A flexible framework based on reinforcement learning for adaptive modulation and coding in OFDM wireless systems

This paper presents a machine learning approach for link adaptation in orthogonal frequency-division multiplexing systems through adaptive modulation and coding. Although machine learning techniques have attracted attention for link adaptation, most of the the schemes proposed so far are based on off-line training algorithms, which make them not well suited for real time operation. The proposed solution, based on the reinforcement learning technique, learns the best modulation and coding scheme for a given signal-to-noise ratio by interacting with the radio channel and it does not rely on an off-line training mode. Simulation results show that under specific conditions, the proposed technique can outperform the well-known solution based on look-up tables for adaptive modulation and coding, and it can potentially adapt itself to distinct characteristics of the radio environment.