摘要翻译：
可再生能源的集成为无线通信系统提供了高效、可持续的资源配置。本文介绍了一种新的框架，用于智能微电网供电的无线蜂窝网络的协调多小区波束形成(CMBF)设计，该网络中的BSs配备了RES采集设备，并可以与主电网进行双向（即买/卖）能量交易。为此，提出了新的模型来考虑随机收获、双向能源交易和基于条件风险价值(CVaR)的能源交易费用。在这些模型的基础上，我们提出了一个分布式CMBF解决方案，在用户服务质量（QoS）约束下最小化网格范围内的交易成本。具体来说，依靠最先进的优化工具，我们表明相关的任务可以被表述为一个非常适合于开发分布式求解器的凸问题。针对RES的随机可用性，利用随机交替方向乘数法(ADMM)提出了一种新的分布式CMBF方案。结果表明，在每个基站只有局部信道状态信息和基站之间信息交换有限的情况下，该方案能够保证得到最优的CMBF解。数值结果验证了该格式的优点。
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英文标题：
《Distributed Coordinated Multicell Beamforming for Wireless Cellular
  Networks Powered by Renewables: A Stochastic ADMM Approach》
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作者：
Shuyan Hu, Chongbin Xu, Xin Wang, Yongwei Huang, and Shunqing Zhang
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  The integration of renewable energy sources (RES) has facilitated efficient and sustainable resource allocation for wireless communication systems. In this paper, a novel framework is introduced to develop coordinated multicell beamforming (CMBF) design for wireless cellular networks powered by a smart microgrid, where the BSs are equipped with RES harvesting devices and can perform two-way (i.e., buying/selling) energy trading with the main grid. To this end, new models are put forth to account for the stochastic RES harvesting, two-way energy trading, and conditional value-at-risk (CVaR) based energy transaction cost. Capitalizing on these models, we propose a distributed CMBF solution to minimize the grid-wide transaction cost subject to user quality-of-service (QoS) constraints. Specifically, relying on state-of-the-art optimization tools, we show that the relevant task can be formulated as a convex problem that is well suited for development of a distributed solver. To cope with stochastic availability of the RES, the stochastic alternating direction method of multipliers (ADMM) is then leveraged to develop a novel distributed CMBF scheme. It is established that the proposed scheme is guaranteed to yield the optimal CMBF solution, with only local channel state information available at each BS and limited information exchange among the BSs. Numerical results are provided to corroborate the merits of the proposed scheme.
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PDF链接：
https://arxiv.org/pdf/1710.06043