• IE interfaces
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• v.25 no.4
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• pp.450-457
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• 2012

This study addresses the call admission control(CAC) problem for OFDMA wireless communication systems in which both subcarriers and power should be considered together as the system resources. To lessen the exccessive allocation of radio resources for protecting handoff calls, the proposed CAC allows the less data rate than their requirements to handoff calls. The CAC problem is formulated as a semi-Markov decision process(SMDP) with constraints on the blocking probabilities of handoff calls. Some extensive experiments are conducted to show the usefulness of the proposed CAC model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3171-3191
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• 2016

Due to growing demand on wireless data traffic, a large number of different types of base stations (BSs) have been installed. However, space-time dependent wireless data traffic densities can result in a significant number of idle BSs, which implies the waste of power resources. To deal with this problem, we propose an active state control algorithm based on semi-Markov decision process (SMDP) for a heterogeneous network. A MDP in discrete time domain is formulated from continuous domain with some approximation. Suboptimal on-line learning algorithm with a random policy is proposed to solve the problem. We explicitly include coverage constraint so that active cells can provide the same signal to noise ratio (SNR) coverage with a targeted outage rate. Simulation results verify that the proposed algorithm properly controls the active state depending on traffic densities without increasing the number of handovers excessively while providing average user perceived rate (UPR) in a more power efficient way than a conventional algorithm.

• ETRI Journal
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• v.39 no.3
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• pp.345-352
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• 2017

Traffic management is a highly beneficial mechanism for satisfying quality-of-service requirements and overcoming the resource scarcity problems in networks. This paper introduces an optimal connection admission control mechanism to decrease the packet loss ratio and end-to-end delay in cognitive radio sensor networks (CRSNs). This mechanism admits data flows based on the value of information sent by the sensor nodes, the network state, and the estimated required resources of the data flows. The number of required channels of each data flow is estimated using a proposed formula that is inspired by a graph coloring approach. The proposed admission control mechanism is formulated as a semi-Markov decision process and a linear programming problem is derived to obtain the optimal admission control policy for obtaining the maximum reward. Simulation results demonstrate that the proposed mechanism outperforms a recently proposed admission control mechanism in CRSNs.