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Sparse Channel Estimation For Massive Mimo Using Quasi Orthogonal Pilots
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Book Synopsis Sparse Channel Estimation for Massive MIMO Using Quasi-orthogonal Pilots by : Ivan Tinjaca
Download or read book Sparse Channel Estimation for Massive MIMO Using Quasi-orthogonal Pilots written by Ivan Tinjaca and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Massive MIMO has been considered recently as the emerging technology to be used in fifth generation wireless communication. The use of a large number of antennas provides many more degrees of freedom when compared to actual fourth generation standards that use basic MIMO. 4G systems are rapidly approaching to their limit of performance and soon will be inadequate to supply the increasing wireless demand. Massive MIMO brings the possibility of establishing communications with more speed, capacity and reliability. However, as in basic MIMO, the knowledge of the channel affects directly its performance. With a higher number of antennas, not only the channel estimation techniques present an increase in the complexity but also, traditional training methods face a limitation in the quantity of pilots to be used. The pilot contamination effect has been intensively studied recently and several methods have been proposed to alleviate the interference caused by the pilots reuse.In this thesis, the estimation process is done using non-orthogonal sequences with low correlation and it is compared to the traditional estimation methods. The sparse characteristics of the channel are also studied when using 2D antenna arrays leading to application of Matching Pursuit algorithms with low complexity. Motivated by the results, a method that uses the non-orthogonal sequences and that incorporates 2D space angular transformation of the channel to exploit its sparse characteristics is proposed. Finally, it is observed that a considerable reduction in the channel estimation error is obtained when using non-orthogonal sequences altogether with Matching Pursuit algorithms. " --
Book Synopsis Channel Estimation for Massive MIMO Systems Based on Sparse Representation and Sparse Signal Recovery by : Yacong Ding
Download or read book Channel Estimation for Massive MIMO Systems Based on Sparse Representation and Sparse Signal Recovery written by Yacong Ding and published by . This book was released on 2018 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive multiple-input multiple-output (MIMO) is a promising technology for next generation communication systems, where the base station (BS) is equipped with a large number of antenna elements to serve multiple user equipments. With the large number of antenna elements, the BS can perform multi-user beamforming with much narrower beamwidth, thereby simultaneously serving more users with less interference among them. Furthermore, the large antenna array results in large array gain which lowers the radiated energy. However, efficient beamforming relies on the availability of channel state information at the BS. In a frequency-division duplexing massive MIMO system, the channel estimation is challenging due to the need to estimate a high dimensional unknown channel vector, which requires large training and feedback overhead for the conventional channel estimation algorithms. Moreover, massive MIMO system with fully digital architecture, where a dedicated radio frequency chain and a high-resolution analog-to-digital converter (ADC) are connected to each antenna element, will cause too much power and hardware cost as the size of the antenna array becomes large. To reduce the training and feedback overhead, compressive sensing methods and sparse recovery algorithms are proposed to robustly estimate the downlink and uplink channel by exploiting the sparse representation of the massive MIMO channel. Previous works model this sparse representation by some predefined matrix, while in this dissertation, a dictionary learning based channel model is proposed which learns an efficient and robust representation from the data. Furthermore, a joint uplink/downlink dictionary learning framework is proposed by observing the reciprocity between the angle of arrival in uplink and the angel of departure in downlink, which enables a joint channel estimation algorithm. To save the power and hardware cost, a hardware-efficient architecture which contains both hybrid analog-digital processing and low-resolution ADCs is proposed. This hardware-efficient architecture poses significant challenges to channel estimation due to the reduced dimension and precision of the measured signal. To address the problem, the sparse nature of the channel is exploited and the transmitted data symbols are utilized as the "virtual pilots", both of which are treated in a unified Bayesian formulation. We formulate the channel estimation into a quantized compressive sensing problem utilizing the sparse Bayesian learning framework, and develop a variational Bayesian algorithm for inference. The performance of the compressive sensing can be further improved by applying a well structured sensing matrix, and we propose a sensing matrix design algorithm which can exploit the partial knowledge of the support.
