[CWN-Summit] Better wireless channel measurements - Where is the discussion?

[Brough Turner]

I just stumbled on a very interesting paper entitled "Predictable 802.11
Packet Delivery from Wireless Channel Measurements"
http://seattle.intel-research.net/~ansheth/papers/effsnr_sigcomm10.pdf. I've
attached a copy of the abstract below.
The paper presents what looks to be a substantial improvement over RSSI and
one that's much faster than the iteratively trying modulation schemes and
measuring packet loss.

I assume there is a better forum for this discussion, but I was hopeful that
someone on this list could point me to the right place.

Thanks,
Brough Turner

netBlazr Inc.  http://netblazr.com/node/2
Mobile: +1 617 285-0433   Skype: brough
Blog: http://blogs.broughturner.com/


*Predictable 802.11 Packet Delivery from Wireless Channel Measurements*
by Halperin, Hu, Sheth & Wetherall

via Anmol Sheth:  http://seattle.intel-research.net/~ansheth/

   *ABSTRACT*


RSSI is known to be a fickle indicator of whether a wireless link will work,
for many reasons. This greatly complicates operation because it requires
testing and adaptation to find the best rate, trans- mit power or other
parameter that is tuned to boost performance. We show that, for the first
time, wireless packet delivery can be accurately predicted for commodity
802.11 NICs from only the channel measurements that they provide. Our model
uses 802.11n Channel State Information measurements as input to an OFDM re-
ceiver model we develop by using the concept of effective SNR. It is simple,
easy to deploy, broadly useful, and accurate. It makes packet delivery
predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of
transmit power and antennas. We re- port testbed experiments that show
narrow transition regions (<2 dB for most links) similar to the near-ideal
case of narrowband, fre- quency-flat channels. Unlike RSSI, this lets us
predict the highest rate that will work for a link, trim transmit power, and
more. We use trace-driven simulation to show that our rate prediction is as
good as the best rate adaptation algorithms for 802.11a/g, even over dy-
namic channels, and extends this good performance to 802.11n.
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