In this paper, a novel codebook design method for sparse code multiple access (SCMA) is proposed and an analytical framework to evaluate the bit error rate (BER) performance is developed. In particular, to meet the codebook design criteria based on the pairwise error probability, a novel codebook with large minimum Euclidean distance employing the star quadrature amplitude modulation signal constellations is designed. In addition, with the aid of the phase distribution of the presented SCMA constellations, BER of SCMA system over downlink Rayleigh fading channel is obtained in closed-form expression. The simulation and analytical results show that the presented SCMA codebook outperforms the existing codebooks and low-density signature, and the proposed design is more efficient for the SCMA codebook with large size and/or high dimension. Moreover, the derived theoretical BER results match well the simulation results, especially in the high signal-to-noise ratio regimes.

Recently, unmanned aerial vehicle (UAV) swarm communication has drawn much attention in search and rescue (SAR) missions owing to its wide wireless coverage and increasing autonomy in navigation. In this article, we consider maximizing the downlink wireless coverage of a UAV swarm in an unknown mission area by controlling the quasistationary deployments of UAVs. Particularly, the stochastic wireless link failures caused by channel fading and noise in UAV-to-UAV communication links are considered in coverage control. Specifically, due to delay sensitivity and onboard energy limitation of UAV-enabled SAR networks, we study a distributed control strategy where swarm UAVs can address the coverage problem by exchanging only the local information. In this case, the wireless coverage problem is divided into several distributed optimization subproblems. However, due to the integer variable and nonlinear constraints, each subproblem is nonconvex and mutually coupling, which makes it difficult to solve via standard convex optimization solvers. Thus, we model the UAV swarm network as an undirected random graph and then solve the optimization subproblems by formulating a UAV swarm wireless coverage game. As per the designed utility function and potential function of the formulated game, existence of the pure Nash equilibrium is discussed and a distributed algorithm is developed to achieve the best Nash equilibrium. We analyze the convergence property and computational complexity of the proposed algorithm. Meanwhile, we analyze effects of the initial learning rate and step size on algorithm performance from both theoretical and simulation results. Simulation results show that the proposed algorithm improves the coverage by around 58% when compared with initial performance.

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been considered promising auxiliary devices to enhance the performance of the wireless network, where users located at different sides of the surfaces can be simultaneously served by the transmitting or reflecting signals. In this article, an energy efficiency (EE) optimization problem for non-orthogonal multiple access (NOMA) assisted STAR-RIS downlink network is investigated. Due to the fractional form of the objective function, it is challenging to solve the EE optimization problem using traditional convex optimization solutions. This article proposes a deep deterministic policy gradient (DDPG)-based algorithm to maximize the EE by jointly optimizing the transmission beamforming vectors at the base station and the coefficients matrices at the STAR-RIS. Simulation results demonstrate that the proposed algorithm can effectively maximize the system EE considering the time-varying channels.

In this paper, we investigate the outage performance of hybrid automatic repeat request with chase combining (HARQ-CC) applied to downlink non-orthogonal multiple access (NOMA) systems. A closed-form expression of individual outage probability and the diversity gain are obtained firstly. Based on the developed analytical outage probability, a tradeoff between the minimum retransmission round and the transmission power allocation coefficient is then provided with a given target rate. The provided simulation results demonstrate the accuracy of the developed analytical results. Moreover, it is shown that NOMA combined with the HARQ-CC can achieve a significant advantage when only average channel state information is known at the transmitter. Specially, the performance of the user with less transmission power in NOMA systems can be efficiently improved by utilizing HARQ-CC.

Hierarchical federated learning (HFL) shows great advantages over conventional two-layer federated learning (FL) in reducing network overhead and interaction latency while still retaining the data privacy of distributed FL clients. However, the communication and energy overhead still pose a bottleneck for HFL performance, especially as the number of clients raises dramatically. To tackle this issue, we propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation in this paper, aiming to minimize the total cost of time and energy at each HFL global round. Specifically, we first propose a novel fuzzy logic based client selection policy considering client heterogeneity in multiple aspects, including channel quality, data quantity and model staleness. Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated. Utilizing problem decomposition, we firstly derive the closed-form solution for the edge server scheduling subproblem via the penalty dual decomposition (PDD) method. Next, a deep deterministic policy gradient (DDPG) based algorithm is proposed to tackle the resource allocation subproblem considering time-varying environments. Finally, extensive simulations demonstrate that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.

Pinching antennas, implemented by applying small dielectric particles on a waveguide, have emerged as a promising flexible-antenna technology ideal for next-generation wireless communications systems. Unlike conventional flexible-antenna systems, pinching antennas offer the advantage of creating line-of-sight (LoS) links by enabling antennas to be activated on the waveguide at a position close to the users. This paper investigates a typical two-user non-orthogonal multiple access (NOMA) down-link scenario, where multiple pinching antennas are activated on a single dielectric waveguide to assist NOMA transmission. We formulate the problem of maximizing the data rate of one user subject to the quality-of-service (QoS) requirement of the other user by jointly optimizing the antenna positions and power allocation coefficients. The formulated problem is nonconvex and difficult to solve due to the impact of antenna positions on large-scale path loss and two types of phase shifts, namely in-waveguide phase shifts and free space propagation phase shifts. To this end, we propose an iterative algorithm based on block coordinate descent and successive convex approximation techniques. Moreover, we consider the special case with a single pinching antenna, which is a simplified version of the multi-antenna case. Although the formulated problem is still nonconvex, by using the inherent features of the formulated problem, we derive the global optimal solution in closed-form, which offers important insights on the performance of pinching-antenna systems. Simulation results demonstrate that the pinching-antenna system significantly outperforms conventional fixed-position antenna systems, and the proposed algorithm achieves performance comparable to the computationally intensive exhaustive search based approach.