Future 6G wireless communications network will increase the data capacity to unprecedented numbers and thus empower the deployment of new real-time applications. Millimeter-Wave (mmWave) band and Massive MIMO are considered as two of the main pillars of 6G to handle the gigantic influx in data traffic and number of mobile users and IoT devices. The small wavelengths at these frequencies mean that more antenna elements can be placed in the same area. Thereby, high spatial processing gains are achievable that can theoretically compensate for the higher isotropic path loss. The propagation characteristics at mmWave band, create sparse channels in typical scenarios, where only few paths convey significant power. Considering this feature, Hybrid (analog-digital) Beamforming introduces a new signal processing framework which enables energy and cost-efficient implementation of massive MIMO with innovative smart arrays. In this setup, the analog beamalignment via beam selection in link access phase, is the critical performance limiting step. Considering the variable operating condition in mmWave channels, a desirable solution should have the following features: efficiency in training (limited coherence time, delay constraints), adaptivity to channel conditions (large SNR range) and robustness to realized channels (LOS, NLOS, Multipath, non-ideal beam patterns). For the link access task, we present a new energy-detection framework based on variable length channel measurements with (orthogonal) beam codebooks. The proposed beam selection technique denoted as composite M-ary Sequential Competition Test (SCT) solves the beam selection problem when knowledge about the SNR operating point is not available. It adaptively changes the test length when the SNR varies to achieve an essentially constant performance level. In addition, it is robust to non-ideal beam patterns and different types of the realized channel. Compared to the conventional fixed length energy-detection techniques, the SCT can increase the training efficiency up to two times while reducing the delay if the channel condition is good. Having the flexibility to allocate resources for channel measurements through different beams adaptively in time, we improve the SCT to eliminate unpromising beams from the remaining candidate set as soon as possible. In this way, the Sequential Competition and Elimination Test (SCET) significantly further reduces training time by increasing the efficiency. The developed ideas can be applied with different codebook types considered for practical applications. The reliable performance of the beam selection technique is evident through experimental evaluation done using the state-of-the-art test-bed developed at the Vodafone Chair that combines a Universal Software Radio Peripheral (USRP) based platform with mmWave frontends.

Massive MIMO communication systems have been highlighted as the main technology for physical layer of next generation communication standards, like 5G. While conventional communication between BS and its covered users is performed in orthogonal time-frequency resources, the improved interuser interference mitigation capability provided by the large number of BS antennas enables the BS to communicate with several users in the same time-frequency resource. This better usage of available but scarce spectrum elevates the spectral efficiency to very appreciable levels, and has a similar effect on energy efficiency, since the transmit power is not increased. On the other hand, if the objective is to provide a target performance for the users, the required transmit power in both direct and reverse links can be made inversely proportional to the number of BS antennas employed. In this Doctoral Thesis, several important aspects of massive MIMO systems are systematically investigated aiming to improve their energy and spectral efficiencies. We can enumerate our main contributions as follows. Considering a cellular massive MIMO network, we proposed an optimized assignment policy of training sequences to the users, which is then combined with suitable power control algorithms. We have also investigated the adoption of alternative waveforms in this scenario, such as single-carrier transmission, in order to overcome the issues of conventional OFDM. Our contributions in this topic are to derive analytical performance expressions for a