Resource Allocation For Max Min Fairness In Multi Cell Massive Mimo

Massive MIMO (multiple-input multiple-output) is considered as an heir of the multi-user MIMO technology and it has recently gained lots of attention from both academia and industry. By equipping base stations (BSs) with hundreds of antennas, this new technology can provide very large multiplexing gains by serving many users on the same time-frequency resources and thereby bring significant improvements in spectral efficiency (SE) and energy efficiency (EE) over the current wireless networks. The transmit power, pilot training, and spatial transmission resources need to be allocated properly to the users to achieve the highest possible performance. This is called resource allocation and can be formulated as design utility optimization problems. If the resource allocation in Massive MIMO is optimized, the technology can handle the exponential growth in both wireless data traffic and number of wireless devices, which cannot be done by the current cellular network technology. In this thesis, we focus on two resource allocation aspects in Massive MIMO: The first part of the thesis studies if power control and advanced coordinated multipoint (CoMP) techniques are able to bring substantial gains to multi-cell Massive MIMO systems compared to the systems without using CoMP. More specifically, we consider a network topology with no cell boundary where the BSs can collaborate to serve the users in the considered coverage area. We focus on a downlink (DL) scenario in which each BS transmits different data signals to each user. This scenario does not require phase synchronization between BSs and therefore has the same backhaul requirements as conventional Massive MIMO systems, where each user is preassigned to only one BS. The scenario where all BSs are phase synchronized to send the same data is also included for comparison. We solve a total transmit power minimization problem in order to observe how much power Massive MIMO BSs consume to provide the requested quality of service (QoS) of each user. A max-min fairness optimization is also solved to provide every user with the same maximum QoS regardless of the propagation conditions. The second part of the thesis considers a joint pilot design and uplink (UL) power control problem in multi-cell Massive MIMO. The main motivation for this work is that the pilot assignment and pilot power allocation is momentous in Massive MIMO since the BSs are supposed to construct linear detection and precoding vectors from the channel estimates. Pilot contamination between pilot-sharing users leads to more interference during data transmission. The pilot design is more difficult if the pilot signals are reused frequently in space, as in Massive MIMO, which leads to greater pilot contamination effects. Related works have only studied either the pilot assignment or the pilot power control, but not the joint optimization. Furthermore, the pilot assignment is usually formulated as a combinatorial problem leading to prohibitive computational complexity. Therefore, in the second part of this thesis, a new pilot design is proposed to overcome such challenges by treating the pilot signals as continuous optimization variables. We use those pilot signals to solve different max-min fairness optimization problems with either ideal hardware or hardware impairments.

Massive MIMO (multiple-input multiple-output) is considered as an heir of the multi-user MIMO technology and it has gained lots of attention from both academia and industry since the last decade. By equipping base stations (BSs) with hundreds of antennas in a compact array or a distributed manner, this new technology can provide very large multiplexing gains by serving many users on the same time-frequency resources and thereby bring significant improvements in spectral efficiency (SE) and energy efficiency (EE) over the current wireless networks. The transmit power, pilot training, and spatial transmission resources need to be allocated properly to the users to achieve the highest possible performance. This is called resource allocation and can be formulated as design utility optimization problems. If the resource allocation in Massive MIMO is optimized, the technology can handle the exponential growth in both wireless data traffic and number of wireless devices, which cannot be done by the current cellular network technology. In this thesis, we focus on the five different resource allocation aspects in Massive MIMO communications: The first part of the thesis studies if power control and advanced coordinated multipoint (CoMP) techniques are able to bring substantial gains to multi-cell Massive MIMO systems compared to the systems without using CoMP. More specifically, we consider a network topology with no cell boundary where the BSs can collaborate to serve the users in the considered coverage area. We focus on a downlink (DL) scenario in which each BS transmits different data signals to each user. This scenario does not require phase synchronization between BSs and therefore has the same backhaul requirements as conventional Massive MIMO systems, where each user is preassigned to only one BS. The scenario where all BSs are phase synchronized to send the same data is also included for comparison. We solve a total transmit power minimization problem in order to observe how much power Massive MIMO BSs consume to provide the requested quality of service (QoS) of each user. A max-min fairness optimization is also solved to provide every user with the same maximum QoS regardless of the propagation conditions. The second part of the thesis considers a joint pilot design and uplink (UL) power control problem in multi-cell Massive MIMO. The main motivation for this work is that the pilot assignment and pilot power allocation is momentous in Massive MIMO since the BSs are supposed to construct linear detection and precoding vectors from the channel estimates. Pilot contamination between pilot-sharing users leads to more interference during data transmission. The pilot design is more difficult if the pilot signals are reused frequently in space, as in Massive MIMO, which leads to greater pilot contamination effects. Related works have only studied either the pilot assignment or the pilot power control, but not