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The thesis of master degree: In this paper, we discuss the application of a new compression technique called compressive sensing (CS) in wireless sensor networks (WSNs). CS is a signal acquisition and compression framework recently developed in the field of signal processing and information theory. We applied this CS technique to WSN which consists of a large number of wireless sensor nodes and a central fusion center (FC). This CS based signal acquisition and compression is done by a simple linear projection at each sensor node. Then, each sensor transmits the compressed samples to the FC. The FC which collects the compressed signals from the sensors jointly reconstructs the signals in polynomial time using a signal recovery algorithm. The distributed sensors observe similar event in designated region. Therefore, the observed signals have considerable correlation each other. We pay some effort in modeling correlation between the signals acquired from the sensors. After modeling the correlated signals, we propose POMP (Phased-OMP) which can recover any type of correlated signals stably and effectively. We introduce the idea of our proposed algorithm in detail and then compare the reconstruction performance of POMP with previous algorithms
3
2012-11-22
Capacity of OFDM Systems over Fading Underwater Acoustic Channels
This paper derives the upper and lower bounds for channel capacity of the OFDM systems over underwater acoustic channels as a function of distance between the transmitter and the receiver. It incorporates frequency dependent path loss at each arrival path at the receiver due to acoustic propagation. This leads the UW channel to be modeled as wide sense stationary and correlated scattering (WSS-non-US) fading channel. Results from both Rayleigh and Rician fading show a gap between the upper and lower bounds which depends, not only on the ranges and shape of the scattering function of the UW channel but also on the distance between the transmitter and the receiver.
2
2012-11-15
On the Recovery Limnit of Sparse Signals Using Orthogonal Matching Pursuit
In the paper, the authors give a sufficient condition of the Orthogonal matching Pursuit (OMP) algorithm. In [2], Wakin and Davenport inisted that OMP can reconstrcut any K sparse signal if delta_(K+1) < 1/(3*sqrt(K)). However, in this talk, an improved sufficient condition that guarantees the perfec recovery of OMP is presented.
1
2012-11-08
Fair Bandwidth Allocation in Wireless Mesh Networks With Cognitive Radios
In this paper authors discuss about fair bandwidth allocation issue in wireless mesh networks with cognitive radios. In order to achieve fairness they define the two allocation problems based upon a simple max-min fairness model and lexicographical max-min fairness model. They solve the allocation problems by using linear programming based heuristic algorithms. The proposed algorithms ensure both fairness and throughput. The presented algorithms are evaluated for their effectiveness and fairness based upon extensive simulations.
