A Novel Partial Joint Processing Architecture for distributed Massive MIMO

Abstract

We propose a new partial joint processing architecture for distributed massive multiple-input multiple-output (MIMO) networks. As opposed to the traditional full-joint processing architecture, where the channel coefficients of all the users within the cooperating cluster are learnt at the base stations, in the proposed architecture, we allow each base station to learn the channel coefficients only of the users that maintain a strong average received signal-to-noise ratio to that base station based on a predefined threshold. This threshold provides extra flexibility, trading-off channel estimation for performance. We assume a zero-forcing receiver at the central processing unit using estimate..