Book Synopsis Sparse Signal Processing for Massive MIMO Communications by : Zhen Gao
Download or read book Sparse Signal Processing for Massive MIMO Communications written by Zhen Gao and published by Springer Nature. This book was released on with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Novel Channel Estimation Methods Under Pilot Contamination in Massive MIMO by : Qingqing Cheng
Download or read book Novel Channel Estimation Methods Under Pilot Contamination in Massive MIMO written by Qingqing Cheng and published by . This book was released on 2016 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive multiple-input multiple-output (Massive MIMO) is a key feature of proposed 5G cellular systems, offering potentially many benefits. However, all benefits rest on the ability to obtain channel state information (CSI) at the base station (BS) during uplink transmission. The BS can in principle measure CSI from known user-transmitted pilot sequences, but in a multiple cell system, the use of non-orthogonal pilot sequences in different cells leads to a problem of pilot contamination.
Book Synopsis EFFECTIVE SPARSE CHANNEL ESTIMATION TECHNIQUE FOR MIMO-OFDM SYSTEM by : Dr.Maddala Vijayalakshmi
Download or read book EFFECTIVE SPARSE CHANNEL ESTIMATION TECHNIQUE FOR MIMO-OFDM SYSTEM written by Dr.Maddala Vijayalakshmi and published by Archers & Elevators Publishing House. This book was released on with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Daniel Verenzuela Publisher :Linköping University Electronic Press ISBN 13 :9179299210 Total Pages :116 pages Book Rating :4.1/5 (792 download)
Book Synopsis Exploring Alternative Massive MIMO Designs by : Daniel Verenzuela
Download or read book Exploring Alternative Massive MIMO Designs written by Daniel Verenzuela and published by Linköping University Electronic Press. This book was released on 2020-01-15 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of information and communication technologies (ICT) provides the means for reaching global connectivity that can help humanity progress and prosper. This comes with high demands on data traffic and number of connected devices which are rapidly growing and need to be met by technological development. Massive MIMO, where MIMO stands for multiple-input multiple-output, is a fundamental component of the 5G wireless communication standard for its ability to provide high spectral and energy efficiency, SE and EE, respectively. The key feature of this technology is the use of a large number of antennas at the base stations (BSs) to spatially multiplex several user equipments (UEs). In the development of new technologies like Massive MIMO, many design alternatives need to be evaluated and compared in order to find the best operating point with a preferable tradeoff between low cost and complexity. In this thesis, two alternative designs for signal processing and hardware in Massive MIMO are studied and compared with the baseline operation in terms of SE, EE, and power consumption. The first design is called superimposed pilot (SP) transmission and is based on superimposing pilot and data symbols to eliminate the need to reserve dedicated time-frequency resources for pilots. This allows more data to be transmitted and supports longer pilot sequences that, in turn, reduce pilot contamination. The second design is mixed analog-to-digital converters (ADCs) and it aims at balancing the SE performance and the power consumption cost by allowing different ADC bit resolutions across the BS antennas. The results show that the Massive MIMO baseline, when properly optimized, is the preferred choice in standard deployments and propagation conditions. However, the SP alternative design can increase the SE compared to the baseline by using the Massive-MIMO iterative channel estimation and decoding (MICED) algorithm proposed in this dissertation. In particular, the SE gains are found in cases with high mobility, high carrier frequencies, or high number of spatially multiplexed UEs. For the mixed-ADCs alternative design, improvements in the SE and EE compared to the Massive MIMO baseline can be achieved in cases with distributed BS antennas where interference suppression techniques are used. El desarrollo en tecnologías de información y comunicación (en inglés, ICT) provee los medios para alcanzar la conectividad global que puede ayudar a la humanidad a progresar y prosperar. Esto implica que el avance tecnológico