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A directionally based bandwidth reservation scheme for call admission control
This paper proposes a new advanced Call Admission Control(CAC) strategy involving for the first time, a bandwidth reservation scheme that is influenced by the direction attribute of a mobile terminal (MT). Aside from the Quality-of-Service (QoS) parameters, the direction attribute plays a key role in efficiently reserving resources for MTs supporting multimedia communications for different QoS classes. The framework for a direction-based CAC system is entirely distributed and may be viewed as a message passing system, where MTs inform their neighbouring base stations (BS) not only of their QoS requirements, but also of their mobility parameters. The base stations then predict future demand and reserve resources accordingly, only admitting those terminals that can be adequately supported. The bandwidth reservation scheme proposed in this paper, integrates the direction attribute into the conventional Guard Channel (GC) scheme. Simulation results prove that this new scheme offers significant improvements in both Call Blocking Probability (CBP) and bandwidth utilization, under a variety of differing traffic conditions
Robustness of optimal channel reservation using handover prediction in multiservice wireless networks
The aim of our study is to obtain theoretical limits for the gain that can be expected when using handover prediction and to determine the sensitivity of the system performance against different parameters. We apply an average-reward reinforcement learning approach based on afterstates to the design of optimal admission control policies in mobile multimedia cellular networks where predictive information related to the occurrence of future handovers is available. We consider a type of predictor that labels active mobile terminals in the cell neighborhood a fixed amount of time before handovers are predicted to occur, which we call the anticipation time. The admission controller exploits this information to reserve resources efficiently. We show that there exists an optimum value for the anticipation time at which the highest performance gain is obtained. Although the optimum anticipation time depends on system parameters, we find that its value changes very little when the system parameters vary within a reasonable range. We also find that, in terms of system performance, deploying prediction is always advantageous when compared to a system without prediction, even when the system parameters are estimated with poor precision. © Springer Science+Business Media, LLC 2012.The authors would like to thank the reviewers for their valuable comments that helped to improve the quality of the paper. This work has been supported by the Spanish Ministry of Education and Science and European Comission (30% PGE, 70% FEDER) under projects TIN2008-06739-C04-02 and TIN2010-21378-C02-02 and by Comunidad de Madrid through project S-2009/TIC-1468.MartĂnez Bauset, J.; GimĂ©nez Guzmán, JM.; Pla, V. (2012). Robustness of optimal channel reservation using handover prediction in multiservice wireless networks. Wireless Networks. 18(6):621-633. https://doi.org/10.1007/s11276-012-0423-6S621633186Ji, S., Chen, W., Ding, X., Chen, Y., Zhao, C., & Hu, C. (2010). Potential benefits of GPS/GLONASS/GALILEO integration in an urban canyon–Hong Kong. Journal of Navigation, 63(4), 681–693.Soh, W., & Kim, H. (2006). A predictive bandwidth reservation scheme using mobile positioning and road topology information. IEEE/ACM Transactions on Networking, 14(5), 1078–1091.Kwon, H., Yang, M., Park, A., & Venkatesan, S. (2008). Handover prediction strategy for 3G-WLAN overlay networks. In Proceedings: IEEE network operations and management symposium (NOMS) (pp. 819–822).Huang, C., Shen, H., & Chuang, Y. (2010). An adaptive bandwidth reservation scheme for 4G cellular networks using flexible 2-tier cell structure. Expert Systems with Applications, 37(9), 6414–6420.Wanalertlak, W., Lee, B., Yu, C., Kim, M., Park, S., & Kim, W. (2011). Behavior-based mobility prediction for seamless handoffs in mobile wireless networks. Wireless Networks, 17(3), 645–658.Becvar, Z., Mach, P., & Simak, B. (2011). Improvement of handover prediction in mobile WiMAX by using two thresholds. Computer Networks, 55, 3759–3773.Sgora, A., & Vergados, D. (2009). Handoff prioritization and