time-domain single-carrier equalizer taking advantage of the large number of BS antennas, and to evaluate and compare the total energy efficiency of OFDM versus single-carrier massive MIMO systems. Finally, considering crowded massive MIMO networks, composed by both human users as well as machine-type communication devices, we proposed an improved random access protocol aiming to decrease the average number of access attempts for the users and decreasing the probability of failed access attempts. / Sistemas de comunicação de múltiplas antenas (multiple-input multiple-output - MIMO) têm se destacado como a principal tecnologia para a camada física dos padrões de comunicação da próxima geração, como o 5G. Enquanto a comunicação convencional entre a estação base (base station - BS) e seus usuários atendidos é realizada em recursos ortogonais de tempo-frequência, a grande capacidade de redução da interferência interusuários possibilitada pelo grande número de antenas da BS habilita a BS a se comunicar com diversos usuários no mesmo recurso tempo-frequência. Este melhor uso do escasso espectro disponível eleva a eficiência espectral a níveis muito apreciáveis, e tem um efeito similar na eficiência energética, pois a potência de transmissão não é aumentada. Por outro lado, se o objetivo é fornecer um desempenho desejado para os usuários, a potência de transmissão necessária em ambos os enlaces direto e reverso pode ser feita inversamente proporcional ao número de antenas na BS. Nesta Tese de Doutorado, diversos aspectos importantes de sistemas MIMO massivo são sistematicamente investigados com o objetivo de melhorar suas eficiências energética e espectral. Pode-se enumerar as principais contribuições alcançadas como se segue. Considerando uma rede celular MIMO massivo, propõe-se uma política de atribuição de sequências de treinamento aos usuários otimizada, a qual é depois combinada com apropriados algoritmos de controle de potência. Também investiga-se a adoção neste cenário de formas de onda alternativas, tal como a transmissão de portadora única, visando superar as deficiências da convencional multiplexagem por divisão de portadoras ortogonais (orthogonal frequency-division multiplexing - OFDM). As principais contribuições obtidas neste tema são derivar expressões de desempenho analíticas para um equalizador de portadora única no domínio do tempo que aproveita o grande número de antenas na BS, e avaliar e comparar a eficiência energética total de sistemas MIMO massivo OFDM versus portadora única. Finalmente, considerando redes MIMO massivo sobrecarregadas, compostas por usuários humanos bem como dispositivos de comunicação do tipo máquina, propõe-se um protocolo de acesso aleatório melhorado visando diminuir o número médio de tentativas de acesso para os usuários e diminuir a probabilidade de falhas de tentativa de acesso.

Abstract With the unprecedented growth in mobile data traffic and network densification, the emerging fifth-generation (5G) wireless network warrants a paradigm shift with respect to system design and technological enablers. In this regard, the prime motivation of this thesis is to propose an integrated access-backhaul (IAB) framework to dynamically schedule users, while efficiently providing a wireless backhaul to dense small cells and mitigating interference. In addition, joint resource allocation and interference mitigation solutions are proposed for two-hop and multi-hop self-backhauled millimeter wave (mmWave) networks. The first contribution of this thesis focuses on a multi-user two-hop relay cellular system in which a massive antenna array enabled macro base station (BS) simultaneously provides high beamforming gains to outdoor users, and wireless backhauling to outdoor small cells. Moreover, a hierarchical interference mitigation scheme is applied to efficiently mitigate cross-tier and co-tier interference. In the second contribution, a multi-hop self-backhauled mmWave communication scenario is studied whereby a joint multi-hop multi-path selection and rate allocation framework is proposed to enable Gbps data rates with reliable communications. Using reinforcement learning techniques, a dynamic and efficient re-routing solution is proposed to cope with blockage and latency constraints. Finally, a risk-sensitive learning solution is leveraged to provide high-reliability and low-latency communications. In