Optimal Rate Sampling in 802.11 Systems

TL;DR

This paper introduces ORS, a family of algorithms for optimal rate sampling in 802.11 systems, formulated as an online stochastic optimization problem, achieving near-optimal throughput in both stationary and non-stationary environments.

Contribution

It formulates rate adaptation as an online stochastic optimization problem and proposes ORS algorithms that provably learn the best (mode, rate) pair efficiently.

Findings

ORS algorithms achieve near-optimal throughput.

Throughput loss is independent of the number of (mode, rate) pairs.

ORS outperforms existing algorithms in simulations and test-bed traces.

Abstract

In 802.11 systems, Rate Adaptation (RA) is a fundamental mechanism allowing transmitters to adapt the coding and modulation scheme as well as the MIMO transmission mode to the radio channel conditions, and in turn, to learn and track the (mode, rate) pair providing the highest throughput. So far, the design of RA mechanisms has been mainly driven by heuristics. In contrast, in this paper, we rigorously formulate such design as an online stochastic optimisation problem. We solve this problem and present ORS (Optimal Rate Sampling), a family of (mode, rate) pair adaptation algorithms that provably learn as fast as it is possible the best pair for transmission. We study the performance of ORS algorithms in both stationary radio environments where the successful packet transmission probabilities at the various (mode, rate) pairs do not vary over time, and in non-stationary environments where these probabilities evolve. We show that under ORS algorithms, the throughput loss due to the need to explore sub-optimal (mode, rate) pairs does not depend on the number of available pairs, which is a crucial advantage as evolving 802.11 standards offer an increasingly large number of (mode, rate) pairs. We illustrate the efficiency of ORS algorithms (compared to the state-of-the-art algorithms) using simulations and traces extracted from 802.11 test-beds.