Towards a Scalable PHY of Cloud-RAN: A Randomized Message Passing Approach
Cloud Radio Access Network (C-RAN) is a promising architecture for unprecedented capacity enhancement in next-generation wireless networks thanks to the centralization and virtualisation of base station processing. However, centralized signal processing in C-RANs involves high computational complexity that quickly becomes unaffordable when the network grows to a huge size. This paper endeavours to design a scalable uplink signal detection algorithm, in the sense that the complexity per unit network area remains constant when the network size grows. To this end, we formulate the signal detection in C-RAN as an inference problem over a bipartite random geometric graph. By passing messages among neighboring nodes, message passing (a.k.a. belief propagation) provides an efficient way to solve the inference problem over a sparse graph. However, the traditional message passing algorithm is not guaranteed to converge, because the corresponding bipartite random geometric graph is locally dense and contains many short loops. As a major contribution of this paper, we propose a randomized Gaussian message passing (RGMP) algorithm to improve the convergence. Instead of exchanging messages simultaneously or in a fixed order, we propose to exchange messages asynchronously in a random order. Numerical results show that the proposed RGMP algorithm has significantly better convergence performance than conventional message passing. The randomness of the message update schedule also simplifies the analysis, and allows the derivation of the convergence conditions for the RGMP algorithm.