Les réseaux radio actuelles utilisent le spectre inefficacement, car une bande de fréquence est allouée de façon permanente à une technologie spécifique. Vu que le spectre est une ressource limitée, cette attribution statique ne pourra bientôt plus combler les besoins des systèmes de transmission qui ne cessent de croître. On peut toutefois optimiser l'utilisation du spectre en permettant des transmissions secondaires (SU) dans les espaces libres du primaire (PU). Cette vision constitue l'objectif principal de la radio cognitive. Nous proposons d'évaluer les stratégies de transmission pour la coexistence des systèmes primaires (PU) et SU dans les mêmes réseaux. Plus concrètement, nous nous focalisons sur un scénario spatial interweave en émettant les signaux SU dans les espaces vides du PU à l'aide d'un précodeur linéaire. Néanmoins, ce précodage nécessite une connaissance a priori des canaux interférents. L'échange d'informations entre le PU et le SU étant proscrit, nous exploitons l'hypothèse de la réciprocité du canal. Cette hypothèse compense l'absence de coopération, mais elle n'est pas si évidente à exploiter en pratique à cause des perturbations des circuits radio fréquence. Nous suggérons de compenser ces perturbations par des méthodes de calibration relative. Nous proposons ensuite une implémentation temps-réel des solutions sur une plateforme LTE. Pour finir, nous généralisons l'approche RC à un système de transmission multi-utilisateurs, à travers une combinaison des techniques RC et massive MIMO, cette approche constitue s’établit comme une solution à la progression exponentielle du trafic. / The accelerated evolution of wireless transmission in recent years has dramatically increased the spectrum overcrowding. Indeed, the spectrum is inefficiently used in the conventional networks, since a frequency band is statically allocated to a specific technology called primary (PU). Whereas the radio spectrum is limited, this static frequency allocation will no longer be able to meet the increasing needs of bandwidth. However, the spectrum can be optimally used in enabling secondary (SU) transmissions, provided the latters do not harm the PU. This opportunistic vision of wireless transmissions is the main aim of Cognitive Radio (CR). CR enables smart use of wireless resources and is a key ingredient to perform high spectral efficiency. We focus on a spatial interweave (SIW) CR scenario which exploits the spatial white spaces to enable SU transmissions. The latter forms spatial beams using precoders, so that there is no interference towards the primary. Nevertheless, this precoding requires acquisition of the crosslink channel. However, due to the lack of cooperation between PU and SU, we acquire the channel thanks to channel reciprocity. Furthermore, the practical use of the reciprocity is not as straightforward as in theory, because it is is jeopardized by the nonreciprocal radio frequency front-ends. These perturbations are compensated in our study by relative calibration algorithms. Subsequently, we propose an implementation of our solutions in a real-time LTE platform. Eventually, we extend the CR model to a MU system in suggesting a combination of SIW and massive MIMO techniques. This scheme is an interesting candidate to overcome the exponential traffic growth.

