Classical Handover Research Articles

Handover Count Based MAP Estimation of Velocity With Prior Distribution Approximated via NGSIM Data-Set

In this paper, we propose a maximum-a-posteriori probability (MAP) based velocity estimation technique in which the prior distribution is defined by current location of the user. Motivation of this work is to improve accuracy of the existing velocity estimation techniques which are either solely based on cellular network measurements or location specific information. Our objective is to exploit both cellular measurements and location information in Bayesian sense; thus, to jointly address the critical applications of mobility management in Heterogeneous-Networks (HetNets), and intelligent transportation system. Here we assume that the Next Generation Simulation (NGSIM) data set for velocity is available at the current location and can be utilized to approximate the prior distribution. Additional information in form of prior distribution function is then exploited to improve the minimum variance unbiased (MVU) estimate of velocity which is based on handover count measurements. Since MVU estimate is a random variable, we first formulate its density function parameterized over the actual velocity. Next, we follow Bayesian approach to accommodate both prior distribution and parametric density function in deriving posterior density function of velocity. Finally, we derive expression of the MAP estimator considering various standard distribution functions which best fit to the density function obtained from NGSIM data set. In order to quantify the quality of estimate, we derive its variance and the corresponding Cramer-Rao-bound (CRB) on the minimum error variance. Numerical results demonstrate that the proposed estimator which incorporates NGSIM data set is asymptotically efficient and outperforms other classical handover count based estimation techniques.

Features detection based blind handover using kullback leibler distance for 5G HetNets systems

<p>The Fifth Generation of Mobile Networks (5G) is changing the cellular network infrastructure paradigm, and Small Cells are a key piece of this shift. But the high number of Small Cells and their low coverage involve more Handovers to provide continuous connectivity, and the selection, quickly and at low energy cost, of the appropriate one in the vicinity of thousands is also a key problem. In this paper, we propose a new method, to have an efficient, blind and rapid handover just by analysing Received Signal probability density function instead of demodulating and analysing Received Signal itself as in classical handover. The proposed method exploits KL Distance, Akaike Information Criterion (AIC) and Akaike weights, in order to decide blindly the best handover and the best Base Station (BS) for each user</p>

Fuzzy logic based layers 2 and 3 handovers in IEEE 802.16e network

In outdoor wireless communication networks such as mobile WiMAX (802.16e), mobile stations move all the time, thus they need to be handed off to different base stations based on certain criteria. In this paper, a fuzzy logic based scheme for fast selection of best base station and of handover technique at the handover time is presented in order to minimize the delay during handover for sensitive multimedia traffic. The scheme considers several parameters such as receiver power levels, handover type, traffic type, base station load and mobile station speed for making the handover decision by the mobile station. Through simulations, we compare our proposed handover scheme based on fuzzy logic modeling with classical handover decision. And finally, we present results based on Quality of Service (QoS) criteria to confirm the validity of the proposed approach.