Joint Communication and Sensing (JCAS) technology enables the sharing of infrastructure, resources, and signals between communication and sensing. However, studying the performance and algorithms using appropriate target reflectivity models for detection poses a significant challenge. Moreover, the increasing demand for efficient sensing systems in large-scale environments necessitates the study of distributed sensing for handling extensive data collection and processing. This study investigates the impact of target mobility on the choice between the Swerling-I and Swerling-II models for target reflectivity and proposes a brief method for reflectivity models in multi-static sensing. This method constructed a dedicated decorrelation area for a single radar detector using its decorrelation angle. Multiple radar system constructs an intersection of these areas. For targets expected to remain in this area, the Swerling-I model is preferred, while for targets likely to move to the outside intersection, the Swerling-II model is more suitable. Additionally, this thesis proposes and derives the test statistics for the distributed sensing in JCAS cell-free massive MIMO (multiple-input multiple-output) systems, where only the statistical distribution of transmitted signals is known at the receiver access points for the sensing purpose. This thesis compares the sensing performance of the proposed distributed processing with the centralized processing. Moreover, the results of a power allocation algorithm that maximizes sensing performance are compared against a baseline algorithm that minimizes total power consumption. In terms of sensing performance via guaranteeing the quality of service of the communication, the results indicate that the sensing algorithm consistently outperforms the power-minimizing algorithm, regardless of the selected reflectivity model. Furthermore, the Swerling-II model performs relatively worse when the reflectivity of the target is low, but exhibits improved performance on a relatively high reflectivity target. Regarding distributed sensing, its implementation may lead to a deterioration in sensing performance. However, the results show that distributed sensing can approach the performance of centralized sensing when the target has high reflectivity. The major advantage of distributed sensing is the reduced fronthaul signaling load in a JCAS cell-free massive MIMO system. / Joint Communication and Sensing (JCAS) teknologi möjliggör delning av infrastruktur, resurser och signaler mellan kommunikation och sensorik. Studier av prestanda och algoritmer med lämpliga modeller för detektering av målets reflektivitet utgör emellertid en betydande utmaning. Dessutom kräver den ökande efterfrågan på effektiva sensorsystem i storskaliga miljöer studier av distribuerad sensorik för att hantera omfattande datainsamling och -bearbetning. Detta studie undersöker påverkan av målets rörlighet på valet mellan SwerlingI och Swerling-II modellerna för målets reflektivitet och föreslår en kort metod för reflektivitetsmodeller i multi-statisk avkänning. Denna metod konstruerar ett dedikerat dekorrelationsområde för en enskild radardetektor med hjälp av dess dekorrelationsvinkel. Ett flertal radarsystem konstruerar en skärningspunkt av dessa områden. För mål som förväntas förbli i detta område föredras Swerling-I-modellen, medan för mål som troligen rör sig till den yttre skärningspunkten är Swerling-II-modellen mer lämplig. Dessutom föreslår och härleder denna avhandling teststatistik för distribuerad avkänning i JCAS cellfri massiv MIMO (multiple-input multiple-output) system, där endast den statistiska fördelningen av överförda signaler är känd vid mottagarens åtkomstpunkter för avkänningsändamål. Denna avhandling jämför avkänningsprestanda för föreslagen distribuerad bearbetning med centraliserad bearbetning. Dessutom jämförs resultaten av en effekttilldelningsalgoritm som maximerar avkänningsprestanda mot en baslinjealgoritm som minimerar total effektförbrukning. När det gäller avkänningsprestanda genom att garantera kommunikationens tjänstekvalitet indikerar resultaten att avkänningsalgoritmen konsekvent presterar bättre än effektminimeringsalgoritmen, oavsett vald reflektivitetsmodell. Dessutom presterar Swerling-II-modellen relativt sämre när målets reflektivitet är låg, men uppvisar förbättrad prestanda på ett relativt högreflekterande mål. När det gäller distribuerad avkänning kan dess implementering leda till försämrad avkänningsprestanda. Resultaten visar dock att distribuerad avkänning kan närma sig prestandan hos centraliserad avkänning när målet har hög reflektivitet. Den största fördelen med distribuerad avkänning är den minskade signalbelastningen i en JCAS cellfri massiv MIMO-system.

