EAGER: Energy-efficient Massive MIMO Processing for Millimeter-wave Communications

摘要：毫米波无线通信的高能效海量MIMO处理摘要：为了满足日益增长的高速、大容量无线业务的需求，毫米波(MmWave)技术已经成为下一代无线网络的一种有前途的选择。毫米波系统在30 GHz及以上运行，频谱不那么拥挤，可用带宽比传统无线系统在相对较低的无线电频率下要宽得多。另一方面，使用这种高频会导致严重的信道路径损耗，这是限制毫米波通信覆盖范围和健壮性的主要因素。解决这一问题的一个自然机会是在毫米波收发机中采用大规模多输入多输出(MIMO)，其中具有数百个天线单元的超大型天线阵可以被封装在微型尺寸中，以提供巨大的分集复用增益，从而显著提高覆盖、吞吐量和抗信道衰落的稳健性。然而，随着天线单元数目的增加，不仅信号捕获和硬件成本急剧增加，而且传统的天线阵处理技术的计算复杂性变得令人望而却步。这项研究的目的是提供大规模阵列处理方面的关键技术创新，以便能够以负担得起的计算成本将大规模MIMO用于高速毫米波无线通信。将开发新的信号处理技术，以节能和稳健的方式执行毫米波大规模MIMO收发机的关键侦听任务，包括到达方向估计和信道估计。解决这种大规模MIMO的计算瓶颈是朝着释放备受赞赏的毫米波技术潜力迈出的关键一步，这将为蜂窝数据服务和物联网的无线连接提供丰富的频谱机会。该项目的另一个重要目标是将研究与教育相结合，努力改善学生在无线通信领域的学习体验。该项目旨在开发一种创新的压缩感知框架，以解决MIMO处理中两个重要的感知任务的计算瓶颈：用于波束形成和空间扇区划分的到达方向估计，以及用于数据解调的信道估计。虽然现有的研究努力寻找在非常大的角度或信道空间上的高维信号估计问题的负担得起的解决方案，但该项目提出了一种通过利用感知任务的内在结构来减少问题空间本身来绕过这一困难的探索性途径。该研究的核心是一种新的压缩协方差稀疏感知(CCSS)框架，它尽可能绕过恢复原始信号的中间步骤，直接提取有用的统计量，从而以较低的采样代价实现强信号压缩和有效的特征提取。基于CCSS框架，将开发新的公式、算法和感知机制，以有效地解决资源受限的毫米波海量MIMO系统中的到达方向和信道估计问题，并在性能和成本之间进行量化权衡。

Abstract Title: Energy-efficient Massive MIMO Processing for Millimeter-wave Wireless CommunicationsAbstract: To meet the ever-growing demands for high-speed, large-capacity wireless services, millimeter-wave (mmWave) technology has emerged as a promising option for next-generation wireless networks. The mmWave systems operate around and above 30GHz, where the spectrum is less crowded and the available bandwidth is much wider than that of legacy wireless systems at relatively lower radio frequencies. On the other hand, the use of such high frequencies incurs severe channel path loss, which is the dominant factor limiting the coverage and robustness of mmWave communications. A natural opportunity to cope with this problem is to adopt massive multiple-input multiple-output (MIMO) in mmWave transceivers, where very large antenna arrays with hundreds of antenna elements can be packaged in a miniature size to provide large diversity-multiplexing gains and hence significantly improved coverage, throughput and robustness against channel fading. However, as the number of antenna elements increases, not only the signal acquisition and hardware costs increase drastically, but also the computational complexity of traditional antenna array processing techniques becomes prohibitively high. The objective of this research is to provide key technological innovations in large-scale array processing such that massive MIMO can be utilized for high-speed mmWave wireless communications at affordable computational costs. New signal processing techniques will be developed to perform the key sensing tasks of mmWave massive MIMO transceivers, including direction of arrival estimation and channel estimation, in an energy-efficient and robust manner. Solving such computational bottlenecks of massive MIMO is an essential step toward unleashing the well-appreciated potential of mmWave technology, which will make available abundant spectrum opportunities for wireless connectivity in both cellular data services and Internet of things. Another important goal of this project is to integrate research with education in effort to enhance the learning experiences for students in the field of wireless communications. This project aims to develop an innovative compressive sensing framework to tackle the computational bottleneck in two important sensing tasks of MIMO processing: direction-of-arrival estimation for beamforming and spatial sectorization, and channel estimation for data demodulation. While existing research seeks strenuously to find affordable solutions to high-dimensional signal estimation problems over a very large angle or channel space, this project sets forth an exploratory path that gets around this difficulty by exploiting the inherent structures of the sensing tasks to reduce the problem space itself. At the core of this research is a new framework of compressive covariance sparse sensing (CCSS), which bypasses the intermediate step of recovering the original signals whenever possible and directly extracts the useful statistics to effect strong signal compression and efficient feature extraction at low sampling costs. Based on the CCSS framework, new formulations, algorithms and sensing mechanisms will be developed to efficiently solve the direction-of-arrival and channel estimation problems in resource-constrained mmWave massive MIMO systems, with quantified performance and cost tradeoffs.