debe satisfacer la alta demanda de tráfico de data y número de equipos conectados que se encuentra en rápido crecimiento. La tecnología de múltiple-entrada múltiple-salida masiva, en inglés Massive MIMO, se considera una pieza fundamental de la quinta generación de comunicaciones inalámbricas (5G) debido a su capacidad de proveer una alta eficiencia espectral y energética (en inglés, SE y EE, respectivamente). Esta tecnología está caracterizada fundamentalmente por el uso de un alto número de antenas en la estación base (en inglés, BS) para multiplexar a varios usuarios en el espacio. En el desarrollo de nuevas tecnologías como Massive MIMO, muchas alternativas de diseño necesitan ser evaluadas y comparadas para encontrar el mejor punto de operación con un balance conveniente entre complejidad y bajo costo. En esta tesis, dos alternativas de diseño para el procesamiento de señales y el hardware de Massive MIMO son estudiadas y comparadas con la operación del diseño base en términos de eficiencia espectral, eficiencia energética y consumo de potencia. El primer diseño se denomina transmisión de pilotos superpuestos (en inglés, SP) y está basado en la superposición de señales piloto y de datos para eliminar la necesidad de asignar recursos dedicados a señales pilotos. Además, la transmisión de pilotos superpuestos permite reducir la interferencia que surge a raíz de reusar las señales pilotos en distintas celdas, este efecto se denomina contaminación de pilotos (en inglés pilot contamination). El segundo diseño se denomina conversores analógico-adigital (en inglés, ADC) mixtos (en inglés, mixed-ADCs) y se basa en permitir distintas resoluciones de bit en los conversores analógico-a-digital de las antenas en la estación base. Este diseño permite que la resolución de los conversores analógico-a-digital se adapte a las condiciones de propagación de las señales para balancear los beneficios en eficiencia espectral con el costo de potencia consumida. Los resultados muestran que el diseño base de Massive MIMO, cuando esta optimizado de manera apropiada, es la opción preferida en despliegues y condiciones de propagación estándares. Sin embargo, la transmisión de pilotos superpuestos puede incrementar la eficiencia espectral en comparación al diseño base cuando se combina con el método iterativo para la estimación de canal y decodificación en Massive MIMO propuesto en esta tesis (en inglés, MICED). En particular, las ganancias en eficiencia espectral son obtenidas en escenarios con alta movilidad de usuarios, alta frecuencia portadora, o alto número de usuarios multiplexados en el espacio. Con respecto al diseño alternativo de conversores analógico-a-digital mixtos, la eficiencia espectral y energética pueden ser incrementadas en comparación al diseño base cuando las antenas de la estación base están distribuidas en el espacio y técnicas para suprimir interferencia entre usuarios son usadas. Die Entwicklung der Informations- und Kommunikationstechnologien (ICT) bietet die Möglichkeit eine globale Konnektivität zu erreichen, die Fortschritt und Wohlstand fördern kann. Dies bedeutet zugleich, dass der steigende Datenverkehr und die wachsende Anzahl verbundener Geräte eines entsprechenden technologischen Fortschritts bedarf. Massive MIMO, wobei MIMO für multiple-input multiple-output steht, ist eine fundamentale Komponente des drahtlosen 5G Kommunikationsstandards, da sie eine hohe spektrale Effizienz (SE) und Energieeffizienz bietet (EE). Die Hauptkomponente dieser Technologie ist die Nutzung einer großen Anzahl an Antennen auf Seiten der Basisstationen (BSs) um mehrere Nutzer zu bedienen, die ihre Signale zur selben Zeit auf derselben Frequenz senden während sie in der räumlichen Domäne getrennt sind (spatial multiplexing). In der Entwicklung neuer Technologien wie Massive MIMO müssen viele Designalternativen evaluiert und verglichen werden um den optimalen Betriebspunkt im Sinne eines sinnvollen Gleichgewichts zwischen Kosteneffizienz und Komplexität zu finden. In dieser Doktorarbeit werden zwei alternative Designs für Signalverarbeitung und Hardware in Massive MIMO Systemen untersucht und in Bezug auf spektrale Effizienz, Energieeffizienz und Stromverbrauch mit dem Massive MIMO Basisdesign verglichen. Das erste Design heißt überlagerte Pilotton Übertragung (superimposed pilot, SP) und basiert auf der Überlagerung von Pilotton und Datensignal, damit nicht mehr die Notwendigkeit besteht bestimmte Ressourcen für