Enabaling LPI/LPD in Wireless Communications Through Deep Learning

reportActive / Technical Report | Accession Number: AD1092036 | Open PDF

Abstract:

The goal of this activity is to develop machine learning techniques to enable resource and latency efficient communication systems. Traditionally, communication system design has focused on exploiting traditional tools such as adaptive signal processing and coding theoretic approaches to determine the best detection, estimation and encodingdecoding algorithms for communication systems. The benefit of doing so is that we obtain explicit algorithms that present provable performance guarantees, such as convergence, error rates and throughput.

Author(s):
Vishwanath, Sriram
Author Organization(s):
University of Texas at Austin Austin United States
Descriptive Note:
Technical Report,28 Sep 2018,27 Jun 2019
Pagination:
0009

Security Markings

DOCUMENT & CONTEXTUAL SUMMARY

Distribution:
Approved For Public Release
Distribution Statement:
Approved For Public Release;

RECORD

Collection: TR
Identifying Numbers
Report Number(s):
73784-NS-II.3
Monitor Series:
73784-NS-II.3
 Subject Terms
Joint Capability Areas:
JCA_1.2.1_Training; JCA_1.2.3_Educating; JCA_5_Command and Control
Modernization Areas:
Autonomy
Communities of Interest:
Autonomy
Descriptor(s):
multiple input multiple output, machine learning, deep learning, signal processing, gaussian noise, amplitude modulation, wireless communications, algorithms
Field(s)/Group(s):
Command, Control and Communications Systems
Keyword(s):
machine learning techniques
Report Date:
2019 Jun 01
Creation Date:
2020 Feb 27