Dans le domaine des télécommunications sans fil, les domaines applicatifs sont de plus en plus larges, s’étendant par exemple du grand public, à la voiture connectée, à l’internet des objets (IoT Internet of Things) et à l’industrie 4.0. Dans ce dernier cas, l’objectif est d’aboutir à une flexibilité et à une versatilité accrues des chaînes de production et à une maintenance prédictive des machines, pour ne citer que quelques exemples. Cependant, les réseaux sans fil actuels ne sont pas encore en mesure de répondre aux nombreuses lacunes de la quatrième génération des réseaux mobiles (4G) et aux exigences de la 5G quant à une connectivité massive, une ultra fiabilité et des temps de latence extrêmement faibles. L’optimisation des ressources spectrales est également un point très important. La 5G était initialement considérée comme une évolution, rendue possible grâce aux améliorations apportées à la LTE (Long Term Evolution), mais elle ne tardera pas à devenir une révolution et une avancée majeure par rapport aux générations précédentes.Dans ce cadre, la technologie des réseaux massifs ou Massive MIMO (Multiple-Input Multiple-Output) s’est imposée comme l’une des technologies de couche physique les plus prometteuses. L'idée principale est d'équiper les stations de base de grands réseaux d’antennes (100 ou plus) pour communiquer simultanément avec de nombreux terminaux ou équipements d’utilisateurs. Grâce à un prétraitement intelligent au niveau des signaux d’émission, les systèmes Massive MIMO promettent d’apporter une grande amélioration des performances, tout en assurant une excellente efficacité spectrale et énergétique. Cependant certains défis doivent encore être relevés avant le déploiement complet des communications basées sur le massive MIMO. Par exemple, l’élaboration de modèles de canaux représentatifs de l’environnement réel, l'impact de la diversité de polarisation, les stratégies de sélection optimale d’antennes et l'acquisition d'informations d'état du canal, sont des sujets importants à explorer. En outre, une bonne compréhension des canaux de propagation en milieu industriel est nécessaire pour optimiser les liens de communication de l'industrie intelligente du futur.Dans cette thèse, nous essayons de répondre à certaines de ces questions en nous concentrant sur trois axes principaux :1) La caractérisation polarimétrique des canaux massive MIMO en environnement industriel. Pour cela, on étudie des scénarios correspondant à des canaux ayant ou non une visibilité directe entre émetteur et récepteur (Line of Sight – LOS) ou Non LOS, et en présence de divers types d’obstacles. Les métriques associées sont soit celles utilisées en propagation telles que le facteur de Rice et la corrélation spatiale, soit orientées système comme la capacité totale du canal incluant des stratégies de précodage linéaire. De plus, les schémas de diversité de polarisation proposés montrent des résultats très prometteurs.2) En massive MIMO, un objectif important est de réduire le nombre de chaînes de fréquences radio et donc la complexité du système, en sélectionnant un ensemble d'antennes distribuées. Cette stratégie de sélection utilisant la corrélation spatiale du récepteur et une métrique de propagation comme facteur de mérite, permet d'obtenir une capacité totale quasi-optimale.3) Une technique efficace de réduction des ressources lors de l’acquisition d’informations du canal de propagation dans les systèmes FDD (frequency-division-duplex) est enfin proposée. Elle repose sur la corrélation spatiale au niveau de l'émetteur et consiste à résoudre un ensemble d'équations auto-régressives simples. Les résultats montrent que cette technique permet d’atteindre des performances qui ne sont pas trop éloignées de celles des systèmes TDD (time-division-duplex) initialement proposés pour le massive MIMO. / 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.