the joint optimization. Furthermore, the pilot assignment is usually formulated as a combinatorial problem leading to prohibitive computational complexity. Therefore, in the second part of this thesis, a new pilot design is proposed to overcome such challenges by treating the pilot signals as continuous optimization variables. We use those pilot signals to solve different max-min fairness optimization problems with either ideal hardware or hardware impairments. The third part of this thesis studies a two-layer decoding method that mitigates inter-cell interference in multi-cell Massive MIMO systems. In layer one, each BS estimates the channels to intra-cell users and uses the estimates for local decoding within the cell. This is followed by a second decoding layer where the BSs cooperate to mitigate inter-cell interference. An UL achievable SE expression is computed for arbitrary two-layer decoding schemes, while a closed form expression is obtained for correlated Rayleigh fading channels, maximum-ratio combining (MRC), and largescale fading decoding (LSFD) in the second layer. We formulate a sum SE maximization problem with both the data power and LSFD vectors as optimization variables. Since the problem is non-convex, we develop an algorithm based on the weighted minimum mean square error (MMSE) approach to obtain a stationary point with low computational complexity. Motivated by recent successes of deep learning in predicting the solution to an optimization problem with low runtime, the fourth part of this thesis investigates the use of deep learning for power control optimization in Massive MIMO. We formulate the joint data and pilot power optimization for maximum sum SE in multi-cell Massive MIMO systems, which is a non-convex problem. We propose a new optimization algorithm, inspired by the weighted MMSE approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime. The solution is suitable for online optimization. Finally, the fifth part of this thesis considers a large-scale distributed antenna system that serves the users by coherent joint transmission called Cell-free Massive MIMO. For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the DL, considering both the hardware consumed and transmit powers, where APs can be turned off to reduce the former part. Even though this is a nonconvex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program (SOCP). We also propose low-complexity algorithms that exploit group-sparsity or received power strength in the problem formulation.

The cellular network operators have witnessed significant growth in data traffic in the past few decades. This growth occurs due to the increases in the number of connected mobile devices, and further, the emerging mobile applications developed for rendering video-based on-demand services. As the frequency bandwidth for cellular communication is limited, significant effort was dedicated to improve the utilization of the available spectrum and increase the system performance via new technologies. For example, 3G and 4G networks were designed to facilitate high data traffic in cellular networks in past decades. Nevertheless, there is a necessity for new cellular network technologies to accommodate the ever-growing data traffic demand. 5G is behind the corner to deal with the tremendous data traffic requirements that will appear in cellular networks in the next decade. Massive MIMO (multiple-input-multi-output) is one of the backbone technologies in 5G networks. Massive MIMO originated from the concept of multi-user MIMO. It consists of base stations (BSs) implemented with a large number of antennas to increase the signal strengths via adaptive beamforming and concurrently serving many users on the same time-frequency blocks. As an outcome of using Massive MIMO technology, there is a notable enhancement of both sum spectral efficiency (SE) and energy efficiency (EE) in comparison with conventional MIMO based cellular networks. Resource allocation is an imperative factor to exploit the specified gains of Massive MIMO. It corresponds to properly allocating resources in the time, frequency, space, and power domains for cellular communication. Power control is one of the resource allocation methods to deliver high spectral and energy efficiency of Massive MIMO networks. Power control refers to a scheme that allocates transmit powers to the data transmitters such that the system maximizes some desirable performance metric. In the first part of this thesis, we investigate reusing the resources of a Massive MIMO system, for direct communication of some specific user pairs known as device-to-device (D2D) underlay communication. D2D underlay can conceivably increase the SE of traditional Massive MIMO systems by enabling more simultaneous transmissions on the same frequencies. Nevertheless, it adds additional mutual interference to the network. Consequently, power control is even more essential in this scenario in comparison with conventional Massive MIMO systems to limit the interference that is caused between the cellular network and the D2D communication, thereby enabling their coexistence. In this part, we propose a novel pilot transmission scheme for D2D users to limit the interference to the channel estimation phase of cellular users in comparison with the case of sharing pilot sequences for cellular and D2D users. We also introduce a novel pilot and data power control scheme for D2D underlaid Massive MIMO systems. This method aims at assuring that D2D communication enhances the SE of the network in comparison with conventional Massive MIMO systems. In the second part of this thesis, we propose a novel power control approach for multi-cell Massive MIMO systems. The new power control approach solves the scalability issue of two well-known power control schemes frequently used in the Massive MIMO literature, which are based on the network-wide max-min and proportional fairness performance metrics. We first explain the scalability issue of these existing approaches. Additionally, we provide mathematical proof for the scalability of our proposed method. Our scheme aims at maximizing the geometric mean of the per-cell max-min SE. To solve this optimization problem, we prove that it can be rewritten in a convex form and then be solved using standard optimization solvers.

