Robust Energy-Efficient Maximization for Cognitive NOMA Networks Under Channel Uncertainties

Abstract

Resource allocation (RA) is a key technique to guarantee the quality of service of users and maximize the system capacity in cognitive nonorthogonal multiple access (NOMA) networks. However, traditional RA approaches have been proposed under perfect channel state information (CSI) which is too ideal in practical systems due to the impact of link delays, quantization errors, etc. In this article, with imperfect CSI, a downlink robust RA algorithm is proposed for robust transmission to maximize the sum energy efficiency (EE) of secondary users (SUs) under channel uncertainties. Meanwhile, it protects the minimum data rates of SUs, satisfying the maximum transmission power constraints of base stations and the maximum interference temperature constraint of each primary user (PU). The formulated problem is nonconvex, thus challenging to solve. For delay-tolerant services, to deal with the intractability of the problem caused by outage probability constraints, the closed-form expressions of outage probabilities of SUs and PUs are derived under Gaussian CSI error models. For delay-sensitive services, the robust constraints with bounded uncertainty sets are transformed into convex ones. Based on successive convex approximation and the parameter transformation approach, the original problem is converted into a closed-form geometric programming problem solved by dual decomposition methods and subgradient methods. Additionally, users’ outage probabilities and the minimum required transmit power under the two modeling approaches are analyzed. Computational complexity and sensitivity analysis are provided. The simulation results show the proposed algorithm serves good robustness and EE.