Cette thèse a permis de travailler sur l'efficacité d'un canal des systèmes massifs MIMO pour lesquels il faille déterminer le débit à l'Uplink des terminaux présents dans leurs cellules respectives. Comme hypothèse, la bande de fréquence en mode TDD est réutilisée dans chaque cellule. Tous les symboles sont propagés de manière asynchrone par les terminaux présents dans les cellules, n'empêchant pas de fait des interactions intra et inter symboles au niveau des stations de base. Ces signaux rencontrent beaucoup d'obstacles sur leur trajet qui entraînent des retards, des pertes de signaux (destructifs), des régénérations de signaux (constructifs) avec divers types de modulation (amplitude, fréquentielle, phase), etc. L’affaiblissement du trajet dans le canal est mis en exergue avec les différentes valeurs prises par le coefficient d'atténuation choisi lors des simulations. Face à cette situation, il a fallu rechercher le meilleur et robuste estimateur de canal à un temps de cohérence donné. La méthode MMSE (Minimum Mean Square Error) est retenue, comparée à d'autres. Pour la performance des systèmes massifs MIMO, nous nous sommes appesantis sur les méthodes de diversité des antennes (diversité d'ordre N), les méthodes de coding, les méthodes d'accès OFDMA et les méthodes d'égalisation pour montrer qu'effectivement le fait d'utiliser de nombreuses antennes au niveau des stations de base améliore et contribue aux gains recherchés en débits. Avec les systèmes massifs MIMO, nous avons montré que l'apport antennaire est bien reconnu dans la gestion des interférences. Un algorithme de calcul de débit à l'Uplink a été réalisé avec trois récepteurs conventionnels que sont le MRC (Maximum Ratio Combiner), le ZF (Zero-Forcing) et le MMSE (Minimum Mean Square Error). Les simulations ont permis de comparer les différentes approches. En faisant varier la puissance de contamination des symboles pilotes, nous observons la convergence des courbes ZF et MMSE. Si le nombre des cellules L augmentent, nous constatons que plus la puissance de contamination des symboles pilotes (pp) est élevée, plus la capacité diminue dans le canal. Après plusieurs itérations, notre algorithme converge vers une asymptote (régime stationnaire et linéaire) où les échantillons à la sortie des détecteurs s’approchent de la séquence de données émises. Le SINR obtenu avec les détecteurs conventionnels permet le calcul des débits respectifs dans le canal avec le théorème de SHANNON. / This thesis made it possible to work on the efficiency of a channel of massive MIMO systems for which it is necessary to determine the throughput at the Uplink of the terminals present in their respective cells. As an assumption, the frequency band in TDD mode is reused in each cell. All symbols are propagated asynchronously by the terminals present in the cells, not effectively preventing intra- and inter-symbol interactions at the base stations. These signals encounter many obstacles on their path that lead to delays, signal losses (destructive), signal regenerations (constructive) with various types of modulation (amplitude, frequency, phase), etc. The path loss in the channel is highlighted with the different values taken by the attenuation coefficient chosen during the simulations. Faced with this situation, it was necessary to look for the best and most robust channel estimator at a given consistency time. The MMSE (Minimum Mean Square Error) method is used, compared to others. For the performance of massive MIMO systems, we have focused on antenna diversity methods (N-order diversity), coding methods, OFDMA access methods and equalization methods to show that effectively using multiple antennas at base stations improves and contributes to the desired rate gains. With massive MIMO systems, we have shown that antennar contribution is well recognized in interference management. An algorithm for calculating the flow rate at the Uplink was developed using three conventional receivers: the MRC (Maximum Ratio Combiner), the ZF (Zero-Forcing) and the MMSE (Minimum Mean Square Error). The simulations made it possible to compare the different approaches. By varying the contamination power of the pilot symbols, we observe the convergence of the ZF and MMSE curves. If the number of L cells increases, we find that the higher the contamination power of the pilot symbols (pp), the lower the capacity in the channel. After several iterations, our algorithm converges to an asymptote (stationary and linear regime) where the samples at the detector output approach the transmitted data sequence. The SINR obtained with conventional detectors allows the calculation of the respective flows in the channel with the SHANNON theorem.

