431 research outputs found
Quantization and Compressive Sensing
Quantization is an essential step in digitizing signals, and, therefore, an
indispensable component of any modern acquisition system. This book chapter
explores the interaction of quantization and compressive sensing and examines
practical quantization strategies for compressive acquisition systems.
Specifically, we first provide a brief overview of quantization and examine
fundamental performance bounds applicable to any quantization approach. Next,
we consider several forms of scalar quantizers, namely uniform, non-uniform,
and 1-bit. We provide performance bounds and fundamental analysis, as well as
practical quantizer designs and reconstruction algorithms that account for
quantization. Furthermore, we provide an overview of Sigma-Delta
() quantization in the compressed sensing context, and also
discuss implementation issues, recovery algorithms and performance bounds. As
we demonstrate, proper accounting for quantization and careful quantizer design
has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing
and Its Applications", 201
A Lite Distributed Semantic Communication System for Internet of Things
The rapid development of deep learning (DL) and widespread applications of
Internet-of-Things (IoT) have made the devices smarter than before, and enabled
them to perform more intelligent tasks. However, it is challenging for any IoT
device to train and run DL models independently due to its limited computing
capability. In this paper, we consider an IoT network where the cloud/edge
platform performs the DL based semantic communication (DeepSC) model training
and updating while IoT devices perform data collection and transmission based
on the trained model. To make it affordable for IoT devices, we propose a lite
distributed semantic communication system based on DL, named L-DeepSC, for text
transmission with low complexity, where the data transmission from the IoT
devices to the cloud/edge works at the semantic level to improve transmission
efficiency. Particularly, by pruning the model redundancy and lowering the
weight resolution, the L-DeepSC becomes affordable for IoT devices and the
bandwidth required for model weight transmission between IoT devices and the
cloud/edge is reduced significantly. Through analyzing the effects of fading
channels in forward-propagation and back-propagation during the training of
L-DeepSC, we develop a channel state information (CSI) aided training
processing to decrease the effects of fading channels on transmission.
Meanwhile, we tailor the semantic constellation to make it implementable on
capacity-limited IoT devices. Simulation demonstrates that the proposed
L-DeepSC achieves competitive performance compared with traditional methods,
especially in the low signal-to-noise (SNR) region. In particular, while it can
reach as large as 40x compression ratio without performance degradation.Comment: Accpeted by JSA
Hardware Impairments Aware Transceiver Design for Bidirectional Full-Duplex MIMO OFDM Systems
In this paper we address the linear precoding and decoding design problem for
a bidirectional orthogonal frequencydivision multiplexing (OFDM) communication
system, between two multiple-input multiple-output (MIMO) full-duplex (FD)
nodes. The effects of hardware distortion as well as the channel state
information error are taken into account. In the first step, we transform the
available time-domain characterization of the hardware distortions for FD MIMO
transceivers to the frequency domain, via a linear Fourier transformation. As a
result, the explicit impact of hardware inaccuracies on the residual
selfinterference (RSI) and inter-carrier leakage (ICL) is formulated in
relation to the intended transmit/received signals. Afterwards, linear
precoding and decoding designs are proposed to enhance the system performance
following the minimum-mean-squarederror (MMSE) and sum rate maximization
strategies, assuming the availability of perfect or erroneous CSI. The proposed
designs are based on the application of alternating optimization over the
system parameters, leading to a necessary convergence. Numerical results
indicate that the application of a distortionaware design is essential for a
system with a high hardware distortion, or for a system with a low thermal
noise variance.Comment: Submitted to IEEE for publicatio
On Distributed and Acoustic Sensing for Situational Awareness
Recent advances in electronics enable the development of small-sized, low-cost, low-power, multi-functional sensor nodes that possess local processing capability as well as to work collaboratively through communications. They are able to sense, collect, and process data from the surrounding environment locally. Collaboration among the nodes are enabled due to their integrated communication capability. Such a system, generally referred to as sensor networks are widely used in various of areas, such as environmental monitoring, asset tracking, indoor navigation, etc.
This thesis consists of two separate applications of such mobile sensors. In this first part, we study decentralized inference problems with dependent observations in wireless sensor networks. Two separate problems are addressed in this part: one pertaining to collaborative spectrum sensing while the other on distributed parameter estimation with correlated additive Gaussian noise. In the second part, we employ a single acoustic sensor with co-located microphone and loudspeaker to reconstruct a 2-D convex polygonal room shape.
For spectrum sensing, we study the optimality of energy detection that has been widely used in the literature. This thesis studies the potential optimality (or sub-optimality) of the energy detector in spectrum sensing. With a single sensing node, we show that the energy detector is provably optimal for most cases and for the case when it is not theoretically optimal, its performance is nearly indistinguishable from the true optimal detector. For cooperative spectrum sensing where multiple nodes are employed, we use a recently proposed framework for distributed detection with dependent observations to establish the optimality of energy detector for several cooperative spectrum sensing systems and point out difficulties for the remaining cases.
The second problem in decentralized inference studied in this thesis is to investigate the impact of noise correlation on decentralized estimation performance. For a tandem network with correlated additive Gaussian noises, we establish that threshold quantizer on local observations is optimal in the sense of maximizing Fisher information at the fusion center; this is true despite the fact that subsequent estimators may differ at the fusion center, depending on the statistical distribution of the parameter to be estimated. In addition, it is always beneficial to have the better sensor (i.e. the one with higher signal-to-noise ratio) serve as the fusion center in a tandem network for all correlation regimes. Finally, we identify different correlation regimes in terms of their impact on the estimation performance. These include the well known case where negatively correlated noises benefit estimation performance as it facilitates noise cancellation, as well as two distinct regimes with positively correlated noises compared with that of the independent case.
In the second part of this thesis, a practical problem of room shape reconstruction using first-order acoustic echoes is explored. Specifically, a single mobile node, with co-located loudspeaker, microphone and internal motion sensors, is deployed and times of arrival of the first-order echoes are measured and used to recover room shape. Two separate cases are studied: the first assumes no knowledge about the sensor trajectory, and the second one assumes partial knowledge on the sensor movement. For either case, the uniqueness of the mapping between the first-order echoes and the room geometry is discussed. Without any trajectory information, we show that first-order echoes are sufficient to recover 2-D room shapes for all convex polygons with the exception of parallelograms. Algorithmic procedure is developed to eliminate the higher-order echoes among the collected echoes in order to retrieve the room geometry. In the second case, the mapping is proved for any convex polygonal shapes when partial trajectory information from internal motion sensors is available.. A practical algorithm for room reconstruction in the presence of noise and higher order echoes is proposed
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