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
This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networ...This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networks.Unlike conventional Deep Reinforcement Learning(DRL)-based approaches for CW size adjustment,which often suffer from overestimation bias and limited exploration diversity,leading to suboptimal throughput and collision performance.Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions.First,SETL adopts a DDQN architecture(SETL-DDQN)to improve Q-value estimation accuracy and enhance training stability.Second,we incorporate a Gumbel distribution-driven exploration mechanism,forming SETL-DDQN(Gumbel),which employs the extreme value theory to promote diverse action selection,replacing the conventional-greedy exploration that undergoes early convergence to suboptimal solutions.Both models are evaluated through extensive simulations in static and time-varying IEEE 802.11 network scenarios.The results demonstrate that our approach consistently achieves higher throughput,lower collision rates,and improved adaptability,even under abrupt fluctuations in traffic load and network conditions.In particular,the Gumbel-based mechanism enhances the balance between exploration and exploitation,facilitating faster adaptation to varying congestion levels.These findings position Gumbel-enhanced DRL as an effective and robust solution for CW optimization in wireless networks,offering notable gains in efficiency and reliability over existing methods.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This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networks.Unlike conventional Deep Reinforcement Learning(DRL)-based approaches for CW size adjustment,which often suffer from overestimation bias and limited exploration diversity,leading to suboptimal throughput and collision performance.Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions.First,SETL adopts a DDQN architecture(SETL-DDQN)to improve Q-value estimation accuracy and enhance training stability.Second,we incorporate a Gumbel distribution-driven exploration mechanism,forming SETL-DDQN(Gumbel),which employs the extreme value theory to promote diverse action selection,replacing the conventional-greedy exploration that undergoes early convergence to suboptimal solutions.Both models are evaluated through extensive simulations in static and time-varying IEEE 802.11 network scenarios.The results demonstrate that our approach consistently achieves higher throughput,lower collision rates,and improved adaptability,even under abrupt fluctuations in traffic load and network conditions.In particular,the Gumbel-based mechanism enhances the balance between exploration and exploitation,facilitating faster adaptation to varying congestion levels.These findings position Gumbel-enhanced DRL as an effective and robust solution for CW optimization in wireless networks,offering notable gains in efficiency and reliability over existing methods.
