Literatura científica selecionada sobre o tema "Distributed massive MIMO"

Abstract As an innovative implementation, Cell-Free Massive Multiple Input Multiple Output (MIMO) has appeared in typical Cellular Massive MIMO Networks. This protocol doesn’t recognize cells, as shown by its name, even though a significant number of APs operate on the same frequency/time resources. Connection from multiple distributed access points through joint signal processing is called Cell-Free Massive MIMO. The Cell-Free Massive MIMO System, a contrast between Cell-Free Massive MIMO Systems and Distributed Massive MIMO, the prime focus in this thesis is on Cell-free Massive MIMO and, along with this discussion, on Cell-free Massive MIMO signal processing, Channel Estimation, Uplink Signal Detection, Cumulative Distribution, Spectral Efficiency & Ubiquitous Cell-Free Massive MIMO Model. Ubiquitous Cell-free Massive MIMO contributes to a Massive MIMO system, a distributed system that implements consistent user-centre distribution to solve that constraint of mobile phone interferences as well as to introduce macro-diversity. We investigated the Cell Radius at different locations in CDF with Spectral Efficiency [bits/s/hertz]. Cell-Free Massive MIMO is an evidence-based preventive of massive MIMOs with distributed high percentage APs that serve even lower margins. The cell-free model is not segregated into cells and any individual is concurrently represented by every Access point.

Distributed MIMO (D-MIMO) systems are expected to play a key role in deployments for future mobile communications. Together with massive MIMO technology, D-MIMO aims to maximize the spectral efficiency and data rate in mobile networks. This paper proposes a deep study on the spectral efficiency of D-MIMO systems for essential channel parameters, such as the channel power balance or the correlation between propagation channels. For that purpose, several propagation channels were acquired in both anechoic and reverberation chambers and were emulated using channel simulators. In addition, several frequency bands were studied, both the sub–6 GHz band and mmWave band. The results of this study revealed the high influence of channel correlation and power balance on the physical channel performance. Low-correlated and high-power balance propagation channels show better performances than high correlated and power unbalance channels in terms of spectral efficiency. Given these results, it will be fundamental to take into account the spectral efficiency of D-MIMO systems when designing criteria to establish multi-connectivity in future mobile network deployments.

Massive MIMO or Large Scale MIMO is a promising solution for achieving superior data rates in 5G communication systems. However, it has limitation in term of scalability and coverage for users that has highly spatial separation. Distributed massive MIMO is expected to enhance these drawbacks. One main problem arises in this scheme is the MIMO interference channel condition that can be copied by interference alignment algorithm. The main consideration for interference alignment algorithm in distributed Massive MIMO is to achieve low complexity precoding to eliminate interference channel condition and to design efficient hardware architecture for its implementation. Previous research regarding IA for Distributed Massive MIMO indicate that the complexity issues is still not widely discussed. This paper proposed the low complexity IA scheme for large scale MIMO system based on limited interferer and the implementation of low cost interference alignment and wireless synchronization for distributed MIMO using software defined radio hardware. From the simulation result, it shows that limited interferer IA algorithm achieve acceptable BER performance, i.e. in order of 10-3. The hardware implementation of the IA precoding matrix computation is also discussed. Based on the experiment, it is show that the proposed algorithm and architecture achieved higher hardware performance compared to the linear IA.

Cell-free massive Multi-input Multi-output (MIMO) can offer higher spectral efficiency compared with cellular massive MIMO by providing services to users through the collaboration of distributed APs, and cell-free massive MIMO systems with distributed operations are attracting a great deal of industry attention due to their simplicity and ease of deployment. This paper aims to find an optimal solution for energy efficiency in the downlink operation in the Industrial Internet based on cell-free massive MIMO systems with distributed operations. A system model is proposed, and a theoretical analysis on energy efficiency is presented. The optimization problem of efficient downlink operation is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is further decomposed into two sub-problems, i.e., maximizing the sum-rate of the downlink transmission and optimizing the total energy consumption. The two sub-problems are addressed via AP selection and power allocation, respectively. The simulation results demonstrate that our algorithms can significantly improve the energy efficiency with low computational complexity in comparison with traditional distributed cell-free massive MIMO. Even in the presence of pilot contamination, the proposed algorithms can still provide significant energy efficiency when a large number of IoTDs are connected.