The development of information and communication technologies (ICT) provides the means for reaching global connectivity that can help humanity progress and prosper. This comes with high demands on data traffic and number of connected devices which are rapidly growing and need to be met by technological development. Massive MIMO, where MIMO stands for multiple-input multiple-output, is a fundamental component of the 5G wireless communication standard for its ability to provide high spectral and energy efficiency, SE and EE, respectively. The key feature of this technology is the use of a large number of antennas at the base stations (BSs) to spatially multiplex several user equipments (UEs). In the development of new technologies like Massive MIMO, many design alternatives need to be evaluated and compared in order to find the best operating point with a preferable tradeoff between low cost and complexity. In this thesis, two alternative designs for signal processing and hardware in Massive MIMO are studied and compared with the baseline operation in terms of SE, EE, and power consumption. The first design is called superimposed pilot (SP) transmission and is based on superimposing pilot and data symbols to eliminate the need to reserve dedicated time-frequency resources for pilots. This allows more data to be transmitted and supports longer pilot sequences that, in turn, reduce pilot contamination. The second design is mixed analog-to-digital converters (ADCs) and it aims at balancing the SE performance and the power consumption cost by allowing different ADC bit resolutions across the BS antennas. The results show that the Massive MIMO baseline, when properly optimized, is the preferred choice in standard deployments and propagation conditions. However, the SP alternative design can increase the SE compared to the baseline by using the Massive-MIMO iterative channel estimation and decoding (MICED) algorithm proposed in this dissertation. In particular, the SE gains are found in cases with high mobility, high carrier frequencies, or high number of spatially multiplexed UEs. For the mixed-ADCs alternative design, improvements in the SE and EE compared to the Massive MIMO baseline can be achieved in cases with distributed BS antennas where interference suppression techniques are used. El desarrollo en tecnologías de información y comunicación (en inglés, ICT) provee los medios para alcanzar la conectividad global que puede ayudar a la humanidad a progresar y prosperar. Esto implica que el avance tecnológico debe satisfacer la alta demanda de tráfico de data y número de equipos conectados que se encuentra en rápido crecimiento. La tecnología de múltiple-entrada múltiple-salida masiva, en inglés Massive MIMO, se considera una pieza fundamental de la quinta generación de comunicaciones inalámbricas (5G) debido a su capacidad de proveer una alta eficiencia espectral y energética (en inglés, SE y EE, respectivamente). Esta tecnología está caracterizada fundamentalmente por el uso de un alto número de antenas en la estación base (en inglés, BS) para multiplexar a varios usuarios en el espacio. En el desarrollo de nuevas tecnologías como Massive MIMO, muchas alternativas de diseño necesitan ser evaluadas y comparadas para encontrar el mejor punto de operación con un balance conveniente entre complejidad y bajo costo. En esta tesis, dos alternativas de diseño para el procesamiento de señales y el hardware de Massive MIMO son estudiadas y comparadas con la operación del diseño base en términos de eficiencia espectral, eficiencia energética y consumo de potencia. El primer diseño se denomina transmisión de pilotos superpuestos (en inglés, SP) y está basado en la superposición de señales piloto y de datos para eliminar la necesidad de asignar recursos dedicados a señales pilotos. Además, la transmisión de pilotos superpuestos permite reducir la interferencia que surge a raíz de reusar las señales pilotos en distintas celdas, este efecto se denomina contaminación de pilotos (en inglés pilot contamination). El segundo diseño se denomina conversores analógico-adigital (en