Energy Efficient Link Adaptation using Machine Learning Techniques for Wireless OFDM

Energy Efficiency in wireless communication is very important due to the slow progress in battery technology with improvement in technology. In this paper, we applied energy efficient link adaptation using Machine learning techniques in Orthogonal Frequency Division Multiplexing (OFDM). We sounding the channel condition periodically and observing the channel parameters. Our aim is to select the optimal mode of the channel which maximizes energy efficiency or throughput of data subject to a given quality of service (QoS) constraint. Simulation results show that the proposed solution achieves significant improvement over existing link adaptation algorithms. Presented work aims on maximizing the throughput and provides orders of magnitude gain in energy efficiency linked to poorly chosen fixed modes when used for energy efficiency maximization purposes.