FIT: On-the-Fly, In-Situ Training for SNR-Based Rate Selection

Abstract

Existing rate adaptation protocols have advocated training to establish the relationship between channel conditions and the optimum modulation and coding scheme. However, innate with in-field operation is encountering scenarios that the rate adaptation mechanism has not yet encountered. Frequently, protocols are optimally tuned for indoor environments but, when taken outdoors, perform poorly. Namely, the decision structure formed by offline training, lacks the ability to adapt to a new situation on the fly . The changing wireless environment calls for a rate adaption scheme that can quickly infer the channel type and adjust accordingly. Typical SNR-based rate adaptation schemes do not capture the nuance of the performance variable in different channel types. In this paper, we propose a novel scheme that allow SNR-based rate selection algorithms to be trained online in the environment in which they are operating. Inspired by the idea that, to do well, an athlete must train for the type of athletic event and environment in which they are competing, we propose FIT, an on-the- f ly, i n-situ t raining mechanism for SNR-based protocols. To do so, we first propose the FIT framework which addresses the challenges of making rate decisions with unpredictable fluctuation and lack of repeatability of real wireless channels. To distinguish between channel types in the training, we then characterize wireless channels according to the link-layer performance and introduce a novel, computationally-efficient, channel performance manifold matching technique to infer the channel type given a sequence of throughput measurements for various link-level parameters. To evaluate our methods, we implement rate selection which uses FIT for training alongside channel performance manifold matching. We then perform extensive experiments on emulated and in-field wireless channels to evaluate the online learning process, showing that the rate decision structure can be updated as channel conditions change using existing traffic flows. The experiments are performed over multiple frequency bands. The proposed FIT framework can achieve large throughput gains compared to traditional SNR-based protocols (8X) and offline-training-based methods (1.3X), particularly in a dynamic wireless propagation environments that lack appropriate training.