Optimality of gaussian fronthaul compression for uplink MIMO cloud radio access networks

Abstract

This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base-stations (BSs) are connected to a cloudcomputing based central processor (CP) via capacity-limited fronthaul links. The BSs perform Wyner-Ziv coding to compress and send the received signals to the CP; the CP performs either joint decoding of both the quantization codewords and the user messages at the same time, or the more practical successive decoding of the quantization codewords first, then the user messages. Under this setup, this paper makes progress toward the optimization of the fronthaul compression scheme by proving two results. First, it is shown that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal Wyner-Ziv quantization scheme for maximizing the achievable rate region is Gaussian. Second, for fixed Gaussian input, under a sum fronthaul capacity constraint and assuming Gaussian quantization, this paper shows that successive decoding and joint decoding achieve the same maximum sum rate. In this case, the optimization of Gaussian quantization noise covariance matrices for maximizing sum rate can be formulated as a convex optimization problem, therefore can be solved efficiently.