[pt] Sistemas de múltiplas antenas livres de células surgiram recentemente como uma combinação de MIMO massivo, sistemas de antenas distribuídas (DAS) e network MIMO. Esta dissertação explora o downlink deste cenário com pontos de acesso (PAs) de uma ou múltiplas antenas e considerando conhecimento perfeito e imperfeito do canal. São desenvolvidos esquemas que combinam pré-codificação, alocação de potência e seleção de PAs (SPA). Para começar, duas estratégias de SPA foram investigadas, uma baseada em busca exaustiva (BE-SPA) e a outra em coeficientes de desvanecimento de larga escala (LE-SPA), com o intuito de reduzir a complexidade das redes livres de células. Subsequentemente, apresentamos duas técnicas iterativas de pré-codificação, todas seguindo o critério Minimum Mean-Square Error (MMSE), combinadas à restrição de potência total. A primeira nós chamamos de MMSE, com restrição de potência total. Nós também incorporamos robustez ao método desenvolvido chamado RMMSE, um pré-codificador robusto com restrição de potência total. Como terceiro elemento da configuração proposta, esquemas de alocação de potência foram desenvolvidos, com abordagens ótimas, adaptativas e uniformes. Um algoritmo de alocação de potência ótima (APO) é apresentado, baseado na maximização da mínima Signal-to-Interference-plus-Noise Ratio (SINR). A solução adaptativa (APA) é caracterizada pelo gradiente estocástico (GE) do mean-square error (MSE) e a alternativa uniforme (UPA) propõe a equalização de todos os coeficientes de potência. Todas as configurações devem respeitar a restrição de potência por antena, imposta pelo sistema. Uma análise de soma das taxas é feita, para todas as técnicas estudadas e o custo computacional de cada uma delas é calculado. Resultados numéricos provam que as técnicas propostas têm performance superior à pré-codificadores Conjugate Beamforming (CB) e Zero-Forcing (ZF), ambos com alocação de potência uniforme e ótima, na forma de taxa de erro de bit (BER), soma das taxas e mínima SINR. Além disso, os resultados atestam que o desempenho pode ser mantido e até melhorado com a aplicação de SPA. / [en] Cell-Free Massive multiple-input multiple-output (MIMO) systems have emerged in recent years as a combination of massive MIMO, distributed antenna systems (DAS) and network MIMO. This thesis explores the downlink channel of such scenario with single and multiple-antenna access points (APs) and takes into account both perfect and imperfect channel state information (CSI). We propose transmit processing schemes that combine precoding, power allocation and AP selection (APS). To begin with, two APS strategies have been investigated, one based on exhaustive search (ES-APS) and the other on the large-scale fading coefficients (LSAPS), in order to reduce the complexity of cell-free networks. Subsequently, we present two iterative precoding techniques following the minimum meansquare error (MMSE) criterion with total power constraint. The first we call MMSE, with total power constraint. We also incorporate robustness in the developed method, called RMMSE, a robust precoder with total power constraint. As the third element of the proposed schemes, power allocation techniques are developed, with optimal, adaptive and uniform approaches. An optimal power allocation (OPA) algorithm is presented based on the maximization of the minimum signal-to-interference-plus-noise ratio (SINR). The adaptive solution (APA) is characterized by the stochastic gradient of the mean-square error (MSE) and the uniform alternative (UPA) proposes to equalize all power coefficients. All configurations must fulfil an antenna power constraint, imposed by the system. A sum-rate analysis is carried out for all studied techniques and the computational cost of each one is calculated. Numerical results prove that the proposed techniques outperform existing conjugate beamforming (CB) and zero-forcing (ZF) precoders, both with uniform and optimal power allocation, in terms of bit error rate (BER), sum-rate and minimum SINR. Furthermore, we also attest that performance can be maintained or even improved in the presence of APS.

