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Full-duplex massive multiple input multiple output systems under hardware impairments
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DOI：10.1016/j.compeleceng.2017.07.001.png)
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In this paper, we study mobile learning (M-Learning) systems from the perspective of physical layer. Due to the characteristic of bidirectional propagation, full-duplex is very suitable for M-Learning systems. Therefore, we apply the full-duplex large-scale antenna technique into M-Learning systems. To be practical, we consider the full-duplex based M-Learning systems under hardware imperfections which pose a huge challenge to the analysis of the uplink achievable rate. According to the principle of minimum mean square error, by resorting to the large dimensional matrix theory, we derive the deterministic equivalence for the uplink achievable rate, which is solely dependent on the statistical channel state information. The deterministic equivalence is proven to be not only tight under high antenna dimension, but also accurate for moderate antenna dimension, as shown by simulations. Moreover, by using the deterministic equivalence, we propose a method to determine the optimal number of M-Learning users in the aim of maximizing uplink sum rate. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
M-Learning
5G
Full-duplex
Massive MIMO
Random matrix theory
Hardware impairments
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