Full-Duplex Cooperative Sensing for Spectrum-Heterogeneous Cognitive Radio Networks

Abstract

In cognitive radio networks (CRNs), spectrum sensing is critical for guaranteeing that the opportunistic spectrum access by secondary users (SUs) will not interrupt legitimate primary users (PUs). The application of full-duplex radio to spectrum sensing enables SU to carry out sensing and transmission simultaneously, improving both spectrum awareness and CRN throughput. However, the issue of spectrum sensing with full-duplex radios deployed in heterogeneous environments, where SUs may observe different spectrum activities, has not been addressed. In this paper, we give a first look into this problem and develop a light-weight cooperative sensing framework called PaCoSIF, which involves only a pairwise SU transmitter (SU-Tx) and its receiver (SU-Rx) in cooperation. A dedicated control channel is not required for pairwise cooperative sensing with instantaneous feedback (PaCoSIF) because sensing results are collected and fused via the reverse channel provided by full-duplex radios. We present a detailed protocol description to illustrate how PaCoSIF works. However, it is a challenge to optimize the sensing performance of PaCoSIF since the two sensors suffer from spectrum heterogeneity and different kinds of interference. Our goal is to minimize the false alarm rate of PaCoSIF given the bound on the missed detection rate by adaptively adjusting the detection threshold of each sensor. We derive an expression for the optimal threshold using the Lagrange method and propose a fast binary-searching algorithm to solve it numerically. Simulations show that, with perfect signal-to-interference-and-noise-ratio (SINR) information, PaCoSIF could decrease the false alarm rate and boost CRN throughput significantly against conventional cooperative sensing when SUs are deployed in spectrum-heterogeneous environments. Finally, the impact of SINR error upon the performance of PaCoSIF is evaluated via extensive simulations.

Keywords: cognitive radio networks; cooperative spectrum sensing; decision fusion; full-duplex radio; spectrum heterogeneity.

Figure 1

A scenario for CRN deployment in spectrum-heterogeneous environments.

Figure 2

Scheduling of sensing, feedback and transmission in PaCoSIF.

Figure 3

Flowcharts of the PaCoSIF. Three typical scenarios are represented: (a) PU’s return is detected by the SU-Tx; (b) PU’s return is detected by the SU-Tx; (c) multiple handshakes due to spectrum heterogeneity.

Figure 4

The false alarm rates of an individual sensor versus cooperative sensing. (a) ROC of an individual sensor (${\rho }_{tx}>{\rho }_{rx}$); (b) false alarm rate of sensing fusion (${\rho }_{tx}>{\rho }_{rx}$); (c) ROC of an individual sensor (${\rho }_{tx}<{\rho }_{rx}$); (d) false alarm rate of sensing fusion (${\rho }_{tx}<{\rho }_{rx}$).

Figure 5

The false alarm rate of the fused decisions in spectrum-heterogeneous environments.

Figure 6

Cooperative gain in spectrum-heterogeneous environments.

Figure 7

The exponent assigned to the SU-Rx in spectrum-heterogeneous environments.

Figure 8

The distribution of the false alarm rate under different noise uncertainties. (a) $\Delta \rho =4\mathrm{dB}$; (b) $\Delta \rho =-4\mathrm{dB}$.

Figure 9

The impact of noise uncertainty upon the minimum cooperative gain. (a) $\Delta \rho =4\mathrm{dB}$; (b) $\Delta \rho =-4\mathrm{dB}$.

Figure 10

The impact of noise uncertainty upon the mean false alarm rate. (a) $\Delta \rho =4\mathrm{dB}$; (b) $\Delta \rho =-4\mathrm{dB}$.

Figure 11

CRN throughput in spectrum-heterogeneous environments.

Figure 12

The impact of noise uncertainty upon the CRN throughput. (a) $\Delta \rho =4\mathrm{dB}$; (b) $\Delta \rho =-4\mathrm{dB}$.