Bridging machine learning technologies to multiple-input-multiple-output (MIMO) communications systems is a primary driving force for next-generation wireless systems. This dissertation introduces a variety of neural network structures for symbol detection/equalization tasks in MIMO systems configured with two different waveforms, orthogonal frequency-division multiplexing (OFDM) and orthogonal time frequency and space (OTFS). The former one is the major air interface in current cellular systems. The latter one is developed to handle high mobility. For the sake of real-time processing, the introduced neural network structures are incorporated with inductive biases of wireless communications signals and operate in an online training manner. The utilized inductive priors include the shifting invariant property of quadrature amplitude modulation, the time-frequency relation inherent in OFDM signals, the multi-mode feature of massive antennas, and the delay-Doppler representation of doubly selective channel. In addition, the neural network structures are rooted in reservoir computing - an efficient neural network computational framework with decent generalization performance for limited training datasets. Therefore, the resulting neural network structures can learn beyond observation and offer decent transmission reliability in the low signal-to-noise ratio (SNR) regime. This dissertation includes comprehensive simulation results to justify the effectiveness of the introduced NN architectures compared with conventional model-based approaches and alternative neural network structures. / Doctor of Philosophy / An important topic for next-generation wireless systems is the integration of machine learning technologies with conventional communications systems. This dissertation introduces several neural network architectures to solve the transmission problems in wireless communications systems. The discussion focuses on the following major modern communications technologies: multiple-input-multiple-output (MIMO), orthogonal frequency-division multiplexing (OFDM), and orthogonal time frequency space (OTFS). In today's cellular networks, MIMO and OFDM are the major air-interface. OTFS is a novel technique that has been designed to work in a high-mobility setting. The implemented neural network structures are integrated with inductive biases of wireless communications signals and operate in an online training mode with limited training datasets. The neural network architectures, in particular, are based on reservoir computing, which is an efficient neural network computational system. A learning algorithm's inductive bias (also known as learning bias) is a collection of assumptions that the learner makes to infer outputs from unknown inputs. The dissertation introduces four different inductive priors from four different perspectives of MIMO communications systems. As a result, the neural network architectures can learn beyond observation and provide good generalization output in scenarios having model mismatch issues. The dissertation provides extensive simulation results to support the efficacy of the implemented NN architectures compared to alternative neural network models and traditional model-based approaches.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/113620 |
| Date | 10 August 2021 |
| Creators | Zhou, Zhou |
| Contributors | Electrical Engineering, Liu, Lingjia, Buehrer, Richard M., Xie, Weijun, Yi, Yang, Ellingson, Steven W. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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