READ ME File For 'Dataset for the paper: Air-to-ground NOMA Systems for the “Internet-Above-the-Clouds”'

Dataset DOI: 10.5258/SOTON/D0640

ReadMe Author: Daryus Chandra, University of Southampton

This dataset supports the publication:

Botsinis, P., Alanis, D., Xu, C., Babar, Z., Chandra, D., Ng, S. X., & Hanzo, L. (2018). Air-to-ground NOMA systems for the “Internet-Above-the-Clouds”. IEEE Access. 

Contents
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Each folder corresponds to a figure in the manuscript.


Paper Abstract
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The provision of high-speed Internet access in aircraft is mainly supported by satellite links at the time of writing, aided by links between the aircraft and the ground stations. It is anticipated that Air-To-Ground (A2G) communications between en-route aircraft and the ground stations will have a major role in providing the required Quality of Service, while complying with the low latency requirements of next generation communications. Non-Orthogonal Multiple Access (NOMA) systems will increase the system throughput by allowing multiple aircraft to simultaneously communicate with the ground station, while requiring fewer resource slots. Due to the limited number of orthogonal resources and the high number of aircraft, interference is expected to be present. In this contribution, we employ beamforming based on the Angle of Arrival (AoA) of the signals and antenna arrays having multiple antenna elements, as well as a novel interference-exploiting Sphere Decoder (iSD), which detects the signals of the supported users, while beneficially exploiting those of the interfering users. We show that an improved performance may be achieved in both Hard-Input Hard-Output (HIHO) scenarios, as well as in iterative Soft-Input Soft-Output (SISO) scenarios, when compared to the conventional Sphere Decoder, the Maximum Likelihood (ML) detector and the Maximum A posteriori Probability (MAP) detector. We also characterize the complexity of the proposed receiver and evaluate its performance with the aid of BER simulations and EXtrinsic Information Transfer (EXIT) charts.


Acknowledgements
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The financial support of the European Research Council under the Advanced Fellow Grant, that of the Royal Society’s Wolfson Research Merit Award and that of the Engineering and Physical Sciences Research Council under Grant EP/L018659/1 is gratefully acknowledged. The use of the IRIDIS High Performance Computing Facility at the University of Southampton is also acknowledged.



Dataset available under a CC BY 4.0 licence


Publisher: University of Southampton, U.K.


Date: September 2018