Recently, unmanned aerial vehicles (UAVs) as flying wireless communication platform have attracted much attention. Benefiting from the mobility, UAV aerial base stations can be deployed quickly and flexibly, and can effectively establish Line-of-Sight communication links. However, there are many challenges in UAV communication system. The first challenge is energy constraint, where the UAV battery lifetime is in the order of fraction of an hour. The second challenge is that the coverage area of UAV aerial base station is limited and the commercial UAV is usually expensive. Thus, covering a large target region all the time with sufficient UAVs is quite challenging. To solve above challenges, in this paper, we propose energy efficient and fair 3-D UAV scheduling with energy replenishment, where UAVs move around to serve users and recharge timely to replenish energy. Inspired by the success of deep reinforcement learning, we propose a UAV Control policy based on Deep Deterministic Policy Gradient (UC-DDPG) to address the combination problem of 3-D mobility of multiple UAVs and energy replenishment scheduling, which ensures energy efficient and fair coverage of each user in a large region and maintains the persistent service. Simulation results reveal that UC-DDPG shows a good convergence and outperforms other scheduling algorithms in terms of data volume, energy efficiency and fairness.

Rate adaptation (RA) is an essential mechanism in 802.11 WLAN. In the latest 802.11ac protocol, there are two emerging problems that need to be addressed for RA. Firstly, 802.11ac supporting more rate selections requires more efforts to find the optimal rate. Secondly, 802.11ac supports higher rate. The difference between optimal rate and non-optimal rate can be so great that non-optimal rate would severely deteriorate the throughput of the WLAN. In order to tackle these problems, we develop a novel RA algorithm termed Rate Adaptation with Thompson Sampling (RATS) for stationary and non-stationary channel environments. In this algorithm, we first consider compacting the search space by removing some rates to accelerate the convergence of the algorithm. Moreover, inspired by multi-armed bandit problem, we design RA algorithm based on Thompson Sampling. Simulation results demonstrate that the performance of the proposed RATS outperforms the existing method.

In order to meet the ever-increasing traffic demand of Wireless Local Area Networks (WLANs), channel bonding is introduced in IEEE 802.11 standards. Although channel bonding effectively increases the transmission rate, the wider channel reduces the number of non-overlapping channels and is more susceptible to interference. Meanwhile, the traffic load differs from one access point (AP) to another and changes significantly depending on the time of day. Therefore, the primary channel and channel bonding bandwidth should be carefully selected to meet traffic demand and guarantee the performance gain. In this paper, we proposed an On-Demand Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) for heterogeneous WLANs to reduce transmission delay, where the APs have different channel bonding capabilities. In this problem, the state space is continuous and the action space is discrete. However, the size of action space increases exponentially with the number of APs by using single-agent DRL, which severely affects the learning rate. To accelerate learning, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is used to train O-DCB. Real traffic traces collected from a campus WLAN are used to train and test O-DCB. Simulation results reveal that the proposed algorithm has good convergence and lower delay than other algorithms.

Existing medium access control (MAC) schemes for wireless local area network (WLAN) have been shown inefficient in dense user environment. Orthogonal frequency division multiple access (OFDMA) which enables multiuser simultaneously transmission is considered as one of the promising technologies to improve MAC efficiency for next generation WLAN. In this paper, an efficient group-based OFDMA (G- OFDMA) MAC protocol for multiuser access is proposed. In the proposed scheme, stations contend for channel resources by transmitting request-to- send (RTS) packets over a randomly selected sub- channel at one of the pre-defined RTS update intervals. Then, an algorithm jointing grouping and sub-channel allocation is proposed to group the stations that transmit RTS successfully and assign them to their favorable sub-channels. Simulation results show that for heavy user loaded WLANs, the proposed G-OFDMA achieves higher throughput than IEEE 802.11 distributed coordination function (DCF) and concurrent OFDMA- based CSMA/CA (C-OFDMA) MAC protocols.

With the advancement of artificial intelligence, Deep Reinforcement Learning (DRL) has been widely applied to traffic signal control (TSC). However, DRL-based TSC methods often require extensive training samples and struggle to generalize across heterogeneous traffic environments, such as different intersection types and traffic flows. Retraining the DRL agent for new environments inevitably increases both costs and time investments, limiting the flexibility of these intelligent TSC algorithms in practical applications. In this paper, we propose an inductive meta-DRL-based traffic signal control method, named IM-TSC, for heterogeneous environments to address these issues. First, we design a feature extraction module composed of a self-feature induction network and a neighborhood feature extraction network to obtain accurate and comprehensive traffic representations, laying the foundation for generalization and coordination. Furthermore, we decouple the scenario inference and agent training processes and design a scenario inference network to obtain contextual encoding for the current intersection scenario through contrastive learning, thereby promoting the transfer of meta-knowledge across similar scenarios. Finally, we design a meta-training framework for the DRL agent with a continuous action space, enabling efficient training of the meta-learner by alternating between local-level learning and global-level learning. We evaluate the proposed IM-TSC method across multiple heterogeneous traffic environments. Experimental results demonstrate that the meta-learner trained with the IM-TSC method can adapt to heterogeneous environments more quickly and effectively. Compared to the suboptimal baseline, it achieves average reductions of 9.20% in the number of stops, 11.55% in waiting time, and 8.29% in fuel consumption.