inglés, ADC) mixtos (en inglés, mixed-ADCs) y se basa en permitir distintas resoluciones de bit en los conversores analógico-a-digital de las antenas en la estación base. Este diseño permite que la resolución de los conversores analógico-a-digital se adapte a las condiciones de propagación de las señales para balancear los beneficios en eficiencia espectral con el costo de potencia consumida. Los resultados muestran que el diseño base de Massive MIMO, cuando esta optimizado de manera apropiada, es la opción preferida en despliegues y condiciones de propagación estándares. Sin embargo, la transmisión de pilotos superpuestos puede incrementar la eficiencia espectral en comparación al diseño base cuando se combina con el método iterativo para la estimación de canal y decodificación en Massive MIMO propuesto en esta tesis (en inglés, MICED). En particular, las ganancias en eficiencia espectral son obtenidas en escenarios con alta movilidad de usuarios, alta frecuencia portadora, o alto número de usuarios multiplexados en el espacio. Con respecto al diseño alternativo de conversores analógico-a-digital mixtos, la eficiencia espectral y energética pueden ser incrementadas en comparación al diseño base cuando las antenas de la estación base están distribuidas en el espacio y técnicas para suprimir interferencia entre usuarios son usadas. Die Entwicklung der Informations- und Kommunikationstechnologien (ICT) bietet die Möglichkeit eine globale Konnektivität zu erreichen, die Fortschritt und Wohlstand fördern kann. Dies bedeutet zugleich, dass der steigende Datenverkehr und die wachsende Anzahl verbundener Geräte eines entsprechenden technologischen Fortschritts bedarf. Massive MIMO, wobei MIMO für multiple-input multiple-output steht, ist eine fundamentale Komponente des drahtlosen 5G Kommunikationsstandards, da sie eine hohe spektrale Effizienz (SE) und Energieeffizienz bietet (EE). Die Hauptkomponente dieser Technologie ist die Nutzung einer großen Anzahl an Antennen auf Seiten der Basisstationen (BSs) um mehrere Nutzer zu bedienen, die ihre Signale zur selben Zeit auf derselben Frequenz senden während sie in der räumlichen Domäne getrennt sind (spatial multiplexing). In der Entwicklung neuer Technologien wie Massive MIMO müssen viele Designalternativen evaluiert und verglichen werden um den optimalen Betriebspunkt im Sinne eines sinnvollen Gleichgewichts zwischen Kosteneffizienz und Komplexität zu finden. In dieser Doktorarbeit werden zwei alternative Designs für Signalverarbeitung und Hardware in Massive MIMO Systemen untersucht und in Bezug auf spektrale Effizienz, Energieeffizienz und Stromverbrauch mit dem Massive MIMO Basisdesign verglichen. Das erste Design heißt überlagerte Pilotton Übertragung (superimposed pilot, SP) und basiert auf der Überlagerung von Pilotton und Datensignal, damit nicht mehr die Notwendigkeit besteht bestimmte Ressourcen für Pilottöne zu reservieren. Dies ermöglicht die Übertragung größerer Datenmengen und reduziert die Interferenz, die aus der wiederholten Nutzung der Pilottöne in verschiedenen Zellen resultiert (pilot contamination). Das zweite Design nennt sich gemischte analog zu digital Konverter (mixed analog-to-digital converters, ADCs) und erlaubt es einen Kompromiss zwischen hoher spektraler Effizienz und niedrigem Stromverbrauch zu finden. Dies geschieht indem die Bit Auflösung an jeder BS Antenne an die Ausbreitungsbedingungen der Signale angepasst wird. Die Resultate zeigen, dass das Massive MIMO Basisdesign, wenn es richtig optimiert ist, bei Standardeinsätzen und unter normalen Ausbreitungsbedingungen, die bevorzugte Wahl ist. Das alternative SP Design kann jedoch die spektrale Effizienz im Vergleich zum Basisdesign durch die Nutzung des in dieser Dissertation vorgeschlagenen Massive MIMO iterativen Kanalschätzungs- und Dekodierungsalgorithmus (MICED) erhöhen. Die verbesserte spektrale Effizienz findet sich insbesondere in Fällen hoher Nutzermobilität, hoher Frequenzen oder hoher Anzahl an gleichzeitig bedienter Nutzer. Das gemischte analog zu digital Konverter Design ermöglicht in Fällen verteilter Basisstationen bei denen