The sub-6 G band is too crowded to accommodate higher data rate, while the millimeter wave(mmWave) bands have abundant spectrum resources and massive MIMO can provide high spectral and energy efficiency. Therefore, th...The sub-6 G band is too crowded to accommodate higher data rate, while the millimeter wave(mmWave) bands have abundant spectrum resources and massive MIMO can provide high spectral and energy efficiency. Therefore, the combination of the two,namely mmWave-MIMO system, has attracted intensive research interests. In this paper, we develop a high-speed mmWave-MIMO communication system and conduct exhaustive field tests. The detail of the system design is provided and the key modules of the testbed are analyzed. The testbed exploits high gain of mmWave RF and flexible configuration of embedded system. The validation and field tests show that the developed testbed can provide up to 2.3 Gbps network layer data rate in single channel with low latency and support point-to-multi-point(PtMP) transmission aided by relay. The testbed can be used in future B5 G and 6 G systems to provide high reliability and low latency wireless coverage.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 Key R&D Program of China ( 2020YFB1807204)。
文摘The sub-6 G band is too crowded to accommodate higher data rate, while the millimeter wave(mmWave) bands have abundant spectrum resources and massive MIMO can provide high spectral and energy efficiency. Therefore, the combination of the two,namely mmWave-MIMO system, has attracted intensive research interests. In this paper, we develop a high-speed mmWave-MIMO communication system and conduct exhaustive field tests. The detail of the system design is provided and the key modules of the testbed are analyzed. The testbed exploits high gain of mmWave RF and flexible configuration of embedded system. The validation and field tests show that the developed testbed can provide up to 2.3 Gbps network layer data rate in single channel with low latency and support point-to-multi-point(PtMP) transmission aided by relay. The testbed can be used in future B5 G and 6 G systems to provide high reliability and low latency wireless coverage.
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.
