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Using LZMA Compression for Spectrum Sensing with SDR Samples

Published

Author(s)

Andre Rosete, Kenneth Baker, Yao Ma

Abstract

Successful spectrum management requires reliable methods for determining whether communication signals are present in a spectrum portion of interest. The widely-used energy detection (ED), or radiometry method is only useful in determining whether a portion of radio-frequency (RF) spectrum contains energy, but not whether this energy carries communication information. In this paper we introduce LZMSA (Lempel-Ziv Markovchain Sum Algorithm), a new spectrum sensing algorithm (SSA) that can detect the presence of a communication signal by leveraging the Liv-Zempel-Markov chain algorithm (LZMA). LZMA is a lossless, general-purpose data compression algorithm that is widely available on many computing platforms. The new algorithm is shown to have good performance at distinguishing between samples of signals that contain communication signals and samples of noise when these samples are collected using a software-defined radio (SDR), while not reacting to Gaussian noise as a present signal. This detection algorithm does not require demodulation of the signal or training.
Conference Dates
November 8-10, 2018
Conference Location
New York City, NY, US
Conference Title
The 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference

Keywords

Spectrum sensing algorithm, ROC, AUC, signal detection, binary classifier, compression.

Citation

Rosete, A. , Baker, K. and Ma, Y. (2018), Using LZMA Compression for Spectrum Sensing with SDR Samples, The 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, New York City, NY, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=926739 (Accessed April 16, 2024)
Created November 9, 2018, Updated April 14, 2022