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GLOBECOM
2010
IEEE

Reinforcement Learning for Link Adaptation in MIMO-OFDM Wireless Systems

9 years 3 months ago
Reinforcement Learning for Link Adaptation in MIMO-OFDM Wireless Systems
Machine learning algorithms have recently attracted much interest for effective link adaptation due to their flexibility and ability to capture more environmental effects implicitly than classical adaptation algorithms. However, past applications are limited to rather simple configurations such as identifying channel condition or link adaptation in fixed or slowly varying channels. Recently, more sophisticated approaches using offline supervised learning have been proposed for link adaptation in complex configurations such as MIMO-OFDM. However, their time complexity and offline training phase hamper their real-world applicability. Approaches using online learning have shown good throughput performance, but the high memory requirement makes them inefficient or even impractical. In this paper, we propose a new effective online learning algorithm for link adaptation. Our computations show that the algorithm performs comparably to the existing online learning approaches, but ours requires...
Sungho Yun, Constantine Caramanis
Added 11 Feb 2011
Updated 11 Feb 2011
Type Journal
Year 2010
Where GLOBECOM
Authors Sungho Yun, Constantine Caramanis
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