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雷达卡
摘要翻译：
为了减轻业务负担，提高系统容量，授权辅助接入(LAA)已经成为一种很有前途的非授权频谱补充利用技术。然而，由于小基站的密集化和重叠区域内Wi-Fi节点数量的动态变化，授权信道干扰和非授权信道冲突会严重影响服务质量（QoS）和能量消耗。本文综合考虑时变无线信道条件、动态业务负载和Wi-Fi节点数目的随机性，提出了一种基于LAA的SBSs和Wi-Fi网络的自适应频谱接入和功率分配问题，该问题能够在一定的队列稳定性约束下使系统功耗最小化。将复杂随机优化问题改写为两个凸（D.C.）的差在Lyapunov优化框架下编程，从而开发了一种在线能量感知优化算法。我们还在功耗和时延之间折衷[O(1=V)；O(V)]的情况下，从理论上给出了该算法的性能界。数值结果验证了这种折衷性，表明在相同的业务时延下，我们的方案比现有方案可以降低高达72.1%的功耗。
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英文标题：
《Energy-aware Adaptive Spectrum Access and Power Allocation in LAA
Networks via Lyapunov Optimization》
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作者：
Yu Gu, Qimei Cui, Yue Wang, Somayeh Soleimani
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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：
To relieve the traffic burden and improve the system capacity, licensed-assisted access (LAA) has been becoming a promising technology to the supplementary utilization of the unlicensed spectrum. However, due to the densification of small base stations (SBSs) and the dynamic variety of the number of Wi-Fi nodes in the overlapping areas, the licensed channel interference and the unlicensed channel collision could seriously influence the Quality of Service (QoS) and the energy consumption. In this paper, jointly considering time-variant wireless channel conditions, dynamic traffic loads, and random numbers of Wi-Fi nodes, we address an adaptive spectrum access and power allocation problem that enables minimizing the system power consumption under a certain queue stability constraint in the LAA-enabled SBSs and Wi-Fi networks. The complex stochastic optimization problem is rewritten as the difference of two convex (D.C.) program in the framework of Lyapunov optimization, thus developing an online energy-aware optimal algorithm. We also characterize the performance bounds of the proposed algorithm with a tradeoff of [O(1=V ); O(V )] between power consumption and delay theoretically. The numerical results verify the tradeoff and show that our scheme can reduce the power consumption over the existing scheme by up to 72.1% under the same traffic delay.
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PDF链接：
https://arxiv.org/pdf/1802.01803
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