The use of directional antennas in high-frequency bands (e.g., millimeter-wave) is essential to support applications requiring high throughput and low latency. However, communications using directional antennas require intricate scheduling by a central coordinator to avoid collision and deafness problems. Thus, in this study, we propose a directional medium access control (DMAC) protocol based on a deep$Q$-network (DQN) framework wireless ad hoc networks (WANETs) for Internet of Things (IoT). In our model, even though there is no central coordinating unit (e.g., edge/cloud server), each IoT device can intelligently avoid the collision and deafness through its learning agent. In addition, to maximize the throughput, we design a reinforcement learning (RL) architecture and propose a DQN-based DMAC such that each IoT device intelligently selects the time-slot and transmitting beam without any central coordinator. The proposed schemes are evaluated using carrier-sense multiple access (CSMA) and adaptive learning-based DMAC (AL-DMAC) protocols. The evaluation results reveal that the proposed double DQN scheme outperforms the existing schemes by approximately 54.1% and 57.2% in terms of the throughput.

Rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) techniques show promise in enhancing spectral efficiency in sixth-generation Internet of Things (IoT) networks. However, optimizing the synergy between these two methods is challenging due to the complex and dynamic environment. This study focuses on maximizing the sum-rate metric in RIS-assisted uplink multiantenna RSMA IoT networks to address this problem. We jointly optimized the base station beamforming design, power allocation, and RIS phase shifts to enhance the spectral efficiency with multiple mobile IoT devices present. The controlled parameters are continuous variables and the mathematical problem is nonconcave. Therefore, we formulated the problem as a Markov decision process and used the deep deterministic policy gradient (DDPG) to determine the optimal joint actions. We proposed a safe action shaping process for the decision-making actor network to address constraint violations. Through a rigorous performance evaluation, we demonstrated that the DDPG approach with action shaping outperforms the current DDPG algorithm regarding the maximum achievable sum rate.

Aerial access networks, comprising a hierarchical model of high-altitude platforms (HAPs) and multiple unmanned aerial vehicles (UAVs), are considered a promising technology to enhance the service experience of Internet of Things Devices (IoTD), especially in underserved areas where terrestrial base stations (TBSs) do not exist. In such scenarios, optimally orchestrating the limited computation, communication, and energy resources in both HAPs and UAVs is crucial toward for an efficient aerial networking infrastructure. Thus, in this study, we investigate and formulate the joint IoTDs association, partial offloading, and communication resource allocations (JAPORAs) decisions problem in heterogeneous Aerial Access IoT (AAIoT) networks to maximize service satisfaction for IoTDs, while minimizing their total energy consumption. In particular, the formulated problem is transformed into a multiagent Markov decision process (MAMDP) to deal with its nonconvexity and environmental dynamicity. To solve the problem, we propose a multiagent policy-gradient-based deep actor–critic algorithm, named MADDPG-JAPORA, with centralized training and decentralized execution. Our extensive numerical experiments demonstrated that MADDPG-JAPORA reliably converges and provides superior performance compared with other state-of-the-art schemes.

Recent studies have shown that rate splitting multiple access (RSMA), which depends on multi-antenna rate splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, successfully controls interference in multi-antenna communication networks. This paper examines RSMA's applicability to video streaming applications in cloud radio access networks (C-RAN). We aim to address a practical challenge to maximize the perceived quality of end users while keeping the delay constraints remained satisfied using RSMA. We propose a learning-based framework to select appropriate video quality together with beamforming vectors according to current defined system state. The simulation figure confirms that the learning behavior of proposed learning scheme is stable.