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Mobile cellular radio systems have become complex multi-layered systems with a mixed architecture of macro and micro cells; therefore, a dynamic method of triggering the handover algorithm in such systems is invaluable and required. In this paper the authors develop a simple handover mechanism to adapt for a fast moving mobile station requesting a handover, in which the authors utilise a Doppler frequency estimation in the downlink for adjustment and apply it to a long-term evolution (LTE) network. This method is beneficial for high speed mobiles in macro and/or micro cells, in which for the latter the cell radius is small and needs a dynamic algorithm to respond in a timely manner. The main objective of this study is to investigate the performance of the proposed algorithm; hence, a system layout is specifically designed for this study. Other factors, such as interference, which may affect the performance of the suggested system are not addressed here. The main concern is to study and compare the proposed algorithm to the standard handover algorithm currently implemented in LTE. Simulations at the system level show a marked decrease in the average number of handovers requested.

In this paper, we investigate the effect of imperfect sensing on the performance of opportunistic spectrum access (OSA) in cognitive radio (CR) networks. We consider a system modeled as a continuous-time Markov chain (CTMC), and then evaluate its performance in terms of the probabilities of users being blocked or dropped. Our results demonstrate that the performance of the underlying system significantly degrades when imperfect sensing is considered; thus, there is a pressing need for a reliable spectrum sensing scheme to improve the overall quality of service in practical scenarios. A simulation study is presented to corroborate the analytical results and to demonstrate the performance of OSA under imperfect sensing conditions.

In this paper, we study the performance of a cooperative spectrum sensing (CSS) scheme over imperfect feedback channels, where N secondary users (SUs) collaborate in order to enhance the detection probability of a primary user (PU). The sensing process is divided into two phases: In the first phase each SU, which is equipped with multiple antennas, independently detect the PU signal. In the second phase, the SUs' local decisions are sent via erroneous and orthogonal feedback channels to a fusion center (FC), where a final decision is made. We utilize Bayesian criterion which accounts for the costs of misdetection and false alarm. Quasi-Newton method is employed to obtain the optimal number of antennas for a given SNR and vice versa. Our simulation results concur the mathematical derivations and validate the optimality of quasi-Newton method. We further show that the derived optimal fusion rule outperforms the OR rule, AND rule and MAJORITY rule.

Cognitive radio (CR) enriches wireless technology systems by harnessing spectrum white spaces. Such systems require continuous and reliable sensing to provide high-quality service to their users and to minimize the interference they may cause to legacy networks. As the simplicity of implementation of energy detectors and their incoherent requirements make them an ideal candidate for this type of application, this work provides a further mathematical analysis to the probability of detection over different fading channels. We derive a tight closed-form expression for the probability of detection over Nakagami channels. In addition, we introduce an accurate recursive algorithm to compute the probability of detection for an odd degree of freedom over additive white Gaussian noise channels, which has been overlooked in the literature so far. Finally, we present the simulation results which concur with the numerical results and which are also a perfect match with the mathematical expressions derived.

In this paper, we propose a handover algorithm for WiMAX networks. The algorithm relies on the computation of the received signal-to-noise ratio (SNR) at a Mobile Station (MS) from neighboring Base Stations (BSs) combined with the capacity estimation of the targeting cell. The proposed handover algorithm is implemented by a joint decision between the MS and BS nodes. The performance of the proposed algorithm is evaluated in terms of call dropping ratio and system throughput. A comparison with the conventional hard handover algorithm is presented.