Jury:

• Jean-François DIOURIS, ,University Professor, ,Ecole polytechnique de l'université de Nantes, ,Rapporteur
• Pascal PAGANI, ,Research Engineer, ,CEA CESTA, ,Rapporteur
• Dinh-Thuy PHAN-HUY, ,Research Engineer, ,Orange - Technology & Global Innovation - Orange Labs Networks - Radio Network and Microwaves - Radio InnOvation- RAdio Performance (TGI/OLN/RNM/RIO/RAP), ,Examiner
• Alain SIBILLE, ,University Professor, ,Telecom ParisTech, ,Examiner
• Martine LIENARD, ,University Professor, ,University of Lille, ,Thesis supervisor
• Davy GAILLOT, ,Senior Lecturer, ,University of Lille, ,Thesis Co-Director

Summary:

In the field of wireless telecommunications, major efforts over the last ten years have focused on developing fast, intelligent, secure and environmentally-friendly information exchange systems. The areas of application are increasingly wide-ranging, extending for example from the general public, to the connected car, the Internet of Things (IOT) and Industry 4.0. In the latter case, the aim is to achieve greater flexibility and versatility of production lines and predictive maintenance of machines, to name but a few examples. However, current wireless networks are not yet able to meet the many shortcomings of the fourth generation of mobile networks (4G) and the requirements of 5G for massive connectivity, ultra-reliability and extremely low latency times. Optimising spectrum resources is also a very important issue. 5G was initially seen as an evolution, made possible by improvements to LTE (Long Term Evolution), but it will soon become a revolution and a major advance on previous generations.
In this context, Massive MIMO (Multiple Input Multiple Output) technology has emerged as one of the most promising physical layer technologies. The main idea is to equip base stations with large antenna arrays (100 or more) to communicate simultaneously with numerous terminals or user equipment. Thanks to intelligent pre-processing of transmit signals, Massive MIMO systems promise to deliver a major improvement in performance, while ensuring excellent spectral and energy efficiency.
Numerous scientific articles have recently developed the theoretical aspects of these systems, whose feasibility has been validated by tests carried out by operators. However, a number of challenges still need to be overcome before Massive MIMO-based communications can be fully deployed. For example, the development of channel models representative of the real environment, the impact of polarisation diversity, optimal antenna selection strategies and the acquisition of channel state information are important subjects to explore. In addition, a good understanding of propagation channels in an industrial environment is needed to optimise communication links for the intelligent industry of the future.

In this thesis, we attempt to answer some of these questions by focusing on three main areas:

1) Polarimetric characterisation of Massive MIMO channels in an industrial environment. To do this, we study scenarios corresponding to channels with or without line-of-sight between transmitter and receiver (Line of Sight - LOS) or non-LOS, and in the presence of various types of obstacle. The associated metrics are either those used in propagation, such as the Rice factor and spatial correlation, or system-oriented metrics such as total channel capacity including linear precoding strategies. In addition, the proposed polarisation diversity schemes show very promising results.

2) In massive MIMO, an important objective is to reduce the number of radio frequency chains and therefore the complexity of the system, by selecting a set of distributed antennas. This selection strategy, which uses the receiver's spatial correlation and a propagation metric as a factor of merit, results in near-optimal total capacity.

3) Finally, an efficient technique for reducing resources during the acquisition of propagation channel information in FDD (frequency-division-duplex) systems is proposed. It is based on spatial correlation at the transmitter and consists of solving a set of simple autoregressive equations. The results show that this technique achieves performances that are not too far removed from those of the TDD (time-division-duplex) systems initially proposed for Massive MIMO.

Abstract:

Over the past decade, mobile connectivity and wireless systems have become a necessity for many applications and use-cases. Faster, smarter, safer and environment-friendlier networks are sought. Continuous efforts have been made to boost wireless systems performance, from analog to digital systems, bulky handheld cellular phone and user equipments to ever-small sensors and smart phones, from mechanization and basic automation systems to the smart industry of the future or Industry 4.0. 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. Over the last 6 years, several scientific papers proved the theoretical aspects and promises of Massive MIMO systems and many trials validated that this technology is not just an academic concept. 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 optimise 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