How Empty is Empty? Weak-Signal Spectrum Survey Measurements and Analysis for Cognitive Radio

Abstract

Spectrum sensing cognitive radios are an emerging technology that promises greater efficiency in utilizing the available wireless spectrum. In particular, spectrum-sensing cognitive radios provide the ability to search for and utilize an unoccupied portion of the wireless spectrum during periods of time when the incumbent user is inactive. A key requirement to operating cognitive radios, therefore, is accurately and reliably identifying unutilized frequency bands, even when those bands may contain extremely weak signals. In this paper, we present initial measurement results from two spectrum occupancy studies conducted in and around Annapolis, Maryland and the US Naval Academy in the frequency range between 700 MHz and 6 000 MHz. At the highest resolution, the measurement system had a noise floor of dBm, allowing it to record signals that were 10-30 dB weaker than those previously reported in the literature. Analysis of the measurement results indicate that spectrum utilization increases exponentially as the receiver's noise floor decreases. Additionally, variations with time, frequency, and receiver threshold are observed. These results imply that, in order to provide reliable detection of incumbent users, cognitive radios will require sensitive receivers and accurate detection algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2008
Accession Number
ADA537808

Entities

People

  • Charles B. Cameron
  • Christopher R. Anderson

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Analyzers
  • Bandwidth
  • Cognitive Radio
  • Communication Systems
  • Data Sets
  • Frequency
  • Frequency Bands
  • High Resolution
  • Low Resolution
  • Maryland
  • Measurement
  • Software Defined Radio
  • Spectrum Analyzers
  • United States
  • United States Naval Academy
  • Wireless Communications

Readers

  • Radio communications and signal processing.