In recent years, there has been a growing research interest in throughput optimization problems in a multi-hop wireless network. MIMO (multiple-input multiple-output), as an advanced physical layer technology, has been employed in multi-hop wireless networks to increase throughput with a given bandwidth or transmit power. It exploits the use of multiple antennas at the transmitter and receiver to increase spectral efficiency by leveraging its spatial multiplexing (SM) and interference cancellation (IC) capabilities. Instead of carrying complex manipulations on matrices, degree-of-freedom(DoF) based MIMO models, which require only simple computations, are widely used in networking research to exploit MIMO's SM and IC capabilities. In this thesis, we employ a new DoF model, which can ensure feasible solution and achieve a higher DoF region than previous DoF-based models. Based on this model, we study the DoF scheduling for a multi-hop MIMO network. Specifically, we aim to maximize the minimum rate among all sessions in the network. Some researches have been done based on this model to solve throughput optimization problems with the assumption that the route of each session is given priori. Although the fixed routing decreases the size of the problem, it also limits the performance of the network to a great extent. The goal of this thesis is to employ this new model to solve the throughput maximization problem by jointly considering flow routing, scheduling, and DoF allocation for SM and IC. We formulate it as a mixed integer linear program (MILP), which cannot be solved efficiently by commercial softwares even for moderate sized networks. Thus, we develop an efficient polynomial time algorithm by customizing the sequential fixing framework. Through simulation results, we show that this algorithm can efficiently provide near-optimal solutions for networks with different sizes. / Master of Science

The recent developments in Internet-of-Things (IoT) and the next-generation wireless communication systems (5G and beyond) are posing unprecedented demands for massive connectivity, enhanced spectrum efficiency, and strengthened reliability. Moreover, the conventional orthogonal multiple access (OMA) techniques have approached their fundamental limits or the improvements in performance are marginal. To this end, a paradigm-shift from OMA to massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) technology is proposed. The proposed techniques are capable of serving multiple spatially-distributed user nodes/IoTs in the same frequency-time resource block by reaping out the benefits of power-domain NOMA, and favorable propagation and channel hardening brought by very large antenna arrays.First, a comprehensively literature survey has been conducted. Next, system, channel and signal models were developed by considering practical transmission impairments of the proposed massive MIMO NOMA. Then, novel NOMA relaying strategies via massive MIMO with pilot designs, per-hop and cascaded channel estimation, statistical-parameter based power allocation policy, and reliable precoding scheme are designed. Then, a complete analytical framework to derive the fundamental performance metrics is developed. A MATLAB-based simulation framework is developed to verify the proposed system designs.Then, the detrimental effects of residual interference caused by intra-cluster pilot sharing and error propagation caused by imperfect successive interference cancellation are quantified. The results acquired can provide insights for refining the proposed techniques in terms of signal model and pilot design.Trade-offs among massive connectivity and spectral efficiency will be established and refined for the proposed relay aided/cell-free massive MIMO NOMA via carefully designing per-hop and cascaded channel estimation, low-complexity statistical-parameter-based power allocation, and conjugate precoding schemes. The proposed technique is expected to significantly outperform the conventional OMA scheme in all overloaded system scenarios by virtue of the proposed aggressive spatial multiplexing and power-domain NOMA techniques. Hence, the proposed technique can simultaneously serve many users with fast data rates than that of the existing OMA techniques. The proposed NOMA techniques are expected to provide higher spectral and energy efficiencies with ultra-low end-to-end latency than those of existing OMA. Thus, the proposed relay-aided/cell-free massive MIMO NOMA can significantly contribute as a novel candidate technology for the next-generation wireless standards.

