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Givan, Robert; Chong, Edwin K. P.; Chang, Hyeong Soo

doi:10.1023/a:1015890105072

Cited by 12 publications

(1 citation statement)

References 14 publications

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“…The second problem is the computation for very large state space is extremely hard. We need an efficient algorithm in dealing with the exponential increase of the decision tree [29].…”

Section: A Analysis On Structure Of Optimal Resultsmentioning

confidence: 99%

DDSA: A Sampling and Validation Based Spectrum Access Algorithm in Wireless Networks

Yang

Wang

Chen³

2009

2009 IEEE International Symposium on Parallel and Distributed Processing With Applications

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Spectrum access scheme is a fundamental component in building efficient wireless networks. Conventional methods such as proactive channel assignment is costly due to large amount of protocol overhead. Also, those algorithms suffer from its inability in dealing with channel dynamics. The opportunistic methods however, spend more time on probing, and suffer from the myopic decisions as well. We present a decision based dynamic spectrum access algorithm (DDSA), which is built upon the Markov decision process (MDP), and could adaptively handle the DSA process for higher throughput. We employ quiet probing and dynamic controlling mechanisms in DDSA, so as to achieve a reduced protocol overhead and improved adaptivity. Different from previous methods, the DDSA is a model driven method, and we use the modeling technique on the IEEE 802.11 DCF for virtual channel state probing. The modeling technique could help us improve the accuracy on channel state, and reduce protocol overhead. Using a heuristic and adaptive algorithm named 'hindsight optimization', we solve the hardness in computing the MDP. Moreover, under the feasibility testing and scaling processes, the validated decision can be confidentially applied for a congestion-free DSA.

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“…The second problem is the computation for very large state space is extremely hard. We need an efficient algorithm in dealing with the exponential increase of the decision tree [29].…”

Section: A Analysis On Structure Of Optimal Resultsmentioning

confidence: 99%