decision schemes in wireless cellular networks: a survey. IEEE Communications Surveys and Tutorials, 11(4), 57–77.Choi, S., & Shin, K. G. (2002). Adaptive bandwidth reservation and admission control in QoS-sensitive cellular networks. IEEE Transactions on Parallel and Distributed Systems, 13(9), 882–897.Ye, Z., Law, L., Krishnamurthy, S., Xu, Z., Dhirakaosal, S., Tripathi, S., & Molle, M. (2007). Predictive channel reservation for handoff prioritization in wireless cellular networks. Computer Networks, 51(3), 798–822.Abdulova, V., & Aybay, I. (2011). Predictive mobile-oriented channel reservation schemes in wireless cellular networks. Wireless Networks, 17(1), 149–166.Ramjee, R., Nagarajan, R., & Towsley, D. (1997). On optimal call admission control in cellular networks. Wireless Networks, 3(1), 29–41.Bartolini, N. (2001). Handoff and optimal channel assignment in wireless networks. Mobile Networks and Applications, 6(6), 511–524.Bartolini, N., & Chlamtac, I. (2002). Call admission control in wireless multimedia networks. In Proceedings: Personal, indoor and mobile radio communications (PIMRC) (pp. 285–289).Pla, V., & Casares-Giner, V. (2003). Optimal admission control policies in multiservice cellular networks. In Proceedings of the international network optimization conference (INOC) (pp. 466–471).Chu, K., Hung, L., & Lin, F. (2009). Adaptive channel reservation for call admission control to support prioritized soft handoff calls in a cellular CDMA system. Annals of Telecommunications, 64(11), 777–791.El-Alfy, E., & Yao, Y. (2011). Comparing a class of dynamic model-based reinforcement learning schemes for handoff prioritization in mobile communication networks. Expert Systems With Applications, 38(7), 8730–8737.Gimenez-Guzman, J. M., Martinez-Bauset, J., & Pla, V. (2007). A reinforcement learning approach for admission control in mobile multimedia networks with predictive information. IEICE Transactions on Communications , E-90B(7), 1663–1673.Sutton R., Barto A. G. (1998) Reinforcement learning: An introduction. The MIT press, Cambridge, MassachusettsBusoniu, L., Babuska, R., De Schutter, B., & Ernst, D. (2010). Reinforcement learning and dynamic programming using function approximators. Boca Raton, FL: CRC Press.Watkins, C., & Dayan, P. (1992). Q-learning. Machine learning, 8(3–4), 279–292.Brown, T. (2001). Switch packet arbitration via queue-learning. Advances in Neural Information Processing Systems, 14, 1337–1344.Proper, S., & Tadepalli, P. (2006). Scaling model-based average-reward reinforcement learning for product delivery. In Proceedings 17th European conference on machine learning (pp. 735–742).Driessens, K., Ramon, J., & Gärtner, T. (2006). Graph kernels and Gaussian processes for relational reinforcement learning. Machine Learning, 64(1), 91–119.Banerjee, B., & Stone, P. (2007). General game learning using knowledge transfer. In Proceedings 20th international joint conference on artificial intelligence (pp. 672–677).Martinez-Bauset, J., Pla, V., Garcia-Roger, D., Domenech-Benlloch, M. J., & Gimenez-Guzman, J. M. (2008). Designing admission control policies to minimize blocking/forced-termination. In G. Ming, Y. Pan & P. Fan (Eds.), Advances in wireless networks: Performance modelling, analysis and enhancement (pp. 359–390). New York: Nova Science Pub Inc.Biswas, S., & Sengupta, B. (1997). Call admissibility for multirate traffic in wireless ATM networks. In Proceedings IEEE INFOCOM (2, pp. 649–657).Evans, J. S., & Everitt, D. (1999). Effective bandwidth-based admission control for multiservice CDMA cellular networks. IEEE Transactions on Vehicular Technology, 48(1), 36–46.Gilhousen, K., Jacobs, I., Padovani, R., Viterbi, A., Weaver, L. A. J., & Wheatley, C. E., III. (1991). On the capacity of a cellular CDMA system. IEEE Transactions on Vehicular Technology, 40(2), 303–312.Hegde, N., & Altman, E. (2006). Capacity of multiservice WCDMA networks with variable GoS. Wireless Networks, 12, 241–253.Ben-Shimol, Y., Kitroser, I., & Dinitz, Y. (2006). Two-dimensional mapping for wireless OFDMA systems. IEEE Transactions on Broadcasting, 52(3), 388–396.Gao, D., Cai, J., & Ngan, K. N. (2005). Admission control in IEEE 802.11e wireless LANs. IEEE Network, 19(4), 6–13.Liu, T., Bahl, P., & Chlamtac, I. (1998). Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE Journal on Selected Areas in Communications, 16(6), 922–936.Hu, F., & Sharma, N. (2004). Priority-determined multiclass handoff scheme with guaranteed mobile qos in wireless multimedia networks. IEEE Transactions on Vehicular Technology, 53(1), 118–135.Chan, J., & Seneviratne, A. (1999). A practical user mobility prediction algorithm for supporting adaptive QoS in wireless networks. In Proceedings IEEE international conference on networks (ICON) (pp. 104–111).Jayasuriya, A., & Asenstorfer, J. (2002). Mobility prediction model for cellular networks based on the observed traffic patterns. In Proceedings of IASTED international conference on wireless and optical communication (WOC) (pp. 386–391).Diederich, J., & Zitterbart, M. (2005). A simple and scalable handoff prioritization scheme. Computer Communications, 28(7), 773–789.Rashad, S., Kantardzic, M., & Kumar, A. (2006). User mobility oriented predictive call admission control and resource reservation for next-generation mobile networks. Journal of Parallel and Distributed Computing, 66(7), 971–988.Soh, W. -S., & Kim, H. (2003). QoS provisioning in cellular networks based on mobility prediction techniques. IEEE Communications Magazine, 41(1), 86 – 92.Lott, M., Siebert, M., Bonjour, S., vonHugo, D., & Weckerle, M. (2004). Interworking of WLAN and 3G systems. Proceedings IEE Communications, 151(5), 507 – 513.Sanabani, M., Shamala, S., Othman, M., & Zukarnain, Z. (2007). An enhanced bandwidth reservation scheme based on road topology information for QoS sensitive multimedia wireless cellular networks. In Proceedings of the 2007 international conference on computational science and its applications—Part II (ICCSA) (pp. 261–274).Mahadevan, S. (1996). Average reward reinforcement learning: Foundations, algorithms, and empirical results. Machine Learning, 22(1–3), 159–196.Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. New York: Wiley.Das, T. K., Gosavi, A., Mahadevan, S., & Marchalleck, N. (1999). Solving semi-markov decision problems using average reward reinforcement learning. Management Science, 45(4), 560–574.Darken, C., Chang, J., & Moody, J. (1992). Learning rate schedules for faster stochastic gradient search. In Proceedings of the IEEE-SP workshop on neural networks for signal processing II. (pp. 3–12)
QoS-aware adaptive call admission control in multiuser OFDM wireless network.
Yu, Xi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 46-49).Abstracts in English and Chinese.Acknowledgement --- p.iAbstract --- p.iiChapter Chapter 1 --- Introduction and Background --- p.1Chapter 1.1 --- Background --- p.3Chapter 1.1.1 --- Brief Review of CAC --- p.3Chapter 1.1.2 --- Dynamic Sub-carrier Allocation in Multi-user OFDM Wireless Network --- p.6Chapter 1.2 --- Problem Statement --- p.11Chapter 1.3 --- The Organization of The Thesis --- p.12Chapter Chapter2 --- System Model and Call Admission Control Framework --- p.13Chapter 2.1 --- System setup --- p.13Chapter 2.2 --- The CAC Strategy Framework --- p.14Chapter Chapter 3 --- QoS-aware Adaptive Call Admission Control´ؤStep One: The QoS-Provisioning CAC --- p.18Chapter 3.1 --- Problem Formulation --- p.19Chapter 3.2 --- Optimal Condition Analysis --- p.21Chapter 3.3 --- Throughput Estimation Algorithm --- p.22Chapter 3.4 --- QoS-Provisioning CAC --- p.25Chapter 3.5 --- Performance Evaluation --- p.26Chapter Chapter 4 --- QoS-aware Adaptive Call Admission Control´ؤStep Two: Average Revenue Maximization CAC --- p.30Chapter 4.1 --- Semi-Markov Decision Process --- p.30Chapter 4.2 --- Investigation of Algorithms for SMDP --- p.34Chapter 4.3 --- The Average Revenue Maximum CAC --- p.37Chapter 4.4 --- Performance Evaluation --- p.40Chapter Chapter 5 --- Conclusion and Future Work --- p.44Bibliography --- p.4
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Performance Modelling and Analysis of Handover and Call Admission Control Algorithm for Next Generation Wireless Networks
The next generation wireless system (NGWS) has been conceived as a ubiquitous wireless environment. It integrates existing heterogeneous access networks, as well as future networks, and will offer high speed data, real-time applications (e.g. Voice over IP, videoconference ) and real-time multimedia (e.g. real-time audio and video) support with a certain Quality of Service (QoS) level to mobile users. It is required that the mobile nodes have the capability of selecting services that are offered by each provider and determining the best path through the various networks.
