Frequency Selective Hybrid Beamforming and Optimal Power Loading for Multiuser Millimeter Wave Cognitive Radio Networks

In this work, frequency selective hybrid precoders and combiners are designed for a millimeter wave (mmWave) multiuser (MU) multiple-input-multiple-output (MIMO) downlink underlay cognitive radio network (CRN) utilizing multiple radio frequency (RF) chains and uniform rectangular planar arrays (URPAs) both at the CR base station (CBS) and the secondary users (SUs). The proposed designs maximize the downlink spectral efficiency (SE) of the secondary users, while keeping the interference introduced to the primary user within a prescribed threshold. In the first phase, considering only the channel knowledge at each subcarrier, a novel blind hybrid MMSE-RC (minimum mean squared error-receiver combiner) design is determined using the modified simultaneous orthogonal matching pursuit (MSOMP). Further, employing feedback for each SU’s effective channel, a two-stage decoupled strategy is developed for hybrid transmit precoder (TPC) design, wherein the RF and stage-1 BB-TPC are designed in the first step relying on a capacity-optimal fully digital (FD)-TPC approximation problem followed by the stage-2 BB-TPC design using a low-complexity ZF approach with the goal of mitigating the MUI. Towards this, a novel Modified Alternating Minimization-Zeroforcing (MAM-ZF) algorithm is proposed to compute the hybrid-TPC weights. Furthermore, a low complexity alternating minimization-zeroforcing (LAM-ZF)-based precoding algorithm is also proposed towards the same. Finally, a per-subcarrier optimal power loading solution is derived in closed-form with the objective of maximizing the sum SE while satisfying the interference and transmit power budget limitations. Our simulation findings show that the proposed schemes outperform other state-of-the-art techniques and achieve a spectral efficiency comparable to that achieved by fully-digital beamforming, while requiring a significantly fewer number of RF chains.