Over the past few years there has been an extensive growth in data traffic consumption devices. Billions of mobile data devices are connected to the global wireless network. Customers demand revived services and up-to-date developed applications, like real-time video and games. These applications require reliable and high data rate wireless communication with high throughput network. One way to meet these requirements is by increasing the number of transmit and/or receive antennas of the wireless communication systems. Massive multiple-input multiple-output (MIMO) has emerged as a promising candidate technology for the next generation (5G) wireless communication. Massive MIMO increases the spatial multiplexing gain and the data rate by adding an excessive number of antennas to the base station (BS) terminals of wireless communication systems. However, building efficient algorithms able to decode a coherently or non-coherently large flow of transmitted signal with low complexity is a big challenge in massive MIMO. In this dissertation, we propose novel approaches to achieve optimal performance for joint channel estimation and signal detection for massive MIMO systems. The dissertation consists of three parts depending on the number of users at the receiver side. In the first part, we introduce a probabilistic approach to solve the problem of coherent signal detection using the optimized Markov Chain Monte Carlo (MCMC) technique. Two factors contribute to the speed of finding the optimal solution by the MCMC detector: The probability of encountering the optimal solution when the Markov chain converges to the stationary distribution, and the mixing time of the MCMC detector. First, we compute the optimal value of the “temperature'' parameter such that the MC encounters the optimal solution in a polynomially small probability. Second, we study the mixing time of the underlying Markov chain of the proposed MCMC detector. We assume the channel state information is known in the first part of the dissertation; in the second part we consider non-coherent signal detection. We develop and design an optimal joint channel estimation and signal detection algorithms for massive (single-input multiple-output) SIMO wireless systems. We propose exact non-coherent data detection algorithms in the sense of generalized likelihood ratio test (GLRT). In addition to their optimality, these proposed tree based algorithms perform low expected complexity and for general modulus constellations. More specifically, despite the large number of the unknown channel coefficients for massive SIMO systems, we show that the expected computational complexity of these algorithms is linear in the number of receive antennas (N) and polynomial in channel coherence time (T). We prove that as $N \rightarrow \infty$, the number of tested hypotheses for each coherent block equals $T$ times the cardinality of the modulus constellation. Simulation results show that the optimal non-coherent data detection algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity. In the part three, we consider massive MIMO uplink wireless systems with time-division duplex (TDD) operation. We propose an optimal algorithm in terms of GLRT to solve the problem of joint channel estimation and data detection for massive MIMO systems. We show that the expected complexity of our algorithm grows polynomially in the channel coherence time (T). The proposed algorithm is novel in two terms: First, the transmitted signal can be chosen from any modulus constellation, constant and non-constant. Second, the algorithm decodes the received noisy signal, which is transmitted a from multiple-antenna array, offering exact solution with polynomial complexity in the coherent block interval. Simulation results demonstrate significant performance gains of our approach compared with suboptimal non-coherent detection schemes. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.

Traditionally, noise in communication systems has been modeled as an additive, white Gaussian noise process with independent, identically distributed samples. Although this model accurately reflects thermal noise present in communication system electronics, it fails to capture the statistics of interference and other sources of noise, e.g. in unlicensed communication bands. Modern communication system designers must take into account interference and non-Gaussian noise to maximize efficiencies and capacities of current and future communication networks. In this work, I develop new multi-dimensional signal processing methods to improve performance of communication systems in three applications areas: (i) underwater acoustic, (ii) powerline, and (iii) multi-antenna cellular. In underwater acoustic communications, I address impairments caused by strong, time-varying and Doppler-spread reverberations (self-interference) using adaptive space-time signal processing methods. I apply these methods to array receivers with a large number of elements. In powerline communications, I address impairments caused by non-Gaussian noise arising from devices sharing the powerline. I develop and apply a cyclic adaptive modulation and coding scheme and a factor-graph-based impulsive noise mitigation method to improve signal quality and boost link throughput and robustness. In cellular communications, I develop a low-latency, high-throughput space-time-frequency processing framework used for large scale (up to 128 antenna) MIMO. This framework is used in the world's first 100-antenna MIMO system and processes up to 492 Gbps raw baseband samples in the uplink and downlink directions. My methods prove that multi-dimensional processing methods can be applied to increase communication system performance without sacrificing real-time requirements. / text