summary, the dissertation analyses key trade-offs between (i) capacity and latency, (ii) reliability and network density. Extensive simulation results were carried out to verify the performance gains of the proposed algorithms compared to several baselines and for different network settings. Key findings show significant improvements in terms of higher data rates, lower latency, and reliable communications with some trade-offs. / Tiivistelmä Liikkuvan dataliikenteen ennennäkemättömän kasvun ja verkkojen tihentymisen seurauksena pian käyttöön tulevien viidennen sukupolven (5G) langattomien verkkojen järjestelmäsuunnittelua ja teknologisten mahdollistajien käyttöä on täytynyt lähestyä kokonaan uudesta näkökulmasta. Niinpä tämän väitöstyön johtavana ajatuksena on ehdottaa integroitua verkkoon pääsyn ja runkoverkkoyhteyden muodostamismallia, jossa käyttäjät resursoidaan dynaamisesti ja samalla muodostetaan tehokkaat runkoverkkoyhteydet piensoluille. Tätä varten tutkitaan resurssiallokaation ja häiriöiden lieventämisen yhteisratkaisuja, jotka tukevat kahden tai useamman hypyn yhteyksiä ja samanaikaista runkoverkkoyhteyden luomista millimetriaaltoalueen verkoissa. Työn alkuosa keskittyy usean käyttäjän välitinavusteiseen kahden hypyn solukkoverkkoon, jossa makrotukiasemassa käytetään suurta antenniryhmää muodostamaan samanaikaisesti suuren vahvistuksen antennikeiloja käyttäjälinkeille ja langattomalle runkoyhteysosuudelle. Lisäksi sovelletaan hierarkkista häiriönvaimennusmenetelmää saman kerroksen ja kerrosten välisen häiriön tehokkaaseen vähentämiseen. Työn seuraavassa osassa arvioidaan usean hypyn runkoverkkoyhteyden muodostuksen tutkimusongelmaa millimetrialueen kommunikaatiossa kehittämällä yhdistetty menetelmä usean hypyn monipolkuvalinnalle ja tiedonsiirtoresurssien allokoinnille. Tällä tähdätään gigabittiluokan datanopeuksiin ja luotettavaan tietoliikenteeseen millimetrialueella. Vahvistavan oppimisen tekniikan avulla esitellään dynaaminen ja tehokas uudelleenreitityskonsepti toimimaan esto- ja viiverajoitusten kanssa. Lopuksi hyödynnetään riskisensitiivistä oppimista ja antennidiversiteettitekniikoita suuren luotettavuuden ja pienen latenssin saavuttamiseksi millimetrialueen tiedonsiirrossa. Näiden avulla analysoidaan kaupankäyntiä esimerkiksi (i) kapasiteetin ja latenssin sekä (ii) luotettavuuden ja verkon tiheyden/kuormituksen välillä. Mittavien suoritettujen simulointien avulla osoitetaan ehdotettujen algoritmien suorituskykyedut suhteessa tunnettuihin verrokkeihin useissa eri skenaarioissa. Tulosten perusteella saavutetaan merkittäviä kustannussäästöjä infrastruktuurin ja runkoverkon osalta sekä päästään suuriin datanopeuksiin ja parannuksiin pienen latenssin luotettavassa tietoliikenteessä.

Massive MIMO communication systems have been highlighted as the main technology for physical layer of next generation communication standards, like 5G. While conventional communication between BS and its covered users is performed in orthogonal time-frequency resources, the improved interuser interference mitigation capability provided by the large number of BS antennas enables the BS to communicate with several users in the same time-frequency resource. This better usage of available but scarce spectrum elevates the spectral efficiency to very appreciable levels, and has a similar effect on energy efficiency, since the transmit power is not increased. On the other hand, if the objective is to provide a target performance for the users, the required transmit power in both direct and reverse links can be made inversely proportional to the number of BS antennas employed. In this Doctoral Thesis, several important aspects of massive MIMO systems are systematically investigated aiming to improve their energy and spectral efficiencies. We can enumerate our main contributions as follows. Considering a cellular massive MIMO network, we proposed an optimized assignment policy of training sequences to the users, which is then combined with suitable power control algorithms. We have also investigated the adoption of alternative waveforms in this scenario, such as single-carrier transmission, in order to overcome the issues of conventional OFDM. Our contributions in this topic are to derive analytical performance expressions for a time-domain single-carrier equalizer taking advantage of the large number of BS antennas, and to evaluate and