Pilottöne zu reservieren. Dies ermöglicht die Übertragung größerer Datenmengen und reduziert die Interferenz, die aus der wiederholten Nutzung der Pilottöne in verschiedenen Zellen resultiert (pilot contamination). Das zweite Design nennt sich gemischte analog zu digital Konverter (mixed analog-to-digital converters, ADCs) und erlaubt es einen Kompromiss zwischen hoher spektraler Effizienz und niedrigem Stromverbrauch zu finden. Dies geschieht indem die Bit Auflösung an jeder BS Antenne an die Ausbreitungsbedingungen der Signale angepasst wird. Die Resultate zeigen, dass das Massive MIMO Basisdesign, wenn es richtig optimiert ist, bei Standardeinsätzen und unter normalen Ausbreitungsbedingungen, die bevorzugte Wahl ist. Das alternative SP Design kann jedoch die spektrale Effizienz im Vergleich zum Basisdesign durch die Nutzung des in dieser Dissertation vorgeschlagenen Massive MIMO iterativen Kanalschätzungs- und Dekodierungsalgorithmus (MICED) erhöhen. Die verbesserte spektrale Effizienz findet sich insbesondere in Fällen hoher Nutzermobilität, hoher Frequenzen oder hoher Anzahl an gleichzeitig bedienter Nutzer. Das gemischte analog zu digital Konverter Design ermöglicht in Fällen verteilter Basisstationen bei denen Interferenz unterdrückende Techniken genutzt werden eine verbesserte spektrale Effizienz und Energieeffizienz. Utvecklingen av informations- och kommunikationsteknik (IKT) gör det möjligt för människor från hela världen att kopplas samman och utbyta kunskaper. Ju mer vi vet och förstår om varandra, desto större är chansen att mänskligheten kan uppnå globala utvecklingsmål och välstånd. IKT-utvecklingen är associerad med höga krav på datatakter och antal uppkopplade enheter. Dessa krav ökar ständigt och måste mötas med teknologisk utveckling. Massiv MIMO, där MIMO står för multiple-input multiple-output, är flerantennteknik och en grundsten i nästa generations trådlösa kommunikationssystem. Huvudanledningen till detta är att tekniken kan förbättra spektraleffektiviteten (SE), vilket är ett mått på hur väl vi kan kommunicera data över begränsade radiofrekvensresurser. Tekniken förbättrar även energieffektiviteten (EE), vilket är ett mått på hur effektivt tekniken använder energi till att kommunicera data. Massiv MIMO bygger på användandet av ett stort antal av antenner på basstationerna för att kommunicera med ett flertal användare samtidigt och på samma frekvensresurser. Detta möjliggörs genom ”rumslig multiplexing” vilket betyder att signaler från användare på olika platser kan separeras på basstationen i den rumsliga domänen. Denna separering kräver att basstationen först mäter egenskaperna hos signaler som kommer från de olika användarnas positioner. När en ny teknik, såsom Massiv MIMO, utvecklas är det viktigt att olika alternativa designer utvärderas och jämförs för att identifiera den bästa varianten. Detta kan exempelvis vara den variant som uppnår en viss balans mellan hög kommunikationsprestanda och låg kostnad. I denna avhandling utvärderas två alternativa sätt att designa signalbehandlingen och hårdvaran i Massiv MIMO. Dessa jämförs med konventionell Massiv MIMO i termer av SE, EE och effektförbrukning. Den första alternativa designen kallas överlagrade piloter och bygger på att kända pilotsignaler och okända datasignaler skickas samtidigt från användarna, istället för efter varandra. Pilotsignalerna används för att mäta upp de trådlösa kanalerna som signalerna färdas över medan datasignalerna innehåller den information som ska kommuniceras. Genom att överlagra pilotsignalerna så behövs inga dedikerade radioresurser för piloter och därmed finns det mer resurser för datasändning. Dessutom minskar överlagrandet de störningar som kommer från andra användare som använder samma pilot, vilket kallas pilotkontaminering. Den andra alternativa designen kallas mixade analog-till-digital (AD) omvandlare. En AD-omvandlare är en krets som behövs på varje antenn för att omvandla analoga radiosignaler till digitala signaler som kan processas i en dator. Bitupplösningen i AD-omvandlaren avgör hur många nivåer som kan användas för att representera den analoga signalen. Ju högre bitupplösning desto fler nivåer och därmed en mer noggrann representation, men detta leder även till högre beräkningskomplexitet och effektförbrukning. Mixade AD-omvandlare försöker balansera mellan hög prestanda och låg komplexitet genom att optimera bitupplösningen på varje antenn i ett Massiv MIMO system. Avhandlingens