50 research outputs found
A Traffic Model for Machine-Type Communications Using Spatial Point Processes
A source traffic model for machine-to-machine communications is presented in
this paper. We consider a model in which devices operate in a regular mode
until they are triggered into an alarm mode by an alarm event. The positions of
devices and events are modeled by means of Poisson point processes, where the
generated traffic by a given device depends on its position and event
positions. We first consider the case where devices and events are static and
devices generate traffic according to a Bernoulli process, where we derive the
total rate from the devices at the base station. We then extend the model by
defining a two-state Markov chain for each device, which allows for devices to
stay in alarm mode for a geometrically distributed holding time. The temporal
characteristics of this model are analyzed via the autocovariance function,
where the effect of event density and mean holding time are shown.Comment: Accepted at the 2017 IEEE 28th Annual International Symposium on
Personal, Indoor, and Mobile Radio Communications (PIMRC) - Workshop WS-07 on
"The Internet of Things (IoT), the Road Ahead: Applications, Challenges, and
Solutions
Turbo-Equalization Using Partial Gaussian Approximation
This paper deals with turbo-equalization for coded data transmission over
intersymbol interference (ISI) channels. We propose a message-passing algorithm
that uses the expectation-propagation rule to convert messages passed from the
demodulator-decoder to the equalizer and computes messages returned by the
equalizer by using a partial Gaussian approximation (PGA). Results from Monte
Carlo simulations show that this approach leads to a significant performance
improvement compared to state-of-the-art turbo-equalizers and allows for
trading performance with complexity. We exploit the specific structure of the
ISI channel model to significantly reduce the complexity of the PGA compared to
that considered in the initial paper proposing the method.Comment: 5 pages, 2 figures, submitted to IEEE Signal Processing Letters on 8
March, 201
Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach
We present a joint message passing approach that combines belief propagation
and the mean field approximation. Our analysis is based on the region-based
free energy approximation method proposed by Yedidia et al. We show that the
message passing fixed-point equations obtained with this combination correspond
to stationary points of a constrained region-based free energy approximation.
Moreover, we present a convergent implementation of these message passing
fixedpoint equations provided that the underlying factor graph fulfills certain
technical conditions. In addition, we show how to include hard constraints in
the part of the factor graph corresponding to belief propagation. Finally, we
demonstrate an application of our method to iterative channel estimation and
decoding in an orthogonal frequency division multiplexing (OFDM) system
Device-Agnostic Millimeter Wave Beam Selection using Machine Learning
Most research in the area of machine learning-based user beam selection
considers a structure where the model proposes appropriate user beams. However,
this design requires a specific model for each user-device beam codebook, where
a model learned for a device with a particular codebook can not be reused for
another device with a different codebook. Moreover, this design requires
training and test samples for each antenna placement configuration/codebook.
This paper proposes a device-agnostic beam selection framework that leverages
context information to propose appropriate user beams using a generic model and
a post processing unit. The generic neural network predicts the potential
angles of arrival, and the post processing unit maps these directions to beams
based on the specific device's codebook. The proposed beam selection framework
works well for user devices with antenna configuration/codebook unseen in the
training dataset. Also, the proposed generic network has the option to be
trained with a dataset mixed of samples with different antenna
configurations/codebooks, which significantly eases the burden of effective
model training.Comment: 30 pages, 19 figures. This article was submitted to IEEE Trans.
Wirel. Commun. on Nov 14 202