LEADER 03611nam 2200565Ia 450 001 9910437899103321 005 20200520144314.0 010 $a3-642-30997-6 024 7 $a10.1007/978-3-642-30997-7 035 $a(CKB)3390000000030196 035 $a(SSID)ssj0000745853 035 $a(PQKBManifestationID)11434881 035 $a(PQKBTitleCode)TC0000745853 035 $a(PQKBWorkID)10859475 035 $a(PQKB)10190800 035 $a(DE-He213)978-3-642-30997-7 035 $a(MiAaPQ)EBC3070987 035 $a(PPN)168318202 035 $a(EXLCZ)993390000000030196 100 $a20120827d2013 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aCall admission control in mobile cellular networks /$fSanchita Ghosh and Amit Konar 205 $a1st ed. 2013. 210 $aBerlin ;$aHeidelberg $cSpringer$dc2013 215 $a1 online resource (XII, 236 p.) 225 1 $aStudies in computational intelligence,$x1860-949X ;$v437 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-30996-8 320 $aIncludes bibliographical references and index. 327 $aAn Overview of Call Admission Control in Mobile -- Cellular Networks -- An Overview of Computational Intelligence Algorithms -- Automatic call Management in a Cellular Mobile -- Network by Fuzzy Threshold Logic -- An Evolutionary Approach to Velocity and Traffic -- Sensitive Call Admission Control -- Call Admission Control Using Bio-geography Based Optimization. 330 $aCall Admission Control (CAC) and Dynamic Channel Assignments (DCA) are important decision-making problems in mobile cellular communication systems. Current research in mobile communication considers them as two independent problems, although the former greatly depends on the resulting free channels obtained as the outcome of the latter. This book provides a solution to the CAC problem, considering DCA as an integral part of decision-making for call admission. Further, current technical resources ignore movement issues of mobile stations and fluctuation in network load (incoming calls) in the control strategy used for call admission. In addition, the present techniques on call admission offers solution globally for the entire network, instead of considering the cells independently.      CAC here has been formulated by two alternative approaches. The first approach aimed at handling the uncertainty in the CAC problem by employing fuzzy comparators.  The second approach is concerned with formulation of CAC as an optimization problem to minimize call drop, satisfying a set of constraints on feasibility and availability of channels, hotness of cells, and velocity and angular displacement of mobile stations.  Evolutionary techniques, including Genetic Algorithm and Biogeography Based Optimization, have been employed to solve the optimization problems. The proposed approaches outperform traditional methods with respect to grade and quality of services. 410 0$aStudies in computational intelligence ;$vv. 437. 606 $aCell phone systems 606 $aWireless communication systems$xManagement 615 0$aCell phone systems. 615 0$aWireless communication systems$xManagement. 676 $a621.384 700 $aGhosh$b Sanchita$01059001 701 $aKonar$b Amit$0542703 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437899103321 996 $aCall admission control in mobile cellular networks$94198697 997 $aUNINA