This study realizes secure near-field location division multiple access through employing both a variational quantumcircuit and a nature-inspired algorithm. With the escalating demands of the sixth generation (6G) and beyond, there is growing interest in using large numbers of antenna elements,making near-field communications more practical Seizing this opportunity, we leverage near-field communications for a distinct multiple access technique, termed location division multipleaccess (LDMA), which capitalizes on spatial orthogonality to distinguish users by both angle and distance. Nevertheless, relying on beamforming to direct signals to distinct users poses a clear physical-layer security risk: adversaries might eavesdrop on messages intended for legitimate users, prompting the need tooptimize beamforming to enhance security while adhering towireless systems’ constraints. To make matters worse, conventional approaches typically entail multiple matrix inversions, and the optimization problem is far from trivial to solve due toits non-convexity and NP-hardness. To this end, our solutionleverages variational quantum circuits (VQC), motivated bythe potential benefits offered by quantum computing. On topof that, we improve upon the existing VQC workflows by integrating a classical algorithm, particularly, the differential evolution algorithm, thereby reducing quantum computational resource demands while maintaining high exploration efficiency. We further investigate how different quantum circuit depths influence the balance between expressibility and convergence. Simulation results reveal that our proposed scheme consistently outperforms conventional benchmarks in terms of secrecyrates, while simultaneously satisfying quality-of-service (QoS)constraints and power allocation requirements.

This paper considers the cell-free integrated sensing and communication (CF-ISAC) networks utilizing reconfigurable intelligent surface (RIS)-mounted uncrewed aerial vehicles (UAVs). We aim to maximize the sum of weighted sum rate within the whole ISAC period by jointly optimizing access points (APs)’ transmit beamformings, RISs’ phase shifts, user-RIS association, and UAVs’ locations. To deal with a highly complex non-convex optimization problem, we propose an alternating optimization solutions by decomposing the original problem into three subproblems. In particular, for optimizing APs’ transmit beamformings, RISs’ phase shifts, and user-RIS association, we convert the log-sum problem into a quadratically constrained quadratic programming problem using the Lagrangian dual principle and multi-ratio fractional programming. For optimizing UAVs’ locations, the successive convex approximation technique is used to transform it into a convex problem. Simulation results highlight the considerable performance advantage of the proposed network compared to benchmark schemes employing fixed RISs, without RIS-mounted UAVs (URISs), and collocated network with URISs.

In this paper, we propose a novel non-orthogonal multiple access (NOMA)-assisted cooperative overlay spectrum-sharing network, in which a full- duplex (FD) secondary transmitter (ST) is selected for relaying the primary transmitter's information and transmitting its own to the primary receiver and secondary receivers, respectively, using NOMA approach. In order to encourage the ST to perform secondary cooperative transmission and maintain its operation, simultaneous wireless information and power transfer (SWIPT) technique is exploited to scavenge energy from the primary signal. To assess the performance of the proposed system, the outage probabilities for the primary and secondary networks are derived in tight closed-form approximations. Numerical results show that the combination of FD, SWIPT, and NOMA technologies greatly benefits the cooperative overlay spectrum- sharing network in terms of outage probability, when compared with that of half-duplex NOMA and conventional orthogonal multiple access networks.