Randomized Gaussian message passing for scalable uplink signal processing in C-RANs

Abstract

In Cloud Radio Access Networks (C-RANs), the high computational complexity of signal processing becomes unaffordable due to the large number of remote radio heads (RRHs) and users. This paper proposes a randomized Gaussian message-passing (RGMP) algorithm to reduce the complexity of uplink signal processing in C-RANs. Specifically, we first propose to use Gaussian message passing to reduce the computational complexity. In C-RANs, RRHs only need to detect signals from nearby users as the signals from distant users are very weak and can be ignored. Thus, in message-passing algorithms, messages only need to be exchanged among nearby RRHs and users. This leads to a linear computational complexity with the number of RRHs and users. Then, to improve the convergence of message passing, we propose to exchange messages in a random order instead of exchanging them simultaneously or in a fixed order. Numerical results show that the proposed RGMP algorithm has better convergence performance than conventional message passing. The randomness of the message update schedule also simplifies the analysis, which allows us to derive some convergence conditions for the RGMP algorithm. Besides analysis, we also compare the convergence rate of RGMP with existing low-complexity algorithms through extensive simulations.