EAGER: Hybrid Precoding for Massive MIMO Communication Networks

ENGING：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设备的日益普及，未来智能和互联社区将对无线通信提出巨大需求。大规模多输入多输出(MIMO)技术与毫米波(毫米波)频谱利用相结合，被认为是实现下一代无线网络数据速率大幅提高的关键突破。在大规模MIMO系统中，基站使用了大量的天线来同时与多个移动用户通信。然而，如果采用传统的方法，每个天线需要一个射频(RF)链，那么这种大量的天线可能导致令人望而却步的成本和功率消耗。本项目致力于新型的混合预编码设计，这种设计不仅可以减少射频链的数量，还可以最大化数据速率。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射频链实现，模拟预编码器由移相器实现。因此，可以显著降低大规模MIMO系统的成本、复杂度和功耗。拟议的研究将对下一代无线网络的设计和开发产生重大影响，大规模MIMO和毫米波是未来无线网络的关键使能技术。通过设计新的课程方案，将研究成果整合到电气工程和计算机工程专业的课堂中。研究成果将通过会议报告和期刊出版物广泛传播。此外，该项目将有助于增加任职人数不足的少数群体的参与，并加强外联活动，以吸引女学生投身工程学职业。本项目旨在研究混合预编码设计方法，在接近全连接、无约束大规模MIMO系统的最佳性能的同时，显著降低成本、复杂度和功耗。该项目将多用户大规模MIMO系统的混合预编码设计转化为模拟和数字预编码器的动态资源分配联合优化，包括子阵选择、功率分配和调制编码率选择。联合优化将使具有显著减少的射频链的混合系统能够以很小的代价获得与全连接的大规模MIMO系统相似的性能。动态资源分配将有助于在给定的信道条件下实现最佳吞吐量。此外，该方法利用有限字母表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，从而提高了优化预编码器在实际系统中的稳健性。该项目的目标预计将通过三项具体任务来实现。首先，对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，算法研究将得到低复杂度的解来解决NP-Hard优化问题。第三，将应用机器学习技术来学习管理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。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.