Massive MIMO (multiple-input–multiple-output) is a multi-antenna technology for cellular wireless communication, where the base station uses a large number of individually controllable antennas to multiplex users spatially.  This technology can provide a high spectral efficiency.  One of its main challenges is the immense hardware complexity and cost of all the radio chains in the base station.  To make massive MIMO commercially viable, inexpensive, low-complexity hardware with low linearity has to be used, which inherently leads to more signal distortion.  This thesis investigates how the degenerated linearity of some of the main components—power amplifiers, analog-to-digital converters (ADCs) and low-noise amplifiers—affects the performance of the system, with respect to data rate, power consumption and out-of-band radiation. The main results are: Spatial processing can reduce PAR (peak-to-average ratio) of the transmit signals in the downlink to as low as 0B; this, however, does not necessarily reduce power consumption.  In environments with isotropic fading, one-bit ADCs lead to a reduction in effective signal-to-interference-and-noise ratio (SINR) of 4dB in the uplink and four-bit ADCs give a performance close to that of an unquantized system.  An analytical expression for the radiation pattern of the distortion from nonlinear power amplifiers is derived.  It shows how the distortion is beamformed to some extent, that its gain never is greater than that of the desired signal, and that the gain of the distortion is reduced with a higher number of served users and a higher number of channel taps.  Nonlinear low-noise amplifiers give rise to distortion that partly combines coherently and limits the possible SINR.  It is concluded that spatial processing with a large number of antennas reduces the impact of hardware distortion in most cases.  As long as proper attention is paid to the few sources of coherent distortion, the hardware complexity can be reduced in massive MIMO base stations to overcome the hardware challenge and make massive MIMO commercial reality. / Massiv MIMO (eng: multiple-input–multiple-output) är en flerantennsteknologi för cellulär trådlös kommunikation, där basstationen använder ett stort antal individuellt styrbara antenner för att multiplexa användare i rummet.  Denna teknologi kan tillhandahålla en hög spektral effektivitet.  En av dess främsta utmaningar är den enorma hårdvarukomplexiteten och kostnaden hos basstationens alla radiokedjor.  För att massiv MIMO skall bli kommersiellt attraktivt, måste billiga, enkla hårdvarukomponenter med låg linjäritet användas, vilket oundvikligen leder till mer signaldistorsion.  Denna avhandling undersöker hur den försämrade linjäriteten hos några av huvudkomponenterna – effektförstärkare, analog-digital-omvandlare (AD-omvandlare) och lågbrusförstärkare – påverkar systemets prestanda, i termer av datatakt, effektförbrukning och utombandsstrålning.  Huvudresultaten är: Rumslig signalbehandling kan reducera sändsignalernas toppvärde i nerlänken ända ner till 0dB, vilket dock inte nödvändigtvis minskar effektförbrukningen.  I miljöer med isotrop fädning leder enbits-AD-omvandlare till 4dB lägre signal-till-interferens-och-brus-förhållande i upplänken, och fyrabits-AD-omvandlare ger en prestanda nära den ett system utan kvantisering kan uppnå.  Ett analytiskt uttryck för strålningsmönstret för distorsionen från icke-linjära effektförstärkare härleds.  Det visar hur distorsionen till viss del lobformas, att dess förstärkning aldrig är starkare än förstärkningen för den önskade signalen och att distorsionens förstärkning minskar med ett högre antal betjänade användare och ett högre antal kanaltappar.  Icke-linjära lågbrusförstärkare ger upphov distorsion som delvis kombinerar koherent och begränsar det möjliga signal-till-brus-och-interferens-förhållandet.  Slutsatsen är att rumslig signalbehandling med ett stort antal antenner reducerar hårdvarudistorsionens inverkan i de flesta fall.  Så länge som de få källorna till koherent distorsion ges tillbörlig uppmärksamhet, kan hårdvarukomplexiteten minskas i basstationer för massiv MIMO för att övervinna hårdvaruutmaningen och göra massiv MIMO kommersiell verklighet. / 蜂窩無線通訊領域中的大規模多天線技術以多個單獨可控的天線通過空間複用的方式服務多個用戶。如是可以大幅提高頻譜效率。實現此技術的主要難題在於基站所用射頻單元的極大複雜度及成本。爲使大規模多天線技術適用在商業系統中，需使用導致失真的低複雜度低成本的非線性硬件。本文探討若將一些主要部件——功放、模數轉換器、低噪聲放大器——的線性程度降低，系統性能是如何受到影響的，即系統的速率、功耗、帶外泄露等指標。主要的結果爲：空間信號處理可以降低下行信號的峯均比，直至０分貝；然而低峯均比不一定能夠降低功耗。用一比特模數轉換器使上行的信干噪比減少４分貝；用四比特模數轉換器可在各向同性衰落的環境裏實現接近無量化系統的性能。本文推導出非線性功放失真輻射方向的解析公式。該公式展示失真在某種程度上會被波束成形的；具體而言，失真的波束成形增益不大於有效信號的增益，波束成形增益會根據服務用戶數量和信道階數的增長而降低。非線性低噪聲放大器引起的失真，一部分會相干地合併，因此會限制信干噪比的增長。結論爲多天線的空間信號處理可以減少硬件失真的影響。只要適當地處理少數相干失真的來源，大規模多天線基站可以降低硬件複雜度，解決硬件難題，使大規模多天線技術成功地應用在商業系統中成爲現實。

