Adaptively Regularized Phase Alignment Precoding for Multiuser Multiantenna Downlink

In multiantenna downlink, linear precoders like channel inversion (CI), regularized CI (RCI), and phase alignment (PA) are more desirable than their nonlinear counterparts due to their reduced complexity. However, to exploit the full benefits of multiuser downlink, the availability of perfect channel state information (CSI) at the base station (BS) is mandatory. Since such an assumption is not realistic in practice, subject to CSI mismatch, the downlink precoders may undergo a severe degradation in performance. Therefore, an adaptive linear precoder is required to achieve better performance under the availability of imperfect CSI at the BS. In this paper, we propose such a linear precoder by judiciously picking up an optimized regularization parameter based on the knowledge of the channel estimation error variance in advance. To do so, we first introduce a generalized CSI mismatch model where the variance of the channel estimation error is a function of the signal-to-noise ratio (SNR) and is thus able to accommodate a variety of distinct scenarios such as reciprocal channels and CSI feedback. It is shown that the proposed precoding scheme, dubbed adaptively regularized PA (RPA), is capable of achieving lower bit error rates (BERs) compared to standard linear precoders under both perfect and imperfect CSI.