The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Structured estimation of channel impulse response (CIR) is considered in orthogonal frequency division multiplexing (OFDM) systems for which the channel exhibits a sparse time-domain response. In particular, fast fading channels encountered in mobile wireless communications are envisaged. Such channels are characterized by time varying frequency selective response. This contribution exploits the much...
Compressed Sensing (CS) has been successfully applied within Wireless Sensors Networks (WSN). We consider the problem of data recovery from a subset of few sensor readings collection at a fusion center in the case of spatially correlated large WSN. We exploit the data spatial correlation to derive a sparse representation of the signal and consider 1-D reading of the WSN. To this end, we propose a...
Compressed Sensing (CS) is an innovative approach allowing to represent signals through a small number of their projections. In this paper, we address the application of CS to the scenario of 2D readings recovery from a Wireless Sensors Network (WSN) with excellent accuracy, while collecting only a small fraction of them at a data gathering point. CS requires a suitable transformation that makes the...
This paper applies the Compressed Sensing (CS) the targets detection in small scale dense Wireless Sensors Networks (WSN). The monitored area is partitioned into cells, each equipped by one sensor. The CS application aims to locate targets from a reduced subset of sensors measurements. A generalized version of a recently proposed Greedy Matching Pursuit algorithm (GMP), designed for point events joint...
The problem of channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems Is here investigated. In particular, channels exhibiting a temporal response with sparse structure are considered. For fast fading channels, we consider the comb type estimation where we address the pilot allocation optimization by applying the recent Compressed Sensing (CS) theory. This optimization reduces...
The problem of events detection in Wireless Sensor Networks (WSN) is investigated from the perspective of Compressed Sensing (CS). In WSN, when the events to detect are rare, the number of sensors with innovative and useful measurements is far lower than that of deployed sensors. In this way, to reduce the data processing step burden, the number of sensors measurements used in the events detection...
The recently emerged Compressed Sensing (CS) theory has widely addressed the problem of sparse targets detection in Wireless Sensor Networks (WSN) in the aim of reducing the deployment cost and energy consumption. In this paper, we apply CS approach for both sparse events recovery and counting. We first propose a novel Greedy version of the Orthogonal Matching Pursuit (GOMP) algorithm allowing to...
This paper addresses the problem of sparse channel estimation in the context of Orthogonal Frequency Division Multiplexing (OFDM) systems. We propose to extend a recently proposed tap-tuned threshold-based Channel Impulse Response (CIR) structure detection scheme to the case of fast varying channels, through the use of a uniform pilot subcarriers placement. The chosen thresholds minimize the estimation...
Channels exhibiting a sparse impulse response arise in a number of communication applications. In this paper, we address the problem of sparse channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems. Our goal is to compare the accuracy of the Matching Pursuit (MP) [1] approach to an optimized threshold-based approach, named the Probabilistic Framework Estimator (PFE) [2]. The...
In this paper, we describe a Threshold-Based Selection (TBS) approach for sparse channel estimation in an Orthogonal Frequency Division Multiplexing (OFDM) system. An optimal tap-tuned threshold is derived by minimizing the Mean Squares Error (MSE) per channel impulse response coefficient. Comparing the proposed TMSE approach to the Probabilistic Framework Estimator (PFE) and to former MSE optimization...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.