Design and experimental evaluation of context-aware link-level adaptation

Abstract

Abstract-Context awareness has received increasing attention with the proliferation of various types of sensors on mobile devices. However, while wireless performance is known to be highly correlated with environmental settings, mobile devices have yet to fully exploit the awareness of context to improve wireless performance. In this paper, we leverage available context information to improve link-level adaptation via decision-tree classifiers and extensively evaluate its performance over emulated channels as well as with in-field trials. We first propose a classification method based on decision trees to select the optimal transmission parameters such as modulation, coding rate and packet size. We then quantify the throughput improvement using the proposed scheme and show that in some scenarios the throughput increases by over 100% compared to traditional SNR-based rate adaptation protocols. Second, we analyze the amount of training to assess the classification scheme. Third, we validate classification-based method by implementation on two different test platforms for extensive experimentation. We reveal the importance of the various contextual attributes used and identify channel type as a key parameter that affects classification performance. Finally, we study and quantify the use of context information across multiple different frequency bands and demonstrate the significant throughput gains that can be obtained.

A global measurement study of context-based propagation and user mobility

“…In particular, we use path loss to characterize the propagation characteristics of a given region [8]. A similar procedure could be used for on-the-fly estimation of regional performance for context-aware applications such as in-situ training to optimize performance [9] or enabling optimized mobile handoff procedures based on directionality, speed, and current distance and propagation characteristics from cell towers [10]. We also examine user mobility within these regions, using a fuzzy logic technique of subtractive clustering [11] to determine groupings of user locations.…”

“…We show how multiple users display similar mobility patterns and display trends among locations and signal strength across similar regions. Such a user mobility characterization is useful to understand a typical smartphone user and could be used to trigger context-aware training in commonly traversed areas [9].…”

“…Surprisingly, we find that, to a large degree, user mobility patterns can be classified as unimodal or bimodal. Since most context-based services require an initial overhead, such an awareness of typical user locations could trigger training to be performed [9] in the typical environment in which they operate to allow large gains in performance for the given scenario.…”

ASTRA: Application of sequential training to rate adaptation

“…As channel fluctuations increase, the ability to converge to optimality becomes more and more elusive [6]. Thus, recent works have proposed using the context information and machine learning to quickly converge to optimality [7], [8].Context-aware rate adaptation schemes attempt to leverage existing patterns in the collected context information to adjust the transmission parameters to improve performance. Examples of such schemes include neural networks and genetic algorithms for parameter adaptation in cognitive radio networks [9], [10], distributed classification with data from different sensors [11], and static classification-based rate adaptation [7].…”

“…Thus, recent works have proposed using the context information and machine learning to quickly converge to optimality [7], [8].…”