Modulation Rate Adaptation In Urban And Vehicular Environments: Cross-layer Implementation And Experimental Evaluation

Abstract

Accurately selecting modulation rates for time-varying channel conditions is critical for avoiding performance degradations due to rate overselection when channel conditions degrade or underselection when channel conditions improve. In this paper, we design a custom cross-layer framework that enables <i>(i)</i> implementation of multiple and previously unimplemented rate adaptation mechanisms, <i>(ii)</i> experimental evaluation and comparison of rate adaptation protocols on controlled, repeatable channels as well as residential urban and downtown vehicular and non-mobile environments in which we accurately measure channel conditions with 100-μs granularity, and <i>(iii)</i> comparison of performance on a per-packet basis with the ideal modulation rate obtained via exhaustive experimental search. Our evaluation reveals that SNR-triggered protocols are susceptible to overselection from the ideal rate when the coherence time is low (a scenario that we show occurs in practice even in a nonmobile topology), and that "in-situ" training can produce large gains to overcome this sensitivity. Another key finding is that a mechanism effective in differentiating between collision and fading losses for hidden terminals has severely imbalanced throughput sharing when competing links are even slightly heterogeneous. In general, we find trained SNR-based protocols outperform loss-based protocols in terms of the ability to track vehicular clients, accuracy within outdoor environments, and balanced sharing with heterogeneous links (even with physical layer capture).