基于偏移的波束形成：一种鲁棒下行链路传输的新方法

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摘要翻译：
当基站的信道状态信息不完全知道时，为多用户多输入单输出(MISO)下行链路设计一组向接收机提供预先规定的服务质量（QoS）水平的波束形成器可能是相当具有挑战性的。通常，使信干噪比满足或超过给定阈值的约束是难以解决的，这导致了从根本上难以解决的问题。在本文中，我们将发展一个高质量的SINR中断约束的近似，连同一个半定松弛，使我们能够把波束形成器设计问题表述为一个可以有效地解决的凸优化问题。对于不确定度较小的系统，进一步的近似产生了基于闭式表达式迭代计算的算法，这些算法具有明显较低的计算成本。由于寻找波束形成方向引起了这些算法的大部分计算量，因此针对预定义的波束形成方向开发了类似的功率负载算法，结果表明它们的性能接近最优。当系统包含大量天线时，所提出的功率负载可以以只随天线数目线性增长的计算代价获得。该算法提供了所需的停电概率与所消耗的功率之间的明确关系，使我们能够精确地控制功率消耗，并自动识别消耗大部分功率资源的用户。通过开发一种最小化平均停电概念的电力负载技术，说明了所提方法的灵活性。
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英文标题：
《Offset-Based Beamforming: A New Approach to Robust Downlink Transmission》
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作者：
Mostafa Medra, Yongwei Huang, Timothy N. Davidson
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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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一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  The design of a set of beamformers for the multiuser multiple-input single-output (MISO) downlink that provides the receivers with prespecified levels of quality-of-service (QoS) can be quite challenging when the channel state information is not perfectly known at the base station. The constraint of having the SINR meet or exceed a given threshold with high probability is intractable in general, which results in problems that are fundamentally hard to solve. In this paper, we will develop a high quality approximation of the SINR outage constraint that, along with a semidefinite relaxation, enables us to formulate the beamformer design problem as a convex optimization problem that can be efficiently solved. For systems in which the uncertainty size is small, a further approximation yields algorithms based on iterative evaluations of closed-form expressions that have substantially lower computational cost. Since finding the beamforming directions incurs most of the computational load of these algorithms, analogous power loading algorithms for predefined beamforming directions are developed and their performance is shown to be close to optimal. When the system contains a large number of antennas, the proposed power loading can be obtained at a computational cost that grows only linearly in the number of antennas. The proposed power loading algorithm provides an explicit relationship between the outage probability required and the power consumed, which allows us to precisely control the power consumption, and automatically identifies users who are consuming most of the power resources. The flexibility of the proposed approach is illustrated by developing a power loading technique that minimizes an average notion of outage.
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PDF链接：
https://arxiv.org/pdf/1710.09786

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