Cross-layer wireless bit rate adaptation
Name
sigcomm258-vutukuru.pdf
Size
792.27 KB
Format
Adobe PDF
Checksum (MD5)
7e88d6c0802a1d7aa969da7653830c22
Author(s)
Vutukuru, Mythili
Balakrishnan, Hari
Jamieson, Kyle
Date Issued
2009
Journal
Proceedings of the ACM SIGCOMM 2009 conference on Data communication (SIGCOMM '09)
Publisher
Association for Computing Machinery (ACM)
Citation
Mythili Vutukuru, Hari Balakrishnan, and Kyle Jamieson. 2009. Cross-layer wireless bit rate adaptation. In Proceedings of the ACM SIGCOMM 2009 conference on Data communication (SIGCOMM '09). ACM, New York, NY, USA, 3-14.
Version
Author's final manuscript
Abstract
This paper presents SoftRate, a wireless bit rate adaptation protocol that is responsive to rapidly varying channel conditions. Unlike previous work that uses either frame receptions or signal-to-noise ratio (SNR) estimates to select bit rates, SoftRate uses confidence information calculated by the physical layer and exported to higher layers via the SoftPHY interface to estimate the prevailing channel bit error rate (BER). Senders use this BER estimate, calculated over each received packet (even when the packet has no bit errors), to pick good bit rates. SoftRate's novel BER computation works across different wireless environments and hardware without requiring any retraining. SoftRate also uses abrupt changes in the BER estimate to identify interference, enabling it to reduce the bit rate only in response to channel errors caused by attenuation or fading. Our experiments conducted using a software radio prototype show that SoftRate achieves 2X higher throughput than popular frame-level protocols such as SampleRate and RRAA. It also achieves 20% more throughput than an SNR-based protocol trained on the operating environment, and up to 4X higher throughput than an untrained SNR-based protocol. The throughput gains using SoftRate stem from its ability to react to channel variations within a single packet-time and its robustness to collision losses.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/1592568.1592571
