Optimal entropy quantization for maximum likelihood estimation based cooperative spectrum sensing

Oluyomi Simpson, Yusuf Abdulkadir, Yichuang Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


This paper focuses on the quantization of soft decision based cooperative spectrum sensing (CSS). The soft data fusion CSS schemes in previous research works provide considerable enhancement in the probability of detection, but at the expense of increased bandwidth required for transmitting the sensing measurements to the Fusion Center (FC). In this paper, Maximum likelihood Estimation (MLE) statistics are quantized and sent to the FC as an alternative of the quantized decision statistics of Log-Likelihood Ratios (LLRs) which assume that the distribution of the received primary user (PU) signal is known. Uniform and optimal entropy quantization's are proposed to reduce the reporting channel overheads and a low complex overhead is proposed which helps speed up the PU signal sensing process. This can be significant in high data rate applications. Simulation results illustrate that the scheme can obtain a high detection rate and a reduction in the reporting channel bandwidth.
Original languageEnglish
Title of host publication2016 Wireless Telecommunications Symposium (WTS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)978-1-5090-0314-3
Publication statusPublished - 2 Jun 2016
EventWireless Telecommunications Symposium - London, United Kingdom
Duration: 18 Apr 201620 Apr 2016
Conference number: 10th


ConferenceWireless Telecommunications Symposium
Abbreviated titleWTS 2016
Country/TerritoryUnited Kingdom


  • Cognitive radio
  • Cooperative Spectrum Sensing
  • Data Fusion
  • Energy Detection
  • Quantization


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