Subband Energy Based Reduced Complexity Spectrum Sensing under Noise Uncertainty and Frequency-Selective Spectral Characteristics
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||IEEE Transactions on Signal Processing|
|Publication status||Published - 2015|
|Publication type||A1 Journal article-refereed|
The present work proposes a subband energy detection method that performs efficiently under noise uncertainty (NU) and frequency selective channels. The critical impact of detrimental modeling uncertainties, such as NU, is analytically quantified and it is shown that the introduced method is robust to both NU and frequency selectivity conditions. This is also the case for eigenvalue based sensing techniques, in contrast to traditional energy detector based sensing. Connections of the subband energy based approach and existing eigenvalue based methods are established analytically which leads to a novel reduced complexity processing technique based on the difference between maximum and minimum subband energies. The proposed method is capable of providing accurate and robust performance with low signal-to-noise ratios (SNR) in the presence of NU. Closed-form expressions are derived for the corresponding probability of false alarm and probability of detection under frequency selectivity due to the primary signal spectrum and/or the transmission channel. The validity of the offered expressions is justified through comparisons with respective results from computer simulations. The sensing performance is evaluated in different communication scenarios, with different frequency selective channel models and primary user waveforms. The offered results indicate that the proposed methods provide quite significant savings in complexity, e.g., 78% reduction in the considered example case, while also improving the detection performance at low SNRs and in the presence of NU.
- Computational complexity, Detectors, Eigenvalues and eigenfunctions, Noise, Uncertainty, Cognitive radio, eigenvalue based sensing, extreme Gumbel distribution, frequency selectivity, noise uncertainty, random matrix theorem, spectrum sensing, subband energy based detector