As a novel beyond fifth-generation (5G) concept, cell-free massive MIMO (multiple-input multiple-output) recently has become a promising physical-layer technology where an enormous number of distributed access points (APs), coordinated by a central processing unit (CPU), cooperate to coherently serve a large number of user equipments (UEs) in the same time/frequency resource. However, denser AP deployment in cell-free networks as well as an exponentially growing number of mobile UEs lead to higher power consumption. What is more, similar to conventional cellular networks, cell-free massive MIMO networks are dimensioned to provide the required quality of service (QoS) to the UEs under heavy traffic load conditions, and thus they might be underutilized during low traffic load periods, leading to inefficient use of both spectral and energy resources. Aiming at the implementation of energy-efficient cell-free networks, several approaches have been proposed in the literature, which consider different AP switch ON/OFF (ASO) strategies for power minimization. Different from prior works, this thesis focuses on additional factors other than ASO that have an adverse effect not only on total power consumption but also on implementation complexity and operation cost. For instance, too frequent ON/OFF switching in an AP can lead to tapering off the potential power saving of ASO by incurring extra power consumption due to excessive switching. Indeed, frequent switching of APs might also result in thermal fatigue and serious lifetime degeneration. Moreover, time variations in the AP-UE association in favor of energy saving in a dynamic network bring additional signaling and implementation complexity. Thus, in the first part of the thesis, we propose a multi-objective optimization problem that aims to minimize the total power consumption together with AP switching and AP-UE association variations in comparison to the state of the network in the previous state. The proposed problem is cast in mixed integer quadratic programming form and solved optimally. Our simulation results show that by limiting AP switching (node switching) and AP-UE association reformation switching (link switching), the total power consumption from APs only slightly increases but the number of average switching drops significantly regardless of node switching or link switching. It achieves a good balance on the trade-off between radio power consumption and the side effects excessive switching will bring. In the second part of the thesis, we consider a larger cell-free massive MIMO network by dividing the total area into disjoint network-centric clusters, where the APs in each cluster are connected to a separate CPU. In each cluster, cell-free joint transmission is locally implemented to achieve a scalable network implementation. Motivated by the outcomes of the first part, we reshape our dynamic network simulator to keep the active APs for a given spatial traffic pattern the same as long as the mean arrival rates of the UEs are constant. Moreover, the initially formed AP-UE association for a particular UE is not allowed to change. In that way, we make the number of node and link switching zero throughout the considered time interval. For this dynamic network, we propose a deep reinforcement learning (DRL) framework that learns the policy of maximizing long-term energy efficiency (EE) for a given spatially-varying traffic density. The active AP density of each network-centric cluster and the boundaries of the clusters are learned by the trained agent to maximize the EE. The DRL algorithm is shown to learn a non-trivial joint cluster geometry and AP density with at least 7% improvement in terms of EE compared to the heuristically-developed benchmarks. / Som ett nytt koncept bortom den femte generationen (5G) har cellfri massiv MIMO (multiple input multiple output) nyligen blivit en lovande teknik för det fysiska lagret där ett enormt antal distribuerade åtkomstpunkter (AP), som samordnas av en central processorenhet (CPU), samarbetar för att på ett sammanhängande sätt betjäna ett stort antal användarutrustningar (UE) i samma tids- och frekvensresurs. En tätare utplacering av AP:er i cellfria nät samt ett exponentiellt växande antal mobila användare leder dock till högre energiförbrukning. Dessutom är cellfria massiva MIMO-nät, i likhet med konventionella cellulära nät, dimensionerade för att ge den erforderliga tjänstekvaliteten (QoS) till enheterna under förhållanden med hög trafikbelastning, och därför kan de vara underutnyttjade under perioder med låg trafikbelastning, vilket leder till ineffektiv användning av både spektral- och energiresurser. För att genomföra energieffektiva cellfria nät har flera metoder föreslagits i litteraturen, där olika ASO-strategier (AP switch ON/OFF) beaktas för att minimera energiförbrukningen. Till skillnad från tidigare arbeten fokuserar den här avhandlingen på andra faktorer än ASO som har en