It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution of this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network (C-RAN), which is a candidate solution in 5G and future communication networks, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. In this study, we consider a downlink C-RAN with a wireless fronthaul and aim to minimize total power spent by jointly designing beamformers for fronthaul and access links. We consider the case where perfect channel state information is not available in the CP. We first derive a novel theoretical performance bound for the problem defined. Then we propose four algorithms with different complexities to show the tightness of the bound. The first two algorithms apply successive convex optimizations with semi-definite relaxation idea where other two are adapted from well-known beamforming design methods. The detailed simulations under realistic channel conditions show that as the complexity of the algorithm increases, the corresponding performance becomes closer to the bound.
翻译:众所周知,标准蜂窝网络的数据率因细胞间干扰而受到限制。这个问题的有效解决办法是使用多细胞合作的理念。在云中无线电接入网络(C-RAN),这是5G和未来通信网络的候选解决方案,通过中央处理器(CPs)与具有有限容量前厅链接的简单远程无线电头进行合作。在本研究中,我们考虑用无线前厅连接C-RAN,目的是通过联合设计前厅和接入链接的横线设计,最大限度地减少使用的总功率。我们考虑了在CP中无法提供完美频道状态信息的情况。我们首先为界定的问题得出了一种新的理论性能。我们随后提出了四种复杂程度不同的算法,以显示约束的紧凑性。前两种算法采用了连续的convex优化和半确定性放松感,而其他两种则根据众所周知的成型设计方法进行调整。在现实频道条件下进行的详细模拟表明,随着算法的复杂程度的提高,相应的性能就更接近约束。