Online adaptive modulation and coding with support vector machines

Optimizing the performance of adaptive modulation and coding (AMC) in practice has proven challenging. Prior research has struggled to find link quality metrics that are suitable for look-up-tables and simultaneously provide an injective mapping to error rate in wireless links that feature selective channels with hardware nonlinearities and non-Gaussian noise effects. This paper proposes a novel online support vector machine algorithm, compatible with accurate multidimensional link quality metrics, that is able to optimize AMC to the unique (potentially dynamic) hardware characteristics of each wireless device in selective channels. IEEE 802.11n simulations show that our proposed algorithm allows each individual wireless device to optimize the operating point in the rate/reliability tradeoff through frame-by-frame error evaluation. These simulations also show that our algorithm displays identical performance to alternative online AMC algorithms while drastically reducing complexity.