Hardware-Efficient and Fast Sensing-Time Maximum-Minimum-Eigenvalue-Based Spectrum Sensor for Cognitive Radio Network

This paper proposes an implementation-friendly maximum-minimum-eigenvalue (MME)-based spectrum sensing algorithm for cognitive radio network. An iterative power method has been applied for the first time to compute maximum and minimum eigenvalues that reduces the computational complexity of this MME algorithm. We suggest new digital architecture of MME-based spectrum sensor with shorter critical-path delay that lowers its sensing time. This spectrum-sensor architecture has been resource shared to further enhance the hardware efficiency. Performance analysis of the suggested MME algorithm with 1024 input-signal samples delivers adequate performance at -10 dB of SNR with the detection probability of 0.8. Suggested MME algorithm performs better than the energy detection-based spectrum sensing under noise uncertainty. It has detection gain of 5.3 dB compared to the cyclostationary feature detection-based spectrum sensing algorithm at 0.7 detection probability. Hardware prototyping of our MME spectrum sensor has been carried out in FPGA platform and its real-time testing is performed using the communication environment of DVB-T standard. We synthesized and post-layout simulated proposed digital sensor in 90 nm-CMOS process that resulted in 0.42 mm(2) of area, operating at a maximum clock frequency of 404 MHz which results in the sensing time of $53.5\mu \text{s}$ . Comparison of our work with literature shows that the suggested MME spectrum-sensor has $2.5\times $ shorter sensing time and lowest area-time-product of 0.023, indicating better hardware-efficiency, than the state-of-the-art implementations.