[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>

Les réseaux radio actuelles utilisent le spectre inefficacement, car une bande de fréquence est allouée de façon permanente à une technologie spécifique. Vu que le spectre est une ressource limitée, cette attribution statique ne pourra bientôt plus combler les besoins des systèmes de transmission qui ne cessent de croître. On peut toutefois optimiser l'utilisation du spectre en permettant des transmissions secondaires (SU) dans les espaces libres du primaire (PU). Cette vision constitue l'objectif principal de la radio cognitive. Nous proposons d'évaluer les stratégies de transmission pour la coexistence des systèmes primaires (PU) et SU dans les mêmes réseaux. Plus concrètement, nous nous focalisons sur un scénario spatial interweave en émettant les signaux SU dans les espaces vides du PU à l'aide d'un précodeur linéaire. Néanmoins, ce précodage nécessite une connaissance a priori des canaux interférents. L'échange d'informations entre le PU et le SU étant proscrit, nous exploitons l'hypothèse de la réciprocité du canal. Cette hypothèse compense l'absence de coopération, mais elle n'est pas si évidente à exploiter en pratique à cause des perturbations des circuits radio fréquence. Nous suggérons de compenser ces perturbations par des méthodes de calibration relative. Nous proposons ensuite une implémentation temps-réel des solutions sur une plateforme LTE. Pour finir, nous généralisons l'approche RC à un système de transmission multi-utilisateurs, à travers une combinaison des techniques RC et massive MIMO, cette approche constitue s'établit comme une solution à la progression exponentielle du trafic.

Multi-antenna wireless communication systems that employ a large number of antennas have recently stirred a lot of research interest. This is mainly due to the possibility of achieving very high spectral efficiency, power efficiency, and link reliability in such large-scale multiple-input multiple-output (MIMO) systems. An emerging architecture for large-scale multiuser MIMO communications is one where each base station (BS) is equipped with a large number of antennas (tens to hundreds of antennas) and the user terminals are equipped with fewer antennas (one to four antennas) each. The backhaul communication between base stations is also carried out using large number of antennas. Because of the high dimensionality of large-scale MIMO signals, the computational complexity of various transceiver operations can be prohibitively large. Therefore, low complexity techniques that scale well for transceiver signal processing in such large-scale MIMO systems are crucial. The transceiver operations of interest include signal encoding at the transmitter, and channel estimation, detection and decoding at the receiver. This thesis focuses on the design and analysis of novel low-complexity transceiver signal processing schemes for large-scale MIMO systems. In this thesis, we consider two types of large-scale MIMO systems, namely, massive MIMO systems and generalized spatial modulation MIMO (GSM-MIMO) systems. In massive MIMO, the mapping of information bits to modulation symbols is done using conventional modulation alphabets (e.g., QAM, PSK). In GSM-MIMO, few of the avail-able transmit antennas are activated in a given channel use, and information bits are conveyed through the indices of these active antennas, in addition to the bits conveyed through conventional modulation symbols. We also propose a novel modulation scheme named as precoder index modulation, where information bits are conveyed through the index of the chosen precoder matrix as well as the modulation symbols transmitted. Massive MIMO: In this part of the thesis, we propose a novel MIMO receiver which exploits channel hardening that occurs in large-scale MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of HH H become much weaker compared to the diagonal terms as the size of the channel gain matrix H increases. We exploit this phenomenon to devise a low-complexity channel estimation scheme and a message passing algorithm for signal detection at the BS receiver in massive MIMO systems. We refer to the proposed receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The key novelties in the proposed CHEMP receiver are: (i) operation on the matched filtered system model, (ii) Gaussian approximation on the off-diagonal terms of the HH H matrix, and (iii) direct estimation of HH H