Interferenz unterdrückende Techniken genutzt werden eine verbesserte spektrale Effizienz und Energieeffizienz. Utvecklingen av informations- och kommunikationsteknik (IKT) gör det möjligt för människor från hela världen att kopplas samman och utbyta kunskaper. Ju mer vi vet och förstår om varandra, desto större är chansen att mänskligheten kan uppnå globala utvecklingsmål och välstånd. IKT-utvecklingen är associerad med höga krav på datatakter och antal uppkopplade enheter. Dessa krav ökar ständigt och måste mötas med teknologisk utveckling. Massiv MIMO, där MIMO står för multiple-input multiple-output, är flerantennteknik och en grundsten i nästa generations trådlösa kommunikationssystem. Huvudanledningen till detta är att tekniken kan förbättra spektraleffektiviteten (SE), vilket är ett mått på hur väl vi kan kommunicera data över begränsade radiofrekvensresurser. Tekniken förbättrar även energieffektiviteten (EE), vilket är ett mått på hur effektivt tekniken använder energi till att kommunicera data. Massiv MIMO bygger på användandet av ett stort antal av antenner på basstationerna för att kommunicera med ett flertal användare samtidigt och på samma frekvensresurser. Detta möjliggörs genom ”rumslig multiplexing” vilket betyder att signaler från användare på olika platser kan separeras på basstationen i den rumsliga domänen. Denna separering kräver att basstationen först mäter egenskaperna hos signaler som kommer från de olika användarnas positioner. När en ny teknik, såsom Massiv MIMO, utvecklas är det viktigt att olika alternativa designer utvärderas och jämförs för att identifiera den bästa varianten. Detta kan exempelvis vara den variant som uppnår en viss balans mellan hög kommunikationsprestanda och låg kostnad. I denna avhandling utvärderas två alternativa sätt att designa signalbehandlingen och hårdvaran i Massiv MIMO. Dessa jämförs med konventionell Massiv MIMO i termer av SE, EE och effektförbrukning. Den första alternativa designen kallas överlagrade piloter och bygger på att kända pilotsignaler och okända datasignaler skickas samtidigt från användarna, istället för efter varandra. Pilotsignalerna används för att mäta upp de trådlösa kanalerna som signalerna färdas över medan datasignalerna innehåller den information som ska kommuniceras. Genom att överlagra pilotsignalerna så behövs inga dedikerade radioresurser för piloter och därmed finns det mer resurser för datasändning. Dessutom minskar överlagrandet de störningar som kommer från andra användare som använder samma pilot, vilket kallas pilotkontaminering. Den andra alternativa designen kallas mixade analog-till-digital (AD) omvandlare. En AD-omvandlare är en krets som behövs på varje antenn för att omvandla analoga radiosignaler till digitala signaler som kan processas i en dator. Bitupplösningen i AD-omvandlaren avgör hur många nivåer som kan användas för att representera den analoga signalen. Ju högre bitupplösning desto fler nivåer och därmed en mer noggrann representation, men detta leder även till högre beräkningskomplexitet och effektförbrukning. Mixade AD-omvandlare försöker balansera mellan hög prestanda och låg komplexitet genom att optimera bitupplösningen på varje antenn i ett Massiv MIMO system. Avhandlingens resultat visar att det går att öka SE i Massiv MIMO genom att använda överlagrade piloter, ifall den föreslagna algoritmen MICED (Massive-MIMO iterative channel estimation and decoding) används. Förbättringarna är särskilt stora när användarna har hög mobilitet, när en hög bärfrekvens används eller när antalet rumsligt multiplexade användare är högt. När det gäller mixade AD-omvandlare så kan små förbättringar i SE uppnås, jämfört med konventionell Massiv MIMO, när bitupplösningen i AD-omvandlarna optimeras under förutsättning att signalstyrkan varierar mellan basstationens antenner. Sammanfattningsvis så kan de alternativa designerna av Massiv MIMO som studerats i avhandlingen ge små prestandaförbättringar jämfört med konventionella metoder. Men trots detta så kan de konventionella metoderna uppnå en bra avvägning mellan hög prestanda och låg komplexitet ifall de optimeras väl.

