Achieving network optima using Stackelberg routing strategies
IEEE/ACM Transactions on Networking (TON)
Conjectural Equilibrium in Multiagent Learning
Machine Learning
Multi-level hierarchies for scalable ad hoc routing
Wireless Networks
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
DOMINO: a system to detect greedy behavior in IEEE 802.11 hotspots
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Performance Evaluation of Ad-Hoc WLAN by M/G/1 Queueing Model
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
Channel assignment schemes for cellular mobile telecommunication systems: A comprehensive survey
IEEE Communications Surveys & Tutorials
Utility-Optimal Random-Access Control
IEEE Transactions on Wireless Communications
IEEE Transactions on Multimedia
Reverse-Engineering MAC: A Non-Cooperative Game Model
IEEE Journal on Selected Areas in Communications
A non-selfish and distributed channel selection scheme for cognitive radio ad hoc networks
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
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In this paper, we study the problem of multi-user channel selection in multi-channel wireless networks. Specifically, we study the case in which the autonomous users deploy delay-sensitive applications. Existing centralized approaches result in efficient allocations, but require intensive message exchanges among the users (i.e. they are not informationally efficient). Current distributed approaches do not require any message exchange for collaboration, but they often result in inefficient allocations, because users only respond to their experienced contention in the network. Alternatively, in this paper we study a distributed channel selection approach, which does not require any message exchanges, and which leads to a system-wise Pareto optimal solution by enabling a foresighted user to predict the implications (based on their beliefs) of their channel selection on their expected future delays and thereby, foresightedly influence the resulting multi-user interaction. We model the multi-user interaction as a channel selection game and show how users can play an Ɛ -consistent conjectural equilibrium by building near-accurate beliefs and competing for the remaining capacities of the channels. We analytically show that when the system has the foresighted user, this self-interested leader can deploy a linear belief function in each channel and manipulates the equilibrium to approach the Stackelberg equilibrium. Alternatively, when the leader is altruistic, the system will converge to the system-wise Pareto optimal solution. We propose a low-complexity learning method based on linear regression for the foresighted user to learn its belief functions.