Distributed massive MIMO is an arising answer for improve 5G limit, throughput, and QoE inside. There are various ways to advance indoor execution. The indoor cell network should have totally different qualities and needs from its outside partner, yet a consistent encounter should be kept up with while moving between the two. New arrangements, like small cells and distributed antenna systems (DAS), have been added to every portable age to work on the presentation and quality of experience (QoE) for indoor clients. Notwithstanding, the goal lines are moved with each new age of purpose cases, and, surprisingly, more significant levels of execution and QoE are required. The improvement in the indoor experience needed in the 5G period should be extensive. The 5G guidelines empower the organizations to help tremendous quantities of clients or associated gadgets in little spaces (up to 1 million for each square kilometer), while consuming information at multi-gigabit speeds and with low idleness and high unwavering quality. This paper give a brief review on improvement in channel capacity and QoE using massiveness of MIMO technology for 5G environment in distributed manner.

Distributed massive multiple-input multiple-output (D-mMIMO) is one of the key candidate technologies for future wireless networks. A D-mMIMO system has multiple, geographically distributed, access points (APs) jointly serving its users. First of all, this paper reports on where to position these APs to minimize the overall transmit power in actual deployments. As a second contribution, we show that it is essential to take into account both the radiation pattern of the antenna array and the environment information when optimizing AP placement. Neglecting the radiation pattern and environment information, as generally assumed in the literature, can lead to a power penalty in the order of 15 dB and 20 dB, respectively. These results have been obtained by formulating the AP placement optimization problem as a combinatorial optimization problem, which can be solved with different approaches where different channel models are applied. The proposed graph-based channel model drastically lowers the computational time with respect to using an ray-tracing simulator (RTS) for channel evaluation. The performance of the graph-based approach is validated via the RTS, showing that it achieves 5 dB power saving on average compared with a Euclidean distance-based approach, which is the most commonly used approach in the literature.

In this thesis, the uplink of distributed massive MIMO where a large number of distributed access point antennas simultaneously serve a relatively smaller number of users is considered. Lattice network coding (LNC), which comprises compute and forward (C&F) and integer forcing (IF), is employed to avoid the potentially enormous backhaul load. Firstly, novel algorithms for coefficient selection in C&F are proposed. For the first time, we propose a low polynomial complexity algorithm to find the optimal solution for the complex valued case. Then we propose a sub-optimal simple linear search algorithm which is conceptually sub-optimal, however numerical results show that the performance degradation is negligible compared to the exhaustive method. The complexity of both algorithms are investigated both theoretically and numerically. The results show that our proposed algorithms achieve better performance-complexity trade-offs compared to the existing algorithms. Both algorithms are suitable for lattices over a wide range of algebraic integer domains. Secondly, the performance of LNC in a realistic distributed massive MIMO model (including fading, pathloss and correlated shadowing) is investigated in this thesis. By utilising the characteristic of pathloss, a low complexity coefficient selection algorithm for LNC is proposed. A greedy algorithm for selecting the global coefficient matrix is proposed. Comprehensive comparisons between LNC and some other promising linear strategies for massive MIMO, such as small cells (SC), maximum ratio combining (MRC), and minimum mean square error (MMSE) are also provided. Numerical results reveal that LNC not only reduces the backhaul load, but also provides uniformly good service to all users in a wide range of applications. Thirdly, the inevitable loss of information due to the quantisation and modulo operation under different backhaul constraints are investigated. An extended C\&F with flexible cardinalities is proposed to adapt to the different backhaul constraints. Numerical results show that by slightly increasing the cardinality, the gap between C\&F to the infinite backhaul case can be significantly reduced.

