Optimizing multiuser MIMO for access point cooperation in dense wireless networks

As the usage of wireless devices continues to grow rapidly in popularity, wireless networks that were once designed to support a few laptops must now host a much wider range of equipments, including smart phones, tablets, and wearable devices, that often run bandwidth-hungry applications. Improvements in wireless local access network (WLAN) technology are expected to help accommodate the huge traffic demands. In particular, advanced multicell Multiple-Input Multiple-Output (MIMO) techniques, involving the cooperation of APs and multiuser MIMO processing techniques, can be used to satisfy the increasing demands from users in high-density environments. The objective of this thesis is to address the fundamental problems for multiuser MIMO with AP cooperation in dense wireless network settings. First, for a very common multiuser MIMO linear precoding technique, block diagonalization, a novel pairing-and-binary-tree based user selection algorithm is proposed. Second, without the zero-forcing constraint on the multiuser MIMO transmission, a general weighted sum rate maximization problem is formulated for coordinated APs. A scalable algorithm that performs a combined optimization procedure is proposed to determine the user selection and MIMO weights. Third, we study the fair and high-throughput scheduling problem by formally specifying an optimization problem. Two algorithms are proposed to solve the problem using either alternating optimization or a two-stage procedure. Fourth, with the coexistence of both stationary and mobile users, different scheduling strategies are suggested for different user types. The provided theoretical analysis and simulation results in this thesis lay out the foundation for the realization of the clustered WLAN networks with AP cooperation.Ph.D

Channel estimation in massive MIMO systems

Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference. The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity. This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes. System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance

Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback

We propose a deep learning-based channel estimation, quantization, feedback, and precoding method for downlink multiuser multiple-input and multiple-output systems. In the proposed system, channel estimation and quantization for limited feedback are handled by a receiver deep neural network (DNN). Precoder selection is handled by a transmitter DNN. To emulate the traditional channel quantization, a binarization layer is adopted at each receiver DNN, and the binarization layer is also used to enable end-to-end learning. However, this can lead to inaccurate gradients, which can trap the receiver DNNs at a poor local minimum during training. To address this, we consider knowledge distillation, in which the existing DNNs are jointly trained with an auxiliary transmitter DNN. The use of an auxiliary DNN as a teacher network allows the receiver DNNs to additionally exploit lossless gradients, which is useful in avoiding a poor local minimum. For the same number of feedback bits, our DNN-based precoding scheme can achieve a higher downlink rate compared to conventional linear precoding with codebook-based limited feedback.Comment: 6 pages, 4 figures, submitted to IEEE Transactions on Vehicular Technolog

Advances in Multi-User Scheduling and Turbo Equalization for Wireless MIMO Systems

Nach einer Einleitung behandelt Teil 2 Mehrbenutzer-Scheduling für die Abwärtsstrecke von drahtlosen MIMO Systemen mit einer Sendestation und kanaladaptivem precoding: In jeder Zeit- oder Frequenzressource kann eine andere Nutzergruppe gleichzeitig bedient werden, räumlich getrennt durch unterschiedliche Antennengewichte. Nutzer mit korrelierten Kanälen sollten nicht gleichzeitig bedient werden, da dies die räumliche Trennbarkeit erschwert. Die Summenrate einer Nutzermenge hängt von den Antennengewichten ab, die wiederum von der Nutzerauswahl abhängen. Zur Entkopplung des Problems schlägt diese Arbeit Metriken vor basierend auf einer geschätzten Rate mit ZF precoding. Diese lässt sich mit Hilfe von wiederholten orthogonalen Projektionen abschätzen, wodurch die Berechnung von Antennengewichten beim Scheduling entfällt. Die Ratenschätzung kann basierend auf momentanen Kanalmessungen oder auf gemittelter Kanalkenntnis berechnet werden und es können Datenraten- und Fairness-Kriterien berücksichtig werden. Effiziente Suchalgorithmen werden vorgestellt, die die gesamte Systembandbreite auf einmal bearbeiten können und zur Komplexitätsreduktion die Lösung in Zeit- und Frequenz nachführen können. Teil 3 zeigt wie mehrere Sendestationen koordiniertes Scheduling und kooperative Signalverarbeitung einsetzen können. Mittels orthogonalen Projektionen ist es möglich, Inter-Site Interferenz zu schätzen, ohne Antennengewichte berechnen zu müssen. Durch ein Konzept virtueller Nutzer kann der obige Scheduling-Ansatz auf mehrere Sendestationen und sogar Relays mit SDMA erweitert werden. Auf den benötigten Signalisierungsaufwand wird kurz eingegangen und eine Methode zur Schätzung der Summenrate eines Systems ohne Koordination besprochen. Teil4 entwickelt Optimierungen für Turbo Entzerrer. Diese Nutzen Signalkorrelation als Quelle von Redundanz. Trotzdem kann eine Kombination mit MIMO precoding sinnvoll sein, da bei Annahme realistischer Fehler in der Kanalkenntnis am Sender keine optimale Interferenzunterdrückung möglich ist. Mit Hilfe von EXIT Charts wird eine neuartige Methode zur adaptiven Nutzung von a-priori-Information zwischen Iterationen entwickelt, die die Konvergenz verbessert. Dabei wird gezeigt, wie man semi-blinde Kanalschätzung im EXIT chart berücksichtigen kann. In Computersimulationen werden alle Verfahren basierend auf 4G-Systemparametern überprüft.After an introduction, part 2 of this thesis deals with downlink multi-user scheduling for wireless MIMO systems with one transmitting station performing channel adaptive precoding:Different user subsets can be served in each time or frequency resource by separating them in space with different antenna weight vectors. Users with correlated channel matrices should not be served jointly since correlation impairs the spatial separability.The resulting sum rate for each user subset depends on the precoding weights, which in turn depend on the user subset. This thesis manages to decouple this problem by proposing a scheduling metric based on the rate with ZF precoding such as BD, written with the help of orthogonal projection matrices. It allows estimating rates without computing any antenna weights by using a repeated projection approximation.This rate estimate allows considering user rate requirements and fairness criteria and can work with either instantaneous or long term averaged channel knowledge.Search algorithms are presented to efficiently solve user grouping or selection problems jointly for the entire system bandwidth while being able to track the solution in time and frequency for complexity reduction. Part 3 shows how multiple transmitting stations can benefit from cooperative scheduling or joint signal processing. An orthogonal projection based estimate of the inter-site interference power, again without computing any antenna weights, and a virtual user concept extends the scheduling approach to cooperative base stations and finally included SDMA half-duplex relays in the scheduling.Signalling overhead is discussed and a method to estimate the sum rate without coordination. Part 4 presents optimizations for Turbo Equalizers. There, correlation between user signals can be exploited as a source of redundancy. Nevertheless a combination with transmit precoding which aims at reducing correlation can be beneficial when the channel knowledge at the transmitter contains a realistic error, leading to increased correlation. A novel method for adaptive re-use of a-priori information between is developed to increase convergence by tracking the iterations online with EXIT charts.A method is proposed to model semi-blind channel estimation updates in an EXIT chart. Computer simulations with 4G system parameters illustrate the methods using realistic channel models.Im Buchhandel erhältlich: Advances in Multi-User Scheduling and Turbo Equalization for Wireless MIMO Systems / Fuchs-Lautensack,Martin Ilmenau: ISLE, 2009,116 S. ISBN 978-3-938843-43-