The main topic of this thesis is based on multiple-input multiple-output (MIMO) wireless communications, which is a novel technology that has attracted great interest in the last twenty years. Conventional MIMO systems using up to eight antennas play a vital role in the urban cellular network, where the deployment of multiple antennas have significantly enhanced the throughput without taking extra spectrum or power resources. The massive MIMO systems “scales” up the benefits that offered by the conventional MIMO systems. Using sixty four or more antennas at the BS not only improves the spectrum efficiency significantly, but also provides additional link robustness. It is considered as a key technology in the fifth generation of mobile communication technology standards network, and the design of new algorithms for these two systems is the basis of the research in this thesis. Firstly, at the receiver side of the conventional MIMO systems, a general framework of bit error rate (BER) approximation for the detection algorithms is proposed, which aims to support an adaptive modulation scheme. The main idea is to utilize a simplified BER approximation scheme, which is based on the union bound of the maximum-likelihood detector (MLD), whereby the bit error rate (BER) performance of the detector for the varying channel qualities can be efficiently predicted. The K-best detector is utilized in the thesis because its quasi- MLD performance and the parallel computational structure. The simulation results have clearly shown the adaptive K-best algorithm, by applying the simplified approximation method, has much reduced computational complexity while still maintaining a promising BER performance. Secondly, in terms of the uplink channel estimation for the massive MIMO systems with the time-division-duplex operation, the performance of the Grassmannian line packing (GLP) based uplink pilot codebook design is investigated. It aims to eliminate the pilot contamination effect in order to increase the downlink achievable rate. In the case of a limited channel coherence interval, the uplink codebook design can be treated as a line packing problem in a Grassmannian manifold. The closed-form analytical expressions of downlink achievable rate for both the single-cell and multi-cell systems are proposed, which are intended for performance analysis and optimization. The numerical results validate the proposed analytical expressions and the rate gains by using the GLP-based uplink codebook design. Finally, the study is extended to the energy efficiency (EE) of the massive MIMO system, as the reduction carbon emissions from the information and communication technology is a long-term target for the researchers. An effective framework of maximizing the EE for the massive MIMO systems is proposed in this thesis. The optimization starts from the maximization of the minimum user rate, which is aiming to increase the quality-of-service and provide a feasible constraint for the EE maximization problem. Secondly, the EE problem is a non-concave problem and can not be solved directly, so the combination of fractional programming and the successive concave approximation based algorithm are proposed to find a good suboptimal solution. It has been shown that the proposed optimization algorithm provides a significant EE improvement compared to a baseline case.

ARAÚJO, D. C. Channel estimation techniques applied to massive MIMO systems using sparsity and statistics approaches. 2016. 124 f. Tese (Doutorado em Engenharia de Teleinformática)–Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2016. / Submitted by Renato Vasconcelos (ppgeti@ufc.br) on 2017-06-21T13:52:26Z No. of bitstreams: 1 2016_tese_dcaraújo.pdf: 1832588 bytes, checksum: a4bb5d44287b92a9321d5fcc3589f22e (MD5) / Approved for entry into archive by Marlene Sousa (mmarlene@ufc.br) on 2017-06-21T16:17:55Z (GMT) No. of bitstreams: 1 2016_tese_dcaraújo.pdf: 1832588 bytes, checksum: a4bb5d44287b92a9321d5fcc3589f22e (MD5) / Made available in DSpace on 2017-06-21T16:17:55Z (GMT). No. of bitstreams: 1 2016_tese_dcaraújo.pdf: 1832588 bytes, checksum: a4bb5d44287b92a9321d5fcc3589f22e (MD5) Previous issue date: 2016-09-29 / Massive MIMO has the potential of greatly increasing the system spectral efficiency by employing many individually steerable antenna elements at the base station (BS). This potential can only be achieved if the BS has sufficient channel state information (CSI) knowledge. The way of acquiring it depends on the duplexing mode employed by the communication system. Currently, frequency division duplexing (FDD) is the most used in the wireless communication system. However, the amount of overhead necessary to estimate the channel scales with the number of antennas which poses a big challenge in implementing massive MIMO systems with FDD protocol. To enable both operating together, this thesis tackles the channel estimation problem by proposing methods that exploit a compressed version of the massive MIMO channel. There are mainly two approaches used to achieve such a compression: sparsity and second order statistics. To derive sparsity-based techniques, this thesis uses a compressive sensing (CS) framework to extract a sparse-representation of the channel. This is investigated initially in a flat channel and afterwards in a frequency-selective one. In the former, we show that the Cramer-Rao lower bound (CRLB) for the problem is a function of pilot sequences that lead to a Grassmannian matrix. In the frequency-selective case, a novel estimator which combines CS and tensor analysis is derived. This new method uses the measurements obtained of the pilot subcarriers to estimate a sparse tensor channel representation. Assuming a Tucker3 model, the proposed solution maps the estimated sparse tensor to a full one which describes the spatial-frequency channel response. Furthermore, this thesis investigates the problem of updating the sparse basis that arises when the user is moving. In this study, an algorithm is proposed to track the arrival and departure directions using very few pilots. Besides the sparsity-based techniques, this thesis investigates the channel estimation performance using a statistical approach. In such a case, a new hybrid beamforming (HB) architecture is proposed to spatially multiplex the pilot sequences and to reduce the overhead. More specifically, the new solution creates a set of beams that is jointly calculated with the channel estimator and the pilot power allocation using the minimum mean square error (MMSE) criterion. We show that this provides enhanced performance for the estimation process in low signal-noise ratio (SNR) scenarios. / Pesquisas em sistemas MIMO massivo (do inglês multiple-input multiple-output) ganha- ram muita atenção da comunidade científica devido ao seu potencial em aumentar a eficiência espectral do sistema comunicações sem-fio utilizando centenas de elementos de antenas na estação de base (EB). Porém, tal potencial só poderá é obtido se a EB possuir suficiente informação do estado de canal. A maneira de adquiri-lo depende de como os recursos de comunicação tempo-frequência são empregados. Atualmente, a solução mais utilizada em sistemas de comunicação sem fio é a multiplexação por divisão na frequência (FDD) dos pilotos. Porém, o grande desafio em implementar esse tipo solução é porque a quantidade de tons pilotos exigidos para estimar o canal aumenta com o número de antenas. Isso resulta na perda do eficiência espectral prometido pelo sistema massivo. Esta tese apresenta métodos de estimação de canal que demandam uma quantidade de tons pilotos reduzida, mas mantendo alta precisão na estimação do canal. Esta redução de tons pilotos é obtida porque os estimadores propostos exploram a estrutura do canal para obter uma redução das dimensões do canal. Nesta tese, existem essencialmente duas abordagens utilizadas para alcançar tal redução de dimensionalidade: uma é através da esparsidade e a outra através das estatísticas de segunda ordem. Para derivar as soluções que exploram a esparsidade do canal, o estimador de canal é obtido usando a teoria de “compressive sensing” (CS) para extrair a representação esparsa do canal. A teoria é aplicada inicialmente ao problem de estimação de canais seletivos e não-seletivos em frequência. No primeiro caso, é mostrado que limitante de Cramer-Rao (CRLB) é definido como uma função das sequências pilotos que geram uma matriz Grassmaniana. No segundo caso, CS e a análise tensorial são combinado para derivar um novo algoritmo de estimatição baseado em decomposição tensorial esparsa para canais com seletividade em frequência. Usando o modelo Tucker3, a solução proposta mapeia o tensor esparso para um tensor cheio o qual descreve a resposta do canal no espaço e na frequência. Além disso, a tese investiga a otimização da base de representação esparsa propondo um método para estimar e corrigir as variações dos ângulos de chegada e de partida, causados pela mobilidade do usuário. Além das técnicas baseadas em esparsidade, esta tese investida aquelas que usam o conhecimento estatístico do canal. Neste caso, uma nova arquitetura de beamforming híbrido é proposta para realizar multiplexação das sequências pilotos. A nova solução consite em criar um conjunto de feixes, que são calculados conjuntamente com o estimator de canal e alocação de potência para os pilotos, usand o critério de minimização erro quadrático médio. É mostrado que esta solução reduz a sequencia pilot e mostra bom desempenho e cenários de baixa relação sinal ruído (SNR).

