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Nowadays, the spectrum becomes more crowded and to solve this problem we use the cognitive radio technology, cognitive radio is a promising technology used to improve of spectrum utilization. Among important functions of the cognitive radio is the spectrum sensing. Most of the research works on the spectrum sensing for the cognitive radio networks are considered in a fixed temporal state, they are ignored impact of the mobility of a secondary user. We interested in the concept of the spectrum sensing in realtime. In this paper, we propose an algorithm that examined the impact of the mobility of a secondary user to determine the parameters that affect the spectrum sensing in cognitive radio networks. The performance of the algorithm proposed is evaluated with simulations and results, and of course we will finish by a conclusion and a future perspective.

<p>Handover is a process that allows a mobile node to change its attachment point. A mobile node connected to a network can, in order to improve the quality of service, have the need to leave it to connect to a cell either of the same network or of a new network. The present paper introduce three techniques using adaptive Variable Step-Size Least Mean Square (VSSLMS) filter combined with spectrum sensing probability method to detect the triggering of handover in heterogeneous LTE networks. These techniques are Normalized LMS (NLMS), Kwong-NLMS and Li-NLMS. The simulation environment is composed of two femtocells belonging to a macrocell. Five User Equipements (UEs) are positioned in one femtocell and are assumed closest to its circumference. Simulation results show that sensing probability with Li-NLMS algorithm has a better performance compared with classical NLMS and Kwong-NLMS.</p>

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The signal strength sensing in the context of cognitive radio networks (CRNs), is very important to predict the primary signal of base station (PBS), particularly when the secondary user (SU) is in a congested environment, and also when is in motion towards the end of coverage of PBS. However, this article presents an analysis on the prediction of primary signal strength in CRNs using an Alpha-Beta Filter (ABF) and a Neyman-Pearson Detector (NPD). The challenge of this contribution is based on a realistic sensing of primary signal strength and to do that, we have assumed that the reporting channels between the SU and the PBS are composited with the shadowing and multipath fading (SMF), and the receiver noise has also added. In this regard, the obtained results were discussed through: the signal-to-noise ratio (SNR) uncertainty, the detection probability (PD) and the False Alarm Probability (PFA), where the average relative error of prediction for the PD will be equal to10-5.

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