compare the total energy efficiency of OFDM versus single-carrier massive MIMO systems. Finally, considering crowded massive MIMO networks, composed by both human users as well as machine-type communication devices, we proposed an improved random access protocol aiming to decrease the average number of access attempts for the users and decreasing the probability of failed access attempts. / Sistemas de comunicação de múltiplas antenas (multiple-input multiple-output - MIMO) têm se destacado como a principal tecnologia para a camada física dos padrões de comunicação da próxima geração, como o 5G. Enquanto a comunicação convencional entre a estação base (base station - BS) e seus usuários atendidos é realizada em recursos ortogonais de tempo-frequência, a grande capacidade de redução da interferência interusuários possibilitada pelo grande número de antenas da BS habilita a BS a se comunicar com diversos usuários no mesmo recurso tempo-frequência. Este melhor uso do escasso espectro disponível eleva a eficiência espectral a níveis muito apreciáveis, e tem um efeito similar na eficiência energética, pois a potência de transmissão não é aumentada. Por outro lado, se o objetivo é fornecer um desempenho desejado para os usuários, a potência de transmissão necessária em ambos os enlaces direto e reverso pode ser feita inversamente proporcional ao número de antenas na BS. Nesta Tese de Doutorado, diversos aspectos importantes de sistemas MIMO massivo são sistematicamente investigados com o objetivo de melhorar suas eficiências energética e espectral. Pode-se enumerar as principais contribuições alcançadas como se segue. Considerando uma rede celular MIMO massivo, propõe-se uma política de atribuição de sequências de treinamento aos usuários otimizada, a qual é depois combinada com apropriados algoritmos de controle de potência. Também investiga-se a adoção neste cenário de formas de onda alternativas, tal como a transmissão de portadora única, visando superar as deficiências da convencional multiplexagem por divisão de portadoras ortogonais (orthogonal frequency-division multiplexing - OFDM). As principais contribuições obtidas neste tema são derivar expressões de desempenho analíticas para um equalizador de portadora única no domínio do tempo que aproveita o grande número de antenas na BS, e avaliar e comparar a eficiência energética total de sistemas MIMO massivo OFDM versus portadora única. Finalmente, considerando redes MIMO massivo sobrecarregadas, compostas por usuários humanos bem como dispositivos de comunicação do tipo máquina, propõe-se um protocolo de acesso aleatório melhorado visando diminuir o número médio de tentativas de acesso para os usuários e diminuir a probabilidade de falhas de tentativa de acesso.

Dans cette thèse, nous avons développé de nouveaux cadres d'analyse pour analyser et optimiser les futurs réseaux cellulaires à l'aide de la géométrie stochastique et des processus ponctuels. Cette thèse fournit quatre contributions techniques principales.Nous analysons d’abord les réseaux émergents capables de communiquer en utilisant la lumière plutôt que les ondes radio. Dans ce contexte, nous proposons un cadre analytique innovant qui nous permet d’estimer la probabilité de couverture et le débit moyen des réseaux distribués dans l’espace, qui sont utilisés pour mieux comprendre l’optimisation du système.Deuxièmement, nous proposons une méthodologie innovante pour modéliser des réseaux cellulaires spatialement corrélés en utilisant des processus ponctuels non homogènes. L'approche proposée est testée par rapport au déploiement pratique de réseaux cellulaires et s'est révélée pratique et précise. Il est appliqué à l'analyse des réseaux de communication à lumière visible et l'impact de la corrélation spatiale est étudié.Troisièmement, nous abordons le problème ouvert de la modélisation de réseaux cellulaires MIMO massifs. Nous étudions les réseaux cellulaires montants et descendants et proposons de nouvelles limites supérieures et inférieures pour l'efficacité spectrale moyenne, ce qui nous permet d'identifier le nombre optimal d'utilisateurs à desservir dans chaque cellule du réseau et l'impact de plusieurs paramètres clés du système.Quatrièmement, nous présentons et analysons les performances d'un nouvel algorithme de planification prenant en compte les interférences pour une application sur la liaison montante