Efficient coupled PHY and MAC use of physical Bursts in WiMAX/IEEE 802.16e networks

Sharon, Oran; Alpert, Yaron; Tabajah, Gassan

doi:10.1016/j.phycom.2012.09.002

Cited by 2 publications

(1 citation statement)

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“…Specifically, the available bandwidth is structured as a two-dimensional rectangular shaped allocation bin, having one dimension associated with frequency (height) and the other with time (width) [5]. Moreover, each downlink request (or a set of requests that share common PHY characteristics, referred to as burst [6]) must comply with the rectangular shape. This rule does not apply to the uplink requests, simplifying, thus, the uplink sub-frame allocation process.…”

Section: Introductionmentioning

confidence: 99%

RLAM: A Dynamic and Efficient Reinforcement Learning-Based Adaptive Mapping Scheme in Mobile WiMAX Networks

Louta

Sarigiannidis

Misra

et al. 2014

Mobile Information Systems

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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-uplinkwidth 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.

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Section: Introductionmentioning

confidence: 99%