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Université de Lille Massive MIMO Channel Characterization and Propagation-based Antenna Selection strategies: Application to 5G and Industry 4.0

Abstract : Continuous efforts have been made to boost wireless systems performance, however, current wireless networks are not yet able to fulfill the many gaps from 4G and requirements for 5G. Thus, significant technological breakthroughs are still required to strengthen wireless networks. For instance, in order to provide higher data rates and accommodate many types of equipment, more spectrum resources are needed and the currently used spectrum requires to be efficiently utilized. 5G, or the fifth generation of mobile networks, is initially being labeled as an evolution, made available through improvements in LTE, but it will not be long before it becomes a revolution and a major step-up from previous generations. Massive MIMO has emerged as one of the most promising physical-layer technologies for future 5G wireless systems. The main idea is to equip base stations with large arrays (100 antennas or more) to simultaneously communicate with many terminals or user equipments. Using smart pre-processing at the array, massive MIMO promises to deliver superior system improvement with improved spectral efficiency, achieved by spatial multiplexing and better energy efficiency, exploiting array gain and reducing the radiated power. Massive MIMO can fill the gap for many requirements in 5G use-cases notably industrial IOT (internet of things) in terms of data rates, spectral and energy efficiency, reliable communication, optimal beamforming, linear processing schemes and so on. However, the hardware and software complexity arising from the sheer number of radio frequency chains is a bottleneck and some challenges are still to be tackled before the full operational deployment of massive MIMO. For instance, reliable channel models, impact of polarization diversity, optimal antenna selection strategies, mutual coupling and channel state information acquisition amongst other aspects, are all important questions worth exploring. Also, a good understanding of industrial channels is needed to bring the smart industry of the future ever closer.In this thesis, we try to address some of these questions based on radio channel data from a measurement campaign in an industrial scenario using a massive MIMO setup. The thesis' main objectives are threefold: 1) Characterization of massive MIMO channels in Industry 4.0 (industrial IoT) with a focus on spatial correlation, classification and impact of cross-polarization at transmission side. The setup consists in multiple distributed user-equipments in many propagation conditions. This study is based on propagation-based metrics such as Ricean factor, correlation, etc. and system-oriented metrics such as sum-rate capacity with linear precoding and power allocation strategies. Moreover, polarization diversity schemes are proposed and were shown to achieve very promising results with simple allocation strategies. This work provides comprehensive insights on radio channels in Industry 4.0 capable of filling the gap in channel models and efficient strategies to optimize massive MIMO setups. 2) Proposition of antenna selection strategies using the receiver spatial correlation, a propagation metric, as a figure of merit. The goal is to reduce the number of radio frequency chain and thus the system complexity by selecting a set of distributed antennas. The proposed strategy achieves near-optimal sum-rate capacity with less radio frequency chains. This is critical for massive MIMO systems if complexity and cost are to be reduced. 3) Proposition of an efficient strategy for overhead reduction in channel state information acquisition of FDD (frequency-division-duplex) systems. The strategy relies on spatial correlation at the transmitter and consists in solving a set of simple autoregressive equations (Yule-Walker equations). The results show that the proposed strategy achieves a large fraction of the performance of TDD (time-division-duplex) systems initially proposed for massive MIMO.
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Contributor : Frederic Challita <>
Submitted on : Sunday, March 1, 2020 - 12:25:36 PM
Last modification on : Monday, October 19, 2020 - 11:05:48 AM
Long-term archiving on: : Sunday, May 31, 2020 - 12:37:53 PM


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  • HAL Id : tel-02495151, version 1


Frédéric Challita. Université de Lille Massive MIMO Channel Characterization and Propagation-based Antenna Selection strategies: Application to 5G and Industry 4.0. Signal and Image processing. Université de Lille, 2019. English. ⟨tel-02495151⟩



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