ACM Home Page
Please provide us with feedback. Feedback
Symbol-level network coding for wireless mesh networks
Full text PdfPdf (624 KB)
Source
Applications, Technologies, Architectures, and Protocols for Computer Communication archive
Proceedings of the ACM SIGCOMM 2008 conference on Data communication table of contents
Seattle, WA, USA
SESSION: Wireless II table of contents
Pages 401-412  
Year of Publication: 2008
ISBN:978-1-60558-175-0
Also published in ...
Authors
Sachin Katti  MIT, Cambridge, MA, USA
Dina Katabi  MIT, Cambridge, MA, USA
Hari Balakrishnan  MIT, Cambridge, MA, USA
Muriel Medard  MIT, Cambridge, MA, USA
Sponsors
ACM: Association for Computing Machinery
SIGCOMM: ACM Special Interest Group on Data Communication
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 71,   Downloads (12 Months): 443,   Citation Count: 3
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1402958.1403004
What is a DOI?

ABSTRACT

This paper describes MIXIT, a system that improves the throughput of wireless mesh networks. MIXIT exploits a basic property of mesh networks: even when no node receives a packet correctly, any given bit is likely to be received by some node correctly. Instead of insisting on forwarding only correct packets, MIXIT routers use physical layer hints to make their best guess about which bits in a corrupted packet are likely to be correct and forward them to the destination. Even though this approach inevitably lets erroneous bits through, we find that it can achieve high throughput without compromising end-to-end reliability.

The core component of MIXIT is a novel network code that operates on small groups of bits, called symbols. It allows the nodes to opportunistically route groups of bits to their destination with low overhead. MIXIT's network code also incorporates an end-to-end error correction component that the destination uses to correct any errors that might seep through. We have implemented MIXIT on a software radio platform running the Zigbee radio protocol. Our experiments on a 25-node indoor testbed show that MIXIT has a throughput gain of 2.8x over MORE, a state-of-the-art opportunistic routing scheme, and about 3.9x over traditional routing using the ETX metric.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
2
 
3
E. M. Gabidulin. Theory of codes with maximum rank distance. Probl. Inform. Transm., pages 1--12, July 1985.
 
4
J. Hagenauer and P. Hoecher. A Viterbi Algorithm with Soft-Decision Outputs and its Applications. In IEEE GLOBECOM, Dallas, USA, 1989.
 
5
T. Ho, R. Koetter, M. M´edard, D. Karger, and M. Effros. The Benefits of Coding over Routing in a Randomized Setting. In ISIT, Yokohoma, Japan, 2003.
 
6
E. Inc. Universal software radio peripheral. http://ettus.com.
 
7
S. Jaggi, M. Langberg, S. Katti, T. Ho, D. Katabi, and M. Médard. Resilient network coding in the presence of byzantine adversaries. In IEEE INFOCOM, Alaska, USA, 2007.
8
 
9
S. Katti. Network Coded Wireless Architecture. PhD thesis, MIT, 2008.
 
10
S. Katti, S. Gollakota, and D. Katabi. Analog network coding. In ACM SIGCOMM, Kyoto, Japan, 2007.
11
 
12
R. Koetter and F. Kschischang. Coding for errors and erasures in random network coding. IEEE Transactions on Information Theory, 2007. To appear.
 
13
 
14
J. N. Laneman, D. N. C. Tse, and G. W. Wornell. Cooperative diversity in wireless networks: Efficient protocols and outage behavior. IEEE Trans. on Inform. Theory, Volume 50, Issue 12, Dec. 2004 Page(s):3062--3080.
 
15
H. Lee. A high-speed low-complexity reed-solomon decoder for optical communications. IEEE Transactions on Circuits and Systems, 52(8):461--465, Aug. 2005.
 
16
 
17
F. J. McWilliams and N. J. A. Sloane. The Theory of Error-Correcting Codes. North-Holland, 1977.
18
19
20
 
21
G. Richter and S. Plass. Error and erasure decoding of rank-codes with a modified berlekamp-massey algorithm. In 5th International ITG Conference on Source and Channel Coding, Erlangen, Germany, 2004.
22
 
23
D. Silva, F. R. Kschischang, and R. Koetter. A rank-metric approach to error control in random network coding. submitted, 2007.
24
 
25
 
26
 
27
M. Wang, X. Weimin, and T. Brown. Soft Decision Metric Generation for QAM with Channel Estimation Error. IEEE Transactions on Communications, 50(7):1058--1061, 2002.
 
28
A. Willig, M. Kubisch, C. Hoene, and A. Wolisz. Measurements of a wireless link in an industrial environment using an ieee 802.11-compliant physical layer. IEEE Transaction on Industrial Electronics, 49(6), 2002.
29
 
30
R. W. Yeung and N. Cai. Network error correction, part 1: Basic concepts and upper bounds. Communications in Information and Systems, 6(1):19--35, 2006.


Collaborative Colleagues:
Sachin Katti: colleagues
Dina Katabi: colleagues
Hari Balakrishnan: colleagues
Muriel Medard: colleagues