Machine Learning for Data-Driven Signal Separation and Interference Mitigation in Radio-Frequency Communication Systems
Single-channel source separation for radio-frequency (RF) systems is a challenging problem relevant to key applications, including wireless communications, radar, and spectrum monitoring. This thesis addresses the challenge by focusing on data-driven approaches for source separation, leveraging data...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/152733 |
| _version_ | 1811080558568013824 |
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| author | Lee, Cheng Feng Gary |
| author2 | Wornell, Gregory W. |
| author_facet | Wornell, Gregory W. Lee, Cheng Feng Gary |
| author_sort | Lee, Cheng Feng Gary |
| collection | MIT |
| description | Single-channel source separation for radio-frequency (RF) systems is a challenging problem relevant to key applications, including wireless communications, radar, and spectrum monitoring. This thesis addresses the challenge by focusing on data-driven approaches for source separation, leveraging datasets of sample realizations when source models are not explicitly provided. To this end, deep learning techniques are employed as function approximators for source separation, with models trained using available data. Two problem abstractions are studied as benchmarks for our proposed deep-learning approaches. Through a simplified problem involving Orthogonal Frequency Division Multiplexing (OFDM), we reveal the limitations of existing deep learning solutions and suggest modifications that account for the signal modality for improved performance. Further, we study the impact of time shifts on the formulation of an optimal estimator for cyclostationary Gaussian time series, serving as a performance lower bound for evaluating data-driven methods. The thesis also introduces the “RFChallenge” as a benchmarking platform, aimed at addressing the gap in current literature for a comprehensive comparison of emerging machine learning solutions for RF signal separation. Finally, we explore an alternative approach of using deep learning to train a library of individual signal models that can be used together for subsequent inference tasks. While showing promise as a scalable strategy for the problem, our preliminary findings uncover the practical limitations of such methods. Ultimately, this thesis seeks to provide insights into judicious choices of data-driven solution architecture based on the signal structures under consideration. Our findings aim to stimulate further research at the intersection of machine learning and RF system design, contributing to the development of next-generation wireless technology through data-driven methodologies. |
| first_indexed | 2024-09-23T11:33:29Z |
| format | Thesis |
| id | mit-1721.1/152733 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:33:29Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1527332023-11-03T03:40:37Z Machine Learning for Data-Driven Signal Separation and Interference Mitigation in Radio-Frequency Communication Systems Lee, Cheng Feng Gary Wornell, Gregory W. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Single-channel source separation for radio-frequency (RF) systems is a challenging problem relevant to key applications, including wireless communications, radar, and spectrum monitoring. This thesis addresses the challenge by focusing on data-driven approaches for source separation, leveraging datasets of sample realizations when source models are not explicitly provided. To this end, deep learning techniques are employed as function approximators for source separation, with models trained using available data. Two problem abstractions are studied as benchmarks for our proposed deep-learning approaches. Through a simplified problem involving Orthogonal Frequency Division Multiplexing (OFDM), we reveal the limitations of existing deep learning solutions and suggest modifications that account for the signal modality for improved performance. Further, we study the impact of time shifts on the formulation of an optimal estimator for cyclostationary Gaussian time series, serving as a performance lower bound for evaluating data-driven methods. The thesis also introduces the “RFChallenge” as a benchmarking platform, aimed at addressing the gap in current literature for a comprehensive comparison of emerging machine learning solutions for RF signal separation. Finally, we explore an alternative approach of using deep learning to train a library of individual signal models that can be used together for subsequent inference tasks. While showing promise as a scalable strategy for the problem, our preliminary findings uncover the practical limitations of such methods. Ultimately, this thesis seeks to provide insights into judicious choices of data-driven solution architecture based on the signal structures under consideration. Our findings aim to stimulate further research at the intersection of machine learning and RF system design, contributing to the development of next-generation wireless technology through data-driven methodologies. Ph.D. 2023-11-02T20:11:53Z 2023-11-02T20:11:53Z 2023-09 2023-09-21T14:26:16.897Z Thesis https://hdl.handle.net/1721.1/152733 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Lee, Cheng Feng Gary Machine Learning for Data-Driven Signal Separation and Interference Mitigation in Radio-Frequency Communication Systems |
| title | Machine Learning for Data-Driven Signal Separation and Interference Mitigation in Radio-Frequency Communication Systems |
| title_full | Machine Learning for Data-Driven Signal Separation and Interference Mitigation in Radio-Frequency Communication Systems |
| title_fullStr | Machine Learning for Data-Driven Signal Separation and Interference Mitigation in Radio-Frequency Communication Systems |
| title_full_unstemmed | Machine Learning for Data-Driven Signal Separation and Interference Mitigation in Radio-Frequency Communication Systems |
| title_short | Machine Learning for Data-Driven Signal Separation and Interference Mitigation in Radio-Frequency Communication Systems |
| title_sort | machine learning for data driven signal separation and interference mitigation in radio frequency communication systems |
| url | https://hdl.handle.net/1721.1/152733 |
| work_keys_str_mv | AT leechengfenggary machinelearningfordatadrivensignalseparationandinterferencemitigationinradiofrequencycommunicationsystems |