Interference-based dynamic pricing for WCDMA networks using neurodynamic programming

Copyright © 2007 IEEEWe study the problem of optimal integrated dynamic pricing and radio resource management, in terms of resource allocation and call admission control, in a WCDMA network. In such interference-limited network, one's resource usage also degrades the utility of others. A new parameter noise rise factor, which indicates the amount of interference generated by a call, is suggested as a basis for setting price to make users accountable for the congestion externality of their usage. The methods of dynamic programming (DP) are unsuitable for problems with large state spaces due to the associated ldquocurse of dimensionality.rdquo To overcome this, we solve the problem using a simulation-based neurodynamic programming (NDP) method with an action-dependent approximation architecture. Our results show that the proposed optimal policy provides significant average reward and congestion improvement over conventional policies that charge users based on their load factor.Siew-Lee Hew and Langford B. Whit

Channel allocation and admission control in cellular communications networks

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 62-64).by Dimitri A. Papaioannou.M.S

Intelligent adaptive bandwidth provisioning for quality of service in umts core networks

Master'sMASTER OF ENGINEERIN

Resource management in QoS-aware wireless cellular networks

2011 Summer.Includes bibliographical references.Emerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study two types of resource allocation problems in QoS-aware wireless cellular networks. First, we develop a rigorous framework to study opportunistic scheduling in multiuser OFDM systems. We derive optimal opportunistic scheduling policies under three common QoS/fairness constraints for multiuser OFDM systems--temporal fairness, utilitarian fairness, and minimum-performance guarantees. To implement these optimal policies efficiently, we provide a modified Hungarian algorithm and a simple suboptimal algorithm. We then propose a generalized opportunistic scheduling framework that incorporates multiple mixed QoS/fairness constraints, including providing both lower and upper bound constraints. Next, taking input queues and channel memory into consideration, we reformulate the transmission scheduling problem as a new class of Markov decision processes (MDPs) with fairness constraints. We investigate the throughput maximization and the delay minimization problems in this context. We study two categories of fairness constraints, namely temporal fairness and utilitarian fairness. We consider two criteria: infinite horizon expected total discounted reward and expected average reward. We derive and prove explicit dynamic programming equations for the above constrained MDPs, and characterize optimal scheduling policies based on those equations. An attractive feature of our proposed schemes is that they can easily be extended to fit different objective functions and other fairness measures. Although we only focus on uplink scheduling, the scheme is equally applicable to the downlink case. Furthermore, we develop an efficient approximation method--temporal fair rollout--to reduce the computational cost

Dynamic capacity adjustment for virtual-path based networks using neuro-dynamic programming

Cataloged from PDF version of article.Dynamic capacity adjustment is the process of updating the capacity reservation of a virtual path via signalling in the network. There are two important issues to be considered: bandwidth (resource) utilization and signaling traffic. Changing the capacity too frequently will lead to efficient usage of resources but has a disadvantage of increasing signaling traffic among the network elements. On the other hand, if the capacity is adjusted for the highest possible value and kept fixed for a long time period, a significant amount of bandwidth will be wasted when the actual traffic rate is small. We proposed two formulations for dynamic capacity adjustment problem. In the first formulation cost parameters are assigned for bandwidth usage and signalling, optimal solutions are reached for different values of these parameters. In the second formulation, our aim is to maximize the bandwidth efficiency with a given signaling requirement. In this formulation, a leaky bucket counter is used in order to regulate the signaling rate. We used dynamic programming and neuro-dynamic programming techniques and we applied our formulations for voice traffic scenario (voice over packet networks) and a general network architecture using flow-based Internet traffic modelling. In the Internet traffic modelling case, we tested two different control strategies: event-driven control and time-driven control. In event-driven control, capacity update epochs are selected to be the time instants of either a flow arrival or a flow departure. In time-driven control, decision epochs are selected to be the equidistant time instants and excessive amount of traffic that cannot be carried will be buffered.Şahin, CemM.S

Science handbook

2002 handbook for the faculty of Scienc