12,363 research outputs found
Asynchronous Channel Training in Multi-Cell Massive MIMO
Pilot contamination has been regarded as the main bottleneck in time division
duplexing (TDD) multi-cell massive multiple-input multiple-output (MIMO)
systems. The pilot contamination problem cannot be addressed with large-scale
antenna arrays. We provide a novel asynchronous channel training scheme to
obtain precise channel matrices without the cooperation of base stations. The
scheme takes advantage of sampling diversity by inducing intentional timing
mismatch. Then, the linear minimum mean square error (LMMSE) estimator and the
zero-forcing (ZF) estimator are designed. Moreover, we derive the minimum
square error (MSE) upper bound of the ZF estimator. In addition, we propose the
equally-divided delay scheme which under certain conditions is the optimal
solution to minimize the MSE of the ZF estimator employing the identity matrix
as pilot matrix. We calculate the uplink achievable rate using maximum ratio
combining (MRC) to compare asynchronous and synchronous channel training
schemes. Finally, simulation results demonstrate that the asynchronous channel
estimation scheme can greatly reduce the harmful effect of pilot contamination
Adaptive Duty Cycling MAC Protocols Using Closed-Loop Control for Wireless Sensor Networks
The fundamental design goal of wireless sensor MAC protocols is to minimize unnecessary power consumption of the sensor nodes, because of its stringent resource constraints and ultra-power limitation. In existing MAC protocols in wireless sensor networks (WSNs), duty cycling, in which each node periodically cycles between the active and sleep states, has been introduced to reduce unnecessary energy consumption. Existing MAC schemes, however, use a fixed duty cycling regardless of multi-hop communication and traffic fluctuations. On the other hand, there is a tradeoff between energy efficiency and delay caused by duty cycling mechanism in multi-hop communication and existing MAC approaches only tend to improve energy efficiency with sacrificing data delivery delay. In this paper, we propose two different MAC schemes (ADS-MAC and ELA-MAC) using closed-loop control in order to achieve both energy savings and minimal delay in wireless sensor networks. The two proposed MAC schemes, which are synchronous and asynchronous approaches, respectively, utilize an adaptive timer and a successive preload frame with closed-loop control for adaptive duty cycling. As a result, the analysis and the simulation results show that our schemes outperform existing schemes in terms of energy efficiency and delivery delay
Asynchrony in image analysis: using the luminance-to-response-latency relationship to improve segmentation
We deal with the probiem of segmenting static images, a procedure known to be difficult in the case of very
noisy patterns, The proposed approach rests on the transformation of a static image into a data flow in which
the first image points to be processed are the brighter ones. This solution, inspired by human perception, in
which strong luminances elicit reactions from the visual system before weaker ones, has led to the notion of
asynchronous processing. The asynchronous processing of image points has required the design of a specific
architecture that exploits time differences in the processing of information. The results otained when very
noisy images are segmented demonstrate the strengths of this architecture; they also suggest extensions of
the approach to other computer vision problem
Covariance estimation via Fourier method in the presence of asynchronous trading and microstructure noise
We analyze the effects of market microstructure noise on the Fourier estimator of multivariate volatilities. We prove that the estimator is consistent in the case of asynchronous data and robust in the presence of microstructure noise. This result is obtained through an analytical computation of the bias and the mean squared error of the Fourier estimator and con¯rmed by Monte Carlo experiments.
Memory-Efficient Topic Modeling
As one of the simplest probabilistic topic modeling techniques, latent
Dirichlet allocation (LDA) has found many important applications in text
mining, computer vision and computational biology. Recent training algorithms
for LDA can be interpreted within a unified message passing framework. However,
message passing requires storing previous messages with a large amount of
memory space, increasing linearly with the number of documents or the number of
topics. Therefore, the high memory usage is often a major problem for topic
modeling of massive corpora containing a large number of topics. To reduce the
space complexity, we propose a novel algorithm without storing previous
messages for training LDA: tiny belief propagation (TBP). The basic idea of TBP
relates the message passing algorithms with the non-negative matrix
factorization (NMF) algorithms, which absorb the message updating into the
message passing process, and thus avoid storing previous messages. Experimental
results on four large data sets confirm that TBP performs comparably well or
even better than current state-of-the-art training algorithms for LDA but with
a much less memory consumption. TBP can do topic modeling when massive corpora
cannot fit in the computer memory, for example, extracting thematic topics from
7 GB PUBMED corpora on a common desktop computer with 2GB memory.Comment: 20 pages, 7 figure
Patterns of Scalable Bayesian Inference
Datasets are growing not just in size but in complexity, creating a demand
for rich models and quantification of uncertainty. Bayesian methods are an
excellent fit for this demand, but scaling Bayesian inference is a challenge.
In response to this challenge, there has been considerable recent work based on
varying assumptions about model structure, underlying computational resources,
and the importance of asymptotic correctness. As a result, there is a zoo of
ideas with few clear overarching principles.
In this paper, we seek to identify unifying principles, patterns, and
intuitions for scaling Bayesian inference. We review existing work on utilizing
modern computing resources with both MCMC and variational approximation
techniques. From this taxonomy of ideas, we characterize the general principles
that have proven successful for designing scalable inference procedures and
comment on the path forward
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