negativ effekt inte bara på den totala energiförbrukningen utan också på komplexiteten i genomförandet och driftskostnaden. Till exempel kan alltför frekventa ON/OFF-omkopplingar i en AP leda till att ASO:s potentiella energibesparingar avtar genom extra energiförbrukning på grund av överdriven omkoppling. Frekventa omkopplingar av AP:er kan också leda till termisk trötthet och allvarlig försämring av livslängden. Dessutom medför tidsvariationer i AP-UE-associationen till förmån för energibesparingar i ett dynamiskt nät ytterligare signalering och komplexitet i genomförandet. I den första delen av avhandlingen föreslår vi därför ett optimeringsproblem med flera mål som syftar till att minimera den totala energiförbrukningen tillsammans med växling av AP och variationer i AP-UE-associationen i jämförelse med nätets tillstånd i det föregående läget. Det föreslagna problemet är en blandad helhetsmässig kvadratisk programmering och löses optimalt. Våra simuleringsresultat visar att genom att begränsa växling av AP (node switching) och växling av AP-UE-association (link switching) ökar den totala energiförbrukningen från AP:erna endast något, men antalet genomsnittliga växlingar ökar, oavsett om det rör sig om node switching eller link switching. Det ger en bra balans mellan radiokraftförbrukning och de bieffekter som överdriven växling medför. I den andra delen av avhandlingen tar vi hänsyn till ett större cellfritt massivt MIMO-nätverk genom att dela upp det totala området i disjunkta nätverkscentrerade kluster, där AP:erna i varje kluster är anslutna till en separat CPU. I varje kluster genomförs cellfri gemensam överföring lokalt för att uppnå en skalbar nätverksimplementering. Motiverat av resultaten i den första delen omformar vi vår dynamiska nätverkssimulator så att de aktiva AP:erna för ett givet rumsligt trafikmönster är desamma så länge som den genomsnittliga ankomsthastigheten för de enskilda enheterna är konstant. Dessutom tillåts inte den ursprungligen bildade AP-UE-associationen för en viss användare att förändras. På så sätt gör vi antalet nod- och länkbyten till noll under hela det aktuella tidsintervallet. För detta dynamiska nätverk föreslår vi ett ramverk för djup förstärkningsinlärning (DRL) som lär sig en strategi för att maximera energieffektiviteten på lång sikt för en given rumsligt varierande trafiktäthet. Den aktiva AP-tätheten i varje nätverkscentrerat kluster och klustrens gränser lärs av den utbildade agenten för att maximera EE. Det visas att DRL-algoritmen lär sig en icke-trivial gemensam klustergeometri och AP-täthet med minst 7% förbättring av EE jämfört med de heuristiskt utvecklade riktmärkena.

Future mobile networks are anticipated to not only enhance communication performance but also facilitate new sensing-based applications. This highlights the essential role of integrated sensing and communication (ISAC) in sixth-generation (6G) and beyond mobile networks. The seamless integration of sensing and communication poses challenges in deployment and resource allocation. Cell-free massive multiple-input multiple-output (MIMO) networks, characterized by multiple distributed access points, offer a promising infrastructure for ISAC implementation. However, the effective realization of ISAC necessitates joint design and resource allocation optimization. In this thesis, we study ISAC within cell-free massive MIMO systems, with a particular emphasis on developing power allocation algorithms under various scenarios. In this thesis, we explore two scenarios: utilizing existing communication signals and incorporating additional sensing signals. We propose power allocation algorithms aiming to maximize the sensing performance while meeting communication and power constraints. In addition, we develop two maximum a posteriori ratio test (MAPRT) target detectors under clutter-free and cluttered scenarios. Results indicate that employing additional sensing signals enhances sensing performance, particularly in scenarios where the target has low reflectivity. Moreover, although the clutter-aware detector requires more advanced processing, it leads to better sensing performance. Furthermore, we introduced sensing spectral efficiency (SE) to measure the effect of resource block utilization, highlighting the integration advantages of ISAC over orthogonal resource sharing approaches.  In the next part of the thesis, we study the energy efficiency aspects of ISAC in cell-free massive MIMO systems with ultra-reliable low-latency communications (URLLC) users. We propose a power allocation algorithm aiming to maximize energy efficiency of the system while meeting communication and sensing requirements. We conduct a comparative analysis between the proposed power allocation algorithms and a URLLC-only approach which takes into account only URLLC and power requirements. The results reveal that while the URLLC-only algorithm excels in energy efficiency, it is not able to support sensing requirements.   