Massive MIMO is an emerging technology for future wireless systems that has received much attention from both academia and industry recently. The most prominent feature of Massive MIMO is that the base station is equiped with a large number of antennas. It is therefore important to create scalable architectures to enable simple deployment in different configurations. In this thesis, a distributed architecture for performing the baseband processing in a massive OFDM MU-MIMO system is proposed and analyzed. The proposed architecture is based on connecting several identical nodes in a K-ary tree. It is shown that, depending on the chosen algorithms, all or most computations can be performed in a distrbuted manner. Also, the computational load of each node does not depend on the number of nodes in the tree (except for some timing issues) which implies simple scalability of the system. It is shown that it should be enough that each node contains one or two complex multipliers and a few complex adders running at a couple of hundres MHz to support specifications similar to LTE. Additionally the nodes must communicate with each other over links with data rates in the order of some Gbps. Finally, a VHDL implementation of the system is proposed. The implementation is parameterized such that a system can be generated from a given specification.

Massive multiple-input multiple-output (MIMO) est l'une des principales technologies de couche physique permettant de répondre à l'exigence de capacité massive exigée par les systèmes 5G. Massive MIMO exploite l'utilisation de grands réseaux d'antennes à la station de base (gNB) pour desservir simultanément plusieurs utilisateurs via un multiplexage spatial sur un canal. Massive MIMO s'appuie sur des pilotes de liaison montante pour obtenir des informations sur l'état du canal (CSI), en exploitant la réciprocité de canal et l'opération de duplexage par répartition dans le temps (TDD). En réalité, cependant, le canal de communication ne se compose pas seulement du canal physique dans l'air, mais également des frontaux radiofréquences (RF) des émetteurs-récepteurs qui ne sont pas réciproques. Par conséquent, le système doit être étalonné avant que la réciprocité des canaux puisse être exploitée. Le MIMO massif distribué avec des antennes spatialement séparées donne une efficacité spectrale plus élevée et une zone de couverture améliorée, par rapport au MIMO massif colocalisé. Néanmoins, la coordination d'un grand nombre d'unités radio distantes (RRU), formant le gNB, est un grand défi. Par conséquent, l'étalonnage de réciprocité TDD et la synchronisation RRU sont les deux facteurs clés pour permettre le MIMO massif distribué. Dans cette thèse, nous nous concentrons sur le déploiement d'un système MIMO massif distribué sur le banc de test Open Air Interface (OAI) 5G et sur l'application d'algorithmes d'étalonnage de canal en temps réel afin d'évaluer leurs performances. Les principales contributions peuvent être résumées comme suit. Tout d'abord, nous implémentons la fonction de précodeur et la parallélisation multi-thread pour des performances optimales des divisions fonctionnelles dans notre système Cloud-RAN (C-RAN) tout en augmentant le nombre de RRU actifs. Deuxièmement, nous présentons les solutions à faible coût pour les problèmes matériels résultant de nos RRUs formant le système d'antenne distribuée (DAS). De plus, nous analysons les méthodes utilisées pour la synchronisation et l'étalonnage temps/fréquence/phase dans notre banc d'essai. Troisièmement, nous avons effectué des mesures en temps réel sur notre C-RAN banc d'essai afin de prouver la synchronisation stable et précise entre plusieurs RRUs et de confirmer l'efficacité du schéma d'étalonnage de réciprocité proposé par groupe. Quatrièmement, nous fournissons une vérité terrain pour l'évaluation du cadre d'étalonnage OTA (over-the-air) basé sur le groupe grâce à des mesures de canal sur un DAS simulé. Enfin, grâce à l'étalonnage de réciprocité TDD, nous avons construit un banc d'essai à entrées multiples et sortie unique (MISO) basé sur la plateforme OAI, afin de faciliter l'évaluation de l'étalonnage relatif et d'accéder simultanément aux performances des prototypes d'antennes MIMO conçu par l'équipe d'Orange labs
Massive multiple-input multiple-output (MIMO) is one of the key enabling physical layer technologies to address the massive capacity requirement demanded by 5G systems. Massive MIMO exploits the use of large antenna arrays at the base station (gNB) to simultaneously serve multiple users through spatial multiplexing over a channel. Massive MIMO relies on uplink pilots to obtain channel state information (CSI), exploiting channel reciprocity and time division duplexing (TDD) operation. In reality, however, the communication channel does not only consist of the physical channel in the air, but also the radio-frequency (RF) front-ends in transceivers which are not reciprocal. Therefore the system needs to be calibrated before channel reciprocity can be exploited. Distributed massive MIMO with spatially separated antennas gives a higher spectral efficiency and enhanced coverage area, compared to collocated massive MIMO. Nevertheless, coordinating a large number of remote radio units (RRUs), forming the gNB, is a big challenge. Hence, TDD reciprocity calibration and RRU synchronization are the two key factors to enable distributed massive MIMO. In this thesis, we focus on deploying a distributed massive MIMO system on the OpenAirInterface (OAI) 5G testbed and applying real-time channel calibration algorithms in order to evaluate their performance. The main contributions can be summarized as follows. First, we implement the precoder function and the multi-thread parallelization for the optimal performance of the functional splits in our Cloud-RAN (C-RAN) system while increasing the number of active RRUs. Second, we present the low-cost solutions for the hardware issues resulting from our RRUs forming the distributed antenna system (DAS). Also, we analyze the methods used for time/frequency/phase synchronization and calibration in our testbed. Third, we carried out real-time measurements on our C-RAN testbed in order to prove the stable and precise synchronization between several RRUs and confirm the efficiency of the proposed group-based reciprocity calibration scheme. Fourth, we provide a ground truth for the evaluation of the group-based over-the-air (OTA) calibration framework through channel measurements on a simulated DAS. Last but not least, enabled by TDD reciprocity calibration, we built up a multiple-input single-output (MISO) testbed based on the OAI platform, in order to facilitate the evaluation of relative calibration and simultaneously access the performance of the MIMO antenna prototypes designed by the team in Orange labs

