Global mobile communication necessitates improved capacity and proper quality assurance for services. To achieve these requirements, small cells have been deployed intensively by long term evolution (LTE) networks operators beside conventional base station structure to provide customers with better service and capacity coverage. Accomplishment of seamless handover between Macrocell layer (first tier) and Femtocell layer (second tier) is one of the key challenges to attain the QoS requirements. Handover related information gathering becomes very hard in high dense femtocell networks, effective handover decision techniques are important to minimize unnecessary handovers occurred and avoid Ping-Pong effect. In this work, we proposed and implemented an efficient handover decision procedure based on users’ profiles using Q-learning technique in an LTE-A macrocell-femtocell networks. New multi-criterion handover decision parameters are proposed in typical/dense femtocells in microcells environment to estimate the target cell for handover. The proposed handover algorithms are validated using the LTE-Sim simulator under an urban environment. The simulation results showed noteworthy reduction in the average number of handovers.
This is an extension of previous work which used an
artificial neural network with a back-propagation algorithm and a lookup
table to find the inverse kinematics for a manipulator arm
moving along pre-defined trajectories. The work now described shows that
the performance of this technique can be improved if the
back-propagation is made to be adaptive. Also, further improvement is
obtained by using the whole workspace to train the neural
network rather than just a pre-defined path. For the inverse
kinematics of the whole workspace, a comparison has also been
done between the adaptive back-propagation algorithm and radial basis function.
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