des réseaux cellulaires. L’approche proposée consiste à assourdir certains utilisateurs afin de réduire le niveau d’interférence. La performance réalisable et l'équité utilisateur de l'approche proposée sont discutées et quantifiées analytiquement. / In this thesis, we have developed new analytical frameworks for analyzing and optimizing future cellular networks with the aid of stochastic geometry and point processes. This thesis provides four main technical contributions.First, we analyze emerging networks that can communicate by using light instead of radio waves. In this context, we propose an innovative analytical framework that allows us to estimate the coverage probability and the average rate of spatially distributed networks, which are used to gain insight for system optimization.Second, we propose an innovative methodology for modeling spatially correlated cellular networks by using inhomogeneous point processes. The proposed approach is tested against practical deployment of cellular networks and found to be tractable and accurate. It is applied to the analysis of visible light communication networks, and the impact of spatial correlation is studied.Third, we tackle the open problem of modeling Massive MIMO cellular networks. We study uplink and downlink cellular networks and propose new upper and lower bounds for the average spectral efficiency, which allow us to identify the optimal number of user to serve in each cell of the network and the impact of several key system parameters.Fourth, we introduce and analyze the performance of a new interference-aware scheduling algorithm for application to the uplink of cellular networks. The proposed approach is based on muting some users in order to reduce the level of interference. The achievable performance and the user-fairness of the proposed approach are discussed and quantified analytically.

In a 5 Generation massive Multiple Input Multiple Output radio network, the Channel State Information is playing a central role in the algorithm design and system evaluation. However, Acquisition of Channel State Information consumes system resources (e.g. time, frequency) which in turn decrease the link utilization, i.e. fewer resources left for actual data transmission. This problem is more apparent in a scenario when User Equipment terminals have multi-antennas and it would be beneficial to obtain Channel State Information between Base Station and different User Equipment antennas e.g. for purpose of high rank (number of streams) transmission towards this User Equipment. Typically, in current industrial implementations, in order to not waste system resources, Channel State Information is obtained for only one of the User Equipment antennas which then limits the downlink transmission rank to 1. Hence, we purpose a method based on Deep learning technique. In this paper, multi-layer perception and convolutional neural network are implemented. Data are generated by MATLAB simulator using the parameters provided by Huawei Technologies Co., Ltd. Finally, the model proposed by this project provides the best performance compared to the baseline algorithms. / I ett 5-generationsmassivt massivt multipel-inmatningsradio-nätverk spelar kanalstatens information en central roll i algoritmdesignen och systemutvärderingen. Förvärv av Channel State Information konsumerar emellertid systemresurser (t.ex. tid, frekvens) som i sin tur minskar länkanvändningen, dvs färre resurser kvar för faktisk dataöverföring. Detta problem är mer uppenbart i ett scenario när användarutrustningsterminaler har flera antenner och det skulle vara fördelaktigt att erhålla kanalstatusinformation mellan basstationen och olika användarutrustningsantenner, t.ex. för överföring av hög rang (antal strömmar) till denna användarutrustning. I nuvarande industriella implementeringar erhålls kanalstatusinformation för endast en av användarutrustningens antenner för att inte slösa bort systemresurser, vilket sedan begränsar överföringsrankningen för nedlänkning till 1. Därför syftar vi på en metod baserad på Deep learning-teknik. I detta dokument implementeras flerskiktsuppfattning och inblandat neuralt nätverk. Data genereras av MATLAB-simulator med hjälp av parametrarna som tillhandahålls av Huawei Technologies Co., Ltd. Slutligen ger modellen som föreslås av detta projekt bästa prestanda jämfört med baslinjealgoritmerna.