A new communication paradigm is foreseen for beyond 2020 society, due to the emergence of new broadband services and the Internet of Things era. The set of requirements imposed by these new applications is large and diverse, aiming to provide a ubiquitous broadband connectivity. Research community has been working in the last decade towards the definition of the 5G mobile wireless networks that will provide the proper mechanisms to reach these challenging requirements. In this framework, three key research directions have been identified for the improvement of capacity in 5G: the increase of the spectral efficiency by means of, for example, the use of massive MIMO technology, the use of larger amounts of spectrum by utilizing the millimeter wave band, and the network densification by deploying more base stations per unit area. This dissertation addresses densification as the main enabler for the broadband and massive connectivity required in future 5G networks. To this aim, this Thesis focuses on the study of the UDN. In particular, a set of technology enablers that can lead UDN to achieve their maximum efficiency and performance are investigated, namely, the use of higher frequency bands for the benefit of larger bandwidths, the use of massive MIMO with distributed antenna systems, and the use of distributed radio resource management techniques for the inter-cell interference coordination. Firstly, this Thesis analyzes whether there exists a fundamental performance limit related with densification in cellular networks. To this end, the UDN performance is evaluated by means of an analytical model consisting of a 1-dimensional network deployment with equally spaced BS. The inter-BS distance is decreased until reaching the limit of densification when this distance approaches 0. The achievable rates in networks with different inter-BS distances are analyzed for several levels of transmission power availability, and for various types of cooperation among cells. Moreover, UDN performance is studied in conjunction with the use of a massive number of antennas and larger amounts of spectrum. In particular, the performance of hybrid beamforming and precoding MIMO schemes are assessed in both indoor and outdoor scenarios with multiple cells and users, working in the mmW frequency band. On the one hand, beamforming schemes using the full-connected hybrid architecture are analyzed in BS with limited number of RF chains, identifying the strengths and weaknesses of these schemes in a dense-urban scenario. On the other hand, the performance of different indoor deployment strategies using HP in the mmW band is evaluated, focusing on the use of DAS. More specifically, a DHP suitable for DAS is proposed, comparing its performance with that of HP in other indoor deployment strategies. Lastly, the presence of practical limitations and hardware impairments in the use of hybrid architectures is also investigated. Finally, the investigation of UDN is completed with the study of their main limitation, which is the increasing inter-cell interference in the network. In order to tackle this problem, an eICIC scheduling algorithm based on resource partitioning techniques is proposed. Its performance is evaluated and compared to other scheduling algorithms under several degrees of network densification. After the completion of this study, the potential of UDN to reach the capacity requirements of 5G networks is confirmed. Nevertheless, without the use of larger portions of spectrum, a proper interference management and the use of a massive number of antennas, densification could turn into a serious problem for mobile operators. Performance evaluation results show large system capacity gains with the use of massive MIMO techniques in UDN, and even greater when the antennas are distributed. Furthermore, the application of ICIC techniques reveals that, besides the increase in system capacity, it brings significant energy savings to UDNs.
A partir del año 2020 se prevé que un nuevo paradigma de comunicación surja en la sociedad, debido a la aparición de nuevos servicios y la era del Internet de las cosas. El conjunto de requisitos impuesto por estas nuevas aplicaciones es muy amplio y diverso, y tiene como principal objetivo proporcionar conectividad de banda ancha y universal. En las últimas décadas, la comunidad científica ha estado trabajando en la definición de la 5G de redes móviles que brindará los mecanismos necesarios para garantizar estos requisitos. En este marco, se han identificado tres mecanismos clave para conseguir el necesario incremento de capacidad de la red: el aumento de la eficiencia espectral a través de, por ejemplo, el uso de tecnologías MIMO masivas, la utilización de mayores porciones del espectro en frecuencia y la densificación de la red mediante el despliegue de más estaciones base por área. Esta Tesis doctoral aborda la densificación como el principal mecanismo que permitirá la conectividad de banda ancha y universal requerida en la 5G, centrándose en el estudio de las Redes Ultra Densas o UDNs. En concreto, se analiza el conjunto de tecnologías habilitantes que pueden llevar a las UDNs a obtener su máxima eficiencia y prestaciones, incluyendo el uso de altas frecuencias para el aprovechamiento de mayores anchos de banda, la utilización de MIMO masivo con sistemas de antenas distribuidas y el uso de técnicas de reparto de recursos distribuidas para la coordinación de interferencias. En primer lugar, se analiza si existe un límite fundamental en la mejora de las prestaciones en relación a la densificación. Con este fin, las prestaciones de las UDNs se evalúan utilizando un modelo analítico de red unidimensional con BSs equiespaciadas, en el que la distancia entre BSs se disminuye hasta alcanzar el límite de densificación cuando ésta se aproxima a 0. Las tasas alcanzables en redes con distintas distancias entre BSs son analizadas, considerando distintos niveles de potencia