Considering reconfigurable intelligent surfaces (RISs), we study a multi-cluster multiple-input-single-output (MISO) non-orthogonal multiple access (NOMA) downlink communication network. In the network, RISs assist the communication from the base station (BS) to all users by passive beamforming. Our goal is to minimize the total transmit power by jointly optimizing the active beamforming matrices at the BS and the reflection coefficient vector at the RISs. Because of the constraints on the RIS reflection amplitudes and phase shifts, the formulated quadratically constrained quadratic problem is highly non-convex. For the aforementioned problem, the conventional semidefinite programming (SDP) based algorithm has prohibitively high computational complexity and deteriorating performance. Here, we propose an effective second-order cone programming (SOCP)-alternating direction method of multipliers (ADMM) based algorithm to obtain the locally optimal solution. To reduce the computational complexity, we also propose a low-complexity zero-forcing based suboptimal algorithm. It is shown through simulation results that our proposed SOCP-ADMM based algorithm achieves significant performance gain over the conventional SDP based algorithm. Furthermore, when the target transmission rates of central and cell-edge users are 0.5 bps/Hz, our proposed NOMA RIS-aided system with 32 RIS elements has about 2.5 dB performance gain over the conventional massive multiple-input-multiple-output system with 64 transmit antennas.

In this paper, we consider a cellular downlink multiple-input-single-output (MISO) nonorthogonal multiple access (NOMA) secure transmission system, where users are grouped as multiple clusters. Each cluster consists of a central user and a cell-edge user. The central user is an entrusted user, and the cell-edge user is a potential eavesdropper. We focus on the secure beamforming and power allocation design optimization problem which maximizes the sum achievable secrecy rate of central users subject to the transmit power constraint at the base station and transmission rate requirements at cell-edge users. The problem is nonconvex because of coupling optimization variables in the considered fractional quadratically constrained quadratic programming. We propose an alternating optimization-based solution and a constrained concave-convex procedure-based solution to the considered problem. Simulation results demonstrate that our proposed NOMA schemes outperform the conventional orthogonal multiple access scheme.

In this paper, an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) server is considered to provide MEC services for multiple ground users by employing a time-division multiple access protocol. Our objective is to solve the joint optimization problem of UAV position, time slot allocation, and computation task partition, which minimizes the system energy consumption of all users while ensuring the successful task computation of all users during a time block. We propose a globally optimal solution which is found by two-dimensional search over possible UAV positions. In each search, we theoretically derive the semi-closed-form solution to the joint optimization of time slot allocation and computation task partition by employing augmented Lagrangian active set method. An alternating optimization scheme to find the locally optimal solution is also proposed. It is shown through numerical results that our proposed schemes are superior to the no offloading, full offloading, and gravity center offloading schemes.

In this paper, we investigate an unmanned aerial vehicle (UAV) enabled wireless powered communication network, where a UAV with constant power supply first charges all users by transmitting wireless energy to all users simultaneously and after that all users send their own information to the UAV. Our target is the joint optimization of the time allocation as well as the position of the UAV to make the uplink sum achievable rate of all users as large as possible. To solve this non-convex problem, we first derive the analytic optimal solution of the time allocation which is expressed as the function of UAV position. The original problem, after substituting the derived optimal time allocation, is reformulated as a new optimization problem whose optimization variable is only UAV position. We propose a sequential unconstrained convex minimization based algorithm to obtain the globally optimal solution. Simulation results demonstrate that the performance of our proposed algorithm matches with that obtained by two-dimensional exhaustive search. To decrease the complexity, a Dinkelbach based algorithm to obtain the locally optimal solution is also proposed. Simulation results show that the performances of our proposed two algorithms are superior to the schemes without time allocation optimization and/or position optimization.

Cooperative non-orthogonal multiple access (NOMA) systems inherit advantages of the NOMA protocol and the cooperative relay. In this paper, we propose cooperative NOMA systems in multiple-input-multiple-output channels. The whole transmission is divided into two phases. In the first phase, the base station broadcasts signals using the NOMA protocol to a central user and a cell-edge user. In the second phase, the central user helps the base station cooperatively relay signals intended for the cell-edge user. Our objective is to maximize achievable rate from the base station to the cell-edge user under transmit power constraints and achievable rate constraint from the base station to the central user. The difficulty of this problem is the joint beamforming of the base station and the central user in the second phase. We propose a constrained convex-concave procedure (CCCP)-based algorithm. To reduce computational complexity, we also propose a closed-form search based suboptimal algorithm. Simulation results demonstrate that our proposed cooperative NOMA system with CCCP-based algorithm outperforms the conventional NOMA scheme. When achievable rate constraint to the central user is low, our proposed cooperative NOMA system with the closed-form search based suboptimal algorithm outperforms the NOMA scheme.

In this letter, we consider a cellular downlink multiple-input-multiple-output nonorthogonal multiple access (NOMA) secure transmission network, which consists of a base station, a central user, and a cell-edge user. The base station and two users are all equipped with multiple antennas. The central user is an entrusted user and the cell-edge user is a potential eavesdropper. We focus on secure beamforming optimization problem, which maximizes achievable secrecy rate of the central user subject to transmit power constraint at the base station and transmission rate requirement at the cell-edge user. The optimization problem is nonconvex. We employ majorization-minimization method to iteratively optimize a sequence of valid surrogate functions for the nonconvex optimization problem. Furthermore, in each iteration, we derive the semi-closed form solution to optimize the valid surrogate functions. Simulation results demonstrate that our proposed NOMA scheme outperforms the zero-forcing-based NOMA scheme and conventional orthogonal multiple access scheme.