Sum-rate maximization for cognitive relay NOMA Systems with channel uncertainty

Abstract

In this paper, we study the sum-rate maximization problem for relay cognitive network based on decoding and forwarding (DF), where the primary network is implemented with the orthogonal frequency division multiple access (OFDMA) technology and the secondary users are in the underlying accessing mode. In the system, multiple primary users are included and one of them plays a role of relay. The relay forwards information to secondary users using the non-orthogonal multiple access (NOMA) technology. We attempt to improve the transmission rate of both primary and secondary users. The block power control strategy is developed by addressing interference of primary users caused by the channel sharing with secondary users. To tackle the tricky issue caused by the channel uncertainty, the worst-case method is adopted when solving the proposed sum-rate maximization problem. Considering the nonlinear objective function and constraints, the successive convex approximation (SCA) method is introduced to determine a suboptimal solution. Also, the Lagrangian dual algorithm is utilized to obtain the closed-form solutions of the transmission power and subchannel allocation. Numerical results verify that the proposed scheme significantly improves the sum-rate under the uncertainty channel gains compared to benchmarks.