disponible en la red y varios grados de cooperación entre celdas. Además, el comportamiento de las UDNs se estudia junto al uso masivo de antenas y la utilización de anchos de banda mayores. Más concretamente, las prestaciones de ciertas técnicas híbridas MIMO de precodificación y beamforming se examinan en la banda milimétrica. Por una parte, se analizan esquemas de beamforming en BSs con arquitectura híbrida en función de la disponibilidad de cadenas de radiofrecuencia en escenarios exteriores. Por otra parte, se evalúan las prestaciones de ciertos esquemas de precodificación híbrida en escenarios interiores, utilizando distintos despliegues y centrando la atención en los sistemas de antenas distribuidos o DAS. Además, se propone un algoritmo de precodificación híbrida específico para DAS, y se evalúan y comparan sus prestaciones con las de otros algoritmos de precodificación utilizados. Por último, se investiga el impacto en las prestaciones de ciertas limitaciones prácticas y deficiencias introducidas por el uso de dispositivos no ideales. Finalmente, el estudio de las UDNs se completa con el análisis de su principal limitación, el nivel creciente de interferencia en la red. Para ello, se propone un algoritmo de control de interferencias basado en la partición de recursos. Sus prestaciones son evaluadas y comparadas con las de otras técnicas de asignación de recursos. Tras este estudio, se puede afirmar que las UDNs tienen gran potencial para la consecución de los requisitos de la 5G. Sin embargo, sin el uso conjunto de mayores porciones del espectro, adecuadas técnicas de control de la interferencia y el uso masivo de antenas, las UDNs pueden convertirse en serios obstáculos para los operadores móviles. Los resultados de la evaluación de prestaciones de estas tecnologías confirman el gran aumento de la capacidad de las redes mediante el uso masivo de antenas y la introducción de mecanismos de I
A partir de l'any 2020 es preveu un nou paradigma de comunicació en la societat, degut a l'aparició de nous serveis i la era de la Internet de les coses. El conjunt de requeriments imposat per aquestes noves aplicacions és ampli i divers, i té com a principal objectiu proporcionar connectivitat universal i de banda ampla. En les últimes dècades, la comunitat científica ha estat treballant en la definició de la 5G, que proveirà els mecanismes necessaris per a garantir aquests exigents requeriments. En aquest marc, s'han identificat tres mecanismes claus per a aconseguir l'increment necessari en la capacitat: l'augment de l'eficiència espectral a través de, per exemple, l'ús de tecnologies MIMO massives, la utilització de majors porcions de l'espectre i la densificació mitjançant el desplegament de més estacions base per àrea. Aquesta Tesi aborda la densificació com a principal mecanisme que permetrà la connectivitat de banda ampla i universal requerida en la 5G, centrant-se en l' estudi de les xarxes ultra denses (UDNs). Concretament, el conjunt de tecnologies que poden dur a les UDNs a la seua màxima eficiència i prestacions és analitzat, incloent l'ús d'altes freqüències per a l'aprofitament de majors amplàries de banda, la utilització de MIMO massiu amb sistemes d'antenes distribuïdes i l'ús de tècniques distribuïdes de repartiment de recursos per a la coordinació de la interferència. En primer lloc, aquesta Tesi analitza si existeix un límit fonamental en les prestacions en relació a la densificació. Per això, les prestacions de les UDNs s'avaluen utilitzant un model analític unidimensional amb estacions base equidistants, en les quals la distància entre estacions base es redueix fins assolir el límit de densificació quan aquesta distància s'aproxima a 0. Les taxes assolibles en xarxes amb diferents distàncies entre estacions base s'analitzen considerant diferents nivells de potència i varis graus de cooperació entre cel·les. A més, el comportament de les UDNs s'estudia conjuntament amb l'ús massiu d'antenes i la utilització de majors amplàries de banda. Més concretament, les prestacions de certes tècniques híbrides MIMO de precodificació i beamforming s'examinen en la banda mil·limètrica. D'una banda, els esquemes de beamforming aplicats a estacions base amb arquitectures híbrides és analitzat amb disponibilitat limitada de cadenes de radiofreqüència a un escenari urbà dens. D'altra banda, s'avaluen les prestacions de certs esquemes de precodificació híbrida en escenaris d'interior, utilitzant diferents estratègies de desplegament i centrant l'atenció en els sistemes d' antenes distribuïdes (DAS). A més, es proposa un algoritme de precodificació híbrida distribuïda per a DAS, i s'avaluen i comparen les seues prestacions amb les de altres algoritmes. Per últim, s'investiga l'impacte de les limitacions pràctiques i altres deficiències introduïdes per l'ús de dispositius no ideals en les prestacions de tots els esquemes anteriors. Finalment, l' estudi de les UDNs es completa amb l'anàlisi de la seua principal limitació, el nivell creixent d'interferència entre cel·les. Per tractar aquest problema, es proposa un algoritme de control d'interferències basat en la partició de recursos. Les prestacions de l'algoritme proposat s'avaluen i comparen amb les d'altres tècniques d'assignació de recursos. Una vegada completat aquest estudi, es pot afirmar que les UDNs tenen un gran potencial per aconseguir els ambiciosos requeriments plantejats per a la 5G. Tanmateix, sense l'ús conjunt de majors amplàries de banda, apropiades tècniques de control de la interferència i l'ús massiu d'antenes, les UDNs poden convertir-se en seriosos obstacles per als operadors mòbils. Els resultats de l'avaluació de prestacions d' aquestes tecnologies confirmen el gran augment de la capacitat de les xarxes obtingut mitjançant l'ús massiu d'antenes i la introducci
Giménez Colás, S. (2017). Ultra Dense Networks Deployment for beyond 2020 Technologies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86204
TESIS

