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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In this issue, we would like to pont to two online courses available via www.edx.org:
1) Sparse Representations in Signal and Image Processing: Fundamentals (https://www.edx.org/course/sparse-representations-signal-image-israelx-236862-1x)
2) Sparse Representations in Image Processing: From Theory to Practice (https://www.edx.org/course/sparse-representations-image-processing-israelx-236862-2x)
The instructors are Dr. Michael Elad (http://www.cs.technion.ac.il/~elad/) and a PhD student, Mr. Yaniv Romano (http://www.cs.technion.ac.il/~yromano/), both from the Technion - Israel Institute of Technology.
In the first course, the instructors will cover the fundamentals of the field of sparse representations, starting with its theoretical concepts, and systematically presenting its key achievements. While the second course will be a follow-up to the first introductory course of sparse representations. Whereas the first course puts emphasis on the theory and algorithms in this field, this course shows how these apply to actual signal and image processing needs.
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