Efficient radio resource management (RRM) is one of the key issues required to support global roaming of the mobile users among different network architectures of the NGWS and a precise call admission control (CAC) scheme satisfies the requirements of high network utilization, cost reduction, minimum handover latency and high-level QoS of all the connections.
This thesis is going to describe an adaptive class-based CAC algorithm, which is expected to prioritize the arriving channel resource requests, based on userÂżs classification and channel allocation policy. The proposed CAC algorithm couples with Fuzzy Logic (FL) and Pre-emptive Resume (PR) theories to manage and improve the performance of the integrated wireless network system. The novel algorithm is assessed using a mathematical analytic method to measure the performance by evaluating the handover dropping probability and the system utilization
Cooperative control of relay based cellular networks
PhDThe increasing popularity of wireless communications and the higher data
requirements of new types of service lead to higher demands on wireless networks.
Relay based cellular networks have been seen as an effective way to meet users’
increased data rate requirements while still retaining the benefits of a cellular
structure. However, maximizing the probability of providing service and spectrum
efficiency are still major challenges for network operators and engineers because of
the heterogeneous traffic demands, hard-to-predict user movements and complex
traffic models.
In a mobile network, load balancing is recognised as an efficient way to increase
the utilization of limited frequency spectrum at reasonable costs. Cooperative
control based on geographic load balancing is employed to provide flexibility for
relay based cellular networks and to respond to changes in the environment.
According to the potential capability of existing antenna systems, adaptive radio
frequency domain control in the physical layer is explored to provide coverage at
the right place at the right time.
This thesis proposes several effective and efficient approaches to improve
spectrum efficiency using network wide optimization to coordinate the coverage
offered by different network components according to the antenna models and
relay station capability. The approaches include tilting of antenna sectors,
changing the power of omni-directional antennas, and changing the assignment of
relay stations to different base stations. Experiments show that the proposed
approaches offer significant improvements and robustness in heterogeneous traffic
scenarios and when the propagation environment changes. The issue of predicting
the consequence of cooperative decisions regarding antenna configurations when
applied in a realistic environment is described, and a coverage prediction model is
proposed. The consequences of applying changes to the antenna configuration on
handovers are analysed in detail. The performance evaluations are based on a
system level simulator in the context of Mobile WiMAX technology, but the
concepts apply more generally
Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks
Hoy en dĂa existen varias tecnologĂas que coexisten en una misma zona formando un sistema heterogĂ©neo. Además, este hecho se espera que se vuelva más acentuado con todas las nuevas tecnologĂas que se están estandarizando actualmente. Hasta ahora, generalmente son los usuarios los que eligen la tecnologĂa a la que se van a conectar, ya sea configurando sus terminales o usando terminales distintos. Sin embargo, esta soluciĂłn es incapaz de aprovechar al máximo todos los recursos. Para ello es necesario un nuevo conjunto de estrategias. Estas estrategias deben gestionar los recursos radioelĂ©ctricos conjuntamente y asegurar la satisfacciĂłn de la calidad de servicio de los usuarios.
Siguiendo esta idea, esta Tesis propone dos nuevos algoritmos. El primero es un algoritmo de asignaciĂłn dinámica de recusos conjunto (JDRA) capaz de asignar recursos a usuarios y de distribuir usuarios entre tecnologĂas al mismo tiempo. El algoritmo está formulado en tĂ©rminos de un problema de optimizaciĂłn multi-objetivo que se resuelve usando redes neuronales de Hopfield (HNNs). Las HNNs son interesantes ya que se supone que pueden alcanzar soluciones sub-Ăłptimas en cortos periodos de tiempo. Sin embargo, implementaciones reales de las HNNs en ordenadores pierden esta rápida respuesta. Por ello, en esta Tesis se analizan las causas y se estudian posibles mejoras.
El segundo algoritmo es un algoritmo de control de admisiĂłn conjunto (JCAC) que admite y rechaza usuarios teniendo en cuenta todas las tecnologĂas al mismo tiempo. La principal diferencia con otros algorimos propuestos es que Ă©stos Ăşltimos toman las dicisiones de admisiĂłn en cada tecnologĂa por separado. Por ello, se necesita de algĂşn mecanismo para seleccionar la tecnologĂa a la que los usuarios se van a conectar. Por el contrario, la tĂ©cnica propuesta en esta Tesis es capaz de tomar decisiones en todo el sistema heterogĂ©neo. Por lo tanto, los usuarios no se enlazan con ninguna tecnologĂa antes de ser admitidos.Calabuig Soler, D. (2010). Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7348Palanci
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