La densification des réseaux allant de pair avec le déploiement de petites cellules, les systèmes Massive MIMO disposent de caractéristiques prometteuses pour accroître la capacité des réseaux au travers des techniques de formation de faisceau, appelées beamforming. Les transmissions aux longueurs d’onde millimétriques (mmWave) sont, quant à elle, très convoitées car, non seulement les bandes passantes exploitables sont extrêmement larges, mais le canal de propagation est principalement Line-of-Sight (LOS), ce qui correspond à la visibilité directe entre le terminal et la station de base. L’attrait que peut avoir un système multi-utilisateurs Massive MIMO à de telles fréquences provient, en partie, du faible encombrement du réseau d’antennes, mais aussi du fort gain de beamforming dont il permet de bénéficier afin de contrecarrer les fortes pertes en espace libre que subissent les signaux à de telles longueurs d’onde. Dans un premier temps nous montrons comment l’augmentation de la fréquence porteuse impacte les performances de deux précodeurs connus. Au travers d’une modélisation déterministe et géométrique du canal, on simule un scénario Outdoor à faible mobilité et à forte densité de population en mettant en avant l’influence du trajet direct et des trajets réfléchis sur les performances. Plus précisément on prouve qu’en configuration purement LOS, le précodeur Zero-Forcing est beaucoup plus sensible à la structure du réseau d’antennes, et à la position des utilisateurs, que le Conjugate Beamforming (aussi connu sous le nom de retournement temporel). On introduit alors un précodeur basé uniquement sur la position angulaire des utilisateurs dans la cellule en référence à la station de base, puis l’on compare ses performances à celles des deux autres. La robustesse d’une telle implémentation à une erreur d’estimation d’angles est illustrée pour un scénario spécifique afin de souligner la pertinence des solutions angulaires, une direction étant plus facile à estimer et son évolution dans le temps plus prévisible.On décrit dans un second temps comment la connaissance des positions angulaires des utilisateurs permet d’accroître la capacité de la cellule par le biais d’un procédé d’allocation de puissance reposant sur une évaluation de l’interférence que chaque faisceau génère sur les autres. On prouve à l’aide de simulations que l’obtention de cette interférence, même exprimée sous une forme très simplifiée, permet d’améliorer très nettement la capacité totale de la cellule. Enfin, nous introduisons les systèmes Hybrides Numériques et Analogiques ayant récemment été proposés afin de permettre à une station de base de conserver un large nombre d’antennes, nécessaire à l’obtention d’un fort gain de beamforming, tout en réduisant le nombre de chaînes Radiofréquences (RF). On commence par décrire une solution permettant à un terminal de former un faisceau dont la direction s’adapte à sa propre inclinaison, en temps réel, pour toujours viser la station de base. On compare ensuite les performances de tels récepteurs, associés à des stations de base Massive MIMO, avec celles d’une solution hybride connue, le nombre de chaînes RF des deux systèmes étant identiques. Principalement, la flexibilité et la capacité d’évolution de ces systèmes est mise en avant, ces deux atouts étant particulièrement pertinents pour de nombreux environnements à forte densité de population. / As wireless communication networks are driven toward densification with small cell deployments, massive MIMO technology shows great promises to boost capacity through beamforming techniques. It is also well known that millimeter-Wave systems are going to be an important part of future dense network solutions because, not only do they offer high bandwidth, but channel is mostly Line-of-Sight (LOS). The attractiveness of using a multi-user Massive MIMO system at these frequencies comes partly from the reduced size of a many antenna base station, but also from the high beamforming gains they provide, which is highly suited to combat the high path losses experienced at such small wavelengths. First we show how raising the carrier frequency impacts the performance of some linear precoders widely used in Massive MIMO systems. By means of a geometrical deterministic channel model, we simulate a dense outdoor scenario and highlight the influence of the direct and multi-paths components. More importantly we prove that, in a Line-of-Sight (LOS) configuration, the discriminating skill of the well-known Zero Forcing precoder is much more sensitive to the antenna array structure and the user location than the Conjugate Beamforming precoder, also known as Time-Reversal. A precoder based on the knowledge of the angular position of all users is then introduced and compared to the other precoders based on channel response knowledge. Its robustness against angle estimation error is illustrated for a specific scenario and serves to back up the importance such a solution represents for future dense 5G networks, angular information being easier to estimate, and more so to keep track of.After that, we show how the knowledge of Directions of Arrival can be used to increase the sum capacity of a multi-user transmission through leakage based power allocation. This allocation uses an estimation of inter-user interference, referred to as Leakage, and we show through simulations how this factor, even under its most simplified form, improves realistic transmissions. Moreover this solution is not iterative and is extremely easy to implement which makes it particularly well suited for high deployment scenarios.Finally we introduce the Hybrid Analog and Digital Beamforming systems that have recently emerged to retain a high number of antennas without as many Radio Frequency (RF) chains, in order to keep high beamforming gains while lowering the complexity of conceiving many antenna base stations. We first describe a user equipment solution allowing the system to form a beam that adapts to its own movement so that it always focuses its energy toward the base station, using an on-board analog array and an Inertial Measurement Unit. Then we compare the performance of a known Hybrid solution with a fully digital Massive MIMO system, having as many RF chains as the Hybrid system, but serving user equipments with beamforming abilities. Mostly we emphasize how such a system allows for great flexibility and evolution, both traits being invaluable features in many future networks.

