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Radio Resource Management of Composite Wireless Networks: Predictive and Reactive Approaches
May 2012 (vol. 11 no. 5)
pp. 807-820
Eng Hwee Ong, University of Newcastle, Australia, Newcastle
Jamil Y. Khan, University of Newcastle, Australia, Newcastle
Kaushik Mahata, University of Newcastle, Australia, Newcastle
Recently, the IEEE 1900.4 standard specified a policy-based radio resource management (RRM) framework in which the decision making process is distributed between network-terminal entities. The standard facilitates the optimization of radio resource usage to improve the overall composite capacity and quality of service (QoS) of heterogeneous wireless access networks within a composite wireless network (CWN). Hence, the study of different RRM techniques to maintain either a load- or QoS-balanced system through dynamic load distribution across a CWN is pivotal. In this paper, we present and evaluate three primary RRM techniques from different aspects, spanning across predictive versus reactive to model-based versus measurement-based approaches. The first technique is a measurement-based predictive approach, known as predictive load balancing (PLB), commonly employed in the network-distributed RRM framework. The second technique is a model-based predictive approach, known as predictive QoS balancing (PQB), typically implemented in the network-centralized RRM framework. The third technique is a measurement-based reactive approach, known as reactive QoS balancing (RQB), anchored in the IEEE 1900.4 network-terminal distributed RRM framework. Comprehensive performance analysis between these three techniques shows that the IEEE 1900.4-based RQB algorithm yields the best improvement in QoS fairness and aggregate end-user throughput while preserving an attractive baseline QoS property.

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Index Terms:
IEEE 1900.4, radio resource management, reactive, predictive, load distribution, WLANs.
Citation:
Eng Hwee Ong, Jamil Y. Khan, Kaushik Mahata, "Radio Resource Management of Composite Wireless Networks: Predictive and Reactive Approaches," IEEE Transactions on Mobile Computing, vol. 11, no. 5, pp. 807-820, May 2012, doi:10.1109/TMC.2011.87
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