Cette thèse a pour object la validation des machines massivement parallèles à passage de messages. Une stratégie de validation hiérarchique est proposée, autour des trois étapes suivantes: un test de routage, un test de mémoire et un test distribué des processeurs. Cette stratégie de validation est complétée par une phase de reconfiguration statique de la machine qui permet son exploitation après validation. Cette stratégie a été appliquée à une machine massivement parallèle, appelée, «machine cellulaire». Le test de routage est basé sur la technique de «Scan périphérique» et la norme IEEE 1149. 1. Le test de mémoire a consisté à évaluer la possibilité d'appliquer les algorithmes classiques de test de mémoire aux mémoires distribuées noyées dans une architecture massivement parallèle. Le test des processeurs est basé sur une stratégie de diagnostic distribué à travers le réseau: cette stratégie est fondée sur un test mutuel des nœuds en se basant sur un algorithme dont l'évolution dépend de l'état du réseau. La génération du programme de test exécuté par chaque processeur a été étudiée en tenant compte des contraintes de la machine cellulaire. La phase de validation permettant de déterminer l'ensemble des liens et des nœuds défectueux, il s'agit alors de proposer une reconfiguration statique de la machine de telle sorte qu'elle puisse supporter des applications avec les seuls nœuds corrects. Des algorithmes de routage tolérants aux fautes ont été proposés et évalués.

