NSF-AoF: CNS Core: Small: Machine Learning Based Physical Layer and Mobility Management Solutions Towards 6G

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

5G evolution and future 6G cellular networks are targeting operations at higher millimeter wave and sub-Tera Hertz (sub-THz) bands due to the availability of large channel bandwidths to further improve data rate, latency, quality-of-service, and reliability. However, the use of these bands for mobile radio access imposes substantial technical challenges, including the quality, cost- and energy-efficiency of the electronics, the extreme path loss and propagation characteristics, and the overall deployment costs to provide indoor and outdoor network coverage with mobility support. Considering these challenges, this project will investigate the utility of machine learning algorithms, that have been successful in solving complex problems in various domains, for designing physical layer technologies and network management procedures, involved in both user equipment and base stations, that aim to improve robustness and reliability of connectivity under mobility. The project’s expected contributions are at the forefront of emerging 6G standard and applications of modern machine learning tools in wireless communications at high frequency bands. The research will address three key thrusts: i) Thrust 1 will develop machine learning assisted and data driven approaches for user equipment beam training and tracking using compressive sensing-based channel probing for low latency and robustness to phased antenna array impairments. In addition, it will accelerate beam training and tracking on the base station side by using deep reinforcement learning for optimizing beam probing strategies based on environment characteristics and user trajectories. The outcome of this thrust will be significant reduction in beam management overhead in the presence of mobility; ii) Thrust 2 will use a novel receiver processing architecture where the signal path exploits convolutional neural network layers in both time and frequency domains to compensate the effects of the power amplifier nonlinearity and phase noise in a wideband orthogonal frequency division multiplexing (OFDM) receiver, while accounting for frequency-selective multipath channel effects. The outcome of this thrust will be improvement in transmitter power efficiency, coverage and bit error probability. iii) Thrust 3 will develop intelligent handover algorithms to minimize disruptions in user connectivity by exploiting position estimates and beam-level reference signal power measurements with distributed deep reinforcement learning while considering user rate requirements and reducing measurement reporting and sharing overheard between base stations. The research methodology will rely on extensive measurements and data set generations using millimeter wave testbeds for the development and evaluation of the proposed solutions. 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.