arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1506.00749cs.ITmath.ITmath.OC
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密集无线协作网络的大规模凸优化

Large-Scale Convex Optimization for Dense Wireless Cooperative Networks

Yuanming Shi, Jun Zhang, Brendan O'Donoghue, Khaled B. Letaief

摘要:

凸优化是无线网络中资源分配和信号处理的有力工具。随着网络密度预计将大幅增加以适应呈指数级增长的移动数据流量,性能优化问题正在进入一个以高维度和/或大量约束为特征的新时代,这带来了重大的设计和计算挑战。在本文中,我们提出了一种新的两阶段的方法来解决大规模的凸优化问题的密集无线合作网络,它可以有效地检测不可行性和享受建模的灵活性。该方法在第一阶段通过矩阵填充将原大规模凸规划问题转化为标准锥规划形式,只需将信道状态信息(CSI)和服务质量(QoS)要求等问题参数复制到标准锥规划形式的预存结构中.通过求解标准型的原始对偶对的齐次自对偶嵌入,获得了不可行性证明和并行计算的能力。在求解阶段,采用算子分裂法,即交替方向乘子法(ADMM)求解大规模齐次自对偶嵌入。与二阶方法相比,ADMM可以在合理的时间内以适度的精度并行解决大规模问题。仿真结果将证明的加速比,可扩展性和可靠性的建议框架相比,国家的最先进的建模框架和求解器。

英文摘要:

Convex optimization is a powerful tool for resource allocation and signal processing in wireless networks. As the network density is expected to drastically increase in order to accommodate the exponentially growing mobile data traffic, performance optimization problems are entering a new era characterized by a high dimension and/or a large number of constraints, which poses significant design and computational challenges. In this paper, we present a novel two-stage approach to solve large-scale convex optimization problems for dense wireless cooperative networks, which can effectively detect infeasibility and enjoy modeling flexibility. In the proposed approach, the original large-scale convex problem is transformed into a standard cone programming form in the first stage via matrix stuffing, which only needs to copy the problem parameters such as channel state information (CSI) and quality-of-service (QoS) requirements to the pre-stored structure of the standard form. The capability of yielding infeasibility certificates and enabling parallel computing is achieved by solving the homogeneous self-dual embedding of the primal-dual pair of the standard form. In the solving stage, the operator splitting method, namely, the alternating direction method of multipliers (ADMM), is adopted to solve the large-scale homogeneous self-dual embedding. Compared with second-order methods, ADMM can solve large-scale problems in parallel with modest accuracy within a reasonable amount of time. Simulation results will demonstrate the speedup, scalability, and reliability of the proposed framework compared with the state-of-the-art modeling frameworks and solvers.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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