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User Selection and Power Minimization in Full-Duplex Cloud Radio Access Networks

机译:全双工云无线电接入网络中的用户选择和功耗最小化

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In this paper, we investigate the network power consumption (NPC) minimization in full-duplex cloud radio access networks (C-RANs) with quality-of-service requirements. We jointly optimize the beamforming vectors, the users' transmit power, the fronthaul compression ratio, and the set of transmitting remote radio heads (RRHs). Users' requirements of signal-to-interferenceand-noise ratio, per-RRH and user power constraints, and fronthaul capacity constraints are considered. Due to these conflicting constraints, the optimization problem may be infeasible. Thus, we solve this problem in two steps. In Step I, a minimum-meansquare-error-based user selection algorithm is proposed to find the largest subset of feasible users. In Step H, an algorithm based on the reweighted l(1) -norm minimization method is proposed to solve the NPC problem with the users selected in Step I. The solutions obtained by the proposed algorithms are proved to monotonically converge and satisfy the Karush-Kuhn-Tucker conditions. Moreover, our proposed algorithms are applicable to both full-duplex and half-duplex C-RANs. We comprehensively compare these two types of C-RANs and propose some valuable insights for system design.
机译:在本文中,我们研究了具有服务质量要求的全双工云无线电接入网络(C-RAN)中的网络功耗(NPC)最小化。我们共同优化了波束成形矢量,用户的发射功率,前传压缩率以及发射远程无线电头端(RRH)的集合。考虑了用户对信号干扰噪声比,每RRH和用户功率限制以及前传容量限制的要求。由于这些冲突的约束,优化问题可能是不可行的。因此,我们分两步解决了这个问题。在步骤I中,提出了一种基于最小均方误差的用户选择算法,以找到可行用户的最大子集。在步骤H中,提出了一种基于重加权l(1)-范数最小化方法的算法,以解决步骤I中选择的用户的NPC问题。证明了该算法获得的解能够单调收敛并满足Karush- Kuhn-Tucker条件。此外,我们提出的算法适用于全双工和半双工C-RAN。我们全面比较了这两种类型的C-RAN,并提出了一些有价值的系统设计见解。

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