Automatic rate adaptation
- 20 October 2010
- conference paper
- Published by Association for Computing Machinery (ACM)
Abstract
Rate adaptation is a fundamental primitive in wireless networks. Since wireless channel strength varies quickly and unpredictably, senders have to constantly measure the channel and correspondingly adapt the bitrate so that the transmitted packet gets correctly decoded. Prior approaches to this problem can be divided into two classes: those that require constant and expensive feedback from the receiver about channel strength, or those that use coarse and often inaccurate inference based on packet losses to measure channel strength and decide what bitrate to use. In this paper we take the opposite approach. Instead of actively adapting the bitrate based on receiver or packet loss feedback, we present a technique where the sender does no measurement or adaptation, yet the receiver manages to receive packets at a bitrate corresponding to whatever channel conditions exist at that point. The technique works with existing coding and modulation techniques (e.g. convolutional codes in WiFi), and requires no changes to them. Our preliminary evaluation shows that our proposed feedback-free technique achieves a performance that is nearly as good as if the sender knew exactly what the channel strength was in advance.Keywords
This publication has 6 references indexed in Scilit:
- Cross-layer wireless bit rate adaptationPublished by Association for Computing Machinery (ACM) ,2009
- Modulation rate adaptation in urban and vehicular environmentsPublished by Association for Computing Machinery (ACM) ,2008
- Efficient channel-aware rate adaptation in dynamic environmentsPublished by Association for Computing Machinery (ACM) ,2008
- Raptor codesIEEE Transactions on Information Theory, 2006
- A Viterbi algorithm with soft-decision outputs and its applicationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- A universal lattice code decoder for fading channelsIEEE Transactions on Information Theory, 1999