Skip to Main Content
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: 1) implementation of multiple and previously unimplemented rate adaptation mechanisms; 2) experimental evaluation and comparison of rate adaptation protocols on controlled, repeatable channels as well as residential urban and downtown vehicular and nonmobile environments in which we accurately measure channel conditions with 100- s granularity; and 3) 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).