Coding and Signal Processing for Emerging Wireless Communication and Sensor Networks
Acronym:CARMEN
Code:TEC2016-75067-C4-2-R
Funder:Spanish Government
Company:MINISTERIO DE ECONOMÍA Y COMPETITIVIDAD
Start date:2016 December 30th
End date:2019 December 29th
Keywords:Wireless Communications, Wireless Sensor Networks, Coding, Signal Processing, Information Theory, Wireless Testbeds
Web site: http://wiki.gtec.udc.es/projects/CARMEN/
Partners:Centro de Estudios e Investigaciones Técnicas de Guipuzcoa, Universidad de Cantabria and Universidad de Coruña
SPCOM Participants:Jaume Del Olmo Alos, Pere Giménez Febrer, Alba Pagès Zamora and Javier Rodríguez Fonollosa
SPCOM Responsible:Javier Rodríguez Fonollosa

Summary

CARMEN aims at developing coding and signal processing techniques for incipient wireless

communication and sensor networks. Two major trends are appreciated in the current

evolution of wireless networks. On the one hand, next generation of cellular systems will use

radio interfaces with unprecedented high data rates to address the huge data traffic

produced by future mobile applications. To satisfy such demand, the radio interfaces will use

new frequency bands located in the 10-300 GHz mmWave region, MIMO transceivers with

wider bandwidths and higher number of transmit/receive antennas, unconventional

waveforms to exploit fragmented spectrum allocations, and network densification through the

use of heterogeneous wireless networks combining macro-, pico-, femtocells, relays and

distributed antennas. On the other, many foreseen applications will use Wireless Sensor

Networks (WSN), i.e. networks with a huge number of low-cost devices, which are spatially

distributed and work cooperatively to communicate information gathered from the monitored

field through wireless links. One of the main challenges that emerging WSNs should face is

the optimization of the way they exchange and compute data in a distributed way. There are

also important challenges related to the wireless connectivity. It is of paramount importance 

to reduce the delay and complexity of the wireless connections to reduce power

consumption.

Latest Related Journal publications

[1] Panagiotis A. Traganitis, A. Pagès Zamora and Georgios B. Giannakis, "Blind Multi-class Ensemble Learning with Unequally Reliable Classifiers", Submitted to Trans. on Signal Processing, December 2017, pp. 1 - 13. | BibTex

Latest Related Conference publications

[1] Panagiotis A. Traganitis, A. Pagès Zamora and Georgios B. Giannakis, "Learning from Unequally Reliable Blind Ensembles of Classifiers", IEEE Global Conference on Signal and Information Processing, November 2017, pp. 1 - 5. | BibTex

[2] Margarita Cabrera-Bean, A. Pagès Zamora, Carles Diaz-Vilor, M. Postigo-Camps, D. Cuadrado-Sanchez and M. Luengo-Oroz, "Counting Malaria Parasites with a two-stage EM based algorithm using crowdsourced data", Annual International Conference of the IEEE Engineering in Medicine and Biology Society , July 2017, pp. 1 - 5. | Details | Full document | BibTex

[3] A. Pagès Zamora, Georgios B. Giannakis, R. López-Valcarce and Pere Giménez-Febrer, "Robust Clustering of Data Collected Via Crowdsourcing", IEEE International Conference on Acoustics, Speech and Signal Processing, March 2017, pp. 4014 - 4018. | Details | Full document | BibTex





©UPC Universitat Politècnica de Catalunya
Signal Processing and Communications group
Powered by Joomla!.