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Encryption of confidential data with a secret key has become a widespread technique for securing wireless transmissions. However, existing key distribution methods that either deliver the secret key with a key distribution center or exchange the secret key using public-key cryptosystems are unable to establish perfect secret keys necessary for symmetric encryption techniques. This research considers secret key establishment, under the broad research area of information theoretic security, using the reciprocal wireless channel as common randomness for the extraction of perfect secret keys in multiple-input multiple-output (MIMO)communication systems. The presentation discusses the fundamental characteristics of the time-variant MIMO wireless channel and establishes a realistic channel simulation model useful for assessing key establishment algorithms. Computational examples show the accuracy and applicability of the model. The discussion then turns to an investigation of the influence of the spatial and temporal correlation of the channel coefficients on the bound of the key size generated from the common channel, and it is found that a sampling approach exists that can generate a key using the minimum sampling time. A practical key generation protocol is then developed based on an enhancement of a published channel coefficient quantization method that incorporates flexible quantization levels, public transmission of the correlation eigenvector matrix and low-density parity-check (LDPC) coding to improve key agreement. This investigation leads to the development of improved channel quantization techniques that dynamically shift the quantization boundaries at one node based on the information provided by the other node. Analysis based on a closed-form bound for the key error rate (KER) and simulations based on the channel model and measurement data show that the enhanced algorithms are able to dramatically reduce key mismatch and asymptotically approach the KER bound. Finally, other secret key generation algorithms based on channel-encryption rather than quantization are discussed, leading to a new concept for secret key generation using the common wireless channel.

In this thesis, several computationally-efficient approximate soft demodulation schemes are developed for multiple-input multiple-output (MIMO) communication systems. These soft demodulators are designed to be deployed in the conventional iterative receiver ('turbo') architecture, and they are designed to provide good performance at substantially lower computational cost than that of the exact soft demodulator. The proposed demodulators are based on the principle of list demodulation and can be classified into two classes, according to the nature of the list-generation algorithm. One class is based on a tree-search algorithm and the other is based on insight generated from the analysis of semidefinite relaxation techniques for hard demodulation. The proposed tree-search demodulators are based on a multi-stack algorithm, developed herein, for efficiently traversing the tree structure that is inherent in the MIMO demodulation problem. The proposed scheme was inspired, in part, by the stack algorithm, which stores all the visited nodes in the tree in a single stack and chooses the next node to expand based on a 'best-first' selection scheme. The proposed algorithm partitions this global stack into a stack for each level of the tree. It examines the tree in the natural ordering of the levels and performs a best-first search in each of the stacks. By assigning appropriate priorities to the level at which the search for the next leaf node re-starts, the proposed demodulators can achieve performance-complexity trade-offs that dominate several existing soft demodulators, including those based on the stack algorithm and those based on 'sphere decoding' principles, especially in the low-complexity region. In the second part of this thesis it is shown that the randomization procedure that is inherent in the semidefinite relaxation (SDR) technique for hard demodulation can be exploited to generate the list members required for list-based soft demodulation. The direct application of this observation yields list-based soft demodulators that only require the solution of one SDP per demodulation-decoding iteration. By approximating the randomization procedure by a set of independent Bernoulli trials, this requirement can be reduced to just one semidefinite program (SDP) per channel use. An advantage of these demodulators over those based on optimal tree-search algorithms is that the computational cost of solving the SDP is a low-order polynomial in the problem size. The analysis and simulation experiments provided in the thesis show that the proposed SDR-based demodulators offer an attractive trade-off between performance and computational cost. The structure of the SDP in the proposed SDR-based demodulators depends on the signaling scheme, and the initial development focuses on the case of QPSK signaling. In the last chapter of this thesis, the extension to MIMO 16-QAM systems is developed, and some interesting observations regarding some existing SDR-based hard demodulation schemes for MIMO 16-QAM systems are derived. The simulation results reveal that the excellent performance-complexity trade-off of the proposed SDR-based schemes is preserved under the extension to 16-QAM signaling. / Thesis / Doctor of Philosophy (PhD)

No / Wavelet based multicarrier systems have attracted the attention of the researchers over the past few years to replace the conventional OFDM systems in the next generation communication systems. In this paper we have investigated the performance of such wavelet based systems using forward error correction with covolutional coding and interleaving in a Wavelet-SISO system and then in a Wavelet multicarrier modulation (WMCM) multiple input multiple output (MIMO) system using Convolutional coding and beamforming to reduce the source bit rate and overall system error and increase the data rate. Results show outstanding Bit Error Rate vs. Signal to Noise Ratio Performance. Other than better performance the proposed systems keep the computational burden off the receiver that has more cost and power constraints.

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