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Cloud Radio Access Network (C-RAN) is a promising architecture forunprecedented capacity enhancement in next-generation wireless networks thanksto the centralization and virtualization of base station processing. However,centralized signal processing in C-RANs involves high computational complexitythat quickly becomes unaffordable when the network grows to a huge size. Amongthe first, this paper endeavours to design a scalable uplink signal detectionalgorithm, in the sense that both the complexity per unit network area and thetotal computation time remain constant when the network size grows. To thisend, we formulate the signal detection in C-RAN as an inference problem over abipartite random geometric graph. By passing messages among neighboring nodes,message passing (a.k.a. belief propagation) provides an efficient way to solvethe inference problem over a sparse graph. However, the traditionalmessage-passing algorithm is not guaranteed to converge, because thecorresponding bipartite random geometric graph is locally dense and containsmany short loops. As a major contribution of this paper, we propose arandomized Gaussian message passing (RGMP) algorithm to improve theconvergence. Instead of exchanging messages simultaneously or in a fixed order,we propose to exchange messages asynchronously in a random order. The proposedRGMP algorithm demonstrates significantly better convergence performance thanconventional message passing. The randomness of the message update schedulealso simplifies the analysis, and allows the derivation of the convergenceconditions for the RGMP algorithm. In addition, we propose a blockwise RGMP(B-RGMP) algorithm for practical implementation. The average computation timeof B-RGMP remains constant when the network size increases.

Scalable Uplink Signal Detection in C-RANs via Randomized Gaussian Message Passing