ASTRA: Application of sequential training to rate adaptation

The application of machine learning algorithms in wireless communications has attracted increasing attention due to the promising performance gains recently achieved. Static classification algorithms have been successfully applied to training protocols that adapt transmission parameters according to context information. However, in reality, there are many time-varying reasons for fading channel quality including mobility of sender, receiver, and/or obstacles within the environment. Moreover, time-varying noise further exacerbates the dynamics of the channel. These problems pose new challenges for the application of static classification algorithms in context-aware algorithms and suggest that sequential classifiers which leverage the temporal dynamics and correlation of context information might be more appropriate. In this paper, we apply sequential training to rate adaptation (ASTRA), leveraging the temporal correlation of context information. In particular, linear and non-linear sequential coding schemes are used in the training process for selecting the modulation/coding rate that achieves the highest throughput for the given context. Experimental results on measurements from emulated and in-field channels demonstrate that ASTRA can significantly increase the accuracy of selecting these target rates by up to 175% and increase the resulting throughput by up to 66% over rate adaptation training which uses static classifier-based methods.