resultat visar att det går att öka SE i Massiv MIMO genom att använda överlagrade piloter, ifall den föreslagna algoritmen MICED (Massive-MIMO iterative channel estimation and decoding) används. Förbättringarna är särskilt stora när användarna har hög mobilitet, när en hög bärfrekvens används eller när antalet rumsligt multiplexade användare är högt. När det gäller mixade AD-omvandlare så kan små förbättringar i SE uppnås, jämfört med konventionell Massiv MIMO, när bitupplösningen i AD-omvandlarna optimeras under förutsättning att signalstyrkan varierar mellan basstationens antenner. Sammanfattningsvis så kan de alternativa designerna av Massiv MIMO som studerats i avhandlingen ge små prestandaförbättringar jämfört med konventionella metoder. Men trots detta så kan de konventionella metoderna uppnå en bra avvägning mellan hög prestanda och låg komplexitet ifall de optimeras väl.
Book Synopsis A Study of Blind Sparse Channel Estimation Based on Compressed Sensing for Massive MIMO Systems with Spatial Modulation by : Han-Wen Tsai
Download or read book A Study of Blind Sparse Channel Estimation Based on Compressed Sensing for Massive MIMO Systems with Spatial Modulation written by Han-Wen Tsai and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Giovanni Interdonato Publisher :Linköping University Electronic Press ISBN 13 :9176852245 Total Pages :35 pages Book Rating :4.1/5 (768 download)
Book Synopsis Signal Processing Aspects of Cell-Free Massive MIMO by : Giovanni Interdonato
Download or read book Signal Processing Aspects of Cell-Free Massive MIMO written by Giovanni Interdonato and published by Linköping University Electronic Press. This book was released on 2019-03-20 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fifth generation of mobile communication systems (5G) promises unprecedented levels of connectivity and quality of service (QoS) to satisfy the incessant growth in the number of mobile smart devices and the huge increase in data demand. One of the primary ways 5G network technology will be accomplished is through network densification, namely increasing the number of antennas per site and deploying smaller and smaller cells. Massive MIMO, where MIMO stands for multiple-input multiple-output, is widely expected to be a key enabler of 5G. This technology leverages an aggressive spatial multiplexing, from using a large number of transmitting/receiving antennas, to multiply the capacity of a wireless channel. A massive MIMO base station (BS) is equipped with a large number of antennas, much larger than the number of active users. The users are coherently served by all the antennas, in the same time-frequency resources but separated in the spatial domain by receiving very directive signals. By supporting such a highly spatially-focused transmission (precoding), massive MIMO provides higher spectral and energy efficiency, and reduces the inter-cell interference compared to existing mobile systems. The inter-cell interference is however becoming the major bottleneck as we densify the networks. It cannot be removed as long as we rely on a network-centric implementation, since the inter-cell interference concept is inherent to the cellular paradigm. Cell-free massive MIMO refers to a massive MIMO system where the BS antennas, herein referred to as access points (APs), are geographically spread out. The APs are connected, through a fronthaul network, to a central processing unit (CPU) which is responsible for coordinating the coherent joint transmission. Such a distributed architecture provides additional macro-diversity, and the co-processing at multiple APs entirely suppresses the inter-cell interference. Each user is surrounded by serving APs and experiences no cell boundaries. This user-centric approach, combined with the system scalability that characterizes the massive MIMO design, constitutes a paradigm shift compared to the conventional centralized and distributed wireless communication systems. On the other hand, such a distributed system requires higher capacity of back/front-haul connections, and the signal co-processing increases the signaling overhead. In this thesis, we focus on some signal processing aspects of cell-free massive MIMO. More specifically, we firstly investigate if the downlink channel estimation, via downlink pilots, brings gains to cell-free massive MIMO or the statistical channel state information (CSI) knowledge at the users is enough to reliably perform data decoding, as in conventional co-located massive MIMO. Allocating downlink pilots is costly resource-wise, thus we also propose resource saving-oriented strategies for downlink pilot assignment. Secondly, we study further fully distributed and scalable precoding schemes in order to outperform cell-free massive MIMO in its canonical form, which consists in single-antenna APs implementing conjugate beamforming (also known as maximum ratio transmission).
