Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Analysis and simulation of a fair queueing algorithm
SIGCOMM '89 Symposium proceedings on Communications architectures & protocols
Convex Optimization
Wireless Communications
Where are the hard knapsack problems?
Computers and Operations Research
Proceedings of the 11th annual international conference on Mobile computing and networking
Non-convex optimization and rate control for multi-class services in the Internet
IEEE/ACM Transactions on Networking (TON)
A scheduling framework for UWB & cellular networks
Mobile Networks and Applications - Special issue: Recent advances in wireless networking
Joint Throughput Optimization for Wireless Mesh Networks
IEEE Transactions on Mobile Computing
Wireless mesh networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Exclusive-Region Based Scheduling Algorithms for UWB WPAN
IEEE Transactions on Wireless Communications
The capacity of wireless networks
IEEE Transactions on Information Theory
The ultra-wide bandwidth indoor channel: from statistical model to simulations
IEEE Journal on Selected Areas in Communications
Optimal power control, scheduling, and routing in UWB networks
IEEE Journal on Selected Areas in Communications
A Cross-Layer Optimization Framework for Multihop Multicast in Wireless Mesh Networks
IEEE Journal on Selected Areas in Communications
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Optimal scheduling for concurrent transmissions in rate-nonadaptive wireless networks is NP-hard. Optimal scheduling in rate-adaptive wireless networks is even more difficult, because, due to mutual interference, each flow's throughput in a time slot is unknown before the scheduling decision of that slot is finalized. The capacity bound derived for rate-nonadaptive networks is no longer applicable either. In this paper, we first formulate the optimal scheduling problems with and without minimum per-flow throughput constraints. Given the hardness of the problems and the fact that the scheduling decisions should be made within a few milliseconds, we propose two simple yet effective searching algorithms which can quickly move towards better scheduling decisions. Thus, the proposed scheduling algorithms can achieve high network throughput and maintain long-term fairness among competing flows with low computational complexity. For the constrained optimization problem involved, we consider its dual problem and apply Lagrangian relaxation. We then incorporate a dual update procedure in the proposed searching algorithm to ensure that the searching results satisfy the constraints. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed scheduling algorithms which are found to achieve throughputs close to the exhaustive searching results with much lower computational complexity.