Performance analysis of the general packet radio service |
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| Affiliation: | 1. Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, PR China;2. Hubei Key Laboratory of Lipid Chemistry and Nutrition, PR China;3. Center for Metabolic Origins of Disease, Sanford Burnham Prebys Medical Discovery Institute, Orlando, FL 32827, USA;4. College of Basic Medical Sciences, Zhejiang Chinese Medical University, 548 Bingwen Road, Hangzhou, Zhejiang 310053, PR China;5. School of Chemical Science, The University of Auckland, Auckland 1142, New Zealand;1. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA;2. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA;3. Weill Medical College of Cornell University, New York, NY, USA;4. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA;5. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA;6. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA;7. Department of Computational Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA;1. University of Cambridge, Chemical Engineering & Biotechnology, New Museums Site, Pembroke Street, Cambridge CB2 3RA, United Kingdom;2. Nanyang Technological University, School of Chemical and Biomedical Engineering, 62 Nanyang Drive, 637459, Singapore;3. National University of Singapore, Department of Mechanical Engineering, 9 Engineering Drive, 117575, Singapore;4. R&B Industrial, Tower Gate Industrial Park, Hampshire SP10 3BB, United Kingdom;1. Dept. of Industrial Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy;2. Dept. of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy |
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| Abstract: | This paper presents an efficient and accurate analytical model for the radio interface of the general packet radio service (GPRS) in a GSM network. The model is utilized for investigating how many packet data channels should be allocated for GPRS under a given amount of traffic in order to guarantee appropriate quality of service. The presented model constitutes a continuous-time Markov chain. The Markov model represents the sharing of radio channels by circuit switched GSM connections and packet switched GPRS sessions under a dynamic channel allocation scheme. In contrast to previous work, the Markov model explicitly represents the mobility of users by taking into account arrivals of new GSM and GPRS users as well as handovers from neighboring cells. Furthermore, we take into account TCP flow control for the GPRS data packets. To validate the simplifications necessary for making the Markov model amenable to numerical solution, we provide a comparison of the results of the Markov model with a detailed simulator on the network level. |
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