Liu Zhiyang, Zhao Yingxin, Wu Hong, Ding Shuxue
Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, 38 Tongyan Road, Tianjin 300350, China.
Entropy (Basel). 2018 Feb 23;20(2):144. doi: 10.3390/e20020144.
Cloud radio access network (C-RAN) has become a promising network architecture to support the massive data traffic in the next generation cellular networks. In a C-RAN, a massive number of low-cost remote antenna ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed low-latency fronthaul links, which enables efficient resource allocation and interference management. As the RAPs are geographically distributed, group sparse beamforming schemes attract extensive studies, where a subset of RAPs is assigned to be active and a high spectral efficiency can be achieved. However, most studies assume that each user is equipped with a single antenna. How to design the group sparse precoder for the multiple antenna users remains little understood, as it requires the joint optimization of the mutual coupling transmit and receive beamformers. This paper formulates an optimal joint RAP selection and precoding design problem in a C-RAN with multiple antennas at each user. Specifically, we assume a fixed transmit power constraint for each RAP, and investigate the optimal tradeoff between the sum rate and the number of active RAPs. Motivated by the compressive sensing theory, this paper formulates the group sparse precoding problem by inducing the ℓ 0 -norm as a penalty and then uses the reweighted ℓ 1 heuristic to find a solution. By adopting the idea of block diagonalization precoding, the problem can be formulated as a convex optimization, and an efficient algorithm is proposed based on its Lagrangian dual. Simulation results verify that our proposed algorithm can achieve almost the same sum rate as that obtained from an exhaustive search.
云无线接入网络（C-RAN）已成为一种很有前景的网络架构，以支持下一代蜂窝网络中的海量数据流量。在C-RAN中，大量低成本的远程天线端口（RAP）通过高速低延迟前传链路连接到单个基带单元（BBU）池，这使得能够进行高效的资源分配和干扰管理。由于RAP在地理上是分布式的，组稀疏波束成形方案吸引了广泛研究，其中将一部分RAP分配为激活状态，从而可以实现高频谱效率。然而，大多数研究假设每个用户配备单个天线。对于多天线用户如何设计组稀疏预编码器仍知之甚少，因为这需要对相互耦合的发射和接收波束成形器进行联合优化。本文针对每个用户具有多天线的C-RAN，提出了一个最优的联合RAP选择和预编码设计问题。具体而言，我们假设每个RAP有固定的发射功率约束，并研究总和速率与激活RAP数量之间的最优权衡。受压缩感知理论的启发，本文通过引入ℓ0范数作为惩罚来构建组稀疏预编码问题，然后使用重加权ℓ1启发式方法来找到解决方案。通过采用块对角化预编码的思想，该问题可以被构建为一个凸优化问题，并基于其拉格朗日对偶提出了一种高效算法。仿真结果验证了我们提出的算法能够实现与穷举搜索几乎相同的总和速率。