Moreover, we study the impact of ISAC on end-to-end (including radio and processing) energy consumption. Particularly, we present giga-operations per second (GOPS) analysis for both communication and sensing tasks. Two optimization problems are formulated and solved to minimize transmission and end-to-end energy through blocklength and power optimization. Results indicate that while end-to-end energy minimization offers substantial energy savings, its efficacy diminishes with sensing integration due to processing energy requirements. / Framtida mobila nätverk förväntas inte bara förbättra kommunikations-prestanda utan även mögliggöra nya applikationer baserade på sensorer. Dettaunderstryker den avgörande rollen för Integrerad avkänning och kommunika-tion (ISAC) i sjätte generationens (6G) och efterföljande mobila nätverk. Densömlösa integrationen av sensorer och kommunikation medför utmaningar iutrullning och resursallokering. Cellfria massiva flerantennsystem (MIMO-nätverk), kännetecknade av flera distribuerade åtkomstpunkter, erbjuder enlovande infrastruktur för implementering av ISAC. Dock kräver den effektivarealiseringen av ISAC samverkande design och optimering av resursallokering.I denna avhandling studerar vi ISAC inom cellfria massiva MIMO-system,med särskild tonvikt på att utveckla effektallokeringsalgoritmer under olikascenarier.Vi utforskar två scenarier: att utnyttja befintliga kommunikationssignaleroch att inkludera ytterligare sensorssignaler. Vi föreslår effektallokeringsalgo-ritmer med målet att maximera sensorsprestandan samtidigt som kommunika-tions och effektbegränsningar uppfylls. Dessutom utvecklar vi två detektorerbaserade på maximum a posteriori ratio test (MAPRT) under störningsfriaoch störda scenarier. Resultaten visar att användning av ytterligare sensors-signaler förbättrar sensorsprestandan, särskilt i scenarier där målet har lågreflektivitet. Dessutom, även om den störkänsliga detektorn kräver mer avan-cerad bearbetning, leder den till bättre sensorsprestanda. Vidare introducerarvi sensorerspektral effektivitet (SE) för att mäta effekten av resursblocksan-vändning och framhäva integrationsfördelarna med ISAC över ortogonala re-sursdelningsmetoder.I den andra delen av avhandlingen studerar vi energieffektivitetsaspek-terna av ISAC i cellfria massiva MIMO-system med användare med ultra-tillförlitlig låg-latens (URLLC) kommunikation. Vi föreslår en effektalloke-ringsalgoritm med syfte att maximera systemets energieffektivitet samtidigtsom kommunikations- och sensorskraven uppfylls. Vi utför en jämförande ana-lys mellan de föreslagna effektallokeringsalgoritmerna och ett URLLC-ensamttillvägagångssätt som tar hänsyn enbart till URLLC- och effektkrav. Resul-taten avslöjar att medan URLLC-ensamma algoritmen utmärker sig i energi-effektivitet, kan den inte stödja sensorskraven. Dessutom studerar vi effektenav ISAC på slut till slut (inklusive radios och bearbetning) energiförbruk-ning. Särskilt presenterar vi giga-operationer per sekund (GOPS) analys förbåde kommunikations- och sensorsuppgifter. Två optimeringsproblem formu-leras och löses för att minimera överförings- och slut till slut energi genomblocklängd- och effektoptimering. Resultaten indikerar att medan slut till slutenergiminimering erbjuder betydande energibesparingar, minskar dess effek-tivitet med sensorintegrationen på grund av bearbetningsenergikrav. / <p>QC 20240513</p>

Owing to its ability to improve both spectral and energy efficiency of wireless networks, massive multiple-input multiple-output (mMIMO) has become one of the key enablers of the fifth-generation (5G) and beyond communication systems. For successful integration of this promising physical layer technique in the upcoming cellular standards, it is essential to have a comprehensive understanding of its network-level performance. Over the last decade, stochastic geometry has been instrumental in obtaining useful system design insights of wireless networks through accurate and tractable theoretical analysis. Hence, it is only natural to consider modeling and analyzing the mMIMO systems using appropriate statistical constructs from the stochastic geometry literature and gain insights for its future implementation. With this broader objective in mind, we first focus on modeling a cellular mMIMO network that uses fractional pilot reuse to mitigate the sole performance-limiting factor of mMIMO networks, namely, pilot contamination. Leveraging constructs from the stochastic geometry literature, such as Johnson-Mehl cells, we derive analytical expressions for the uplink (UL) signal-to-interference-and-noise ratio (SINR) coverage probability and average spectral efficiency for a random user. From our system analysis, we present a partitioning rule for the number of pilot sequences to be reserved for the cell-center and cell-edge users that improves the average cell-edge user spectral efficiency while achieving similar cell-center