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
Jon Barker - University of Sheffield
Emmanuel Vincent - INRIA
We are glad to announce a new mailing list dedicated to the hot topic of Machine Listening.
The list aims to attract members from diverse research communities united by an interest in building robust Machine Listening applications, be it for speech, music or environmental sounds. This includes researchers and engineers in the fields of Audio and Acoustic Signal Processing, Speech and Language Processing, Multichannel and Multimodal Signal Processing and Machine Learning for Signal Processing.
The list will serve as a place for announcing events (workshops, challenges, journal special issues, etc), publicizing opportunities (jobs, PhD places, etc) and we hope it will also serve as a valuable discussion forum for people working in the field.
We have decided to use Google Groups as the platform for the list. In order to subscribe, please follow the link below.
Jon Barker, University of Sheffield
Emmanuel Vincent, INRIA
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