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首页> 外文期刊>IEEE Transactions on Information Theory >On the Optimal Fronthaul Compression and Decoding Strategies for Uplink Cloud Radio Access Networks
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On the Optimal Fronthaul Compression and Decoding Strategies for Uplink Cloud Radio Access Networks

机译：上行云无线接入网的最佳前传压缩和解码策略

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This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base stations (BSs) are connected to a cloud-computing-based central processor (CP) via capacity-limited fronthaul links. The BSs compress the received signals with Wyner-Ziv coding and send the representation bits to the CP; the CP performs the decoding of all the users’ messages. Under this setup, this paper makes progress toward the optimal structure of the fronthaul compression and CP decoding strategies for the compress-and-forward scheme in the C-RAN. On the CP decoding strategy design, this paper shows that under a sum fronthaul capacity constraint, a generalized successive decoding strategy of the quantization and user message codewords that allows arbitrary interleaved order at the CP achieves the same rate region as the optimal joint decoding. Furthermore, it is shown that a practical strategy of successively decoding the quantization codewords first, then the user messages, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. On the joint optimization of user transmission and BS quantization strategies, this paper shows that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal quantization scheme for maximizing the achievable rate region is Gaussian. Moreover, Gaussian input and Gaussian quantization with joint decoding achieve to within a constant gap of the capacity region of the Gaussian multiple-input multiple-output (MIMO) uplink C-RAN model. Finally, this paper addresses the computational aspect of optimizing uplink MIMO C-RAN by showing that under fixed Gaussian input, the sum rate maximization problem over the Gaussian quantization noise covariance matrices can be formulated as convex optimization problems, thereby facilitating its efficient solution.
机译：本文研究了上行云无线接入网（C-RAN）模型的压缩转发方案，其中多天线基站（BS）通过容量连接到基于云计算的中央处理器（CP）有限的前传链接。 BS利用Wyner-Ziv编码压缩接收到的信号，并将表示比特发送给CP。 CP对所有用户的消息进行解码。在这种设置下，本文对C-RAN中的压缩转发方案的前传压缩和CP解码策略的最佳结构取得了进展。在CP解码策略设计中，本文表明，在总前传容量约束下，允许CP处任意交错顺序的量化和用户消息码字的广义连续解码策略可实现与最佳联合解码相同的速率区域。此外，示出了在各个前传约束下，先对量化码字然后对用户消息进行连续解码的实用策略，实现了与联合解码相同的最大和速率。关于用户传输和BS量化策略的联合优化，本文表明，如果假设输入分布为高斯分布，则在联合解码下，用于最大化可实现速率区域的最佳量化方案为高斯分布。此外，具有联合解码的高斯输入和高斯量化实现在高斯多输入多输出（MIMO）上行链路C-RAN模型的容量区域的恒定间隙内。最后，本文通过表明在固定的高斯输入下，可以将高斯量化噪声协方差矩阵上的和率最大化问题公式化为凸优化问题，从而促进其高效求解，从而解决了优化上行链路MIMO C-RAN的计算问题。

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