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Publication - Professor Angela Doufexi

    Adaptive User Grouping Based on EVM Prediction for Efficient & Robust Massive MIMO in TDD

    Citation

    Hasan, WB, Harris, P, Bromell, H, Doufexi, A & Beach, M, 2019, ‘Adaptive User Grouping Based on EVM Prediction for Efficient & Robust Massive MIMO in TDD’. IEEE Access.

    Abstract

    Massive multiple-input, multiple-output (Ma-MIMO) offers significant capacity improvements for sub6GHz wireless access. Evaluating the practicalities for real-world deployments and identifying solutions is the next critical step in the roll-out of this technology. Here novel adaptive user grouping algorithms for a single cell Ma-MIMO scenario are proposed and shown to further enhance performance. For the first time, the methodology addresses the interference caused by the user channel vectors as well as hardware impairments. Here, this is uniquely achieved by extracting the Error Vector Magnitude (EVM), and three different methodologies are applied to address specific wireless connectivity objectives. The first, maximizes the spectral efficiency which is more desirable for cellular networks. The second, maximizes the number of users for low data rate bearers, which is suitable for the Internet of Things (IoT). Whilst, the third approach focuses on specific quality per user, for applications such as wireless cameras at major cultural or sporting events. Importantly, real-world experimental data-sets have been used to evaluate the proposed user grouping algorithms.

    Full details in the University publications repository