Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty

This paper proposes novel spectrum sensing algorithms for cognitive radio networks. By assuming known transmitter pulse shaping filter, synchronous and asynchronous receiver scenarios have been considered. For each of these scenarios, the proposed algorithm is explained as follows: First, by introducing a combiner vector, an over-sampled signal of total duration equal to the symbol period is combined linearly. Second, for this combined signal, the Signal-to-Noise ratio (SNR) maximization and minimization problems are formulated as Rayleigh quotient optimization problems. Third, by using the solutions of these problems, the ratio of the signal energy corresponding to the maximum and minimum SNRs are proposed as a test statistics. For this test statistics, analytical probability of false alarm ($P_f$) and detection ($P_d$) expressions are derived for additive white Gaussian noise (AWGN) channel. The proposed algorithms are robust against noise variance uncertainty. The generalization of the proposed algorithms for unknown transmitter pulse shaping filter has also been discussed. Simulation results demonstrate that the proposed algorithms achieve better $P_d$ than that of the Eigenvalue decomposition and energy detection algorithms in AWGN and Rayleigh fading channels with noise variance uncertainty. The proposed algorithms also guarantee the desired $P_f(P_d)$ in the presence of adjacent channel interference signals

Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple Antennas

In this paper, we propose a signal-selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple-antenna capability. This method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. The analytical threshold for achieving constant false alarm rate using this detection method is presented, verified through simulations, and shown to be independent of both the number of samples used and the noise variance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown, through numerical simulations, to outperform existing multiple-antenna cyclostationary-based spectrum sensing algorithms under a quasi-static Rayleigh fading channel, in both spatially correlated and uncorrelated noise environments. The algorithm also has significantly lower computational complexity than these other approaches.Comment: 6 pages, 6 figures, accepted to IEEE GLOBECOM 201

Cooperative spectrum sensing: performance analysis and algorithms

The employment of cognitive (intelligent) radios presents an opportunity to efficiently use the scarce spectrum with the condition that it causes a minimal disturbance to the primary user. So the cognitive or secondary users use spectrum sensing to detect the presence of primary user. In this thesis, different aspects related to spectrum sensing and cognitive radio performance are theoretically studied for the discussion and in most cases, closedform expressions are derived. Simulations results are also provided to verify the derivations. Firstly, robust spectrum sensing techniques are proposed considering some realistic conditions, such as carrier frequency offset (CFO) and phase noise (PN). These techniques are called the block-coherent detector (N2 -BLCD), the secondorder matched filter-I (SOMF-I) and the second-order matched filter-II (SOMF-II). The effect of CFO on N2 -BLCD and SOMF-I is evaluated theoretically and by simulation for SOMF-II. However, the effect of PN is only evaluated by simulation for all proposed techniques. Secondly, the detection performance of an energy detector (ED) is analytically investigated over a Nakagami-m frequency-selective (NFS) channel. Thirdly, the energy efficiency aspect of cooperative spectrum sensing is addressed, whereby the energy expenditure is reduced when secondary users report their test statistics to the fusion center (FC). To alleviate the energy consumption overhead, a censored selection combining based power censoring (CSCPC) is proposed. The accomplishment of energy saving is conducted by not sending the test statistic that does not contain robust information or it requires a lot of transmit power. The detection performance of the CSCPC is analytically derived using stochastic geometry tools and verified by simulation. Simulation results show that that the CSCPC technique can reduce the energy consumption compared with the conventional techniques while a detection performance distortion remains negligible. Finally, an analytical evaluation for the cognitive radio performance is presented while taking into consideration realistic issues, such as noise uncertainty (NU) and NFS channel. In the evaluation, sensing-throughput tradeoff is used as an examination metric. The results illustrate the NU badly affects the performance, but the performance may improve when the number of multipath increases

DVB-T signal detection for indoor environments in low-SNR regime

The problem of coexistence between the primary (licensed) and secondary (non-licensed) users can be solved in various ways. One of them assumes the application of the detailed Radio Environment Maps being a kind of database, where some crucial information about the licensed transmission can be stored. In this paper we propose the new methods for signal detection in low signal-to-noise regime and compare it through hardware experiment with other known techniques used for spectrum sensing.Peer ReviewedPostprint (author’s final draft