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EAGER: Hybrid Precoding for Massive MIMO Communication Networks

EAGER：大规模 MIMO 通信网络的混合预编码

基本信息

批准号：
1827592
负责人：
Chengshan Xiao
金额：
$ 20万
依托单位：
Lehigh University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827592&HistoricalAwards=false
关键词：
EAGER Hybrid Precoding Massive MIMO

项目摘要

EAGER: Hybrid Precoding for Massive Multiple-Input Multiple-Output Millimeter Wave Wireless Communication NetworksFuture smart and connected communities will place tremendous demand on wireless communications due to the ever-growing popularity of smartphones, autonomous vehicles, and mobile devices. The massive Multiple-Input Multiple-Output (MIMO) technology, in combination with millimeter wave (mmWave) spectrum utilization, is considered as a key breakthrough for enabling enormous data-rate increase for next generation wireless networks. In massive MIMO systems, a very large number of antennas is employed at the base station to communicate many mobile users simultaneously. However, this large number of antennas can lead to prohibitive cost and power consumption if conventional approach which requires one radio frequency (RF) chain per antenna is adopted. This project focuses on novel hybrid precoding designs which will not only reduce the number of RF chains but also maximize the data rate. The hybrid precoding consists of analog and digital precoders, where the digital precoder is realized by a small amount of RF chains, and the analog precoder is realized by phase shifters. Therefore, the cost, complexity and power consumption of massive MIMO systems can be reduced dramatically. The proposed research can have a large impact on the design and development of future generation of wireless networks, for which massive MIMO and mmWave are key enabling technologies. The research results will be integrated into the classes for electrical engineering and computer engineering majors through designing new course projects. Research findings will be broadly disseminated through conference presentations and journal publications. Moreover, the project will help increase participation of under-represented minorities and enhance outreach activities to attract female students to careers in engineering. This project aims to investigate hybrid precoding design methods that can drastically reduce the cost, complexity and power consumption while approaching the optimal performance of fully-connected, unconstrained massive MIMO systems. The project formulates the hybrid precoding design of multi-user massive MIMO systems into a joint optimization of analog and digital precoders with dynamic resource allocation which includes subarray selection, power allocation, and modulation-coding-rate selection. The joint optimization will enable the hybrid system with a significantly reduced number of RF chains to achieve similar performance of fully-connected massive MIMO systems at a fractional cost. The dynamic resource allocation will help to achieve best throughput for given channel conditions. Furthermore, the proposed approach utilizes finite-alphabet inputs and statistical channel state information (CSI) instead of the idealistic Gaussian inputs and instantaneous CSI, thus improving the robustness of the optimized precoders for practical systems. The objective of this project is expected to be accomplished by three specific tasks. First, theoretical studies of the achievable data rates will address the fundamental tradeoff between performance and cost of hybrid precoding. Second, algorithm research will derive low-complexity solutions to solve the NP-hard optimization problems. Third, machine learning techniques will be applied to learn the features and transition probabilities of the Markov decision processes governing the online resource allocation and hybrid precoding.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

EAGER：大规模多输入多输出毫米波无线通信网络的混合预编码由于智能手机、自动驾驶汽车和移动的设备的日益普及，未来的智能互联社区将对无线通信提出巨大的需求。大规模多输入多输出（MIMO）技术与毫米波（mmWave）频谱利用相结合，被认为是实现下一代无线网络的巨大数据速率增加的关键突破。在大规模MIMO系统中，在基站处采用非常大量的天线来同时与许多移动的用户通信。然而，如果采用每个天线需要一个射频（RF）链的常规方法，则这种大量的天线可能导致过高的成本和功耗。该项目的重点是新型的混合预编码设计，不仅可以减少RF链的数量，而且还可以最大限度地提高数据速率。混合预编码由模拟预编码器和数字预编码器组成，其中数字预编码器由少量RF链实现，模拟预编码器由移相器实现。因此，大规模MIMO系统的成本、复杂性和功耗可以大幅降低。拟议的研究可能会对下一代无线网络的设计和开发产生重大影响，其中大规模MIMO和毫米波是关键的使能技术。研究成果将通过设计新的课程方案融入到电气工程和计算机工程专业的课程中。研究结果将通过会议介绍和期刊出版物广泛传播。此外，该项目将有助于增加代表性不足的少数群体的参与，并加强外联活动，以吸引女生从事工程职业。本项目旨在研究混合预编码设计方法，可以大大降低成本，复杂性和功耗，同时接近全连接，无约束的大规模MIMO系统的最佳性能。该方案将多用户大规模MIMO系统的混合预编码设计问题转化为模拟和数字预编码器的联合优化问题，包括子阵选择、功率分配和调制编码速率选择等动态资源分配。联合优化将使具有显著减少的RF链数量的混合系统能够以较小的成本实现与完全连接的大规模MIMO系统相似的性能。动态资源分配将有助于在给定信道条件下实现最佳吞吐量。此外，该方法利用有限字母输入和统计信道状态信息（CSI），而不是理想的高斯输入和瞬时CSI，从而提高了实际系统的优化预编码器的鲁棒性。该项目的目标预计将通过三项具体任务来实现。首先，可实现的数据速率的理论研究将解决混合预编码的性能和成本之间的基本权衡。其次，算法研究将得出低复杂度的解决方案，以解决NP难优化问题。第三，机器学习技术将被应用于学习控制在线资源分配和混合预编码的马尔可夫决策过程的特征和转移概率。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。