Consistency in the face of change: an adaptive approach to physical layer cooperation

TL;DR

This paper introduces an adaptive PHY cooperation method that dynamically selects relays to maintain performance in changing wireless environments, demonstrating significant throughput improvements over static and IP-level adaptive schemes.

Contribution

It presents a theoretical analysis of relay network diminishing returns, a simple adaptive relay selection algorithm, and experimental validation showing substantial throughput gains.

Findings

Diminishing returns with increasing relays.

Efficient relay selection maintains target reliability.

2x throughput gain over nonadaptive PHY schemes.

Abstract

Most existing works on physical-layer (PHY) cooperation (beyond routing) focus on how to best use a given, static relay network--while wireless networks are anything but static. In this paper, we pose a different set of questions: given that we have multiple devices within range, which relay(s) do we use for PHY cooperation, to maintain a consistent target performance? How can we efficiently adapt, as network conditions change? And how important is it, in terms of performance, to adapt? Although adapting to the best path when routing is a well understood problem, how to do so over PHY cooperation networks is an open question. Our contributions are: (1) We demonstrate via theoretical evaluation, a diminishing returns trend as the number of deployed relays increases. (2) Using a simple algorithm based on network metrics, we efficiently select the sub-network to use at any given time to maintain a target reliability. (3) When streaming video from Netflix, we experimentally show (using measurements from a WARP radio testbed employing DIQIF relaying) that our adaptive PHY cooperation scheme provides a throughput gain of 2x over nonadaptive PHY schemes, and a gain of 6x over genie-aided IP-level adaptive routing.