1,212 research outputs found
Network vector quantization
We present an algorithm for designing locally optimal vector quantizers for general networks. We discuss the algorithm's implementation and compare the performance of the resulting "network vector quantizers" to traditional vector quantizers (VQs) and to rate-distortion (R-D) bounds where available. While some special cases of network codes (e.g., multiresolution (MR) and multiple description (MD) codes) have been studied in the literature, we here present a unifying approach that both includes these existing solutions as special cases and provides solutions to previously unsolved examples
Graded quantization for multiple description coding of compressive measurements
Compressed sensing (CS) is an emerging paradigm for acquisition of compressed
representations of a sparse signal. Its low complexity is appealing for
resource-constrained scenarios like sensor networks. However, such scenarios
are often coupled with unreliable communication channels and providing robust
transmission of the acquired data to a receiver is an issue. Multiple
description coding (MDC) effectively combats channel losses for systems without
feedback, thus raising the interest in developing MDC methods explicitly
designed for the CS framework, and exploiting its properties. We propose a
method called Graded Quantization (CS-GQ) that leverages the democratic
property of compressive measurements to effectively implement MDC, and we
provide methods to optimize its performance. A novel decoding algorithm based
on the alternating directions method of multipliers is derived to reconstruct
signals from a limited number of received descriptions. Simulations are
performed to assess the performance of CS-GQ against other methods in presence
of packet losses. The proposed method is successful at providing robust coding
of CS measurements and outperforms other schemes for the considered test
metrics
Regularized principal manifolds
Many settings of unsupervised learning can be viewed as quantization problems - the minimization
of the expected quantization error subject to some restrictions. This allows the
use of tools such as regularization from the theory of (supervised) risk minimization for
unsupervised learning. This setting turns out to be closely related to principal curves, the
generative topographic map, and robust coding.
We explore this connection in two ways: (1) we propose an algorithm for nding principal
manifolds that can be regularized in a variety of ways; and (2) we derive uniform
convergence bounds and hence bounds on the learning rates of the algorithm. In particular,
we give bounds on the covering numbers which allows us to obtain nearly optimal
learning rates for certain types of regularization operators. Experimental results demonstrate
the feasibility of the approach
Tight Probabilistic SINR Constrained Beamforming Under Channel Uncertainties
In downlink multi-user beamforming, a single bases- tation is serving a number of users simultaneously. However, energy intended for one user may leak to other unintended users, causing interference. With signal-to-interference-plus-noise ratio (SINR) being one of the most crucial quality metrics to users, beamforming design with SINR guarantee has always been an important research topic. However, when the channel state information is not accurate, the SINR requirements become probabilistic constraints, which unfortunately are not tractable analytically for general uncertainty distribution. Therefore, ex- isting probabilistic beamforming methods focus on the relatively simple Gaussian and uniform channel uncertainties, and mainly rely on probability inequality based approximated solutions, resulting in conservative SINR outage realizations. In this paper, based on the local structure of the feasible set in the probabilistic beamforming problem, a systematic method is proposed to realize tight SINR outage control for a large class of channel uncertainty distributions. With channel estimation and quantization errors as examples, simulation results show that the SINR outage can be re- alized tightly, which results in reduced transmit power compared to the existing inequality based probabilistic beamformers.published_or_final_versio
- …