Book Synopsis mmWave Massive MIMO by : Shahid Mumtaz
Download or read book mmWave Massive MIMO written by Shahid Mumtaz and published by Academic Press. This book was released on 2016-12-02 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: mmWave Massive MIMO: A Paradigm for 5G is the first book of its kind to hinge together related discussions on mmWave and Massive MIMO under the umbrella of 5G networks. New networking scenarios are identified, along with fundamental design requirements for mmWave Massive MIMO networks from an architectural and practical perspective. Working towards final deployment, this book updates the research community on the current mmWave Massive MIMO roadmap, taking into account the future emerging technologies emanating from 3GPP/IEEE. The book's editors draw on their vast experience in international research on the forefront of the mmWave Massive MIMO research arena and standardization. This book aims to talk openly about the topic, and will serve as a useful reference not only for postgraduates students to learn more on this evolving field, but also as inspiration for mobile communication researchers who want to make further innovative strides in the field to mark their legacy in the 5G arena. Contains tutorials on the basics of mmWave and Massive MIMO Identifies new 5G networking scenarios, along with design requirements from an architectural and practical perspective Details the latest updates on the evolution of the mmWave Massive MIMO roadmap, considering future emerging technologies emanating from 3GPP/IEEE Includes contributions from leading experts in the field in modeling and prototype design for mmWave Massive MIMO design Presents an ideal reference that not only helps postgraduate students learn more in this evolving field, but also inspires mobile communication researchers towards further innovation
Book Synopsis Channel Estimation for Massive MIMO Systems by : Mingduo Liao
Download or read book Channel Estimation for Massive MIMO Systems written by Mingduo Liao and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Pilot Contamination Mitigation for Massive MIMO System Using Novel Channel Estimation Techniques by : Hayder Qahtan Kshash Al-Salihi
Download or read book Pilot Contamination Mitigation for Massive MIMO System Using Novel Channel Estimation Techniques written by Hayder Qahtan Kshash Al-Salihi and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Signal-perturbation-free Semi-blind Channel Estimation for MIMO-OFDM Systems by : Feng Wan
Download or read book Signal-perturbation-free Semi-blind Channel Estimation for MIMO-OFDM Systems written by Feng Wan and published by . This book was released on 2009 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Channel Estimation in TDD Massive MIMO Systems with Subsampled Data at BS by : Yichuan Tian
Download or read book Channel Estimation in TDD Massive MIMO Systems with Subsampled Data at BS written by Yichuan Tian and published by . This book was released on 2016 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel state information at transmitter side (CSIT) is essential. Frequency division duplex (FDD) is widely employed by the most cellular systems today. However, it requires unaffordable pilot overhead and has high computational complexity. On the other hand, by exploiting the channel reciprocity using uplink pilots, the time division duplex (TDD) can overcome the overwhelming pilot training as well as the pilot feedback overhead. Considering these advantages, we propose a subsampling algorithm that can be implemented in a TDD mode. Particularly, we first exploit the intrinsic sparsity of CSIT, and then employ the Walsh-Hadamard Transform (WHT), which will subsample the received signal at BS, to perform channel estimation. Additionally, we discuss the proposed channel estimation scheme in a multicell scenario. Simulation results demonstrate that the proposed algorithm can accurately estimate channels with reduced computational complexity.
