Discrete Mathematics - Topics on domination
Approximation schemes for covering and packing problems in image processing and VLSI
Journal of the ACM (JACM)
NC-approximation schemes for NP- and PSPACE-hard problems for geometric graphs
Journal of Algorithms
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
The IEEE 802.11 Handbook: A Designer's Companion
The IEEE 802.11 Handbook: A Designer's Companion
SCHEDULING IN A QUEUING SYSTEM WITH ASYNCHRONOUSLY VARYING SERVICE RATES
Probability in the Engineering and Informational Sciences
Achieving 100% throughput in an input-queued switch
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 1
Channel assignment schemes for cellular mobile telecommunication systems: A comprehensive survey
IEEE Communications Surveys & Tutorials
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Channel allocation schemes that have been used in cellular wireless ave limited applicability to Wireless LANs (WLANs) because of the small number of available channels and irregular cell geometries in WLAN environments. In this paper, we propose a dynamic, frame-based channel allocation architecture for WLANs. In this architecture, time is divided into a sequence of consecutive frames (in the order of milliseconds), and in each frame, only a non-interfering subset of access points (APs) is activated. Under broad traffic assumptions, we prove that the attainable system throughput can be optimized by scheduling APs and allocating channels in each frame such that a weighted sum of queue sizes at the activated APs is maximized. This optimality criterion for AP scheduling and channel allocation leads to a novel graph problem which is a variant of the well-known maximum independent set problem. We develop two heuristics for solving this problem. The first is a greedy heuristic that yields an approximation algorithm that has quadratic time complexity (in the number of APs) and, under certain conditions, yields a constant (6) factor approximation bound. The second heuristic is a graph decomposition heuristic. This heuristic, again under certain conditions, yields better approximation ratio, $$1+\epsilon,$$ but has complexity that grows exponentially with $$1/\epsilon^{2}$$ for arbitrarily small $$\epsilon 0.$$ Using the ns2 simulator we conducted experiments to compare our frame-based approach to static channel allocation. Results of our simulation indicate that our approach is able to deliver system throughput improvements of more than 50%.