DMCA
Traffic-Aware Channel Sensing Order in Dynamic Spectrum Access Networks
| Citations: | 3 - 1 self |
BibTeX
@MISC{Liu_traffic-awarechannel,
author = {Chun-hao Liu and Jason A. Tran and Student Member and Przemysław Pawełczak and Cabric Member Ieee},
title = {Traffic-Aware Channel Sensing Order in Dynamic Spectrum Access Networks},
year = {}
}
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Abstract
Abstract—In this paper we present new results on the problem of finding the best channel sensing order for multi-channel Dynamic Spectrum Access (DSA) networks. We start with the general assumption that all Secondary Users (SUs) cooperatively sense each Primary User (PU) channel at one time. Then, the SU sensing results are reported to a DSA base station that schedules SU transmissions in order to maximize DSA network throughput. We then assume that PU traffic parameters are not perfectly known to DSA network and change over time, and propose a novel PU channel sensing order scheme based on the quality of PU traffic estimation. We adopt a maximum likelihood estimator to estimate the traffic statistics of PU channels and derive the Cramér-Rao (CR) bounds for the PU traffic estimation performance. Based on the CR bound and its Gaussian approximation, we analyze the impact of the estimation error on the DSA network throughput by computing a new metric called sensing order confidence, i.e., the probability that the best selected sensing order is not affected by PU traffic estimation errors. Finally, we formulate a convex optimization problem to determine the minimum number of PU channel state samples required for estimating PU traffic parameters after determining a certain constraint on the sensing order confidence metric to achieve the best sensing order. Index Terms—Dynamic spectrum access networks, channel sensing order, throughput maximization, traffic estimation, sens-ing order confidence, convex optimization. I.
Keyphrases
dynamic spectrum access network traffic-aware channel order confidence dsa network throughput pu traffic parameter dsa base station estimation error maximum likelihood estimator throughput maximization general assumption order scheme pu traffic estimation performance dsa network pu channel traffic statistic pu traffic estimation error primary user certain constraint sensing order secondary user pu traffic estimation present new result traffic estimation su sensing result novel pu channel cr bound pu channel state sample crame r-rao gaussian approximation su transmission convex optimization problem sens-ing order confidence convex optimization multi-channel dynamic spectrum access minimum number
