摘要翻译：
本研究通过联合优化基站发射功率和小区活动来解决无线通信网络能量消耗最小化的问题。提出了一个混合整数非线性优化问题，给出了一种计算简便的线性内逼近算法。该方法通过综合考虑多个系统参数来优化网络运行，具有很大的灵活性，克服了现有基于启发式的网络运行优化方法的一个主要缺陷。仿真结果表明，该方法在降低能耗方面表现出较高的性能，并在困难的高需求场景下提供隐式负载均衡。
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英文标题：
《Energy Consumption Optimization in Mobile Communication Networks》
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作者：
Florian Bahlke and Marius Pesavento
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最新提交年份：
2018
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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：
  This work addresses the challenge of minimizing the energy consumption of a wireless communication network by joint optimization of the base station transmit power and the cell activity. A mixed-integer nonlinear optimization problem is formulated, for which a computationally tractable linear inner approximation algorithm is provided. The proposed method offers great flexibility in optimizing the network operation by considering multiple system parameters jointly, which mitigates a major drawback of existing state-of-the-art schemes that are mostly based on heuristics. Simulation results show that the proposed method exhibits high performance in decreasing the energy consumption, and provides implicit load balancing in difficult high demand scenarios.
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PDF链接：
https://arxiv.org/pdf/1807.02651