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In MIMO-OFDM systems, by matching transmitter parameters such as modulation order and coding rate, link adaptation can increase the throughput significantly. However, creating a tractable mathematical mapping model from environmental variables to transmitter parameters that allows the latter to be optimized in any sense, presents serious challenging due to the large number of variables involved, as well as the complexity required in any model with the ability to accurately capture and explain all factors that affect performance. Machine learning algorithms, which make no mathematical assumptions and use only past observations to model the input-output relationship, have recently been explored for adaptation in MIMO-OFDM systems.
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