Future fifth generation (5G) cellular networks have to cope with the expected ten-fold increase in mobile data traffic between 2015 and 2021. To achieve this goal, new technologies are being considered, including massive multiple-input multiple-output (MIMO) systems and millimeter-wave (mmWave) communications. Massive MIMO involves the use of large antenna array sizes at the base station, while mmWave communications employ frequencies between 30 and 300 GHz. In this thesis we study the impact of these technologies on the performance of channel estimators. Our results show that the characteristics of the propagation channel at mmWave frequencies improve the channel estimation performance in comparison with current, low frequency-based, cellular networks. Furthermore, we demonstrate the existence of an optimal angular spread of the multipath clusters, which can be used to maximize the capacity of mmWave networks. We also propose efficient noise variance estimators, which can be employed as an input to existing channel estimators.

In the first part of the dissertation, we provide an extensive overview of sub-6 GHz wireless access technology known as massive multiple-input multiple-output (MIMO) systems, highlighting its benefits, deployment challenges, and the key enabling technologies envisaged for 5G networks. We investigate the fundamental issues that degrade the performance of massive MIMO systems such as pilot contamination, precoding, user scheduling, and signal detection. In the second part, we optimize the performance of the massive MIMO system by proposing several algorithms, system designs, and hardware architectures. To mitigate the effect of pilot contamination, we propose a pilot reuse factor scheme based on the user environment and the number of active users. The results through simulations show that the proposed scheme ensures the system always operates at maximal spectral efficiency and achieves higher throughput. To address the user scheduling problem, we propose two user scheduling algorithms bases upon the measured channel gain. The simulation results show that our proposed user scheduling algorithms achieve better error performance, improve sum capacity and throughput, and guarantee fairness among the users. To address the uplink signal detection challenge in the massive MIMO systems, we propose four algorithms and their system designs. We show through simulations that the proposed algorithms are computationally efficient and can achieve near-optimal bit error rate performance. Additionally, we propose hardware architectures for all the proposed algorithms to identify the required physical components and their interrelationships.

Cloud radio access network (C-RAN) is a promising architecture for the next-generation RAN to meet the diverse and stringent requirements envisioned by fifth generation mobile communication systems (5G) and future generation mobile networks. C-RAN offers several advantages, such as reduced capital expenditure (CAPEX) and operational expenditure (OPEX), increased spectral efficiency (SE), higher capacity and improved cell-edge performance, and efficient hardware utilization through resource sharing and network function virtualization (NFV). However, these centralization gains come with the need for a fronthaul, which is the transport link connecting remote radio units (RRUs) to the base band unit (BBU) pool. In conventional C-RAN, legacy common public radio interface (CPRI) protocol is used on the fronthaul network to transport the raw, unprocessed baseband in-phase/quadrature-phase (I/Q) samples between the BBU and the RRUs, and it demands a huge fronthaul bandwidth, a strict low-latency, in the order of a few hundred microseconds, and a very high reliability. Hence, in order to relax the excessive fronthaul bandwidth and stringent low-latency requirements, as well as to enhance the flexibility of the fronthaul, it is utmost important to redesign the fronthaul, while still profiting from the acclaimed centralization benefits. Therefore, a flexibly centralized C-RAN with different functional splits has been introduced. In addition, 5G mobile fronthaul (often also termed as an evolved fronthaul ) is envisioned to be packet-based, utilizing the Ethernet as a transport technology. In this thesis, to circumvent the fronthaul bandwidth constraint, a packetized fronthaul considering an appropriate functional split such that the fronthaul data rate is coupled with actual user data rate, unlike the classical C-RAN where fronthaul data rate is always static and independent of the traffic load, is