Affiliations: Department of Informatics and Telecommunications Engineering, University of Western
Macedonia, Kozani, Greece | School of Information Technology, Indian Institute of
Technology, Kharagpur, West Bengal, India | Department of Informatics, Aristotle University of
Thessaloniki, Thessaloniki, Greece
Note: [] Corresponding author: M. Louta, Department of Informatics and
Telecommunications Engineering, University of Western Macedonia, 50100, Kozani,
Greece. Tel.: +30 2461056566; mobile: +306977973322; Fax: +30 2461056501; E-mail: louta@uowm.gr
Abstract: WiMAX (Worldwide Interoperability for Microwave Access) constitutes
a candidate networking technology towards the 4G vision realization. By
adopting the Orthogonal Frequency Division Multiple Access (OFDMA) technique,
the latest IEEE 802.16x amendments manage to provide QoS-aware access services
with full mobility support. A number of interesting scheduling and mapping
schemes have been proposed in research literature. However, they neglect a
considerable asset of the OFDMA-based wireless systems: the dynamic adjustment
of the downlink-to-uplink width ratio. In order to fully exploit the supported
mobile WiMAX features, we design, develop, and evaluate a rigorous adaptive
model, which inherits its main aspects from the reinforcement learning field.
The model proposed endeavours to efficiently determine the downlink-to-uplink
width ratio, on a frame-by-frame basis, taking into account both the downlink and uplink traffic in the
Base Station (BS). Extensive evaluation results indicate that the model proposed succeeds in providing quite accurate estimations,
keeping the average error rate below 15% with respect to the optimal
sub-frame configurations. Additionally, it presents improved performance
compared to other learning methods (e.g., learning automata) and notable
improvements compared to static schemes that maintain a fixed predefined ratio
in terms of service ratio and resource utilization.