Book Synopsis Channel Estimation Methods by Using Prebeamforming Technique in Massive MIMO by : Sadjad Sedighi
Download or read book Channel Estimation Methods by Using Prebeamforming Technique in Massive MIMO written by Sadjad Sedighi and published by . This book was released on 2017 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: The number of connected wireless devices is anticipated to increase heavily in the next few years. Thus, there is a need for a new system which is able to handle billions of wireless devices. The category of massive multiple input multiple output (MIMO) systems is a great candidate for this purpose. Because of the large number of antennas in massive MIMO there is a need to reduce the dimension of the MIMO channel effectively to decrease the complexity. This could be achieved by using a particular prebeamforming technique that is introduced recently. An important aspect of wireless communication systems is the channel state information (CSI). In order to send and receive data through the channel, the transmitter and the receiver must know the CSI or at least have an estimation for it. In this thesis, different algorithms for estimating the channel vector coefficient and their performance are studied. Different approaches are used in order to find the best algorithm based on the performance of the estimating channel and the complexity of the algorithm. Also, algorithms are used to estimate the channel vector coefficient for different channel models.
Book Synopsis Wireless Channel Estimation and Channel Prediction for MIMO Communication Systems by : Farnoosh Talaei
Download or read book Wireless Channel Estimation and Channel Prediction for MIMO Communication Systems written by Farnoosh Talaei and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, channel estimation and channel prediction are studied for wireless communication systems. Wireless communication for time-variant channels becomes more important by the fast development of intelligent transportation systems which motivates us to propose a reduced rank channel estimator for time-variant frequency-selective high-speed railway (HSR) systems and a reduced rank channel predictor for fast time-variant flat fading channels. Moreover, the potential availability of large bandwidth channels at mm-wave frequencies and the small wavelength of the mm-waves, offer the mm-wave massive multiple-input multiple-output (MIMO) communication as a promising technology for 5G cellular networks. The high fabrication cost and power consumption of the radio frequency (RF) units at mm-wave frequencies motivates us to propose a low-power hybrid channel estimator for mm-wave MIMO orthogonal frequency-division multiplexing (OFDM) systems. The work on HSR channel estimation takes advantage of the channel's restriction to low dimensional subspaces due to the time, frequency and spatial correlation of the channel and presents a low complexity linear minimum mean square error (LMMSE) estimator for MIMO-OFDM HSR channels. The channel estimator utilizes a four-dimensional (4D) basis expansion channel model obtained from band-limited generalized discrete prolate spheroidal (GDPS) sequences. Exploiting the channel's band-limitation property, the proposed channel estimator outperforms the conventional interpolation based least square (LS) and MMSE estimators in terms of estimation accuracy and computational complexity, respectively. Simulation results demonstrate the robust performance of the proposed estimator for different delay, Doppler and angular spreads. Channel state information (CSI) is required at the transmitter for improving the performance gain of the spatial multiplexing MIMO systems through linear precoding. In order to avoid the high data rate feedback lines, which are required in fast time-variant channels for updating the transmitter with the rapidly changing CSI, a subframe-wise channel tracking scheme is presented. The proposed channel predictor is based on an assumed DPS basis expansion model (DPS-BEM) for exploiting the variation of the channel coefficients inside each sub-frame and an autoregressive (AR) model of the basis coefficients over each transmitted frame. The proposed predictor properly exploits the channel's restriction to low dimensional subspaces for reducing the prediction error and the computational complexity. Simulation results