650 research outputs found
Very Low-Rate Variable-Length Channel Quantization for Minimum Outage Probability
We identify a practical vector quantizer design problem where any
fixed-length quantizer (FLQ) yields non-zero distortion at any finite rate,
while there is a variable-length quantizer (VLQ) that can achieve zero
distortion with arbitrarily low rate. The problem arises in a
multiple-antenna fading channel where we would like to minimize the channel
outage probability by employing beamforming via quantized channel state
information at the transmitter (CSIT). It is well-known that in such a
scenario, finite-rate FLQs cannot achieve the full-CSIT (zero distortion)
outage performance. We construct VLQs that can achieve the full-CSIT
performance with finite rate. In particular, with denoting the power
constraint of the transmitter, we show that the necessary and sufficient VLQ
rate that guarantees the full-CSIT performance is . We also
discuss several extensions (e.g. to precoding) of this result
Fusing Censored Dependent Data for Distributed Detection
In this paper, we consider a distributed detection problem for a censoring
sensor network where each sensor's communication rate is significantly reduced
by transmitting only "informative" observations to the Fusion Center (FC), and
censoring those deemed "uninformative". While the independence of data from
censoring sensors is often assumed in previous research, we explore spatial
dependence among observations. Our focus is on designing the fusion rule under
the Neyman-Pearson (NP) framework that takes into account the spatial
dependence among observations. Two transmission scenarios are considered, one
where uncensored observations are transmitted directly to the FC and second
where they are first quantized and then transmitted to further improve
transmission efficiency. Copula-based Generalized Likelihood Ratio Test (GLRT)
for censored data is proposed with both continuous and discrete messages
received at the FC corresponding to different transmission strategies. We
address the computational issues of the copula-based GLRTs involving
multidimensional integrals by presenting more efficient fusion rules, based on
the key idea of injecting controlled noise at the FC before fusion. Although,
the signal-to-noise ratio (SNR) is reduced by introducing controlled noise at
the receiver, simulation results demonstrate that the resulting noise-aided
fusion approach based on adding artificial noise performs very closely to the
exact copula-based GLRTs. Copula-based GLRTs and their noise-aided counterparts
by exploiting the spatial dependence greatly improve detection performance
compared with the fusion rule under independence assumption
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Information theoretic approach to quantization and classification for signal processing, communications, and machine learning applications
There are five main contributions of this dissertation. The first contribution is new closed-form expressions for channel capacity of a new class of channel matrices. The second contribution is the discovery of the structure for optimal binary quantizer and the associated methods for finding an optimal quantizer that maximizes mutual information between the input and output for a given input distribution. The third contribution is the discovery of the structure for an optimal -ary quantizer that maximizes the mutual information subject to an arbitrary constraint on the output distribution. The fourth contribution is the joint design of an optimal quantizer that maximizes the mutual information over both the input distribution and the quantization parameters for an arbitrary binary noisy channel with a given noise density. The last contribution is the development and analysis of novel efficient classification algorithms for finding the minimum impurity partition using mutual information as the metric
At low SNR, asymmetric quantizers are better
We study the capacity of the discrete-time Gaussian channel when its output is quantized with a 1-bit quantizer. We focus on the low signal-to-noise ratio (SNR) regime, where communication at very low spectral efficiencies takes place. In this regime, a symmetric threshold quantizer is known to reduce channel capacity by a factor of 2/pi i.e., to cause an asymptotic power loss of approximately 2 dB. Here, it is shown that this power loss can be avoided by using asymmetric threshold quantizers and asymmetric signaling constellations. To avoid this power loss, flash-signaling input distributions are essential. Consequently, 1-bit output quantization of the Gaussian channel reduces spectral efficiency. Threshold quantizers are not only asymptotically optimal: at every fixed SNR, a threshold quantizer maximizes capacity among all 1-bit output quantizers. The picture changes on the Rayleigh-fading channel. In the noncoherent case, a 1-bit output quantizer causes an unavoidable low-SNR asymptotic power loss. In the coherent case, however, this power loss is avoidable provided that we allow the quantizer to depend on the fading level
On the Capacity-Achieving Input of Channels with Phase Quantization
Several information-theoretic studies on channels with output quantization
have identified the capacity-achieving input distributions for different fading
channels with 1-bit in-phase and quadrature (I/Q) output quantization. But can
analytical results on the capacity-achieving input also be obtained for
multi-bit quantization? We answer the question in the affirmative by
considering multi-bit phase quantization. We first consider a complex Gaussian
channel with -bit phase-quantized output and prove that the
capacity-achieving distribution is a rotated -phase shift keying (PSK).
The analysis is then extended to multiple fading scenarios. We show that the
optimality of rotated -PSK continues to hold under noncoherent fast fading
Rician channels with -bit phase quantization when line-of-sight (LoS) is
present. When channel state information (CSI) is available at the receiver, we
identify -symmetry and constant amplitude as the necessary
and sufficient conditions for the ergodic capacity-achieving input
distribution; which a -PSK satisfies. Finally, an optimum power control
scheme is presented which achieves ergodic capacity when CSI is also available
at the transmitter.Comment: Submitted to IEEE Transactions on Information Theor
Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs
With the congestion of the sub-6 GHz spectrum, the interest in massive
multiple-input multiple-output (MIMO) systems operating on millimeter wave
spectrum grows. In order to reduce the power consumption of such massive MIMO
systems, hybrid analog/digital transceivers and application of low-resolution
digital-to-analog/analog-to-digital converters have been recently proposed. In
this work, we investigate the energy efficiency of quantized hybrid
transmitters equipped with a fully/partially-connected phase-shifting network
composed of active/passive phase-shifters and compare it to that of quantized
digital precoders. We introduce a quantized single-user MIMO system model based
on an additive quantization noise approximation considering realistic power
consumption and loss models to evaluate the spectral and energy efficiencies of
the transmit precoding methods. Simulation results show that
partially-connected hybrid precoders can be more energy-efficient compared to
digital precoders, while fully-connected hybrid precoders exhibit poor energy
efficiency in general. Also, the topology of phase-shifting components offers
an energy-spectral efficiency trade-off: active phase-shifters provide higher
data rates, while passive phase-shifters maintain better energy efficiency.Comment: Published in IEEE Journal of Selected Topics in Signal Processin
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