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

机译:C-RAN上行链路中加权总和率和资源分配的交替信息瓶颈优化

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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.
机译:研究了云无线电接入网(C-RAN)上行链路中的量化器设计和资源分配。在C-RAN中,多个无线电单元(RU)通过将相关的接收信号同时通过有限容量的前传链路压缩并转发到云中的中央处理器(CP),从而充当软中继。利用Wyner-Ziv编码是为了利用相邻RU处接收信号之间的相关性。因此,量化器的联合优化是必要的。此外,当容量资源在前传之间共享时,量化器的设计与资源分配紧密相关。在本文中,我们旨在通过联合优化所有量化器和资源分配来最大化可实现的加权总和率。为了使问题更易于处理,首先我们假设资源分配是预先确定的,并对所有量化器执行联合优化,该优化算法是交替信息瓶颈(AIB)方法和交替两部分方法的组合,这是我们以前的工作中提出的。我们扩展它来解决这项工作中的问题。然后,我们基于该资源优化资源分配。仿真结果证明了所提出算法的正确性和有效性。

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