This paper studied the robust beamforming for dense transmission systems with imperfect channel state information (CSI). The objective was to maximize the minimum signal to interference plus noise ratio (SINR) in the constraint of the per base station (BS) power. By performing the uplink–downlink duality theory, the referred non-convex optimization problem can be changed into the equivalent uplink decoupling optimization problem. Then, we proposed the instantaneous updating uplink–downlink power algorithm, which relies on instantaneous CSI for the finite system. For the massive MIMO system, to obtain the solution to the problem required for power to instantaneously update, we proposed the updating uplink–downlink power algorithm, which only requires statistical CSI, by applying the random matrix theory. The simulation results show the feasibility and effectiveness of our proposed algorithms.
Aiming at the influence of channel uncertainty, user information leakage and harvested energy improvement, this paper proposes a robust resource allocation algorithm for reconfigurable intelligent reflector (RIS) multiple-input single-output systems based on imperfect channel state information. First, considering the legal user minimum secret rate constraint, the base station maximum transmit power constraint and the RIS phase shift constraint with the bounded channel uncertainty, a joint optimization of the base station active beam, energy beam and RIS phase shift is established. A multivariate coupled nonlinear resource allocation problem for matrices is addressed. Then, using S-procedure and alternating optimization methods, the original non-convex problem is transformed into a deterministic convex optimization problem and an alternating optimization algorithm based on continuous convex approximation is proposed. The simulation results show that the proposed algorithm has better fairness harvested energy compared with the traditional robust algorithm.
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