Ever increasing development of wireless devices and wireless networks have increased the value of spectral space. Many efforts have been conducted to increase spectral utilization. The radio environment mapping opens new gates for developing low cost wireless devices. In this paper, a new method is proposed for increasing spectral utilization in Distributed networks. In this method Distributed Kalman filter, which is modified to increase estimation accuracy, is used to estimate position, velocity and power of primary transmitters. These data are used to select spectrum holes optimally and increase spectral utilization compared to centralized methods. Obtained r esults are evaluated through practical implementations and simulations. Innovations of this research include introducing and employing a linear model for estimating position of a transmitter using received power in line of sight (LoS) and non-line of sight (NLS) conditions, modifying extended kalman filter and implementation of Distributed spectrum sensing; advantages of this method are illustrated compared to centralized methods.

The emerging field of compressive sensing enables the reconstruction of the signal from a small set of linear projections. Traditional compressive sensing approaches deal with a single signal; while one can jointly reconstruct multiple signals via Distributed compressive sensing algorithm, which exploits both the inter-and intra-signal correlations via joint sparsity models. Since the wavelet coefficients of many signals is sparse, in this paper, the wavelet transform is used as sparsifying transform, and a new wavelet-based Bayesian Distributed compressive sensing algorithm is proposed, which takes into account the inter-scale dependencies among the wavelet coefficients via hidden Markov tree model, as well as the inter-signal correlations. This paper uses Bayesian procedure to statistically model this correlation via the prior distributions. Also, in this work, a type-1 joint sparsity model is used for jointly sparse signals, in which every sparse coefficient vector is considered as the sum of a common component and an innovation component. In order to jointly reconstruct multiple sparse signals, the centralized approach is used in Distributed compressive sensing, in which all the data is processed in the fusion center. Also, variational Bayes procedure is used to infer the posterior distributions of unknown variables. Simulation results demonstrate that the structure exploited within the wavelet coefficients provides superior performance in terms of average reconstruction error and structural similarity index.

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In this paper a cyclostationarya-a based wideband spectrum sensing method is proposed. The received signal passes through a rough and flexible filter with its effective band tuned to a specific part of the received signal spectrum. This part belongs to a target signal which potentially exists in the received signal. Some cyclic frequencies of the target signal are employed to derive a normalized least mean square (NLMS) adaptive algorithm that estimates the output of the filter from its frequency shifted samples. If the target is absent in the received signal, the norm of the weights of the NLMS algorithm is almost zero. On the other hand, in the case of presence of the target, the norm of the weights will be greater than a certain threshold. The procedure is repeated to cover the entire band of the received signal and therefore it detects all cyclostationary signals with known cyclic frequencies in the received signal. The overall system is very easy to implement and fast and its performance is comparable to other spectrum sensing counterparts.

Cognitive radio (CR) is an effective technique for dealing with scarcity in spectrum resources and enhancing overall spectrum utilization. CR attempts to enhance spectrum sensing by detecting the primary user (PU) and allowing the secondary user (SU) to utilize the spectrum holes. The rapid growth of CR technology increases the required standards for spectrum sensing (SS) performance, especially in regions with low Signal-to-Noise Ratios (SNRs). In Cognitive Radio Networks (CRN), SS is an essential process for detecting the available spectrum. SS is divided into sensing time and transmission time; the more the sensing time, the higher the detection probability) and the lower the probability of a false alarm). So, this paper proposes a novel two-stage SS optimization model for CR systems. The proposed model consists of two techniques: Interval Dependent De-noising (IDD) and Energy Detection (ED), which achieve optimum sensing time, maximum throughput, lower and higher. The Simulation results demonstrated that the proposed model decreases the, achieves a higher especially at low SNRs ranging, and obtains the optimum sensing time, achieving maximum throughput at different numbers of sensing samples (N) and different SNRs from -10 to -20 dB in the case of N = 1000 to 10000 samples. The proposed model achieves a throughput of 5.418 and 1.98 Bits/Sec/HZ at an optimum sensing time of 0.5ms and 1.5ms respectively, when N increases from 10000 to 100000 samples. The proposed model yields an achievable throughput of 5.37 and 4.58 Bits/Sec/HZ at an optimum sensing time of 1.66ms and 13ms respectively. So, it enhances the SS process than previous related techniques.

Joint optimization of spectrum sensing and spectrum access parameters of a cognitive radio sensor network (CRSN) leads to a higher sum throughput of secondary users (SUs) while the interference introduced to primary users (PUs) is kept under certain tolerable level. In this work, first, by using the con-cept of probabilistic spectrum access, joint spectrum sensing and power allocation is performed in a multiband CRSN. The considered optimization problem is formulated with the aim of maximizing the average opportunistic secondary data rate under constraints on the interference introduced to PU and lim-ited power budget of SU. The considered system model leads to a non-convex optimization problem which is converted into a convex problem. Based on using genetic algorithms, optimal solution of this problem is obtained using two different approaches: i) Lagrange multipliers method and ii) Linear programming meth-od. We provide several numerical simulation results to evaluate the performance of our proposed methods in terms of achievable CR data rate, interference introduced to the PU and convergence properties of the proposed algorithms.

One of most emerging technology in the recent years in the field of wireless communication is the Cognitive Radio (CR) technology, which reduces spectrum scarcity significantly. The main function of CR technology is detecting spectrum holes or unused spectrum of primary users (PUs), also called as licensed users, and assigning this unused spectrum to the secondary users (SUs), also called unlicensed users. As the CR technology is open to every user, there are many security issues such as Primary User Emulsion Attack (PUEA), Jamming Attack, spectrum sensing Data Falsification (SSDF) Attack, Lion Attack, and Sink Hole Attack and so on. SSDF attack is the one of major security attack in cognitive radio in which a malicious user sends false data intentionally to the other secondary users. The main aim of the SSDF attack is to disturb the communication between the secondary users or to gain more channel resources. One of the solutions to mitigating SSDF attack is the cooperative spectrum sensing. In this paper, we propose a new algorithm of cooperative sensing based on trust values of secondary users, and compares with the conventional cooperative spectrum sensing with the proposed algorithm. The simulation of cooperative sensing also performed in both time variant channel and time invariant (Rayleigh) channel. The authors also compare the three basic hard fusion techniques such as AND, OR, MAJORITY rule