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Fronthaul Compression and Transmit Beamforming Optimization for Multi-Antenna Uplink C-RAN

机译:多天线上行C-RAN的前传压缩和发射波束成形优化

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This paper considers the joint fronthaul compression and transmit beamforming design for the uplink cloud radio access network (C-RAN), in which multi-antenna users communicate with a cloud-computing based centralized processor (CP) through multi-antenna base-stations (BSs) serving as relay nodes. A compress-and-forward relaying strategy, named the virtual multiple-access channel (VMAC) scheme, is employed, in which the BSs can either perform single-user compression or Wyner-Ziv coding to quantize the received signals and send the quantization bits to the CP via capacity-limited fronthaul links; the CP performs successive decoding with either successive interference cancellation (SIC) receiver or linear minimum-mean square-error (MMSE) receiver. Under this setup, this paper investigates the joint optimization of the transmit beamformers and the quantization noise covariance matrices for maximizing the network utility. A novel weighted minimum-mean-square-error successive convex approximation (WMMSE-SCA) algorithm is first proposed for maximizing the weighted sum rate under the user transmit power and fronthaul capacity constraints with single-user compression. Assuming a heuristic decompression order, the proposed algorithm is then adapted for optimizing the transmit beamforming and fronthaul compression under Wyner-Ziv coding. This paper also proposes a low-complexity separate design consisting of optimizing transmit beamformers for the Gaussian vector multiple-access channel along with per-antenna quantizers with uniform quantization noise levels across the antennas at each BS. Numerical results show that majority of the performance gain brought by C-RAN comes from the implementation of SIC at the CP. Furthermore, the low complexity separate design already performs very close to the optimized joint design in regime of practical interest.
机译:本文考虑了上行云无线电接入网(C-RAN)的联合前传压缩和发射波束成形设计,其中多天线用户通过多天线基站与基于云计算的集中处理器(CP)通信( BS)充当中继节点。采用一种称为虚拟多路访问信道(VMAC)方案的压缩转发中继策略,其中BS可以执行单用户压缩或Wyner-Ziv编码来量化接收信号并发送量化位通过容量受限的前传链路到CP; CP使用连续干扰消除(SIC)接收器或线性最小均方误差(MMSE)接收器执行连续解码。在这种设置下,本文研究了发射波束形成器和量化噪声协方差矩阵的联合优化,以最大程度地利用网络。首先提出了一种新颖的加权最小均方误差逐次凸逼近(WMMSE-SCA)算法,以在单用户压缩的情况下,在用户发射功率和前传容量约束下最大化加权和速率。假设启发式解压缩顺序,则将所提出的算法适配为在Wyner-Ziv编码下优化发射波束成形和前传压缩。本文还提出了一种低复杂度的单独设计,该设计包括针对高斯向量多路访问信道优化发射波束成形器,以及在每个BS的整个天线上具有均匀量化噪声水平的逐天线量化器。数值结果表明,C-RAN带来的大部分性能提升来自CP上SIC的实现。此外,在实际应用中,低复杂度的独立设计已经非常接近优化的联合设计。

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