Scalable Uplink Processing via Sparse Message Passing in C-RAN

Cloud radio access network (C-RAN) emerges as a promising solution to sustain the mobile data explosion with low cost and high energy efficiency. The centralized base band processing of C-RAN facilitates coordinated signal processing at the cloud server, which can potentially lead to huge capacity gain. However, full-scale coordination in a large-scale system inevitably results in high computational complexity that limits the scalability of the system. To address this issue, this paper proposes a scalable uplink signal processing algorithm based on message passing. By exploiting near-sparsity of large C- RAN channel matrices, we derive a sparse message- passing algorithm that reduces the computational complexity of signal detection to be linear with the number of RRHs and users. This implies that the average computational complexity per user does not grow with the network size, and hence the system is scalable. In addition, we discuss the convergence of the sparse message-passing algorithm and propose a block-wise message-passing algorithm that significantly improves the probability of convergence.