Communications in the observation limited regime

We consider the design of communications systems when the principal cost is observing the channel, as opposed to transmit energy per bit or spectral efficiency. This is motivated by energy constrained communications devices where sampling the signal, rather than transmitting or processing it, dominates energy consumption. We show that sequentially observing samples with the maximum a posteriori entropy can reduce observation costs by close to an order of magnitude using a (24,12) Golay code. This is the highest performance reported over the binary input AWGN channel, with or without feedback, for this blocklength. Sampling signal energy, rather than amplitude, lowers circuit complexity and power dissipation significantly, but makes synchronization harder. We show that while the distance function of this non-linear coding problem is intractable in general, it is Euclidean at vanishing SNRs, and root Euclidean at large SNRs. We present sequences that maximize the error exponent at low SNRs under the peak power constraint, and under all SNRs under an average power constraint. Some of our new sequences are an order of magnitude shorter than those used by the 802.15.4a standard.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Bhardwaj, Manish, 1976-

Advisor dc:contributor.advisor

• Anantha Chandrakasan.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/53191

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/53191