Coordinated regularized zero-forcing beamforming with channel statistics based adaptive feedback for cooperative massive MIMO networks

In cooperative massive multiple-input multiple-output (MIMO) networks, channel statistics based adaptive feedback can considerably reduce feedback overhead for channel state information (CSI) acquisition as well as backhaul overhead for CSI sharing. When regularized zero-forcing beamforming is considered to coordinate interference with the skewed codebook, average sum rate depends on not only regularization parameters, but also quantization error impacts of serving and interfering channels according to their channel covariance matrices. To improve the average sum rate by effectively controlling the desired signal strength and the interference cancellation, we propose joint optimization of regularization parameters and feedback bit allocation by leveraging adaptive feedback according to the channel covariance matrices. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V. This is an open access

Automated summary

In cooperative MIMO networks, adaptive feedback can reduce the overhead of CSI acquisition and sharing. By jointly optimizing regularization parameters and feedback bit allocation, the average sum rate can be effectively improved.