justifiably chosen. We adapt queuing and spatial traffic models to derive the mathematical expressions for statistical multiplexing gains that can be obtained from the randomness in the user traffic. Through this, we show that the required fronthaul bandwidth can be reduced significantly, depending on the overall traffic demand, correlation distance and outage probability. Furthermore, an iterative optimization algorithm is developed, showing the impacts of number of pilots on a bandwidth-constrained fronthaul. This algorithm achieves additional reduction in the required fronthaul bandwidth. Next, knowing the multiplexing gains and possible fronthaul bandwidth reduction, it is beneficial for the mobile network operators (MNOs) to deploy the optical transceiver (TRX) modules in C-RAN cost efficiently. For this, using the same framework, a cost model for fronthaul TRX cost optimization is presented. This is essential in C-RAN, because in a wavelength division multiplexing-passive optical network (WDM-PON) system, TRXs are generally deployed to serve at a peak load. But, because of variations in the traffic demands, owing to tidal effect, the fronthaul can be dimensioned requiring a lower capacity allowing a reasonable outage, thus giving rise to cost saving by deploying fewer TRXs, and energy saving by putting the unused TRXs in sleep mode. The second focus of the thesis is the fronthaul latency analysis, which is a critical performance metric, especially for ultra-reliable and low latency communication (URLLC). An analytical framework to calculate the latency in the uplink (UL) of C-RAN massive multiple-input multiple-output (MIMO) system is presented. For this, a continuous-time queuing model for the Ethernet switch in the fronthaul network, which aggregates the UL traffic from several massive MIMO-aided RRUs, is considered. The closed-form solutions for the moment generating function (MGF) of sojourn time, waiting time and queue length distributions are derived using Pollaczek–Khinchine formula for our M/HE/1 queuing model, and evaluated via numerical solutions. In addition, the packet loss rate – due to the inability of the packets to reach the destination in a certain time – is derived. Due to the slotted nature of the UL transmissions, the model is extended to a discrete-time queuing model. The impact of the packet arrival rate, average packet size, SE of users, and fronthaul capacity on the sojourn time, waiting time and queue length distributions are analyzed. While offloading more signal processing functionalities to the RRU reduces the required fronthaul bandwidth considerably, this increases the complexity at the RRU. Hence, considering the 5G New Radio (NR) flexible numerology and XRAN functional split with a detailed radio frequency (RF) chain at the RRU, the total RRU complexity is computed first, and later, a tradeoff between the required fronthaul bandwidth and RRU complexity is analyzed. We conclude that despite the numerous C-RAN benefits, the stringent fronthaul bandwidth and latency constraints must be carefully evaluated, and an optimal functional split is essential to meet diverse set of requirements imposed by new radio access technologies (RATs). / Ein cloud-basiertes Mobilfunkzugangsnetz (cloud radio access network, C-RAN) stellt eine vielversprechende Architektur für das RAN der nächsten Generation dar, um die vielfältigen und strengen Anforderungen der fünften (5G) und zukünftigen Generationen von Mobilfunknetzen zu erfüllen. C-RAN bietet mehrere Vorteile, wie z.B. reduzierte Investitions- (CAPEX) und Betriebskosten (OPEX), erhöhte spektrale Effizienz (SE), höhere Kapazität und verbesserte Leistung am Zellrand sowie effiziente Hardwareauslastung durch Ressourcenteilung und Virtualisierung von Netzwerkfunktionen (network function virtualization, NFV). Diese Zentralisierungsvorteile erfordern jedoch eine Transportverbindung (Fronthaul), die die Antenneneinheiten (remote radio units, RRUs) mit dem Pool an Basisbandeinheiten (basisband unit, BBU) verbindet. Im konventionellen C-RAN wird das bestehende CPRI-Protokoll (common public radio interface) für das Fronthaul-Netzwerk verwendet, um die rohen, unverarbeitet n Abtastwerte der In-Phaseund Quadraturkomponente (I/Q) des Basisbands zwischen der BBU und den RRUs zu transportieren. Dies erfordert eine enorme Fronthaul-Bandbreite, eine strenge niedrige Latenz in der Größenordnung von einigen hundert Mikrosekunden und eine sehr hohe Zuverlässigkeit. Um die extrem große Fronthaul-Bandbreite und die strengen Anforderungen an die geringe Latenz zu lockern und die