Distributed throughput-maximization using the up- and downlink duality in wireless networks
IWCMC '07 Proceedings of the 2007 international conference on Wireless communications and mobile computing
Stochastic learning solution for distributed discrete power control game in wireless data networks
IEEE/ACM Transactions on Networking (TON)
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Proceedings of the 5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness
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WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
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Wireless Networks
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Journal of Parallel and Distributed Computing
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Wireless Personal Communications: An International Journal
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In this paper, we discuss a cross-layer design for joint user scheduling and adaptive rate control for downlink wireless transmission. We take a stochastic learning-based approach to achieve this. The scheduling is performed at the medium access control (MAC) layer, whereas the rate selection takes place at the physical/link (PHY/LINK) layer. These two components residing in the two layers exchange information to ensure that user defined rate requests are satisfied by the right combination of transmission schedules and rate selections. The method is highly efficient for low mobility applications with mobile speeds in the order of a few kilometers per hour. While simple to implement, this technique requires no explicit channel estimation phase. The only feedback used are the single bit ACK/NACK signal indicating the correct reception/failure of the packet. As shown in the convergence theorems, the algorithm achieves optimal performance in "stationary" channels. With slowly varying channels, the rate selection algorithm sees a "quasi-stationary" channel and adaptively converges to an optimal solution. Simulations performed using a third-generation wireless system, namely, high-speed downlink packet access (HSDPA) validate the theoretical results.