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Rateless codes, and especially Raptor codes, have received considerable attention in the recent past due to their inherent ability to adapt to channel conditions and their capacity-approaching performance. Since decoding of rateless codes typically involves multiple decoding attempts, early termination of such attempts is mandatory for overall efficient(More)
We propose a novel receiver for Ultra-Wide-band Impulse-Radio communication in Wireless Sensor Networks, which are characterized by bursty traffic and severe power constraints. The receiver is based on the principle of Compressed Sensing, and exploits the sparsity of the transmitted signal to achieve reliable demodulation from a relatively small number of(More)
Fountain codes have recently gained wide attention in communications due to their capacity-approaching performance and rateless properties that allow them to seamlessly adapt to unknown channel statistics. In this paper, we consider the problem of low complexity decoding of Luby transform codes, which are a class of linear fountain codes. We adapt the(More)
Free-space optical (FSO) transmission systems enable high-speed communication with relatively small deployment costs. However, FSO suffers a critical disadvantage, namely susceptibility to fog, smoke, and conditions alike. A possible solution to this dilemma is the use of hybrid systems employing FSO and radio frequency (RF) transmission. In this paper we(More)
We have recently proposed a novel receiver for Ultra-Wide-band Impulse-Radio communication in bursty applications like Wireless Sensor Networks. The receiver, based on the principle of Compressed Sensing (CS), exploits the sparsity of the transmitted signal to achieve reliable demodulation. It acquires a modest number of projections of the received signal(More)
The task of tracking targets carrying active radiofrequency identification (RFID) tags based on the received signal strength indication (RSSI) values of tag transmissions is a classical Bayesian filtering problem. Since the problem is nonlinear, no closed-form solution is known and tractable approximations must be used. Unscented Kalman Filtering (UKF) and(More)
Wireless sensor networks (WSNs) comprise of highly power constrained nodes that observe a hidden natural field and reconstruct it at a distant data fusion center. Algorithmic strategies for extending the lifetime of such networks invariably require a knowledge of the statistical model of the underlying field. Since centralized model identification is(More)
We consider a wireless sensor network (WSN) that monitors a physical field and communicates pertinent data to a distant fusion center (FC). We study the case of a binary valued hidden natural field observed in a significant amount of Gaussian clutter, which is relevant to applications like detection of plumes or oil slicks. The considerable spatio-temporal(More)
Wireless Sensor Networks are well suited for tracking targets carrying RFID tags in indoor environments. Tracking based on the received signal strength indication (RSSI) is by far the cheapest and simplest option, but suffers from secular biases due to effects of multi-path, occlusions and decalibration, as well as large unbiased errors due to measurement(More)
A Wireless Sensor Network (WSN) observes a natural field and aims to recreate it with sufficient fidelity at a, perhaps distant, Fusion Center (FC) using a wireless communication channel of arbitrary capacity. We propose a universal and power efficient method for such data extraction, based on Digital Fountain Codes (DFCs) and joint-source channel decoding.(More)