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Titlebook: Call Admission Control in Mobile Cellular Networks; Sanchita Ghosh,Amit Konar Book 2013 Springer-Verlag Berlin Heidelberg 2013 Automatic C

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11#
發(fā)表于 2025-3-23 09:51:31 | 只看該作者
An Overview of Call Admission Control in Mobile Cellular Networks,ictive nature of the network and implicitness of the network, call queuing strategy, and channel borrowing schemes. Application of soft computing techniques, including artificial neural nets, genetic algorithm, fuzzy relational approach and particle swarm optimization, in call admission control is illustrated.
12#
發(fā)表于 2025-3-23 16:58:20 | 只看該作者
An Overview of Computational Intelligence Algorithms,upervised, unsupervised and reinforcement learning algorithms will be outlined under neural nets. The chapter ends with scope of applications of computational intelligence algorithms in call admission.
13#
發(fā)表于 2025-3-23 20:47:40 | 只看該作者
14#
發(fā)表于 2025-3-24 00:32:43 | 只看該作者
Robotic Systems for Gait Rehabilitationmic traffic load in the network. The constrained objective function has been minimized using an evolutionary algorithm. Experimental results and computer simulations envisage that the proposed algorithm outperforms most of the existing approaches on call admission control, considering either of the two issues addressed above.
15#
發(fā)表于 2025-3-24 05:48:09 | 只看該作者
16#
發(fā)表于 2025-3-24 09:19:44 | 只看該作者
17#
發(fā)表于 2025-3-24 11:10:27 | 只看該作者
18#
發(fā)表于 2025-3-24 15:41:34 | 只看該作者
19#
發(fā)表于 2025-3-24 21:32:09 | 只看該作者
20#
發(fā)表于 2025-3-25 00:34:51 | 只看該作者
Robotic Systems for Gait Rehabilitationl admission control either ignore both variation in traffic conditions or velocity of mobile devices, or at most consider one of them. This chapter overcomes the above problems jointly by formulating call admission control as a constrained optimization problem, where the primary objective is to mini
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