Alternating Information Bottleneck Optimization for Weighted Sum Rate and Resource Allocation in the Uplink of C-RAN

Abstract:
The quantizer design and resource allocation in the uplink of Cloud Radio Access Network (C-RAN) are studied. In C-RAN, multiple Radio Units (RUs) act as soft relays by compressing and forwarding the correlated received signals simultaneously to the Central Processor (CP) in the cloud, via the fronthaul links with finite capacities. Wyner-Ziv coding is utilized in order to exploit the correlation between the received signals at neighboring RUs. Thus, a joint optimization of the quantizers is necessary. Moreover, when the capacity resource is shared among fronthauls, the design of quantizers is closely related to the resource allocation. In this paper we aim to maximize the achievable weighted sum rate by joint optimizing all the quantizers and resource allocation. To make the problem more tractable, at first we assume that the resource allocation is predetermined, and perform a joint optimization of all quantizers, the optimization algorithm is a combination of the Alternating Information Bottleneck (AIB) method and the Alternating Bi-Section method, which is proposed in our previous work. We extend it to solve the problem in this work. Then we optimize the resource allocation based on it. The simulation results justify the correctness and effectiveness of our proposed algorithms.