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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率最小化

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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-RANS)中的网络功耗(NPC)最小化。我们共同优化波束成形矢量,用户发射功率,Fronthault压缩比和传输远程无线电头(RRHS)的集合。用户对噪声到干扰噪声比,每RRH和用户功率约束以及Fronthaul Cafoft Constaints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,我们在两个步骤中解决了这个问题。在步骤I中,提出了基于最小的基于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,提出了一种基于重量的L(1)-norm最小化方法的算法,以解决步骤i中选择的用户的NPC问题。证明了所提出的算法获得的解决方案可以单调地收敛并满足karush- Kuhn-tucker条件。此外,我们的建议算法适用于全双工和半双工C-RAN。我们全面比较这两种类型的C rans,并为系统设计提出了一些有价值的见解。

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