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 October 2016
Waheed U. Bajwa (Rutgers University, USA) and Nelly Pustelnik (CNRS, Ens de Lyon, France)
The Machine Learning for Signal Processing Technical Committee (MLSP TC) is involved with activities that facilitate the use of machine learning techniques for signal processing problems through advances in both the theory and practice of machine learning. The scope of this TC is fairly broad, ranging from traditional machine learning and pattern recognition problems, to approaches that combine insights from both disciplines of machine learning and signal processing. In particular, the MLSP research community has made and continues to make central contributions to important emerging problems such as privacy-preserving analytics, decentralized processing of big data, deep learning, and machine learning for the internet-of-things.
The MLSP TC manages its activities through 40 TC members, who are elected for three-year terms, as well as through several associate members and affiliate members. Dr. Vince Calhoun (University of New Mexico and Mind Research Network, USA) and Dr. Raviv Raich (Oregon State University, USA) currently serve as the Chair and the Vice Chair of the TC, respectively. The TC accepted eight new members within its folds this year. These include Waheed U. Bajwa (Rutgers University, USA), Yuejie Chi (Ohio State University, USA), Tomoko Matsui (The Institute of Statistical Mathematic, Japan), David J. Miller (Penn State University, USA), Nelly Pustelnik (CNRS, Ens de Lyon, France), Alain Rakotomamonjy (University of Rouen, France), Venkatesh Saligrama (Boston University, USA), and Yao Xie (Georgia University of Technology, USA). In addition, two members of the TC were re-elected for their second three-year terms this year, namely, Cédric Richard (University of Nice Sophia Antipolis, France) and Saeid Sanei (University of Surrey, UK). We would also like to thank our departing members, whose terms ended in 2015, for their past service and ongoing support. These members include Suleyman Kozat (Bilkent University, Turkey), Weifeng Liu (University of Copenhagen, Denmark), Morten Moerup (Technical University of Denmark), Asoke Nandi (Brunel University London, UK), Vincent Tan (National University of Singapore ), and Marc T. Van Hulle (KU Leuven, Belgium).
Each year, the MLSP TC organizes its namesake, flagship International Workshop on Machine Learning for Signal Processing (MLSP). This year, the workshop took place in Vietri sul mare, Italy, which was attended by 120 researchers. The General Chair, Francesco A. N. Palmieri (Seconda Università di Napoli, Italy), and his team organized a very vibrant and exciting workshop. In addition to 102 contributed and invited papers (acceptance rate of 62%), the workshop included participation of Alexandre Alahi (Stanford University, USA), Richard E. Turner (University of Cambridge, UK), and Bernhard Schölkopf (Max Planck Institute for Intelligent Systems Tübingen, Germany) as keynote lecturers, all of whom provided very interesting insights for promising research at the intersection of Machine Learning and Signal Processing. Next year, the MLSP’17 workshop will take place in Tokyo, Japan under the leadership of Tomako Matsui (The Institute of Statistical Mathematic, Japan) and Jen-Tzung Chien (National Chiao Tung University, Taiwan); final dates for the workshop are currently being finalized and will be shared on the SPS website. The MLSP is expected to continue its involvement in organizing annual data competitions, which have received considerable interest over the years. An expected change in this workshop from previous years is the involvement in IEEE-wide discussions regarding the creation of standardized datasets for validation of machine learning algorithms under the leadership of an MLSP TC Sub-Committee.
In addition to the MLSP Workshop, the MLSP TC also plays an active role within the organization of the SPS flagship ICASSP Conference. The number of papers submitted to ICASSP under the MLSP track has been increasing each year, which is resulting in a more competitive publication process for the MLSP track each year. This year, there also have been some changes to the ICASSP EDICS in relation to the MLSP track. These include the addition of "Tensor and Structured Matrix Methods", "Machine Learning from Big Data", and "Scalable Learning Algorithms" to the EDICS, while "Cognitive Information Processing" has been removed from the EDICS.
We conclude by encouraging our readers to become a part of the thriving community of MLSP researchers through submission of research papers to MLSP Workshop and ICASSP under the MLSP track. We would also like to encourage our readers to engage with the MLSP TC by either nominating themselves (or others) for the TC membership or signing up to become an affiliate member of the TC. While the affiliate membership is open all year round to everyone who wishes to engage with the MLSP TC, full membership of the TC does require a formal vote as per the IEEE SPS rules. The deadline for nominating oneself or others to the TC is typically at the end of the summer each year and elected members are expected to serve a term of three years, with typical duties involving reviewing of papers submitted to ICASSP under the MLSP track, to the MLSP Workshop, and to workshops owned or co-owned by the MLSP TC, serving on the subcommittees established by the TC, and performing other duties as assigned by the Chair and the Vice Chair of the TC. Further information about the MLSP TC activities and member responsibilities is available at https://signalprocessingsociety.org/get-involved/machine-learning-signal.... We hope you will consider engaging with and becoming a part of this vibrant TC that we call our technical home within the Signal Processing Society.
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