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Spectrum sensing with multiple receive antennas is addressed in the cognitive radio network under impulsive noise environments. Based on order statistics, we propose a non-linear combining scheme to cope with the heavy-tail characteristics of the probability density function of impulsive noise. Through computer simulations, it is shown that the proposed scheme exhibits better detection performance than the conventional schemes in impulsive noise environments with Rayleigh fading.

Unlike most studies assuming exact knowledge of the transmit power and path-loss exponent, we address the localization problem under a more practical assumption that the exact values of the transmit power and/or path-loss exponent are unavailable. We propose an algorithm based on the differential evolution, opposition based learning, and adaptive redirection that jointly estimates the position of the target node and the parameters. Results from simulation and indoor experiments show that the proposed algorithm provides higher localization accuracy and requires less computational time than conventional algorithms especially when the exact values of the transmit power and path-loss exponent are unavailable simultaneously.

To deal with the multimodality, nonlinearity, and nonconvexity of the maximum likelihood (ML) cost function when solving the localization problem with received signal strength (RSS) in wireless sensor networks, we propose a localization approach based on the differential evolution algorithm, opposition-based learning, and adaptive redirection in a unified way. In the proposed approach, we need neither to approximate the ML cost function nor to provide a good initial point while most conventional approaches, including those based on the semidefinite programming, second-order cone programming, and unscented transformation. The performance and computational complexity of the proposed approach are analyzed and compared with those of other approaches for three practical scenarios. Simulation results confirm that the proposed approach performs better than other approaches with relatively low computational complexity. Note to Practitioners —This article was motivated by the problem of localization in wireless sensor networks that can be found in different areas, such as intelligent farming, health care monitoring, and environmental monitoring. In this article, a technique is proposed, which exploits the RSS measurements to estimate the position of a target node. This technique is quite popular due to its simplicity and low cost. Most of the techniques based on the ML formulation approximate the ML cost function due to its high nonlinearity and nonconvexity. This article suggests a new approach using differential evolution and opposition-based learning a redirection, which does not require approximating the ML cost function to find the location of a target. In addition, in this article, we analyze the sensor nodes’ placement and its effect on localization accuracy. Numerical results in three practical scenarios are provided to demonstrate the effectiveness of the proposed approach and its superiority compared with state-of-the-art algorithms. In future research, we will address a more realistic scenario with multiple targets.

For detecting the presence of the primary user under malicious attacks in cooperative cognitive radio networks, we propose a detection scheme based on the order statistics and recursive updating algorithm with aging factor. The aging factor not only makes the detector sensitive to the change in the behavior of secondary users but also allows reduction in the storage space. The order statistics help select secondary users with higher degree of reputation for cooperation, and consequently, reduce the influence of malicious users. Computer simulations show the proposed scheme can defend the network against malicious attacks more effectively than the conventional scheme. Keywords: Aging factor, Cooperative spectrum sensing, Order statistics, Recursive updating algorithm

We obtain explicit formulae for the expected values $$E \{ \prod \nolimits _{i=1}^{3}g_i ( X_i ) \}$$ of standard tri-variate Gaussian random vector $${\underline{X}}= \left( X_1, X_2 , X_3 \right) $$ over the set $$g_i (x) \in \left\{ \delta (x), \mathrm {sgn}(x), |x|, x \right\} $$ of nonlinear and linear functions. Based on the results, we also suggest corrections to long-known formulae for two incomplete moments. Applications of the formulae in practical examples are also illustrated concisely. For easy reference for the readers, the explicit formulae derived in this paper and related well-known results are summarized in tables.

We consider the problem of designing waveforms for mitigating single tone jamming (STJ) signals with an estimated jamming frequency in time-hopping spread spectrum (TH SS) systems. The proposed design of waveforms optimizes the anti-jamming (AJ) performance of TH SS systems by minimizing the correlation between the template and STJ signals, in which the problem of waveform optimization is simplified by employing a finite number of rectangular pulses. The simplification eventually makes the design of waveforms be converted into a problem of finding eigenvalues and eigenvectors of a matrix. Simulation results show that the waveforms designed by the proposed scheme provide us with performance superior not only to the conventional waveforms but also to the clipper receiver in the mitigation of STJ. The waveforms from the proposed design also exhibit a desirable AJ capability even when the estimated frequency of the STJ is not perfect.

With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.