Massive multiple-input multiple-output (MIMO) is widely acknowledged as the key enabling technology for the next-generation mobile communication networks. Pilot contamination (PC) is one of the key performance limiting factors for realizing the full potential offered by massive MIMO. This thesis investigates various aspects of PC in massive MIMO. First, we present methods to mitigate PC through pilot sequence design and location-aware pilot allocation. Second, we examine the impact of PC attack on the performance of massive MIMO. Third, we investigate practical aspects of massive MIMO deployment in wireless networks through distributed antenna arrays (DAAs). In the first part of the thesis, we present two methods to mitigate PC. The first method, i.e., the pilot sequence design method, generates pilot sequences and devises power allocation at base stations (BSs) for downlink transmission. The pilot sequences and the proposed power allocation ensure that the predefined signal-to-interference-plus-noise ratio (SINR) requirements of all users are met. We derive new closed-form expressions for the user capacity and the user capacity region. Built upon these expressions, we develop an algorithm to obtain the required pilot sequences and power allocation. The second method, i.e., the location-aware pilot allocation method, exploits the behavior of line-of-sight (LOS) interference among the users and allocates the same pilot sequence to the users with small LOS interference. Numerical results demonstrate that the proposed method significantly outperforms the existing methods. In the second part of the thesis, we present an active attack strategy in massive MIMO, where correlated pilots are used. The use of correlated pilots in massive MIMO has some intrinsic limitations, which can be exploited by an attacker to increase PC. To this end, we analyze the user capacity-achieving pilot sequence design for vulnerabilities that can be exploited by an active attacker. Furthermore, we present an effective active attack strategy. The active attacker transmits a known pilot sequence during the uplink training and artificial noise during the downlink data transmission phase. Numerical results reveal that the user capacity region of the network is significantly reduced in the presence of an active attack. Consequently, the SINR requirements of all the users are no longer guaranteed in the presence of active attackers. In the third part of the thesis, we investigate downlink power control in DAA massive MIMO. We assume that the BS in each cell consists of multiple DAAs, which are deployed in arbitrary locations. Due to the spatial separation between antenna arrays, power control in DAA massive MIMO is a challenging problem as compared to conventional co-located massive MIMO. Based on the channel estimates obtained via uplink training, the BSs perform maximum ratio transmission for downlink. We then derive a closed-form spectral efficiency expression, where the channels are subject to correlated fading. Utilizing the derived expression, we propose a max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. The methods, algorithms, and techniques presented in this thesis give practical insights for solving challenges related to the deployment of massive MIMO in the next generation wireless networks.

