Downlink Training-Based Massive MIMO NOMA

Abstract

The benefits of facilitating downlink (DL) channel estimation at users are investigated for multi-cell massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. By leveraging channel reciprocity of time division duplexing mode and uplink channel estimates, the DL pilots are beamformed towards the clusters of NOMA users such that the DL pilot sequence length does not scale with the number of antennas at the base-station. This enables users to estimate their effective DL channels and adopt those to facilitate successive interference cancellation. Aiming at establishing the performance bounds, the achievable DL rates are derived based on the estimated DL channels, and thereby, the adverse joint impact of beamforming uncertainty, imperfect SIC, and residual intra/inter-cluster/cell interference is quantified. The residual inter-cell interference is mitigated by using pilot contamination precoding and the underlying asymptotically achievable user rates are derived. Based on our analytical results, we conclude that DL channel estimates at user nodes are essential in successfully reaping benefits of integrating NOMA with massive MIMO.