Maximum Likelihood Detection of Random Primary Networks for Cognitive Radio Systems

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概要

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• In this letter, we focus on detecting a random primary user (PU) network for cognitive radio systems in a cooperative manner by using maximum likelihood (ML) detection. Different from traditional PU network models, the random PU network model in this letter considers the randomness in the PU network topology, and so is better suited for describing the infrastructure-less PU network such as an ad hoc network. Since the joint pdf required for the ML detection is hard to obtain in a closed form, we derive approximate ones from the Gaussian approximation. The performance of the proposed algorithm is comparable to the optimal one.

著者

• Kim Seong-lyun

  Radio Resource Management & Optimization Laboratory Graduate School Of Management Information An

• LEE Sunyoung

  Radio Resource Management and Optimization (RAMO) Laboratory, School of Electrical & Electronic Engineering, Yonsei University

• CHOI Kae

  Department of Computer Science & Engineering, Seoul National University of Science and Technology (SeoulTech)

• CHOI Kae

  Department of Computer Science & Engineering, Seoul National University of Science and Technology (SeoulTech)

• KIM Seong-Lyun

  Radio Resource Management and Optimization (RAMO) Laboratory, School of Electrical & Electronic Engineering, Yonsei University

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• Effect of Reading Errors on Location Prediction in RFID Indoor Networks
• Mobility-Based Topology Control for Wireless Ad Hoc Networks
• Maximum Likelihood Detection of Random Primary Networks for Cognitive Radio Systems

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