553 research outputs found
Asymptotic Task-Based Quantization with Application to Massive MIMO
Quantizers take part in nearly every digital signal processing system which
operates on physical signals. They are commonly designed to accurately
represent the underlying signal, regardless of the specific task to be
performed on the quantized data. In systems working with high-dimensional
signals, such as massive multiple-input multiple-output (MIMO) systems, it is
beneficial to utilize low-resolution quantizers, due to cost, power, and memory
constraints. In this work we study quantization of high-dimensional inputs,
aiming at improving performance under resolution constraints by accounting for
the system task in the quantizers design. We focus on the task of recovering a
desired signal statistically related to the high-dimensional input, and analyze
two quantization approaches: We first consider vector quantization, which is
typically computationally infeasible, and characterize the optimal performance
achievable with this approach. Next, we focus on practical systems which
utilize hardware-limited scalar uniform analog-to-digital converters (ADCs),
and design a task-based quantizer under this model. The resulting system
accounts for the task by linearly combining the observed signal into a lower
dimension prior to quantization. We then apply our proposed technique to
channel estimation in massive MIMO networks. Our results demonstrate that a
system utilizing low-resolution scalar ADCs can approach the optimal channel
estimation performance by properly accounting for the task in the system
design
Multiple-Description Coding by Dithered Delta-Sigma Quantization
We address the connection between the multiple-description (MD) problem and
Delta-Sigma quantization. The inherent redundancy due to oversampling in
Delta-Sigma quantization, and the simple linear-additive noise model resulting
from dithered lattice quantization, allow us to construct a symmetric and
time-invariant MD coding scheme. We show that the use of a noise shaping filter
makes it possible to trade off central distortion for side distortion.
Asymptotically as the dimension of the lattice vector quantizer and order of
the noise shaping filter approach infinity, the entropy rate of the dithered
Delta-Sigma quantization scheme approaches the symmetric two-channel MD
rate-distortion function for a memoryless Gaussian source and MSE fidelity
criterion, at any side-to-central distortion ratio and any resolution. In the
optimal scheme, the infinite-order noise shaping filter must be minimum phase
and have a piece-wise flat power spectrum with a single jump discontinuity. An
important advantage of the proposed design is that it is symmetric in rate and
distortion by construction, so the coding rates of the descriptions are
identical and there is therefore no need for source splitting.Comment: Revised, restructured, significantly shortened and minor typos has
been fixed. Accepted for publication in the IEEE Transactions on Information
Theor
Hardware-Limited Task-Based Quantization
Quantization plays a critical role in digital signal processing systems.
Quantizers are typically designed to obtain an accurate digital representation
of the input signal, operating independently of the system task, and are
commonly implemented using serial scalar analog-to-digital converters (ADCs).
In this work, we study hardware-limited task-based quantization, where a system
utilizing a serial scalar ADC is designed to provide a suitable representation
in order to allow the recovery of a parameter vector underlying the input
signal. We propose hardware-limited task-based quantization systems for a fixed
and finite quantization resolution, and characterize their achievable
distortion. We then apply the analysis to the practical setups of channel
estimation and eigen-spectrum recovery from quantized measurements. Our results
illustrate that properly designed hardware-limited systems can approach the
optimal performance achievable with vector quantizers, and that by taking the
underlying task into account, the quantization error can be made negligible
with a relatively small number of bits
A user's guide for the signal processing software for image and speech compression developed in the Communications and Signal Processing Laboratory (CSPL), version 1
A complete documentation of the software developed in the Communication and Signal Processing Laboratory (CSPL) during the period of July 1985 to March 1986 is provided. Utility programs and subroutines that were developed for a user-friendly image and speech processing environment are described. Additional programs for data compression of image and speech type signals are included. Also, programs for the zero-memory and block transform quantization in the presence of channel noise are described. Finally, several routines for simulating the perfromance of image compression algorithms are included
Hardware-Limited Task-Based Quantization
Quantization plays a critical role in digital signal
processing systems. Quantizers are typically designed to obtain
an accurate digital representation of the input signal, operating
independently of the system task, and are commonly implemented
using serial scalar analog-to-digital converters (ADCs). In this
work, we study hardware-limited task-based quantization, where
a system utilizing a serial scalar ADC is designed to provide a suitable representation in order to allow the recovery of a parameter
vector underlying the input signal. We propose hardware-limited
task-based quantization systems for a fixed and finite quantization
resolution, and characterize their achievable distortion. We then
apply the analysis to the practical setups of channel estimation
and eigen-spectrum recovery from quantized measurements. Our
results illustrate that properly designed hardware-limited systems
can approach the optimal performance achievable with vector
quantizers, and that by taking the underlying task into account,
the quantization error can be made negligible with a relatively
small number of bits
TWO-DIMENSIONAL GMM-BASED CLUSTERING IN THE PRESENCE OF QUANTIZATION NOISE
In this paper, unlike to the commonly considered clustering, wherein data attributes are accurately presented, it is researched how successful clustering can be performed when data attributes are represented with smaller accuracy, i.e. by using the small number of bits. In particular, the effect of data attributes quantization on the two-dimensional two-component Gaussian mixture model (GMM)-based clustering by using expectation–maximization (EM) algorithm is analyzed. An independent quantization of data attributes by using uniform quantizers with the support limits adjusted to the minimal and maximal attribute values is assumed. The analysis makes it possible to determine the number of bits for data presentation that provides the accurate clustering. These findings can be useful in clustering wherein before being grouped the data have to be represented with a finite small number of bits due to their transmission through the bandwidth-limited channel.
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