Adaptive Perturbation-Aided Opportunistic Hybrid Beamforming for mmWave Systems

In this paper, we propose an adaptive perturbation-aided opportunistic hybrid beamforming (AP-OHBF) scheme for temporally correlated millimeter wave (mmWave) channels. The basic idea is that the proposed scheme perturbs the channel parameters to track the channels of the scheduled mobile stations (MSs), whose channels are in favorable conditions, based on only the signal-to-interference-plus-noise ratio (SINR) information of MSs. Specifically, in each time slot, the proposed scheme makes random perturbations to the 'best' known channel parameters in the memory, which corresponds to the maximum sum scheduling metric. Then the base station (BS) constructs the channel estimates from these parameters, which are subsequently used to generate the precoders. For data transmission, the BS adopts the successful perturbation, which achieves performance improvements. By contrast, if a perturbation results in performance degradation, it is discarded, whereas the precoders corresponding to the best-known parameters in the memory are instead used for transmission. Simulation results show that in mmWave channels, the proposed AP-OHBF scheme significantly improves the system throughput with respect to the conventional opportunistic beamforming schemes for the same amount of feedback information.