Main Article Content
This paper presents a machine learning approach based on the concept of reinforcement learning andMarkov Decision Processes for link adaptation in orthogonal frequency-division multiplexing systemsthrough adaptive modulation and coding. Although machine learning techniques have attracted attentionfor link adaptation, the schemes proposed so far are based on off-line training algorithms, which makethem not well suited for real time and real-world operation. The proposed solution learns the bestmodulation and coding scheme for a given signal-to-noise ratio by interacting with the environment(the radio channel). Unlike the other solutions, it does not rely on an off-line and computationallyintensive training phase. Simulation results show that the proposed technique outperforms the wellknownsolution based on look-up tables for adaptive modulation and coding, and it can potentiallyadapt itself to distinct characteristics of the environment or the receiver radio frequency (RF) front end.
How to Cite
de Carvalho, P. H. P., Vieira, R. D., & Leite, J. P. (2015). A Continuous-State Reinforcement Learning Strategy for Link Adaptation in OFDM Wireless Systems. Journal of Communication and Information Systems, 30(1). https://doi.org/10.14209/jcis.2015.6
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