Eigenvalue-based spectrum sensing algorithms for cognitive radio
IEEE Transactions on Communications
An Implementation of Matrix Eigenvalue Decomposition with Improved Jacobi Algorithm
PCSPA '10 Proceedings of the 2010 First International Conference on Pervasive Computing, Signal Processing and Applications
A survey of spectrum sensing algorithms for cognitive radio applications
IEEE Communications Surveys & Tutorials
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In this paper, a novel multiple antenna, high-resolution eigenvalue-based spectrum sensing algorithm based on the FFT of the received signal is introduced. The proposed platform overcomes the SNR wall problem in the conventional energy detection (ED) algorithm, enabling the detection of the weak signals at 驴10 dB SNR. Moreover, the utilization of FFT for the input signal channelization provides a simple, low-power design for a high-resolution spectrum sensing regime. A real-time, low-area, and low-power VLSI architecture is also developed for the algorithm, which is implemented in a 0.18 μm CMOS technology. The implemented design is the first eigenvalue-based detection (EBD) architecture proposed to-date capable of detecting weak signals at 驴10 dB. Despite having more algorithmic complexity in comparison to the ED, the proposed EBD architecture shows no significant increase in the core area and the power consumption, due to the FFT utilization for the input signal channelization. The proposed design occupies a total area of 3.4 mm2 and dissipates 78 mW for a 40 MHz sensing bandwidth consisting of 32 sub-channels.