In this paper, the performance and system complexity of the candidate multiple access (MA) techniques for the next generation of cellular systems, namely, power domain nonorthogonal multiple access (PD-NOMA) and sparse code multiple access (SCMA), are investigated. To this end, for each MA technique, a resource allocation problem considering heterogeneous cellular networks (HetNet) is formulated. We apply successive convex approximation (SCA) method to each problem and obtain their solutions. Simulation results show that SCMA-based system achieves better performance than PD-NOMA-based one at the cost of more complexity.

This paper investigates the problem of age of information (AoI) aware radio resource management for a platooning system. Multiple autonomous platoons exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the cooperative awareness messages (CAMs) to their followers while ensuring timely delivery of safety-critical messages to the Road-Side Unit (RSU). To lower the computational load at the RSU and cope with the challenges of dynamic channel conditions, we exploit a distributed resource allocation framework based on multi-agent reinforcement learning (MARL), where each platoon leader (PL) acts as an agent and interacts with the environment to learn its optimal policy. Motivated by the existing literature in RL, we propose two novel MARL frameworks based on the multi-agent deep deterministic policy gradient (MADDPG), named Modified MADDPG, and Modified MADDPG with task decomposition. Both algorithms train two critics with the following goals: A global critic which estimates the global expected reward and motivates the agents toward a cooperating behavior and an exclusive local critic for each agent that estimates the local individual reward. Furthermore, based on the tasks each agent has to accomplish, in the second algorithm, the holistic individual reward of each agent is decomposed into multiple sub-reward functions where task-wise value functions are learned separately. Numerical results indicate our proposed algorithms' effectiveness compared with other contemporary RL frameworks, e.g., federated reinforcement learning (FRL) in terms of AoI performance and CAM message transmission probability.

This paper proposes a novel approach to improve the performance of a heterogeneous network (HetNet) supported by dual connectivity (DC) by adopting multiple unmanned aerial vehicles (UAVs) as passive relays that carry reconfigurable intelligent surfaces (RISs). More specifically, RISs are deployed under the UAVs termed as UAVs-RISs that operate over the micro-wave ( $\mu \text{W}$ ) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the $\mu \text{W}$ channel based on orthogonal multiple access (OMA), while small base stations (SBSs) operate over the millimeter-wave (mmW) channel based on non-orthogonal multiple access (NOMA). We study the problem of total transmit power minimization by jointly optimizing the trajectory/velocity of each UAV, RISs’ phase shifts, subcarrier allocations, and active beamformers at each BS. The underlying problem is highly non-convex and the global optimal solution is intractable. To handle it, we decompose the original problem into two subproblems, i.e., a subproblem which deals with the UAVs’ trajectories/velocities, RISs’ phase shifts, and subcarrier allocations for $\mu \text{W}$ ; and a subproblem for active beamforming design and subcarrier allocation for mmW. In particular, we solve the first subproblem via the dueling deep Q-Network (DQN) learning approach by developing a distributed algorithm which leads to a better policy evaluation. Then, we solve the active beamforming design and subcarrier allocation for the mmW via the successive convex approximation (SCA) method. Simulation results exhibit the effectiveness of the proposed resource allocation scheme compared to other baseline schemes. In particular, it is revealed that by deploying UAVs-RISs, the transmit power can be reduced by 6 dBm while maintaining similar guaranteed QoS.

In this letter, we propose a cooperative relaying system (CRS) based on non-orthogonal multiple access (NOMA) which uses an amplify-and-forward (AF) relay. This study proposes a novel detection scheme in which the received signals of two consecutive time slots at destination is applied. We calculate asymptotic outage probability (OP) and derive diversity order of two for each symbol. Simulations show that our system have much better outage performance than NOMA-based CRS with decode-and-forward (DF) relay. Also, asymptotic ergodic sum rate increases log-linearly with transmit SNR and outperforms NOMA-based CRS with DF relay and conventional CRS. Finally, we verify analytical results by Monte-Carlo simulations.

In this paper, the trade-off among the energy efficiency, fairness, harvested energy, and system sum rate is studied. In this regard, various fairness methods namely: max-min fairness, proportional fairness, and minimum delay potential fairness in power domain non-orthogonal multiple access (PDNOMA)- based heterogeneous cellular networks (HetNet) are investigated. In order to perform successive interference cancellation (SIC), we use two ordering approaches and compare their performance. To this end, we propose joint subcarrier and power allocation algorithms to achieve fair energy efficient resource allocation for each fairness method and SIC ordering. Since the proposed optimization problems are non-convex and intractable, the existing methods to solve the convex problems could not be directly used. To overcome this difficulty, an iterative algorithm based on successive convex approximation is used. Moreover, to show the optimality gap of the proposed solution method, an optimal approach based on the monotonic optimization is applied in which we first transform each of the proposed optimization problems into a monotonic optimization problem of canonical form, and then, we obtain the optimal solution of each problem which coincides with the optimal solution of the original non-convex problem. We finally study the performance of the proposed schemes using simulations for different values of the system parameters.

Covert communication is often limited in rate be- cause it is difficult to hide the signal in the background noise. Recent work has shown that jamming can significantly improve the rate at which covert communication can be conducted, but this jamming signal has a devastating effect on the receiver. To tackle this issue, we equip the jammer with multiple antennas so that it can employ beamforming to place the intended receiver in the null space of the jamming. Moreover, in order to increase rate in this network, we equip the transmitter with an antenna array to be able to perform 3-dimensional (3D) beamforming toward its intended recipient. To evaluate this design, we formulate an optimization problem and present an iterative algorithm to solve it. Moreover, since Willie is passive, it is possible that Alice is not able to estimate the location of Willie perfectly. Hence, we investigate a new practical scenario in which the information about Willie's location is imperfect at Alice. Numerical results consider both the rate of covert communication with the proposed architecture and the gap between the result from our optimization and that obtained from exhaustive search. Numerical results highlight that the optimality gap is on average approximately 10%, and 3D beamforming compared to 2D beamforming in- creases covert rate by approximately 70%.