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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The image blurring that results from moving a camera with the shutter open is normally regarded as undesirable. However, the blurring of the images encapsulates information that can be extracted to recover the light rays present within the scene. Given the correct recovery of the light rays that resulted in a blurred image, it is possible to reconstruct images of the scene from different camera locations. Therefore, rather than resharpening an image with motion blur, the goal of this paper is to recover the information needed to resynthesize images of the scene from different viewpoints. Estimation of the light rays within a scene is achieved by using a layer-based model to represent objects in the scene as layers, and by using an extended level set method to segment the blurred image into planes at different depths. The algorithm described in this paper has been evaluated on real and synthetic images to produce an estimate of the underlying epipolar plane image.
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