instead of H and use of this estimate of HH H for detection The performance and complexity results show that the proposed CHEMP receiver achieves near-optimal performance in large-scale MIMO systems at complexities less than those of linear receivers like minimum mean squared error (MMSE) receiver. We also present a log-likelihood ratio (LLR) analysis that provides an analytical reasoning for this better performance of the CHEMP receiver. Further, the proposed message passing based detection algorithm enables us to combine it with low density parity check (LDPC) decoder to formulate a joint message passing based detector-decoder. For this joint detector-decoder, we design optimized irregular binary LDPC codes specific to the massive MIMO channel and the proposed receiver through EXIT chart matching. The LDPC codes thus obtained are shown to achieve improved coded bit error rate (BER) performance compared to off-the-shelf irregular LDPC codes. The performance of the CHEMP receiver degrades when the system loading factor (ratio of the number of users to the number of BS antennas) and the modulation alpha-bet size are large. It is of interest to devise receiver algorithms that work well for high system loading factors and modulation alphabet sizes. For this purpose, we propose a low-complexity factor-graph based vector message passing algorithm for signal detection. This algorithm uses a scalar Gaussian approximation of interference on the basic sys-tem model. The performance results show that this algorithm performs well for large modulation alphabets and high loading factors. We combine this detection algorithm with a non-binary LDPC decoder to obtain a joint detector-decoder, where the field size of the non-binary LDPC code is same as the size of the modulation alphabet. For this joint message passing based detector-decoder, we design optimized non-binary irregular LDPC codes tailored to the massive MIMO channel and the proposed detector. GSM-MIMO: In this part of the thesis, we consider GSM-MIMO systems in point-to-point as well as multiuser communication settings. GSM-MIMO has the advantage of requiring only fewer transmit radio frequency (RF) chains than the number of transmit antennas. We analyze the capacity of point-to-point GSM-MIMO, and obtain lower and upper bounds on the GSM-MIMO system capacity. We also derive an upper bound on the BER performance of maximum likelihood detection in GSM-MIMO systems. This bound is shown to be tight at moderate to high signal-to-noise ratios. When the number of transmit and receive antennas are large, the complexity of en-coding and decoding of GSM-MIMO signals can be prohibitively high. To alleviate this problem, we propose a low complexity GSM-MIMO encoding technique that utilizes com-binatorial number system for bits-to-symbol mapping. We also propose a novel layered message passing (LaMP) algorithm for decoding GSM-MIMO signals. Low computational complexity is achieved in the LaMP algorithm by detecting the modulation bits and the antenna index bits in two deferent layers. We then consider large-scale multiuser GSM-MIMO systems, where multiple users employ GSM at their transmitters to communicate with a BS having a large number of receive antennas. For this system, we develop computationally efficient message passing algorithms for signal detection using vector Gaussian approximation of interference. The performance results of these algorithms show that the GSM-MIMO system outperforms the massive MIMO system by several dBs for the same spectral efficiency. Precoder index modulation: It is known that the performance of a communication link can be enhanced by exploiting time diversity without reducing the rate of transmission using pseudo random phase preceding (PRPP). In order to further improve the performance of GSM-MIMO, we apply PRPP technique to GSM-MIMO systems. PRPP provides additional diversity advantage at the receiver and further improves the performance of GSM-MIMO systems. For PRPP-GSM systems, we propose methods to simultaneously precode both the antenna index bits and the modulation symbols using rectangular precoder matrices. Finally, we extend the idea of index modulation to pre-coding and propose a new modulation scheme referred to as precoder index modulation (PIM). In PIM, information bits are conveyed through the index of a prehared PRPP matrix, in addition to the information bits conveyed through the modulation symbols. PIM is shown to increase the achieved spectral efficiency, in addition to providing diver-sity advantages.

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>