This thesis presents a comparison of a GPU implementation of the Conjugate Residual method as a sequence of generic library kernels against implementations ofthe method with custom kernels to expose the performance gains of a keyoptimization strategy, kernel fusion, for memory-bound operations which is to makeefficient reuse of the processed data. For massive MIMO the iterative solver is to be employed at the linear detection stageto overcome the computational bottleneck of the matrix inversion required in theequalization process, which is 𝒪(𝑛3) for direct solvers. A detailed analysis of howone more of the Krylov subspace methods that is feasible for massive MIMO can beimplemented on a GPU as a unified kernel is given. Further, to show that kernel fusion can improve the execution performance not onlywhen the input data is large matrices-vectors as in scientific computing but also inthe case of massive MIMO and possibly similar cases where the input data is a largenumber of small matrices-vectors that must be processed in parallel.In more details, focusing on the small number of iterations required for the solver toachieve a close enough approximation of the exact solution in the case of massiveMIMO, and the case where the number of users matches the size of a warp, twodifferent approaches that allow to fully unroll the algorithm and gradually fuse allthe separate kernels into a single, until reaching a top-down hardcodedimplementation are proposed and tested. Targeting to overcome the algorithms computational burden which is the matrixvector product, further optimization techniques such as two ways to utilize the faston-chip memories, preloading the matrix in shared memory and preloading thevector in shared memory, are tested and proposed to achieve high efficiency andhigh parallelism.

This thesis considers the modulation division based on the concept of uniquely factorable constellation pair (UFCP) and uniquely decodable constellation group (UDCG) in multiuser wireless communication networks. We first consider a two-hop relay network consisting of two single-antenna users and a two-antenna relay node, for which a novel distributed concatenated Alamouti code is devised. This new design allows the source and relay nodes to transmit their own information to the destination node concurrently at the symbol level with the aid of the UFCP generated from both PSK and square QAM constellations as well as by jointly processing the noisy signals received at the relay node. Moreover, an asymptotic symbol error probability (SEP) formula is derived for the ML receiver, showing that the maximum diversity gain function is achieved, which is proportional to $\ln \mathtt{SNR}/\mathtt{SNR}^2$. Then, we concentrate on the point-to-point correlated multiple-input and multiple-output (MIMO) communication systems where full knowledge of channel state information (CSI) is available at the receiver and only the first- and second-order statistics of the channels are available at the transmitter. When the number of antenna elements of both ends goes to infinity while keeping their ratio constant, the asymptotic SEP analysis is carried out for either optimally precoded or uniformly precoded correlated large MIMO fading channels using the zero-forcing (ZF) detector with equally likely PAM, PSK or square QAM constellations. For such systems, we reveal some very nice structures which inspire us to explore two very useful mathematical tools (i.e., the Szego's theorem on large Hermitian Toeplitz matrices and the well-known limit: $\lim_{x\to\infty}(1+1/x)^x=e$), for the systematic study of asymptotic behaviors on their error performance. This new approach enables us to attain a very simple expression for the SEP limit as the number of the available antenna elements goes to infinity. In what follows, the problem of precoder design using a zero-forcing decision-feedback (ZF-DF) detector is also addressed. For such a MIMO system, our principal goal is to efficiently design an optimal precoder that minimizes the asymptotic SEP of the ZF-DF detector under a perfect decision feedback. By fully taking advantage of the product majorization relationship among eigenvalues, singular-values and Cholesky values of the precoded channel matrix parameters, a necessary condition for the optimal solution to satisfy is first developed and then the structure of the optimal solution is characterized. With these results, the original non-convex problem is reformulated into a convex one that can be efficiently solved by using an interior-point method. In addition, by scaling up the antenna array size of both terminals without bound for such a network, we propose a novel method as we did for the ZF receiver scenario to analyze the asymptotic SEP performance of an equal-diagonal QRS precoded large MIMO system when employing an abstract Toeplitz correlation model for the transmitter antenna array. This new approach has a simple expression with a fast convergence rate and thus, is efficient and effective for error performance evaluation. For multiuser communication networks, we first consider a discrete-time multiple-input single-output (MISO) Gaussian broadcast channel (BC) where