Joint Pilot Allocation and Robust Transmission Design for Ultra-Dense User-Centric TDD C-RAN With Imperfect CSI
Joint Pilot Allocation and Robust Transmission Design for Ultra-Dense User-Centric TDD C-RAN With Imperfect CSI
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DOI：
10.1109/twc.2017.2788001
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发表时间：
2018-03-01
影响因子：
10.4
通讯作者：
Nallanathan, Arumugam
中科院分区：
文献类型：
--
作者：
Pan, Cunhua;Mehrpouyan, Hani;Nallanathan, Arumugam
研究了超密集云无线接入网络中信道状态信息的不可用性问题。采用以用户为中心的簇来降低计算复杂度，而不完整的CSI被认为是减少沉重的信道训练开销，其中只有大规模的簇间CSI是可用的。还考虑了集群内CSI的信道估计，其中我们制定了一个联合导频分配和用户设备（UE）选择问题，以最大限度地增加具有固定导频数量的被接纳UE的数量。提出了一种新的考虑多用户导频干扰的导频分配算法。然后，我们考虑鲁棒的波束向量优化问题受到UE的数据速率要求和前传容量约束，其中信道估计误差和不完整的簇间CSI被认为是。精确的数据速率很难以封闭形式获得，相反，我们保守地用它的下限来代替它。由此产生的问题是非凸的，组合的，甚至是不可行的。基于UE选择、逐次凸逼近和半定松弛方法，提出了一种求解该问题的保证收敛的实用算法。我们严格证明了半定松弛是紧的概率为1。最后，大量的仿真结果表明，我们提出的算法的快速收敛性，并证明其优于现有的算法。
This paper considers the unavailability of complete channel state information (CSI) in ultra-dense cloud radio access networks. The user-centric cluster is adopted to reduce the computational complexity, while the incomplete CSI is considered to reduce the heavy channel training overhead, where only large-scale inter-cluster CSI is available. Channel estimation for intra-cluster CSI is also considered, where we formulate a joint pilot allocation and user equipment (UE) selection problem to maximize the number of admitted UEs with fixed number of pilots. A novel pilot allocation algorithm is proposed by considering the multi-UE pilot interference. Then, we consider robust beam-vector optimization problem subject to UEs' data rate requirements and fronthaul capacity constraints, where the channel estimation error and incomplete inter-cluster CSI are considered. The exact data rate is difficult to obtain in closed form, and instead we conservatively replace it with its lower-bound. The resulting problem is non-convex, combinatorial, and even infeasible. A practical algorithm, based on UE selection, successive convex approximation and semi-definite relaxation approach, is proposed to solve this problem with guaranteed convergence. We strictly prove that the semidefinite relaxation is tight with probability 1. Finally, extensive simulation results are presented to show the fast convergence of our proposed algorithm and demonstrate its superiority over the existing algorithms.