The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years. Based on these predictions, a 127-fold increase of the IP traffic is expected from 2005 to 2021. It is also anticipated that the mobile data traffic will reach 3.3 zettabytes per year by 2021, and that the number of mobile-connected devices will reach 3.5 per capita.
With such demands for higher data rates and for better quality of service (QoS), fifth generation (5G) standardization initiatives, whose initial phase was specified in June 2018 under the umbrella of Long Term Evolution (LTE) Release 15, have been under vibrant investigation. In particular, the International Telecommunication Union (ITU) has identified three usage scenarios (service categories) for 5G wireless networks: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and low latency communications (uRLLC), and (iii) massive machine type communications (mMTC). The vast variety of applications for beyond 5G wireless networks has motivated the necessity of novel and more flexible physical layer (PHY) technologies, which are capable of providing higher spectral and energy efficiencies, as well as reduced transceiver implementations.
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10 years of news and resources for members of the IEEE Signal Processing Society
Published in TC News on 1 December 2014
by Paris Smaragdis
The Machine Learning for Signal Processing Technical Committee (MLSP TC) is involved with activities that support the use of Machine Learning techniques for Signal Processing problems. The scope of this TC is fairly wide, ranging from traditional machine learning and pattern recognition, to approaches that combine material from both disciplines. Under the scope of the MLSP TC, we find areas such as source separation, graphical and kernel methods for time-series, Bayesian non-parametrics, and matrix and tensor factorizations among many more.
The MLSP TC recently concluded the new member elections and it is happy to announce the following new members starting in 2015: Nikos Sidiropoulos (University of Minessota), Qi Zhao (National University of Singapore), Peder Olsen (IBM), Wee Peng Tay (Nanyang Technological University), Robert Jenssen (University of Tromso), Jen-Tzung Chien (National Cheng Kung University), Karim Seghouane (University of Melbourne), Jun Wang (Columbia University), and Simo Särkkä (Aalto University).
We would like to thank our departing members Jerónimo Arenas-García, Gustavo Camps-Valls, Andrzej Cichocki, Konstantinos I. Diamantaras, Kenneth Kreutz-Delgado, Jan Larsen, Elias S. Manolakos, Atsushi Nakamura, Ignacio Santamaria, Peter Schreier, Marc M. Van Hulle, Z. Jane Wang for their valuable service and ongoing support.
The main workshop of the MLSP TC took place last September in Rheims, France, in which we had 91 accepted papers and 120 attendees. The plenary speakers were Jean-François Cardoso (CNRS), Lars Kai Hansen (DTU) and Sabine Van Huffel (KU Leuven). As is customary, we also had our data competition as part of the workshop. This year the competition was announced in Kaggle.com and involved a Schizophrenia Classification Challenge from MRI scans. We had a record 313 teams submitting material for this competition with the first place going to Arno Solin from Espoo, Finland.
The 2015 MLSP workshop will take place in Boston, USA next September. Additionally, MLSP TC members are co-organizing a GlobalSIP 2014 symposium on Machine Learning Applications in Signal Processing, on which you can find more information here: http://www.ieeeglobalsip.org/symposium/mlasp.html
More information about the MLSP TC can be found at our web page at: http://www.signalprocessingsociety.org/technical-committees/list/mlsp-tc/
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