Abstract
In cognitive radio networks, wideband spectrum sensing (WSS) has been advocated as an effective approach to increase the spectrum access opportunity of the secondary users. On the other hand, because of the increase of sampling rate and sensing time duration cost by the analog-to-digital converter (ADC), energy saving has been a significant problem for WSS. In this paper, taking advantage of the frequency-domain sparsity of the wideband spectrum, a WSS scheme combining compressed sensing and multi-band joint detection technique is proposed to reduce the energy consumption. Based on extensive analysis and simulation, we identify the sparsity order of the wideband spectrum, the received signal-to-noise ratio (SNR) of the primary signal, and the compression rate employed in sampling as three key factors that affect the sensing performance. In particular, we derive a closed-form analytical model of the scheme. Based on these observations, the energy efficiency, defined as the ratio of the spectrum access opportunity to the energy consumption, is maximized through the optimization process of the compression rate under the sensing performance constraints. We also indicate the uniqueness of the optimal compression rate for maximizing the energy efficiency. Numerical and simulation results show that our proposed scheme is more energy efficient if the wideband spectrum is sparser in the frequency domain.
Highlights
In cognitive radio networks (CRNs), the secondary users (SUs) can opportunistically access the spectrum bands unoccupied by the primary users (PUs)
We propose a reconstruction structure aiming at optimizing the energy efficiency, which is defined as the ratio of SU’s spectrum access opportunity to the energy consumption
Based on extensive analysis and simulation, we identify the sparsity order, the received signal-to-noise ratio (SNR), and the compression rate as three key factors when performing our proposed scheme and quantify these contributions by mathematical approximation to support the theoretical derivation
Summary
In cognitive radio networks (CRNs), the secondary users (SUs) can opportunistically access the spectrum bands unoccupied by the primary users (PUs). The parallel multi-band detection scheme is more energy efficient by improving the sampling rate to shorten the sensing time duration. Only a minority of channels are occupied by PUs at the same time, namely that the wideband spectrum is sparser in the frequency range Taking advantage of such sparsity of wideband spectrum, compressed sensing (CS) has recently been proposed to reduce the sampling rate below the Nyquist rate [5,6,7,8,9,10,11]. For this reason, the CS-based spectrum sensing methods have been proposed as an efficient approach for energy saving in WSS.
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