EAGER: Hybrid Precoding for Massive MIMO Communication Networks

Lehigh University, Bethlehem PA

Investigators

Abstract

EAGER: Hybrid Precoding for Massive Multiple-Input Multiple-Output Millimeter Wave Wireless Communication Networks Future 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.