radio resource allocation method based on 802.11be multi-AP cooperation.First,to reduce the network overhead associated with centralized AP management,we introduce a distributed interference-aware AP clustering method that groups APs into cooperative sets.Second,methods for multi-AP cooperation information exchange,and cooperation transmission processes are designed.To support network state collection,capability advertisement,and cooperative trigger execution at the protocol level,this paper enhances the 802.11 frame structure with dedicated fields for multi-AP cooperation.Finally,considering the mutual influence between power and channel allocation,this paper proposes a joint radio resource allocation algorithm that employs an enhanced genetic algorithm for resource unit(RU)allocation and Q-learning for power control,interconnected via an inner-outer dual-loop architecture.Simulation results demonstrate the effectiveness of the proposed CCI avoidance mechanism and radio resource allocation algorithm in enhancing throughput in dense WLAN scenarios.展开更多
由于人们对无线通信的需求的增加以及通信质量的不断提升,无线局域网(Wireless Local Area Networks,WLAN)的接入点(Access Point,AP)部署问题在优化领域受到了越来越多的关注。目前,大量的文献都在研究WLAN规划问题,并提出了一系列优...由于人们对无线通信的需求的增加以及通信质量的不断提升,无线局域网(Wireless Local Area Networks,WLAN)的接入点(Access Point,AP)部署问题在优化领域受到了越来越多的关注。目前,大量的文献都在研究WLAN规划问题,并提出了一系列优化技术。然而,这些技术的寻优能力在面对复杂的AP部署场景时就会下降。为了解决复杂的WLAN规划问题,本文在前人工作的基础上,提出了一种改进型多智能体优化算法。该算法基于分布式人工智能,从AP角度和全局角度来优化AP的布局。展开更多
为解决重大自然灾害发生后灾区电力系统的应急通信问题,近年来出现了利用Wi-Fi结合北斗技术的解决方案.如何增大系统覆盖能力、数据传输能力、用户承载能力,增强系统部署便宜性、灵活性是该应急通信系统面临的核心挑战.针对灾后电力应...为解决重大自然灾害发生后灾区电力系统的应急通信问题,近年来出现了利用Wi-Fi结合北斗技术的解决方案.如何增大系统覆盖能力、数据传输能力、用户承载能力,增强系统部署便宜性、灵活性是该应急通信系统面临的核心挑战.针对灾后电力应急通信系统上述核心需求,本研究基于第7代Wi-Fi多接入点(access point, AP)协作通信技术,提出一种协作式功率控制技术:增加无线接入过程中用户类型,并调整部分协议帧结构,具化相关字段用于指示新增用户类型;结合软频率复用技术设计了频谱资源分配算法和AP中心功率控制算法.仿真结果表明,与现有Wi-Fi系统多AP协作技术和软频率复用技术相比,所提算法有效提高了数据传输能力;与现有多AP协作技术相比,在用户承载能力相近条件下,可提高信号覆盖范围.展开更多
基金supported by National Natural Science Foundation of China(No.62201074),Reliable Mechanism for Edge Collaboration Service in Highly Dynamic Scenarios.
文摘With the exponential growth of mobile terminals and the widespread adoption of Internet of Things(IoT)technologies,an increasing number of devices rely on wireless local area networks(WLAN)for data transmission.To address this demand,deploying more access points(APs)has become an inevitable trend.While this approach enhances network coverage and capacity,it also exacerbates co-channel interference(CCI).The multi-AP cooperation introduced in IEEE 802.11be(Wi-Fi 7)represents a paradigm shift from conventional single-AP architectures,offering a novel solution to CCI through joint resource scheduling across APs.However,designing efficient cooperation mechanisms and achieving optimal resource allocation in dense AP environment remain critical research challenges.To mitigate CCI in high-density WLANs,this paper proposes a radio resource allocation method based on 802.11be multi-AP cooperation.First,to reduce the network overhead associated with centralized AP management,we introduce a distributed interference-aware AP clustering method that groups APs into cooperative sets.Second,methods for multi-AP cooperation information exchange,and cooperation transmission processes are designed.To support network state collection,capability advertisement,and cooperative trigger execution at the protocol level,this paper enhances the 802.11 frame structure with dedicated fields for multi-AP cooperation.Finally,considering the mutual influence between power and channel allocation,this paper proposes a joint radio resource allocation algorithm that employs an enhanced genetic algorithm for resource unit(RU)allocation and Q-learning for power control,interconnected via an inner-outer dual-loop architecture.Simulation results demonstrate the effectiveness of the proposed CCI avoidance mechanism and radio resource allocation algorithm in enhancing throughput in dense WLAN scenarios.
文摘由于人们对无线通信的需求的增加以及通信质量的不断提升,无线局域网(Wireless Local Area Networks,WLAN)的接入点(Access Point,AP)部署问题在优化领域受到了越来越多的关注。目前,大量的文献都在研究WLAN规划问题,并提出了一系列优化技术。然而,这些技术的寻优能力在面对复杂的AP部署场景时就会下降。为了解决复杂的WLAN规划问题,本文在前人工作的基础上,提出了一种改进型多智能体优化算法。该算法基于分布式人工智能,从AP角度和全局角度来优化AP的布局。
文摘为解决重大自然灾害发生后灾区电力系统的应急通信问题,近年来出现了利用Wi-Fi结合北斗技术的解决方案.如何增大系统覆盖能力、数据传输能力、用户承载能力,增强系统部署便宜性、灵活性是该应急通信系统面临的核心挑战.针对灾后电力应急通信系统上述核心需求,本研究基于第7代Wi-Fi多接入点(access point, AP)协作通信技术,提出一种协作式功率控制技术:增加无线接入过程中用户类型,并调整部分协议帧结构,具化相关字段用于指示新增用户类型;结合软频率复用技术设计了频谱资源分配算法和AP中心功率控制算法.仿真结果表明,与现有Wi-Fi系统多AP协作技术和软频率复用技术相比,所提算法有效提高了数据传输能力;与现有多AP协作技术相比,在用户承载能力相近条件下,可提高信号覆盖范围.