Flexibilität des Fronthauls zu erhöhen, ist es daher äußerst wichtig, das Fronthaul neu zu gestalten und dabei trotzdem von den erwarteten Vorteilen der Zentralisierung zu profitieren. Daher wurde ein flexibel zentralisiertes CRAN mit unterschiedlichen Funktionsaufteilungen eingeführt. Außerdem ist das mobile 5G-Fronthaul (oft auch als evolved Fronthaul bezeichnet) als paketbasiert konzipiert und nutzt Ethernet als Transporttechnologie. Um die Bandbreitenbeschränkung zu erfüllen, wird in dieser Arbeit ein paketbasiertes Fronthaul unter Berücksichtigung einer geeigneten funktionalen Aufteilung so gewählt, dass die Fronthaul-Datenrate mit der tatsächlichen Nutzdatenrate gekoppelt wird, im Gegensatz zum klassischen C-RAN, bei dem die Fronthaul-Datenrate immer statisch und unabhängig von der Verkehrsbelastung ist. Wir passen Warteschlangen- und räumliche Verkehrsmodelle an, um mathematische Ausdrücke für statistische Multiplexing- Gewinne herzuleiten, die aus der Zufälligkeit im Benutzerverkehr gewonnen werden können. Hierdurch zeigen wir, dass die erforderliche Fronthaul-Bandbreite abhängig von der Gesamtverkehrsnachfrage, der Korrelationsdistanz und der Ausfallwahrscheinlichkeit deutlich reduziert werden kann. Darüber hinaus wird ein iterativer Optimierungsalgorithmus entwickelt, der die Auswirkungen der Anzahl der Piloten auf das bandbreitenbeschränkte Fronthaul zeigt. Dieser Algorithmus erreicht eine zusätzliche Reduktion der benötigte Fronthaul-Bandbreite. Mit dem Wissen über die Multiplexing-Gewinne und die mögliche Reduktion der Fronthaul-Bandbreite ist es für die Mobilfunkbetreiber (mobile network operators, MNOs) von Vorteil, die Module des optischen Sendeempfängers (transceiver, TRX) kostengünstig im C-RAN einzusetzen. Dazu wird unter Verwendung des gleichen Rahmenwerks ein Kostenmodell zur Fronthaul-TRX-Kostenoptimierung vorgestellt. Dies ist im C-RAN unerlässlich, da in einem WDM-PON-System (wavelength division multiplexing-passive optical network) die TRX im Allgemeinen bei Spitzenlast eingesetzt werden. Aufgrund der Schwankungen in den Verkehrsanforderungen (Gezeiteneffekt) kann das Fronthaul jedoch mit einer geringeren Kapazität dimensioniert werden, die einen vertretbaren Ausfall in Kauf nimmt, was zu Kosteneinsparungen durch den Einsatz von weniger TRXn und Energieeinsparungen durch den Einsatz der ungenutzten TRX im Schlafmodus führt. Der zweite Schwerpunkt der Arbeit ist die Fronthaul-Latenzanalyse, die eine kritische Leistungskennzahl liefert, insbesondere für die hochzuverlässige und niedriglatente Kommunikation (ultra-reliable low latency communications, URLLC). Ein analytisches Modell zur Berechnung der Latenz im Uplink (UL) des C-RAN mit massivem MIMO (multiple input multiple output) wird vorgestellt. Dazu wird ein Warteschlangen-Modell mit kontinuierlicher Zeit für den Ethernet-Switch im Fronthaul-Netzwerk betrachtet, das den UL-Verkehr von mehreren RRUs mit massivem MIMO aggregiert. Die geschlossenen Lösungen für die momenterzeugende Funktion (moment generating function, MGF) von Verweildauer-, Wartezeit- und Warteschlangenlängenverteilungen werden mit Hilfe der Pollaczek-Khinchin-Formel für unser M/HE/1-Warteschlangenmodell hergeleitet und mittels numerischer Verfahren ausgewertet. Darüber hinaus wird die Paketverlustrate derjenigen Pakete, die das Ziel nicht in einer bestimmten Zeit erreichen, hergeleitet. Aufgrund der Organisation der UL-Übertragungen in Zeitschlitzen wird das Modell zu einem Warteschlangenmodell mit diskreter Zeit erweitert. Der Einfluss der Paketankunftsrate, der durchschnittlichen Paketgröße, der SE der Benutzer und der Fronthaul-Kapazität auf die Verweildauer-, dieWartezeit- und dieWarteschlangenlängenverteilung wird analysiert. Während das Verlagern weiterer Signalverarbeitungsfunktionalitäten an die RRU die erforderliche Fronthaul-Bandbreite erheblich reduziert, erhöht sich dadurch im Gegenzug die Komplexität der RRU. Daher wird unter Berücksichtigung der flexiblen Numerologie von 5G New Radio (NR) und der XRAN-Funktionenaufteilung mit einer detaillierten RF-Kette (radio frequency) am RRU zunächst die gesamte RRU-Komplexität berechnet und später ein Kompromiss zwischen der erforderlichen Fronthaul-Bandbreite und der RRU-Komplexität untersucht. Wir kommen zu dem Schluss, dass trotz der zahlreichen Vorteile von C-RAN die strengen Bandbreiten- und Latenzbedingungen an das Fronthaul sorgfältig geprüft werden müssen und eine optimale funktionale Aufteilung unerlässlich ist, um die vielfältigen Anforderungen der neuen Funkzugangstechnologien (radio access technologies, RATs) zu erfüllen.