The fifth generation of mobile communication systems (5G) promises unprecedented levels of connectivity and quality of service (QoS) to satisfy the incessant growth in the number of mobile smart devices and the huge increase in data demand. One of the primary ways 5G network technology will be accomplished is through network densification, namely increasing the number of antennas per site and deploying smaller and smaller cells. Massive MIMO, where MIMO stands for multiple-input multiple-output, is widely expected to be a key enabler of 5G. This technology leverages an aggressive spatial multiplexing, from using a large number of transmitting/receiving antennas, to multiply the capacity of a wireless channel. A massive MIMO base station (BS) is equipped with a large number of antennas, much larger than the number of active users. The users are coherently served by all the antennas, in the same time-frequency resources but separated in the spatial domain by receiving very directive signals. By supporting such a highly spatially-focused transmission (precoding), massive MIMO provides higher spectral and energy efficiency, and reduces the inter-cell interference compared to existing mobile systems. The inter-cell interference is however becoming the major bottleneck as we densify the networks. It cannot be removed as long as we rely on a network-centric implementation, since the inter-cell interference concept is inherent to the cellular paradigm. Cell-free massive MIMO refers to a massive MIMO system where the BS antennas, herein referred to as access points (APs), are geographically spread out. The APs are connected, through a fronthaul network, to a central processing unit (CPU) which is responsible for coordinating the coherent joint transmission. Such a distributed architecture provides additional macro-diversity, and the co-processing at multiple APs entirely suppresses the inter-cell interference. Each user is surrounded by serving APs and experiences no cell boundaries. This user-centric approach, combined with the system scalability that characterizes the massive MIMO design, constitutes a paradigm shift compared to the conventional centralized and distributed wireless communication systems. On the other hand, such a distributed system requires higher capacity of back/front-haul connections, and the signal co-processing increases the signaling overhead. In this thesis, we focus on some signal processing aspects of cell-free massive MIMO. More specifically, we firstly investigate if the downlink channel estimation, via downlink pilots, brings gains to cell-free massive MIMO or the statistical channel state information (CSI) knowledge at the users is enough to reliably perform data decoding, as in conventional co-located massive MIMO. Allocating downlink pilots is costly resource-wise, thus we also propose resource saving-oriented strategies for downlink pilot assignment. Secondly, we study further fully distributed and scalable precoding schemes in order to outperform cell-free massive MIMO in its canonical form, which consists in single-antenna APs implementing conjugate beamforming (also known as maximum ratio transmission).