user spectral efficiency with respect to unity pilot reuse. In addition, using the analytical approach developed for the cell-center user performance evaluation, we study the performance of a small cell system where user and base station (BS) locations are coupled. The impact of distance-dependent UL power control on the performance of an mMIMO network with unity pilot reuse is analyzed and subsequent system design guidelines are also presented. Next, we focus on the performance analysis of the cell-free mMIMO network, which is a distributed implementation of the mMIMO system that leads to the second and third contributions of this dissertation. Similar to the cellular counterpart, the cell-free systems also suffer from pilot contamination due to the reuse of pilot sequences throughout the network. Inspired by a hardcore point process known as the random sequential adsorption (RSA) process, we develop a new distributed pilot assignment algorithm that mitigates the effect of pilot contamination by ensuring a minimum distance among the co-pilot users. This pilot assignment scheme leads to the construction of a new point process, namely the multilayer RSA process. We study the statistical properties of this point process both in one and two-dimensional spaces by deriving approximate but accurate expressions for the density and pair correlation functions. Leveraging these new results, for a cell-free network with the proposed RSA-based pilot assignment scheme, we present an analytical approach that determines the minimum number of pilots required to schedule a user with probabilistic guarantees. In addition, to benchmark the performance of the RSA-based scheme, we propose two optimization-based centralized pilot allocation schemes using linear programming principles. Through extensive numerical simulations, we validate the efficacy of the distributed and scalable RSA-based pilot assignment scheme compared to the proposed centralized algorithms. Apart from pilot contamination, another impediment to the performance of a cell-free mMIMO is limited fronthaul capacity between the baseband unit and the access points (APs). In our fourth contribution, using appropriate stochastic geometry-based tools, we model and analyze the downlink of such a network for two different implementation scenarios. In the first scenario, we consider a finite network where each AP serves all the users in the network. In the second scenario, we consider an infinite network where each user is served by a few nearby APs in order to limit the load on fronthaul links. From our analyses, we observe that for the finite network, the achievable average system sum-rate is a strictly quasi-concave function of the number of users in the network, which serves as a key guideline for scheduler design for such systems. Further, for the user-centric architecture, we observe that there exists an optimal number of serving APs that maximizes the average user rate. The fifth and final contribution of this dissertation focuses on the potential improvement that is possible by the use of mMIMO in citizen broadband radio service (CBRS) spectrum sharing systems. As a first concrete step, we present comprehensive modeling and analysis of this system with omni-directional transmissions. Our model takes into account the key guidelines by the Federal Communications Commission for co-existence between licensed and unlicensed networks in the 3.5 GHz CBRS frequency band. Leveraging the properties of the Poisson hole process and Matern hardcore point process of type II, a.k.a. ghost RSA process, we analytically characterize the impact of different system parameters on various performance metrics such as medium access probability, coverage probability, and area spectral efficiency. Further, we provide useful system design guidelines for successful co-existence between these networks. Building upon this omni-directional model, we also characterize the performance benefits of using mMIMO in such a spectrum sharing network. / Doctor of Philosophy / The emergence of cloud-based video and audio streaming services, online gaming platforms, instantaneous sharing of multimedia contents (e.g., photos, videos) through social networking platforms, and virtual collaborative workspace/meetings require the cellular communication networks to provide high data-rate as well as reliable and ubiquitous connectivity. These constantly evolving requirements can be met by designing a wireless network that harmoniously exploits the symbiotic co-existence among different types of cutting-edge wireless technologies. One such technology is massive multiple-input multiple-output (mMIMO), whose core idea is to equip the cellular base stations (BSs) with a large number of antennas that can be leveraged through appropriate signal processing algorithms to simultaneously accommodate multiple users with reduced network interference. For successful deployment of mMIMO in the upcoming