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.

Nonorthogonal multiple access (NOMA) is a promising technology in future mobile communication systems. In this paper, considering that the base station knows imperfect channel state information (CSI), we investigate the robust beamforming design problem for NOMA systems in multiple-input-single-output (MISO) channels. Modeling channel uncertainties by the worst-case model, we aim at maximizing the worst-case achievable sum rate subject to the transmit power constraint at the base station. We propose to decouple the nonconvex optimization problem into four optimization problems and employ an alternating optimization algorithm to solve the problem. Simulation results demonstrate that our proposed robust beamforming scheme outperforms the orthogonal multiple-access scheme.

For simultaneous wireless information and power transfer (SWIPT), secure communication is an important issue. In this correspondence, we study the secure relay beamforming (SRB) scheme for SWIPT in a nonregenerative multiantenna relay network. We propose a constrained concave convex procedure (CCCP)-based iterative algorithm that is able to achieve a local optimum, where the secrecy rate is maximized, and the relay transmit power and energy harvesting constraints are satisfied. Simulation results have shown that our proposed CCCP-based iterative algorithm achieves a larger secrecy rate and lower computational complexity than the convectional SRB schemes. Since the CCCP-based iterative algorithm is still complex, we propose a semidefinite programming (SDP)-based noniterative suboptimal algorithm and a closed-form suboptimal algorithm. It is shown that when the maximum transmit power of the relay to noise power ratio is high, the SDP-based noniterative suboptimal algorithm performs close to the CCCP-based iterative algorithm.

The secure transmission in the multiple-input-single-output simultaneous wireless information and power transfer (SWIPT) system is an important issue. Considering channel uncertainties, we investigate the robust secure transmission scheme, which maximizes the worst-case secrecy rate under transmit power constraint and energy-harvesting (EH) constraint. The optimization problem is a nonconvex problem. Omitting the rank-one constraint on transmit covariance, it is transformed into a solvable semidefinite program (SDP) where the rank relaxation performance upper bound is obtained. Since the obtained rank relaxation transmit covariance may not be rank one, we propose a lower bound-based rank-one suboptimal (LB-Sub) solution by employing Charnes-Cooper transformation. We also propose the suboptimal Gaussian randomized (GR) solutions based on the rank relaxation upper bound and the lower bound, respectively. Simulation results have shown that our proposed LB-Sub solution and suboptimal GR solution based on the rank relaxation upper bound outperform the nonrobust scheme.

In this paper, we investigate the secure relay beamforming problem for simultaneous wireless information and power transfer (SWIPT) in an amplify-and-forward (AF) two-way relay network. We consider scenarios that the eavesdropper's channel state information (CSI) is and is not available. When the eavesdropper's CSI is available, our objective is to maximize the achievable secrecy sum rate under transmit power constraint and energy harvesting constraint. Since the optimization problem is nonconvex, we derive its performance upper bound, which requires 2-D search, where a semidefinite programming is solved in each step. We also propose an upper bound-based rank-one solution by employing the Gaussian randomization method. To reduce computational complexity, we transform the optimization problem into a difference-of-convex programming and propose a sequential parametric convex approximation (SPCA)-based iterative algorithm to find a locally optimal solution. Furthermore, we also propose a zero-forcing (ZF)-based suboptimal solution. Simulation results demonstrate that the upper bound-based rank-one solution archives the performance almost the same as the upper bound that has high computational complexity. The low-complexity SPCA-based locally optimal solution performs close to the upper bound. The ZF-based suboptimal solution has the lowest computational complexity among the proposed solutions. When the eavesdropper's CSI is not available, we propose an artificial noise-aided secure relay beamforming scheme.

In this paper, we investigate beamforming design for cooperative secure transmission in cognitive two-way relay networks, where the cognitive transmitter (CT) with multiple antennas helps to forward the signals of two primary transmitters (PTs) and tries to protect the PTs from wiretapping by a single-antenna eavesdropper. With the objective of maximizing the secrecy sum rate (SSR) for PTs, we jointly design the beamforming matrix for the PTs’ signals, the beamforming vector for the cognitive receiver (CR)’s signal, and the artificial noise (AN)’s beamforming matrix, under the quality of service constraint at the CR and the transmit power constraint at the CT. We propose the monotonic optimization based algorithm to obtain the global optimal solution to the SSR maximization problem, which is a double-layer iterative algorithm and has very high complexity. To balance the complexity and the performance, we also propose a sequential parametric convex approximation based single-layer iterative algorithm and a zero-forcing based closed-form algorithm, the latter of which has the lowest complexity. Furthermore, we derive the asymptotic achievable secrecy sum rate when the CT transmit power goes to infinity. We present numerical results to verify the effectiveness of our proposed algorithms.