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

Abstract

Abstract-Cloud Radio Access Network (C-RAN) is a promising architecture for unprecedented capacity enhancement in nextgeneration wireless networks thanks to the centralization and virtualization 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. Among the first, this paper endeavours to design a scalable uplink signal detection algorithm, in the sense that both the complexity per unit network area and the total computation time remain 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. The proposed RGMP algorithm demonstrates significantly better convergence performance than conventional message passing. The randomness of the message updating schedule also simplifies the analysis, and allows the derivation of the convergence conditions for the RGMP algorithm. In addition, we generalize the RGMP algorithm to a blockwise RGMP (B-RGMP) algorithm, which allows parallel implementation. The average computation time of B-RGMP remains constant when the network size increases.

CNN-Based Signal Detection for Banded Linear Systems

“…In the input preprocessing module, we use an input reshaping approach to ensure the shiftinvariance property of the input {h k,m } and y. 3 3 The realization of the input preprocessing module to achieve shift-invariance is not unique. The input reshaping approach proposed in this paper is just an example.…”

Linear state estimation via 5G C-RAN cellular networks using Gaussian belief propagation

“…, f xM } serve as virtual inputs needed for initialization of measurement nodes. Similarly, the set of edges E can be divided as As noted earlier, the state estimation problem using GBP over factor graph G A , and the uplink C-RAN signal detection problem using GBP over factor graph G H , have been recently investigated in detail in [9] and [7], respectively.…”

“…The advantage of GBP solution is accuracy that matches the MMSE estimation, low complexity due to lack of scheduling MTC-UE transmissions, low latency due to simultaneous data transfer, scalability to large-scale systems (due to the fact that the underlying factor graph is usually sparse), and ease of parallelization and distributed implementation in future distributed F-RAN architectures. For the future work, we aim to provide rigorous convergence analysis of GBP in the presented framework, motivated by similar analysis in [7] and [9], and provide extensive numerical simulation study.…”

“…For the joint problem, it is straightforward to derive (and implement at a central location) the standard minimum mean-square error (MMSE) estimator, however, such a solution comes with prohibitive O(N 3 )-complexity that hinders its application for large-scale systems. By exploiting inherent sparsity within both of the component problems, an approximate MMSE solution for each problem can be obtained using the tools from probabilistic graphical models, as recently investigated for both (power system) state estimation [9] and uplink signal detection in C-RANs [7]. In particular, an instance of the Belief-Propagation (BP) algorithm, called Gaussian BP (GBP) [10], can be applied to produce an exact MMSE estimate with O(N )-complexity, thus scaling the MMSE solution to large-scale system scenarios.…”

Centralized and Distributed Sparsification for Low-Complexity Message Passing Algorithm in C-RAN Architectures

“…About the reduction of signal processing burden in up-link detection, it has been proposed in [4] to cluster both users and RRHs based on the distance of terminals from RRH thus parallelizing MMSE operations into small size matrix operations. A further step forward has been done in [5] where it is proposed to implement the MMSE receiver by the message passing (MP). By exploiting the Gaussian distribution of the noise, a simple solution is obtained where the complexity per unit network area remains constant with growing network sizes.…”