cellular standards, i.e., fifth-generation (5G) and beyond systems, it is necessary to characterize its performance in a large-scale wireless network taking into account the inherent spatial randomness in the BS and user locations. To achieve this goal, in this dissertation, we propose different statistical methods for the performance analysis of mMIMO networks using tools from stochastic geometry, which is a field of mathematics related to the study of random patterns of points. One of the major deployment issues of mMIMO systems is pilot contamination, which is a form of coherent network interference that degrades user performance. The main reason behind pilot contamination is the reuse of pilot sequences, which are a finite number of known signal waveforms used for channel estimation between a user and its serving BS. Further, the effect of pilot contamination is more severe for the cell-edge users, which are farther from their own BSs. An efficient scheme to mitigate the effect of pilot contamination is fractional pilot reuse (FPR). However, the efficiency of this scheme depends on the pilot partitioning rule that decides the fraction of total pilot sequences that should be used by the cell-edge users. Using appropriate statistical constructs from the stochastic geometry literature, such as Johnson-Mehl cells, we present a partitioning rule for efficient implementation of the FPR scheme in a cellular mMIMO network. Next, we focus on the performance analysis of the cell-free mMIMO network. In contrast to the cellular network, where each user is served by a single BS, in a cell-free network each user can be served by multiple access points (APs), which have less complex hardware compared to a BS. Owing to this cooperative and distributed implementation, there are no cell-edge users. Similar to the cellular counterpart, the cell-free systems also suffer from pilot contamination due to the reuse of pilot sequences throughout the network. Inspired by a hardcore point process known as the random sequential adsorption (RSA) process, we develop a new distributed pilot assignment algorithm that mitigates the effect of pilot contamination by ensuring a minimum distance among the co-pilot users. Further, we show that the performance of this distributed pilot assignment scheme is appreciable compared to different centralized pilot assignment schemes, which are algorithmically more complex and difficult to implement in a network. Moreover, this pilot assignment scheme leads to the construction of a new point process, namely the multilayer RSA process. We derive the statistical properties of this point process both in one and two-dimensional spaces. Further, in a cell-free mMIMO network, the APs are connected to a centralized baseband unit (BBU) that performs the bulk of the signal processing operations through finite capacity links, such as fiber optic cables. Apart from pilot contamination, another implementational issue associated with the cell-free mMIMO systems is the finite capacity of fronthaul links that results in user performance degradation. Using appropriate stochastic geometry-based tools, we model and analyze this network for two different implementation scenarios. In the first scenario, we consider a finite network where each AP serves all the users in the network. In the second scenario, we consider an infinite network where each user is served by a few nearby APs. As a consequence of this user-centric implementation, for each user, the BBU only needs to communicate with fewer APs thereby reducing information load on fronthaul links. From our analyses, we propose key guidelines for the deployment of both types of scenarios. The type of mMIMO systems that are discussed in this work will be operated in the sub-6 GHz frequency range of the electromagnetic spectrum. Owing to the limited availability of spectrum resources, usually, spectrum sharing is encouraged among different cellular operators in such bands. One such example is the citizen broadband radio service (CBRS) spectrum sharing systems proposed by the Federal Communications Commission (FCC). The final contribution of this dissertation focuses on the potential improvement that is possible by the use of mMIMO in the CBRS systems. As our first step, using tools from stochastic geometry, we model and analyze this system with a single antenna at the BSs. In our model, we take into account the key guidelines by the FCC for co-existence between licensed and unlicensed operators. Leveraging properties of the Poisson hole process and hardcore process, we provide useful theoretical expressions for different performance metrics such as medium access probability, coverage probability, and area spectral efficiency. These results are used to obtain system design guidelines for successful co-existence between these networks. We further highlight the potential improvement in the user performance with multiple antennas at the unlicensed BS.

