Anti-sampling-distortion compressive wideband spectrum sensing for Cognitive Radio

Abstract

Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio in mobile communication. Compressed sensing (CS) is introduced to transfer the sampling burden. The standard sparse signal recovery of CS does not consider the distortion in the analogue-to-information converter (AIC). To mitigate performance degeneration casued by the mismatch in least square distortionless constraint which doesn't consider the AIC distortion, we define the sparse signal with the sampling distortion as a bounded additive noise, and An anti-sampling-distortion constraint (ASDC) is deduced. Then we combine the ℓ 1 norm based sparse constraint with the ASDC to get a novel robust sparse signal recovery operator with sampling distortion. Numerical simulations demonstrate that the proposed method outperforms standard sparse wideband spectrum sensing in accuracy, denoising ability, etc.Index Terms -cognitive radio, wideband spectrum sensing, compressive sensing, sparse signal recovery, mobile communication, analogue to information converter, sampling distortion.

Enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access

“…Thus it is reasonable that only a small part of the constituent signals will be simultaneously active at a given location and a certain range of frequency band. The sparsity inherently exists in the wideband spectrum [10,[22][23][24][25][26][27][28]. It is also the reason that DSA can work.…”

“…N samples are needed to recover the frequency spectrum r without aliasing, where T 0 is the Nyquist sampling duration. A digital receiver converts the continuous signal x(t) to a discrete complex sequence y t of length M. For illustration convenience, we formulate the sampling model in discrete setting as it does in [10,[22][23][24][25][26][27][28]:…”

Efficient blind spectrum sensing for cognitive radio networks based on compressed sensing

“…Actually, Equation (10) is a second-order cone program and many software packages are available to solve this problem [9]. On the other hand, some variants of LASSO algorithm have been developed to deal with the noisy signals by minimizing the usual sum of squared errors.…”

“…A number of convex optimization software packages have been developed to solve the LASSO problem, such as cvx, SeDumi, Yalmip, and so on [9]. Recently, the authors of [9] improved Equation (11) with a weighted scheme of LASSO …”

“…A compressible signal means that it can sparsely be represented in some basis, and can exactly be reconstructed only with a small set of random projections on an incoherent basis [8][9][10]. Recently, many research efforts have been done on random projection.…”