Subject: [NEXTEL-1] Nonlinear neural nets smooth WiFi packets

From: [email-masked]
Date: Wed, 05 May 2004 19:23:15 -0400

-------- Original Message --------
From: Shreyas Pandit <[email-masked]>
Subject: Nonlinear neural nets smooth WiFi packets

Smart Packets, Inc has developed the Smart WiFi Algorithm, a packet
sizing 
technology which can predict the near future of network conditions based
on 
the recent past. The development was originally started to enable smooth 
real-time data delivery for applications such as streaming video, but
when 
tested on 802.11b networks it was shown to increase data throughput by 
100%. The technology can be applied at the application level, the
operating 
system level, or at the firmware level.

http://www.smartpacketsinc.com/

http://www.commsdesign.com/news/tech_beat/showArticle.jhtml?articleID=19501137

Nonlinear neural nets smooth WiFi packets

By Chappell Brown
	
EE Times
May 04, 2004 	
	
HANCOCK, N.H. — The packet sizing problem in IP networks is getting a
new 
twist from non-linear neural network theory with the advent of the Smart 
WiFi algorithm developed at SmartPackets, Inc. (Manchester, N.H.).

Smooth real-time data delivery for demanding applications such as
digital 
video has been a thorny problem for broadcast over IP networks due to
the 
high variability of network traffic conditions. To be effective in
solving 
that problem, a packet sizing algorithm needs to be adept at predicting
the 
near future of network conditions based on the recent past. Engineers at 
SmartPackets discovered that non-linear neural nets excelled at that
task, 
creating a powerful algorithm for enhancing IP networks.

The result is a 100 percent increase in data throughput on the target 
application of 802.11b wireless networks, said Murat Bahadiroglu, chief 
technology officer at SmartPackets. The system has been developed as a 
C-code kernel that can be inserted into the packet-sizing segment of the 
802.11b wireless standard. Occupying only 19 kbytes of memory, the
kernel 
analyzes network activity and predicts the ideal data packet size for 
optimal throughput.
"About five years ago, Bahadiroglu developed this packet-resizing
approach 
while working on a wireless videoconferencing system," explained Paul 
Introcaso, SmartPackets' vice president of business development. "He
needed 
to get a smooth video response over a 3-kbit/second channel, so it was a 
big challenge."

Bahadiroglu's basic discovery was that packet sizing could go a lot
further 
in boosting performance than data compression with the right predictive 
approach. While IP applications developers have looked at packet sizing 
methods as a means of improving the real-time performance of packet
based 
networks, the methods used never seemed to pay off in significantly
better 
performance, Introcaso explained.

Once the basic system was worked out, the company realized that it could
be 
applied to any Internet Protocol-based network that uses packets. That
led 
it to target the 802.11b market.

"We realized that if we stripped the packet-sizing function out of the 
video application and applied it to any packet-based network, we would
have 
a product with a wide application area," Introcaso said.

In wireless networks, if an error is detected in a packet, the receiver 
requests a resend. In noisy channels, large packets that must be resent 
will produce long, redundant retransmissions while short packets reduce
the 
amount of wasted communications. Thus, for each level of noise, there is
an 
optimal packet size that will keep redundant transmissions to a minimum. 
Since noise levels are random on a wireless network, any fixed formula
for 
sizing packets will not provide optimal throughput.

"The challenge was how to determine packet size right at a point in the 
transmission. Channel conditions change dramatically and they change
very 
fast, so we needed a mathematical model that would capture the behavior
of 
the network," Bahadiroglu explained. In addition, up to 12 parameters
need 
to monitored simultaneously, making the problem highly complex.

Bahadiroglu hired two mathematicians to look at the problem and a number
of 
approaches were tried. A database look-up model, time series, fuzzy
logic 
and expert systems were all considered, but the problem turned out to be 
resistant to those methods.

"The margins are very thin. If 550 bits is the optimum packet size, 540 
would still be ok, but 520 would not be adequate," he said. Eventually,
the 
group realized that any linear model would not be able to respond to the 
real-time demands of the problem.

The solution, a nonlinear recurrent-feedback neural network, turns out
to 
be very similar to the Echo State Networks (ESNs) that German researcher 
Herbert Jaeger is using to optimize channel allocation in cellular
networks.

The feedback circuits in a recurrent neural network allow a past series
of 
inputs to affect the prediction of the next event in the series, which 
makes them very accurate in predicting events. For example, in benchmark 
tests, Jaeger found that ESNs were more accurate than standard
algorithms 
by a factor of 2,400 in predicting chaotic time series.

The neural network is one of three modules in the company's WiFi
Speedzone 
system which has reached the beta test stage. A second module monitors
and 
analyzes network traffic and the third handles the packet-sizing
operation. 
WiFi Speedzone can be implemented at the application level, the
operating 
system level or as firmware embedded in an 802.11b device, the company
said.

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