[CCoE Notice] Ph.D. Defense Announcement

[Khator, Suresh]

Ph.D. Defense

COMPRESSIVE SENSING APPLICATIONS FOR WIRELESS
COMMUNICATIONS

By JIA MENG
Electrical and Computer Engineering

Date: Nov, 15th, (Monday)

Time: 12:00pm-1:30pm

Location: W342-D3 Engineering Bldg. #2

Advisor: Zhu Han
Abstract
Compressive sensing theory which links data acquisition, compression, dimensionality reduction, and optimization, has attracted great many research attentions in recent years. The CS theory consists of three key components, namely, signal sparsity, incoherent measurement matrix, and signal recovery. It claims that, as long as the signal to be measured is sparse, or can become sparse after some known transformation; the information in the signal can be preserved in a small number of its incoherent measurements, and convex optimization offers overwhelming signal recovery probability.

The bulk of the dissertation explores the CS framework and proposes several implementations in wireless communications. Specifically, we first propose to apply compressive sensing for collaborative spectrum sensing in cognitive radio networks to reduce the amount of sensing and transmission overhead. This is realized by innovatively equipping each cognitive radio nodes with an on-board frequency selective filter set, through which the sensing information is blended incoherently and can be decoded at the fusion center via joint sparsity recovery or matrix completion technique. Then, we design a "high resolution OFDM channel estimation with low speed ADC using compressive sensing" system. Aiming at increasing the channel estimation resolution without increasing the costly ADC speed, we form a random convolution sensing scheme by carefully arranging the pilot tones and taking advantage of the channel- signal convolution nature. Moreover, based on the observation that the received signals are sparse in the time domain due to the limited multipath effects at 60 GHz UWB wireless transmission, we design a CS based low speed DC to reduce the sampling rate, while still be able to reconstruct the signal with high fidelity. Finally, we implement CS framework for sparse events detection in wireless sensor networks, in which the sensor activation events are sparse. We propose the use of a small number of monitoring tubes to take very limited number of incoherent measurements, which are then decoded through the Bayesian framework with a heuristic algorithm to enhancing the detection probability.


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