Unsupervised Radio Scene Analysis Using Neural Expectation Maximization

Files

Unsupervised_Radio_Scene_Analysis_Using_Neural_Expectation_Maximization.pdf (2.08 MB)

Links to Files

https://ieeexplore.ieee.org/abstract/document/10017594

Permanent Link

https://doi.org/10.1109/MILCOM55135.2022.10017594
 http://hdl.handle.net/11603/26901

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0002-5504-4997

Date

2023-01-24

Type of Work

conference papers and proceedings
preprints
Text

Department

Program

Citation of Original Publication

H. Chen and S. -J. Kim, "Unsupervised Radio Scene Analysis Using Neural Expectation Maximization," MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM), Rockville, MD, USA, 2022, pp. 368-373, doi: 10.1109/MILCOM55135.2022.10017594.

Rights

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Subjects

Abstract

An unsupervised learning-based blind RF scene analysis method is proposed. The method can analyze a complex radio scene containing a mixture of different transmission types and estimate the constituent signals with associated channel vectors from multi-antenna measurements. A deep neural network is trained to learn the unique time-frequency patterns of various signal types. The channels, noise powers, and encodings input to the neural network are estimated in a maximum likelihood framework via an expectation-maximization algorithm. Numerical tests using scenes constructed from real RF measurements verify the effectiveness of the proposed method.