Wireless Communications
Optimal multiband joint detection for spectrum sensing in cognitive radio networks
IEEE Transactions on Signal Processing
Spectrum sensing for cognitive radio
RWS'09 Proceedings of the 4th international conference on Radio and wireless symposium
OFDM system identification for cognitive radio based on pilot-induced cyclostationarity
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Multi-window spectrum sensing of unsynchronized OFDM signal at very low SNR
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A survey of spectrum sensing algorithms for cognitive radio applications
IEEE Communications Surveys & Tutorials
Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs
IEEE Communications Magazine
Cyclostationary Signatures in Practical Cognitive Radio Applications
IEEE Journal on Selected Areas in Communications
Defense against Primary User Emulation Attacks in Cognitive Radio Networks
IEEE Journal on Selected Areas in Communications
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Spectrum sensing is one of the most challenging issues of Cognitive Radio communications. The possibility of extremely low signal-to-noise ratio (SNR) of the received signal poses a fundamental challenge to spectrum sensing. In this paper, pilot-based spectrum sensing for OFDM signals is investigated. It is shown that the existing pilot-based OFDM spectrum sensing algorithms suffer from the frequency offset between the transmitter and sensing devices, as well as the noise uncertainty in the sensing threshold design. We consequently propose a robust pilot-based spectrum sensing algorithm for low SNR OFDM signals using a sliding frequency correlator. The proposed algorithm processes additional bandwidth to eliminate the impact of frequency offset. In addition, considering the unknown noise statistics and its time-varying nature, a ratio threshold which is not sensitive to the noise power level is derived for spectrum sensing. Our theoretical analysis and simulation results show that this algorithm can achieve exceptionally good sensing performance at very low SNR, while being insensitive to time and frequency offsets and requiring no information of the noise statistics.