perfect CSI is available at both the transmitter and all the receivers. We propose a flexible and explicit design of a uniquely decomposable constellation group (UDCG) based on PAM and rectangular QAM constellations. With this new concept, a modulation division (MD) transmission scheme is developed for the considered MISO BC. The proposed MD scheme enables each receiver to uniquely and efficiently recover their desired signals from the superposition of mutually interfering cochannel signals in the absence of noise. Using max-min fairness as a design criterion, the optimal transmitter beamforming problem is solved in a closed-form for two-user MISO BC. Then, for a general case with more than two receivers, a user-grouping based beamforming scheme is developed, where the grouping method, beamforming vector design and power allocation problems are addressed by employing weighted max-min fairness. Then, we consider an uplink massive single-input and multiple-output (SIMO) network consisting of a base station (BS) and several single-antenna users. To recover the transmitted signal matrix of all the users when the antenna array size is large, a novel multi-user space-time modulation (MUSTM) scheme is proposed for the considered network based on the explicit construction of QAM uniquely-decomposable constellation groups (QAM-UDCGs). In addition, we also develop a sub-constellation allocation method at the transmitter side to ensure the signal matrix is always invertible. In the meanwhile, an efficient training correlation receiver (TCR) is proposed which calculates the correlation between the received sum training signal vector and the sum information carrying vector. Moreover, the optimal power allocation problems are addressed by maximizing the coding gain or minimizing the average SEP of the received sum signal under both average and peak power constraints on each user. The proposed transmission scheme not only allows the transmitted signals with strong mutual interference to be decoded by a simple TCR but it also enables the CSI of all the users to be estimated within a minimum number of time slots equal to that of the users. Comprehensive computer simulations are carried out to verify the effectiveness of the proposed uniquely decomposable space-time modulation method in various network topologies and configurations. Our modulation division method will be one of the promising technologies for the fifth generation (5G) communication systems. / Dissertation / Doctor of Philosophy (PhD)

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.

The next generation 5G systems has attracted more and more attention to the rapid development of information and communication technology (ICT). Positioning is an important research area in 5G mobile networks, especially in M-MIMO wireless networks. Because the base station needs to have a knowledge of the position of the user equipment, which can direct the signal to the user equipment while reducing interference and hence improve the user throughput. But the problem is that it is difficult to determine position when users are in NLOS scenarios. In the master thesis, we conclude the existing positioning method by the literature review, as well as get the inspiration to design our research environment. Through the MATLAB simulation, we restore the Madrid city three-dimensional environment, design and confirm a new method for the positioning by the AoA and the path power in NLOS scenarios. Our results indicate that the new positioning method demonstrates a nice performance of the accurate level even in the NLOS scenarios, in which the error of the position of the user equipment is within o.5 meters. Although the method is very sensitive to the strange signal paths, we can use the path with the strongest path power to overcome this problem. Meanwhile, the performance of our new positioning method is not impacted by the existence of the scattering loss. / Nästa generations 5G-system har fått mer och mer uppmärksamhet åt den snabba utvecklingen av informations- och kommunikationsteknik (IKT). Positionering är ett viktigt forskningsområde i 5G-mobilnät, särskilt i trådlösa M-MIMO-nätverk. Eftersom basstationen behöver ha kunskap om användarutrustningens position, vilket kan rikta signalen till användarutrustningen samtidigt som störningen minskas och därmed förbättra användarens genomströmning. Men problemet är att det är svårt att bestämma positionen när användare är i NLOS-scenarier. I magisteruppsatsen avslutar vi den befintliga positioneringsmetoden genom litteraturöversikten och får inspiration att designa vår forskningsmiljö. Genom MATLAB-simuleringen återställer vi stadens tredimensionella miljö, designar och bekräftar en ny metod för positionering av AoA och bankraften i NLOS-scenarier. Våra resultat indikerar att den nya positioneringsmetoden visar en bra prestanda för den exakta nivån även i NLOS-scenarierna, där felet i användarutrustningens position ligger inom o.5 meter. Även om metoden är mycket känslig för de konstiga signalvägarna kan vi använda banan med den starkaste vägkraften för att övervinna detta problem. Samtidigt påverkas inte vår nya positioneringsmetod av spridningsförlusten.