20,399 research outputs found
Compressive sensing based Bayesian sparse channel estimation for OFDM communication systems: high performance and low complexity
In orthogonal frequency division modulation (OFDM) communication systems,
channel state information (CSI) is required at receiver due to the fact that
frequency-selective fading channel leads to disgusting inter-symbol
interference (ISI) over data transmission. Broadband channel model is often
described by very few dominant channel taps and they can be probed by
compressive sensing based sparse channel estimation (SCE) methods, e.g.,
orthogonal matching pursuit algorithm, which can take the advantage of sparse
structure effectively in the channel as for prior information. However, these
developed methods are vulnerable to both noise interference and column
coherence of training signal matrix. In other words, the primary objective of
these conventional methods is to catch the dominant channel taps without a
report of posterior channel uncertainty. To improve the estimation performance,
we proposed a compressive sensing based Bayesian sparse channel estimation
(BSCE) method which can not only exploit the channel sparsity but also mitigate
the unexpected channel uncertainty without scarifying any computational
complexity. The propose method can reveal potential ambiguity among multiple
channel estimators that are ambiguous due to observation noise or correlation
interference among columns in the training matrix. Computer simulations show
that propose method can improve the estimation performance when comparing with
conventional SCE methods.Comment: 24 pages,16 figures, submitted for a journa
An objective based classification of aggregation techniques for wireless sensor networks
Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented
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