215 research outputs found
On the Design of a Novel Joint Network-Channel Coding Scheme for the Multiple Access Relay Channel
This paper proposes a novel joint non-binary network-channel code for the
Time-Division Decode-and-Forward Multiple Access Relay Channel (TD-DF-MARC),
where the relay linearly combines -- over a non-binary finite field -- the
coded sequences from the source nodes. A method based on an EXIT chart analysis
is derived for selecting the best coefficients of the linear combination.
Moreover, it is shown that for different setups of the system, different
coefficients should be chosen in order to improve the performance. This
conclusion contrasts with previous works where a random selection was
considered. Monte Carlo simulations show that the proposed scheme outperforms,
in terms of its gap to the outage probabilities, the previously published joint
network-channel coding approaches. Besides, this gain is achieved by using very
short-length codewords, which makes the scheme particularly attractive for
low-latency applications.Comment: 28 pages, 9 figures; Submitted to IEEE Journal on Selected Areas in
Communications - Special Issue on Theories and Methods for Advanced Wireless
Relays, 201
Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants
The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.This work has been partially supported by the EU project iDev40. This project has received funding
from the ECSEL Joint Undertaking (JU) under grant agreement No 783163. The JU receives support from the
European Union’s Horizon 2020 research and innovation programme and Austria, Germany, Belgium, Italy,
Spain, Romania. It has also been supported by the Basque Government (Spain) through the project VIRTUAL
(KK-2018/00096), and by Ministerio de EconomÃa y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P)
A Comparison of Modelling Approaches for the Long-term Estimation of Origin Destination Matrices in Bike Sharing Systems
Micro-mobility services have gained popularity in the last years, becoming a relevant part of the transportation
network in a plethora of cities. This has given rise to a fruitful research area, covering from the impact and
relationships of these transportation modes with preexisting ones to the different ways for estimating the demand of
such services in order to guarantee the quality of service. Within this domain, docked bike sharing systems constitute
an interesting surrogate for understanding the mobility of the whole city, as origin-destination matrices can be obtained
straightforward from the information available at the docking stations. This work elaborates on the characterization of such
origin-destination matrices, providing an essential set of insights on how to estimate their behavior in the long-term. To do so, the
main non-mobility features that affect mobility are studied and used to train different machine learning algorithms to produce
viable mobility patterns. The case study performed over real data captured by the bike sharing system of Bilbao (Spain)
reveals that, by virtue of a properly selected set of features and the adoption of specialized modeling algorithms, reliable
long-term estimations of such origin-destination matrices can be effectively achieved
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