Massive MIMO (Multiple-Input--Multiple-Output) is a cellular-network technology in which the base station is equipped with a large number of antennas and aims to serve several different users simultaneously, on the same frequency resource through spatial multiplexing. This is made possible by employing efficient beamforming, based on channel estimates acquired from uplink reference signals, where the base station can transmit the signals in such a way that they add up constructively at the users and destructively elsewhere. The multiplexing together with the array gain from the beamforming can increase the spectral efficiency over contemporary systems. One challenge of practical importance is how to transmit data in the downlink when no channel state information is available. When a user initially joins the network, prior to transmitting uplink reference signals that enable beamforming, it needs system information---instructions on how to properly function within the network. It is transmission of system information that is the main focus of this thesis. In particular, the thesis analyzes how the reliability of the transmission of system information depends on the available amount of diversity. It is shown how downlink reference signals, space-time block codes, and power allocation can be used to improve the reliability of this transmission. In order to estimate the uplink and downlink channels from uplink reference signals, which is imperative to ensure scalability in the number of base station antennas, massive MIMO relies on channel reciprocity. This thesis shows that the principles of channel reciprocity can also be exploited by a jammer, a malicious transmitter, aiming to disrupt legitimate communication between two single-antenna devices. A heuristic scheme is proposed in which the jammer estimates the channel to a target device blindly, without any knowledge of the transmitted legitimate signals, and subsequently beamforms noise towards the target. Under the same power constraint, the proposed jammer can disrupt the legitimate link more effectively than a conventional omnidirectional jammer in many cases. Massiv MIMO (eng: Multiple-Input--Multiple-Output) är en teknologi inom cellulär kommunikation som förutspås ha en betydande roll i framtida kommunikationssystem på grund av de många fördelar som denna teknologi medför. Massiv MIMO innebär att basstationen har ett stort antal antenner där varje antenn kan styras individuellt. De många antennerna gör att basstationen kan rikta de elektromagnetiska signalerna på ett sådant sätt att de förstärks på positioner där användarna befinner sig men släcks ut i övrigt. Detta i sin tur innebär att flera användare kan betjänas samtidigt, på samma frekvensband utan att de stör varandra. Detta medför att massiv MIMO kan erbjuda en högre datatakt än nutida cellulära kommunikationssystem. För att kunna rikta signalerna på ett effektivt sätt måste basstationen känna till kanalen, eller utbredningsmiljön, mellan sig själv och de användare som betjänas. När en användare precis kommer in i systemet vet basstationen inte var användaren befinner sig, men måste likväl tillgodose användaren med information om hur systemet fungerar. Nu måste alltså basstationen kommunicera med användaren, utan möjligheten att kunna rikta signalen på ett effektivt sätt. Det är detta problem som vi i huvudsak studerar i denna avhandling: hur man kan utnyttja de många antennerna på basstationen för att skicka information till användarna utan någon kanalkännedom. Vi studerar även hur en gruppantenn med många antenner, baserad på samma teknologi som massiv MIMO, kan användas som en störsändare. Störsändarens mål är att hindra kommunikationen mellan två enheter på ett effektivt sätt. En störsändare med ett stort antal antenner kan, utan någon kännedom av vad de två enheterna skickar, i många fall prestera bättre än en konventionell störsändare på grund av att störsignalen kan riktas mot en specifik enhet.

The development of information and communication technologies (ICT) provides the means for reaching global connectivity that can help humanity progress and prosper. This comes with high demands on data traffic and number of connected devices which are rapidly growing and need to be met by technological development. Massive MIMO, where MIMO stands for multiple-input multiple-output, is envisioned as a fundamental component of next generation wireless communications for its ability to provide high spectral and energy efficiency, SE and EE, respectively. The key feature of this technology is the use of a large number of antennas at the base stations (BS) to spatially multiplex several user equipments (UEs). In the development of new technologies like Massive MIMO, many design alternatives need to be evaluated and compared in order to find the best operating point with a preferable tradeoff between high performance and low cost. In this thesis, two alternative designs for signal processing and hardware in Massive MIMO are studied and compared with the baseline operation in terms of SE, EE, and power consumption. The first design is called superimposed pilot (SP) transmission and is based on superimposing pilot and data symbols to remove the overhead from pilot transmission and reduce pilot contamination. The second design is mixed analog-to-digital converters (ADCs) and it aims at balancing high performance and low complexity by allowing different ADC bit resolutions across the BS antennas. The results show that the baseline operation of Massive MIMO, properly optimized, is the preferred choice. However, SP and mixed ADCs still have room for improvement and further study is needed to ascertain the full capabilities of these alternative designs.

This book begins with a historical overview of the evolution of mobile technologies and addresses two key questions: why do we need 6G? and what will 6G be? The remaining chapters of this book are organized into three parts: Part I covers the foundation of an end-to-end 6G system by presenting 6G vision, driving forces, key performance indicators, and societal requirements on digital inclusion, sustainability, and intelligence. Part II presents key radio technology components for the 6G communications to deliver extreme performance, including new radio access technologies at high frequencies, joint communications and sensing, AI-driven air interface, among others. Part III describes key enablers for intelligent 6G networking, including network disaggregation, edge computing, data-driven management and orchestration, network security and trustworthiness, among others. This book is relevant to researchers, professionals, and academics working in 5G/6G and beyond.