The ever-growing demand of wireless traffic poses a challenge for current cellular networks. Each new generation must find new ways to boost the network capacity and spectral efficiency (SE) per device. A pillar of 5G is massive multiple-input-multiple-output (MIMO) technology. Through utilizing a large number of antennas at each transmitting node, massive MIMO has the ability to multiplex several user equipments (UEs) on the same time-frequency resources via spatial multiplexing. Looking beyond 5G, cell-free massive MIMO has attracted a lot of attention for its ability to utilize spatial macro diversity and higher resilience to interference. The cell-free architecture is based on a large number of distributed access points (APs) jointly serving the UEs within a coverage area without creating artificial cell boundaries. It provides a promising solution that is focused on delivering uniform service quality throughout the mobile network. The main challenges of the cell-free network architecture lie in the computational complexity for signal processing and the huge fronthaul requirements for information exchange among the APs. In this thesis, we tackle some of the inherent problems of the cell-free network architecture by providing distributed solutions to the power allocation and mobility management problems. We then introduce a new method for characterizing unknown interference in wireless networks. For the problem of power allocation, a distributed learning-based solution that provides a good trade-off between SE performance and applicability for implementation in large-scale networks is developed with reduced fronthaul requirements and computational complexity. The problem is divided in a way that enables each AP (or group of APs) to separately decide on the power coefficients to the UEs based on the locally available information at the AP without exchanging information with the other APs, however, still attempting to achieve a network wide optimization objective.  Regarding mobility management, a handover procedure is devised for updating the serving sets of APs and assigned pilot to each UE in a dynamic scenario considering UE mobility. The algorithm is tailored to reduce the required number of handovers per UE and changes in pilot assignment. Numerical results show that our proposed solution identifies the essential refinements since it can deliver comparable SE to the case when the AP-UE association is completely redone. Finally, we developed a new technique based on a Bayesian approach to model the distribution of the unknown interference arising from scheduling variations in neighbouring cells. The method is shown to provide accurate modelling for the unknown interference power and an effective tool for robust rate allocation in the uplink with a guaranteed target outage performance. / Den ständigt växande efterfrågan på trådlös datatrafik är en stor utmaning för dagens mobilnät. Varje ny nätgeneration måste hitta nya sätt att öka den totala kapaciteten och spektraleffektiviteten (SE) per uppkopplad enhet. En pelare i 5G är massiv-MIMO-teknik (multiple-input-multiple-output). Genom att använda ett stort antal antenner på varje mobilmast har massiv MIMO förmågan att kommunicera med flera användarutrustningar (eng. user equipment, UE) på samma tid/frekvensresurser via så kallad rumslig multiplexing. Om man ser bortom 5G-tekniken så har cellfri massiv-MIMO väckt stort intresse tack vare sin förmåga att utnyttja rumslig makrodiversitet för att förbättra täckningen och uppnå högre motståndskraft mot störningar. Den cellfria arkitekturen bygger på att ha ett stort antal distribuerade accesspunkter (AP) som gemensamt serverar UE:erna inom ett täckningsområde utan att dela upp området konstgjorda celler. Detta är en lovande lösning som är fokuserad på att leverera enhetliga datahastigheter i hela mobilnätet. De största forskningsutmaningarna med den cellfria nätverksarkitekturen ligger i beräkningskomplexiteten för signalbehandling och de enorma kraven på fronthaul-kablarna som möjliggör informationsutbyte mellan AP:erna. I den här avhandlingen löser vi några av de grundläggande utmaningarna med den cellfria nätverksarkitekturen genom att tillhandahålla distribuerade algoritmlösningar på problem relaterade till signaleffektreglering och mobilitetshantering. Vi introducerar sedan en ny metod för att karakterisera okända störningar i trådlösa nätverk. När det gäller signaleffektreglering så utvecklas en distribuerad inlärnings-baserad metod som ger en bra avvägning mellan SE-prestanda och tillämpbarhet för implementering i storskaliga cellfria nätverk med reducerade fronthaulkrav och lägre beräkningskomplexitet. Lösningen är uppdelat på ett sätt som gör det möjligt för varje AP (eller grupp av AP) att separat besluta om effektkoefficienterna relaterade till varje UE baserat på den lokalt tillgängliga informationen vid AP:n utan att utbyta information med de andra AP:erna, men ändå försöka uppnå ett nätverksomfattande optimeringsmål. När det gäller mobilitetshantering utformas en överlämningsprocedur som dynamiskt uppdaterar vilken uppsättning av AP:er som servar en viss UE och vilken pilotsekvens som används när den rör sig över täckningsområdet. Algoritmen är skräddarsydd för att minska antalet överlämningar per UE och förändringar i pilottilldelningen. Numeriska resultat visar att vår föreslagna lösning identifierar de väsentliga förfiningarna eftersom den kan leverera jämförbar SE som när AP-UE-associationen görs om helt och hållet. Slutligen utvecklade vi en ny Bayesiansk metod för att modellera den statistiska fördelningen av de okända störningarna som uppstår på grund av schemaläggningsvariationer i närliggande celler. Metoden har visat sig ge en korrekt modell av den okända störningseffekten och är ett effektivt verktyg för robust SE-allokering i upplänken med en garanterad maximal avbrottsnivå. / <p>QC 20230503</p>