Abstract (300 word limit)
Statement of the Problem:

Given the benefit of massive connectivity, improved user fairness and spectral efficiency, non-orthogonal multiple access(NOMA) has become a promising candidate of multiple access(MA) technology for beyond 5G networks. By exploiting the channel disparities, NOMA is capable of serving multiple users sharing same time-frequency resources by exploiting superposition coding at the transmitter as well as successive interference cancellation (SIC) at the receiver. Furthermore,with the directional transmission employed for mmWave systems, it is highly likely that multiple users share the same spatial beam. In this scenario, the NOMA assisted transmission can be employed for MA by exploiting the power-domain, i.e. channel disparities, of the users sharing the specific beam.In the state-of-the-art NOMA systems, SIC assisted detection is employed relying on the simplifying assumption of having perfect CSI at the receiver. However, in the face of channel impairments, the SIC assisted detection degrades the performance because of the error propagation nature observed in SIC, which is highly sensitive to the CSI imperfections.Additionally, as the number of users in the cluster grow large,the co-channel interference further degrades the performance owing to both SIC complexity and error propagation. This degradation is even more pronounced if the non-linear components introduced by the hardware are considered. Owing to these reasons, it becomes crucial to employ machine learning techniques for jointly modeling the CSI impairments and SIC detector.In this talk, I discuss how machine learning can help circumvent this issue.

At present, the current 4G systems provide a universal platform for broadband mobile services; however, mobile traffic is still growing at an unprecedented rate and the need for more sophisticated broadband services is pushing the limits on current standards to provide even tighter integration between wireless technologies and higher speeds. This has led to the need for a new generation of mobile communications: the so-called 5G. Although 5G systems are not expected to penetrate the market until 2020, the evolution towards 5G is widely accepted to be the logical convergence of internet services with existing mobile networking standards leading to the commonly used term “mobile internet” over heterogeneous networks, with several Gbits/s data rate and very high connectivity speeds. Therefore, to support highly increasing traffic capacity and high data rates, the next generation mobile network (5G) should extend the range of frequency spectrum for mobile communication that is yet to be identified by the ITU-R. The mm-wave spectrum is the key enabling feature of the next-generation cellular system, for which the propagation channel models need to be predicted to enhance the design guidance and the practicality of the whole design transceiver system.
The proposed work addressed the main concepts of the propagation channel behaviour using ray tracing software package for simulation and then results were tested and compared against practical analysis in a real-time environment including indoor and outdoor for mm Wave frequency bands. The characteristics of Indoor-Indoor (LOS and NLOS), propagations channels were intensively investigated at millimetre wave (mm Wave) frequencies by proving the data rate in gigahertz/sec. The computed data achieved from the 3-D Shooting and Bouncing Ray (SBR)