Robust Beamforming in NOMA Cognitive Radio Networks: Gaussian CSI

Even though we use a different channel model, the residual interference due to imperfect Channel State Information (CSI) estimation affects the message detection similarly to the bounded error model. Thus the achievable data rate expression of SU k remains the same except that Δh_k is in a new set. In contrast to the existing non‐orthogonal multiple access (NOMA) contributions on imperfect CSI, this chapter uses the Gaussian estimation error model to form an optimization problem. It considers the optimization problem of maximizing the total harvested energy. This problem has important real‐world applications, since most of the consumer electronics products are battery‐driven and thus their energy efficiency is critical. Simulation results for characterizing the performance of the proposed robust beamforming were conceived with NOMA under both bounded and Gaussian estimation error models.