As the quest for higher data rates continues, future generations of wireless communications are expected to concur even higher frequency bands, particularly at terahertz (THz) frequencies. Even though the vast bandwidths at the THz band promise terabit-per-second (Tbps) data rates, current baseband technologies do not support such high rates. In particular, the complexities of Tbps channel code decoding and ultra-massive multiple-input multiple-output data detection are prohibitive. This work addresses the efficient data detection and channel-code decoding problem under THz-band channel conditions and Tbps baseband processing limitations. We propose ultra-massive multiple-input multiple-output THz channel models, then investigate the corresponding performance of several candidate data detection and coding schemes. We further investigate the complexity of different detectors and decoders, motivating parallelizability at both levels. We recommend which detector to combine best with which channel code decoder under specific THz channel characteristics.

5G est prévu pour s'attaquer, en plus d'une augmentation considérable du volume de trafic, la tâche de connecter des milliards d'appareils avec des exigences de service hétérogènes. Afin de relever les défis de la 5G, nous préconisons une utilisation plus efficace des informations disponibles, avec plus de sensibilisation par rapport aux services et aux utilisateurs, et une expansion de l'intelligence du RAN. En particulier, nous nous concentrons sur deux activateurs clés de la 5G, à savoir le MIMO massif et la mise en cache proactive. Dans le troisième chapitre, nous nous concentrons sur la problématique de l'acquisition de CSI dans MIMO massif en TDD. Pour ce faire, nous proposons de nouveaux schémas de regroupement spatial tels que, dans chaque groupe, une couverture maximale de la base spatiale du signal avec un chevauchement minimal entre les signatures spatiales des utilisateurs est obtenue. Ce dernier permet d'augmenter la densité de connexion tout en améliorant l'efficacité spectrale. MIMO massif en TDD est également au centre du quatrième chapitre. Dans ce cas, en se basant sur les différents taux de vieillissement des canaux sans fil, la périodicité d'estimation de CSI est supplémentaire. Nous le faisons en proposant un exploité comme un degré de liberté supplémentaire. Nous le faisons en proposant une adaptation dynamique de la trame TDD en fonction des temps de cohérence des canaux hétérogènes. Les stations de bases MIMO massif sont capables d'apprendre la meilleure politique d’estimation sur le uplink pour de longues périodes. Comme les changements de canaux résultent principalement de la mobilité de l'appareil, la connaissance de l'emplacement est également incluse dans le processus d'apprentissage. Le problème de planification qui en a résulté a été modélisé comme un POMDP à deux échelles temporelles et des algorithmes efficaces à faible complexité ont été fournis pour le résoudre. Le cinquième chapitre met l'accent sur la mise en cache proactive. Nous nous concentrons sur l'amélioration de l'efficacité énergétique des réseaux dotes de mise en cache en exploitant la corrélation dans les modèles de trafic en plus de la répartition spatiale des demandes. Nous proposons un cadre qui établit un compromis optimal entre la complexité et la véracité dans la modélisation du comportement des utilisateurs grâce à la classification adaptative basée sur la popularité du contenu. Il simplifie également le problème du placement de contenu, ce qui se traduit par un cadre d'allocation de contenu rapidement adaptable et économe en énergie. / 5G is envisioned to tackle, in addition to a considerable increase in traffic volume, the task of connecting billions of devices with heterogeneous service requirements. In order to address the challenges of 5G, we advocate a more efficient use of the available information, with more service and user awareness, and an expansion of the RAN intelligence. In particular, we focus on two key enablers of 5G, namely massive MIMO and proactive caching. In the third chapter, we focus on addressing the bottleneck of CSI acquisition in TDD Massive MIMO. In order to do so, we propose novel spatial grouping schemes such that, in each group, maximum coverage of the signal’s spatial basis with minimum overlapping between user spatial signatures is achieved. The latter enables to increase connection density while improving spectral efficiency. TDD Massive MIMO is also the focus of the fourth chapter. Therein, based on the different rates of wireless channels aging, CSI estimation periodicity is exploited as an additional DoF. We do so by proposing a dynamic adaptation of the TDD frame based on the heterogeneous channels coherence times. The Massive MIMO BSs are enabled to learn the best uplink training policy for long periods. Since channel changes result primarily from device mobility, location awareness is also included in the learning process. The resulting planning problem was modeled as a two-time scale POMDP and efficient low complexity algorithms were provided to solve it. The fifth chapter focuses on proactive caching. We focus on improving the energy efficiency of cache-enabled networks by exploiting the correlation in traffic patterns in addition to the spatial repartition of requests. We propose a framework that strikes the optimal trade-off between complexity and truthfulness in user behavior modeling through adaptive content popularity-based clustering. It also simplifies the problem of content placement, which results in a rapidly adaptable and energy efficient content allocation framework.