Zeroing Unknown Terms: A Novel Clustering Architecture for Low Network Overhead in Distributed Massive MIMO

We propose a novel user-centric clustering architecture for distributed massive multiple-input-multiple-output networks. We examine the uplink of a general multi-cell scenario in which a cluster of base stations (BSs) with large antenna arrays detect the signals of multiple users simultaneously. As opposed to traditional clustering schemes, where the channel coefficients of all the users within the cooperating cluster are learned at the BSs, in the proposed approach, BSs in the network learn the channels of only the strongest users based on the received signal-to-noise ratio. We consider two linear receivers, namely, zero forcing and linear minimum mean squared error receivers at the central processing unit. The two receivers operate with only partial knowledge of the user channels and the unknown terms are set to zero. We conduct a thorough analytical investigation and derive novel approximations for the instantaneous received signal-to-interference-and-noise ratio of an arbitrary user processed by a cluster of BSs. Furthermore, we develop simple closed-form expressions for the achievable rate and symbol error probability of an arbitrary user processed by a cluster of BSs. Numerical examples are used to illustrate the accuracy of the proposed approach and also to compare it to existing approaches. We show that our proposed architecture outperforms traditional clustering methods, particularly for cluster edge users.