In this paper, the authors study the compensation of high-power amplifier (HPA) non-linear distortion in the multi-user (MU) massivemultiple-inputmultiple-output (MIMO) systems and focus on uplink transmission, wher...
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In this paper, the authors study the compensation of high-power amplifier (HPA) non-linear distortion in the multi-user (MU) massivemultiple-inputmultiple-output (MIMO) systems and focus on uplink transmission, where the base station (BS) uses a large antenna array. First, the authors present a non-linear distortion iterative cancellation (NDIC) algorithm-based MMSE and approximate message passing (AMP) at the receiver level, in order to estimate and mitigate jointly a non-linear distortion and the channel noise. Second, the authors propose a novel distortion cancellation technique based on deep learning. At this level, the authors first introduce a multilayer neural network, trained in the Levenberg-Marquardt algorithm by eliminating HPA non-linearities on the 'Pre distortion' transmitter and 'Post distortion' receiver side. Next, the authors developed a novel end-to-end (E2E) learning approach for the joint transmitter and non-coherent receiver in the Rayleigh fading channel. The basic idea lies in the use of deep neural networks (DNNs), auto encoder (AE) for unknown channels, where DNNs are applied to perform several functions and modules existing in the transmission chain. The simulation results demonstrate the strong potential of the proposed approach E2E in terms of improving the link quality and symbol error rate (SER) compared to other compensation techniques presented in this work.
Spectral efficiency, energy efficiency, and security are cornerstones for the upcoming 5G systems. In this study, the issue of how the energy and spectral efficiency of multiusermassivemultiple-inputmultiple-output...
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Spectral efficiency, energy efficiency, and security are cornerstones for the upcoming 5G systems. In this study, the issue of how the energy and spectral efficiency of multiusermassivemultiple-inputmultiple-output (Ma-MIMO) systems are affected in the presence of a secrecy constraint is addressed. The performance of the two most prominent linear precoding techniques, the matched filter (MF) and zero-forcing (ZF) precoders, for secure downlink multiuser Ma-MIMO in the presence of multi-antenna passive eavesdropper is investigated. The authors consider three performance metrics, namely, the achievable ergodic secrecy rate, the secrecy spectral efficiency (SSE), and the secrecy energy efficiency (SEE), assuming perfect and imperfect channel state information. The tradeoff between SSE and SEE is also studied. Moreover, the authors derive tight lower bounds on the achievable ergodic secrecy rate for MF and ZF precoding techniques. The derived lower bounds provide insights on the tradeoff between the SSE and SEE. It is shown that ZF precoder outperforms MF precoder at high transmit power, whereas at very low transmit power, MF outperforms ZF. Moreover, it is shown that using large number of transmit antennas can improve the SSE and the SEE with orders of magnitude compared to a single-input single-output system.
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