demonstrate that the proposed channel predictor out-performs the DPS based minimum energy (ME) predictor for different ranges of normalized Doppler frequencies and has better performance than the conventional Wiener predictor for slower time-variant channels and almost the similar performance to it for very fast time-variant channels with the reduced amount of computational complexity. The work on the hybrid mm-wave channel estimator considers the sparse nature of the mm-wave channel in angular domain and leverages the compressed sensing (CS) tools for recovering the angular support of the MIMO-OFDM mm-wave channel. The angular channel is treated in a continuous framework which resolves the limited angular resolution of the discrete sparse channel models used in the previous CS based channel estimators. The power leakage problem is also addressed by modeling the continuous angular channel as a multi-band signal with the bandwidth of each sub-band being proportional to the amount of power leakage. The RF combiner is designed to be implemented using a network of low-power switches for antenna subset selection based on a multi-coset sampling pattern. Simulation results validate the effectiveness of the proposed hybrid channel estimator both in terms of the estimation accuracy and the RF power consumption.
Book Synopsis Sparse Bayesian Learning, Beamforming Techniques and Asymptotic Analysis for Massive MIMO by : Christo Kurisummoottil Thomas
Download or read book Sparse Bayesian Learning, Beamforming Techniques and Asymptotic Analysis for Massive MIMO written by Christo Kurisummoottil Thomas and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple antennas at the base station side can be used to enhance the spectral efficiency and energy efficiency of the next generation wireless technologies. Indeed, massive multi-input multi-output (MIMO) is seen as one promising technology to bring the aforementioned benefits for fifth generation wireless standard, commonly known as 5G New Radio (5G NR). In this monograph, we will explore a wide range of potential topics in multi-userMIMO (MU-MIMO) relevant to 5G NR,• Sum rate maximizing beamforming (BF) design and robustness to partial channel stateinformation at the transmitter (CSIT)• Asymptotic analysis of the various BF techniques in massive MIMO and• Bayesian channel estimation methods using sparse Bayesian learning.One of the potential techniques proposed in the literature to circumvent the hardware complexity and power consumption in massive MIMO is hybrid beamforming. We propose a globally optimal analog phasor design using the technique of deterministic annealing, which won us the best student paper award. Further, in order to analyze the large system behaviour of the massive MIMO systems, we utilized techniques from random matrix theory and obtained simplified sum rate expressions. Finally, we also looked at Bayesian sparse signal recovery problem using the technique called sparse Bayesian learning (SBL). We proposed low complexity SBL algorithms using a combination of approximate inference techniques such as belief propagation (BP), expectation propagation and mean field (MF) variational Bayes. We proposed an optimal partitioning of the different parameters (in the factor graph) into either MF or BP nodes based on Fisher information matrix analysis.
Book Synopsis Massive MIMO Systems by : Kazuki Maruta
Download or read book Massive MIMO Systems written by Kazuki Maruta and published by MDPI. This book was released on 2020-07-03 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple-input, multiple-output (MIMO), which transmits multiple data streams via multiple antenna elements, is one of the most attractive technologies in the wireless communication field. Its extension, called ‘massive MIMO’ or ‘large-scale MIMO’, in which base station has over one hundred of the antenna elements, is now seen as a promising candidate to realize 5G and beyond, as well as 6G mobile communications. It has been the first decade since its fundamental concept emerged. This Special Issue consists of 19 papers and each of them focuses on a popular topic related to massive MIMO systems, e.g. analog/digital hybrid signal processing, antenna fabrication, and machine learning incorporation. These achievements could boost its realization and deepen the academic and industrial knowledge of this field.
