In this paper, we explore the integration of a reconfigurable intelligent surface (RIS) with a multi-antenna base station (BS) for downlink multi-user multiple-input-single-output (MU-MISO) systems. We aim to enhance energy efficiency (EE) by jointly optimizing beamforming and phase shifts at the BS and RIS, respectively, while ensuring each mobile user meets their link budget requirements. The resulting optimization problem is inherently non-convex. To address this challenge, we employ proximal policy optimization (PPO), known for efficiently managing non-convex problems and reducing training overhead in continuous action spaces through a clip factor. Furthermore, by leveraging deep neural networks (DNN), the proposed PPO-based solution provides the optimum values for the beamforming at the BS and the phase shift at the RIS, respectively. Finally, we demonstrate the effectiveness and accuracy of the proposed PPO-based algorithm through an extensive simulation campaign, comparing its performance against baseline methods (i.e., fractional programming (FP) and deep deterministic policy gradient (DDPG)). The results show that our proposed PPO-based algorithm outperforms the considered baseline approaches (i.e., FP and DDPG) in terms of EE by 34.2% and 15.8%, respectively.

Internet-of-Things (IoT) networks provide massive connectivity for many application scenarios. Recently, much work has been dedicated to develop spectrum access strategies for IoT networks with a massive number of nodes and sporadic data traffic behavior. The case becomes more challenging in critical applications when Ultra-Reliable Low-Latency (URLL) transmissions are required. Such networks entail spectrum-efficient transmission schemes in which Non-Orthogonal Multiple-Access (NOMA) is considered a key enabler. We proposed a Distributed Queuing (DQ) approach in NOMA for critical massive IoT (mIoT) applications. More specifically, we introduce a frame structure to support DQ-based NOMA so that dynamic NOMA clustering (at the nodes) and dynamic Successive Interference Cancellation (SIC) ordering at the Base Station (BS) are supported. We also use adaptive power back-off strategy to reduce power collisions by utilizing both nodes’ and clusters’ activation index. We investigate network performance metrics, such as reliability, delay violation probability, and effective sum rate. These metrics are derived analytically, and the effect of different network parameters such as blocklength, active node arrival rate, and the number of contention subslots on the network metrics are investigated and compared with the S-ALOHA-TD benchmark.

We investigate the use of reconfigurable intelligent surfaces (RISs) in wireless networks to maximize the sum secrecy rate (i.e., the sum maximum rate that can be communicated under perfect secrecy). Specifically, we focus on a network that utilizes RIS-assisted unmanned aerial vehicles (UAVs) under imperfect channel state information (CSI). Our objective is to maximize the sum secrecy rate while dealing with the presence of multiple eavesdroppers. To achieve this, we jointly optimize the active (UAV) and passive (RIS) beamforming together with the UAV's trajectories. The formulated problem is non-convex due to the coupling of CSI with the maneuverability of the UAV. To overcome this challenge, we propose a policy-based deep reinforcement learning (DRL) approach that solves the non-convex optimization problem in a centralized fashion. Finally, simulation results show that our proposed approach significantly improves average sum secrecy rates over conventional approaches.

Spectrum awareness is one of the most challenging requirements in cognitive radio (CR). To adequately adapt to the changing radio environment, it is necessary for the CR to be able to perform joint detection and classification of low signal-to-noise ratio (SNR) signals. In this paper we propose a joint detection and classification algorithm for the mobile Worldwide Interoperability for Microwave Access (WiMAX) and Long Term Evolution (LTE) signals, which exploits the second-order signal cyclostationarity. Simulation results are presented, which show the efficiency of the proposed algorithm under diverse scenarios. Furthermore, we provide new analytical findings related to the second-order cyclostationarity of the signals of interest.

In this paper we present a new algorithm for detecting mobile Worldwide Interoperability for Microwave Access (WiMAX) signals in cognitive radio systems, which is based on preamble-induced second-order cyclostationarity. We derive closed form expressions for the cyclic autocorrelation function (CAF) and cyclic frequencies (CFs) due to the preamble, and use these results in the proposed algorithm for signal detection. The algorithm is illustrated with numerical results obtained from simulations, which demonstrate its efficiency under diverse scenarios.

In this paper, a distinctive cyclostationarity-based feature of the Long Term Evolution (LTE) Orthogonal Frequency Division Multiplexing (OFDM) signals used in the Frequency Division Duplex (FDD) downlink transmission is proved, and further employed for their detection. This relates to the existence of the reference signals (RSs) used for channel estimation and cell search/acquisition purposes. The analytical closed form expressions for the RS-induced cyclic autocorrelation function (CAF) and cyclic frequencies (CFs) are derived. Based on these findings, a signal detection algorithm is then developed. Simulation results show that the proposed algorithm achieves a good detection performance for low signal-to-noise ratios (SNRs), short sensing times, and under diverse channel conditions.