碩士
國立交通大學
電信工程研究所
104
We investigate transform domain based estimators for both two-dimensional (2D) and three(full)-dimensional (3D) massive MIMO channels with single or multiple narrowband arriving clusters. The estimators take advantage of the channel's transform domain sparsity but require side information about the energy spectrum of the channel to determine the dominant channel rank and the associated subspace. Closed-form expressions which relate the dominant signal subspace support to the mean angle of arrival (AoA) and the corresponding angle spread (AS), which used to be estimated by iterative algorithms, are obtained. We analyze the mean squared error (MSE) of the estimators and the bit error rate (BER) performance of the corresponding (adaptive) zero-forcing and MMSE receiver in the presence of channel estimation error. We compare the MSE- and BER-minimizing channel estimators and study the associated rank distribution and performance. Optimal power allocation between pilot and data symbols is investigated and the resulting performance analyzed. Various numerical results are provided to examine behaviors of our channel estimators and related receivers and to validate the superiority of the proposed schemes. We show that the resulting receivers perform Rake-like combining in the spatial domain that automatically capture most of the energy of the desired signal while rejecting noise and interference from other directions.

Compressed sensing has been a very active area of research and several elegant algorithms have been developed for the recovery of sparse signals in the past few years. However, most of these algorithms are either computationally expensive or make some assumptions that are not suitable for all real world problems. Recently, focus has shifted to Bayesian-based approaches that are able to perform sparse signal recovery at much lower complexity while invoking constraint and/or a priori information about the data. While Bayesian approaches have their advantages, these methods must have access to a priori statistics. Usually, these statistics are unknown and are often difficult or even impossible to predict. An effective workaround is to assume a distribution which is typically considered to be Gaussian, as it makes many signal processing problems mathematically tractable. Seemingly attractive, this assumption necessitates the estimation of the associated parameters; which could be hard if not impossible. In the thesis, we focus on this aspect of Bayesian recovery and present a framework to address the challenges mentioned above. The proposed framework allows Bayesian recovery of sparse signals but at the same time is agnostic to the distribution of the unknown sparse signal components. The algorithms based on this framework are agnostic to signal statistics and utilize a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. In the thesis, we propose several algorithms based on this framework which utilize the structure present in signals for improved recovery. In addition to the algorithm that considers just the sparsity structure of sparse signals, tools that target additional structure of the sparsity recovery problem are proposed. These include several algorithms for a) block-sparse signal estimation, b) joint reconstruction of several common support sparse signals, and c) distributed estimation of sparse signals. Extensive experiments are conducted to demonstrate the power and robustness of our proposed sparse signal estimation algorithms. Specifically, we target the problems of a) channel estimation in massive-MIMO, and b) Narrowband interference mitigation in SC-FDMA. We model these problems as sparse recovery problems and demonstrate how these could be solved naturally using the proposed algorithms.

MIMO: Multiple-input multiple-output technology uses multiple antennas to use reflected signals to provide channel robustness and throughput gains. It is advantageous in several applications like cellular systems, users are distributed over a wide coverage area in various applications such as mobile systems, improving channel state information (CSI) processing efficiency in massive MIMO systems. This chapter proposes two channel-based deep learning methods gated recurrent unit and a Legendre memory unit to enhance the performance in a massive MIMO system and compares the complexity analysis to the previous methods, The complexity analysis is based on the channel state information network combined with gated recurrent units and Legendre memory units compared to indicator parameters which show the difference between literature-based techniques.

MIMO: multiple-input multiple-output technology uses multiple antennas to use reflected signals to provide channel robustness and throughput gains. It is advantageous in several applications like cellular systems, and users are distributed over a wide coverage area in various applications such as mobile systems, improving channel state information (CSI) processing efficiency in massive MIMO systems. This chapter proposes two channel-based deep learning methods to enhance the performance in a massive MIMO system and compares our proposed technique to the previous methods. The proposed technique is based on the channel state information network combined with the gated recurrent unit’s technique CsiNet-GRUs, which increases recovery efficiency. Besides, a fair balance between compression ratio (CR) and complexity is given using correlation time in training samples. The simulation results show that the proposed CsiNet-GRUs technique fulfills performance improvement compared with the existing literature techniques, namely CS-based methods Conv-LSTM CsiNet, LASSO, Tval3, and CsiNet.