Discrimination on the Grassmann Manifold: Fundamental Limits of Subspace Classifiers

We present fundamental limits on the reliable classification of linear and affine subspaces from noisy, linear features. Drawing an analogy between discrimination among subspaces and communication over vector wireless channels, we propose two Shannon-inspired measures to characterize asymptotic classifier performance. First, we define the classification capacity, which characterizes necessary and sufficient conditions for the misclassification probability to vanish as the signal dimension, the number of features, and the number of subspaces to be discerned all approach infinity. Second, we define the diversity-discrimination tradeoff which, by analogy with the diversity-multiplexing tradeoff of fading vector channels, characterizes relationships between the number of discernible subspaces and the misclassification probability as the noise power approaches zero. We derive upper and lower bounds on these measures which are tight in many regimes. Numerical results, including a face recognition application, validate the results in practice.Comment: 19 pages, 4 figures. Revised submission to IEEE Transactions on Information Theor

Mobile radio alternative systems study, executive summary

Present day mobile communication technologies, systems and equipment are described from background in evaluating the concepts generated in the study. Average propagation ranges are calculated for terrestrial installations in each of seven physiographic areas of the contiguous states to determine the number of installations that would be required for nationwide coverage. Four system concepts are defined and analyzed to determine how well terrestrial systems can fulfill the requirements at acceptable costs

Advanced wireless communications using large numbers of transmit antennas and receive nodes

The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. First, we propose practical open-loop and closed-loop training frameworks to reduce the overhead of the downlink training phase. We then discuss efficient CSI quantization techniques using a trellis search. The proposed CSI quantization techniques can be implemented with a complexity that only grows linearly with the number of transmit antennas while the performance is close to the optimal case. We also analyze distributed reception using a large number of geographically separated nodes, a scenario that may become popular with the emergence of the Internet of Things. For distributed reception, we first propose coded distributed diversity to minimize the symbol error probability at the fusion center when the transmitter is equipped with a single antenna. Then we develop efficient receivers at the fusion center using minimal processing overhead at the receive nodes when the transmitter with multiple transmit antennas sends multiple symbols simultaneously using spatial multiplexing

Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation

Cell-free Massive MIMO is considered as a promising technology for satisfying the increasing number of users and high rate expectations in beyond-5G networks. The key idea is to let many distributed access points (APs) communicate with all users in the network, possibly by using joint coherent signal processing. The aim of this paper is to provide the first comprehensive analysis of this technology under different degrees of cooperation among the APs. Particularly, the uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary linear processing. It turns out that it is possible to outperform conventional Cellular Massive MIMO and small cell networks by a wide margin, but only using global or local minimum mean-square error (MMSE) combining. This is in sharp contrast to the existing literature, which advocates for maximum-ratio combining. Also, we show that a centralized implementation with optimal MMSE processing not only maximizes the SE but largely reduces the fronthaul signaling compared to the standard distributed approach. This makes it the preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding is also investigated and shown to bring negligible improvements.Comment: 14 pages, 6 figures, To appear in IEEE Transactions on Wireless Communication

Making Cell-Free Massive MIMO Competitive with MMSE Processing and Centralized Implementation

Cell-free Massive MIMO is considered as a promising technology for satisfying the increasing number of users and high rate expectations in beyond-5G networks. The key idea is to let many distributed access points (APs) communicate with all users in the network, possibly by using joint coherent signal processing. The aim of this paper is to provide the first comprehensive analysis of this technology under different degrees of cooperation among the APs. Particularly, the uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary linear processing. It turns out that it is possible to outperform conventional Cellular Massive MIMO and small cell networks by a wide margin, but only using global or local minimum mean-square error (MMSE) combining. This is in sharp contrast to the existing literature, which advocates for maximum-ratio combining. Also, we show that a centralized implementation with optimal MMSE processing not only maximizes the SE but largely reduces the fronthaul signaling compared to the standard distributed approach. This makes it the preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding is also investigated and shown to bring negligible improvements

Optical Communication

Optical communication is very much useful in telecommunication systems, data processing and networking. It consists of a transmitter that encodes a message into an optical signal, a channel that carries the signal to its desired destination, and a receiver that reproduces the message from the received optical signal. It presents up to date results on communication systems, along with the explanations of their relevance, from leading researchers in this field. The chapters cover general concepts of optical communication, components, systems, networks, signal processing and MIMO systems. In recent years, optical components and other enhanced signal processing functions are also considered in depth for optical communications systems. The researcher has also concentrated on optical devices, networking, signal processing, and MIMO systems and other enhanced functions for optical communication. This book is targeted at research, development and design engineers from the teams in manufacturing industry, academia and telecommunication industries

Linear Transmit-Receive Strategies for Multi-user MIMO Wireless Communications

Die Notwendigkeit zur Unterdrueckung von Interferenzen auf der einen Seite und zur Ausnutzung der durch Mehrfachzugriffsverfahren erzielbaren Gewinne auf der anderen Seite rueckte die raeumlichen Mehrfachzugriffsverfahren (Space Division Multiple Access, SDMA) in den Fokus der Forschung. Ein Vertreter der raeumlichen Mehrfachzugriffsverfahren, die lineare Vorkodierung, fand aufgrund steigender Anzahl an Nutzern und Antennen in heutigen und zukuenftigen Mobilkommunikationssystemen besondere Beachtung, da diese Verfahren das Design von Algorithmen zur Vorcodierung vereinfachen. Aus diesem Grund leistet diese Dissertation einen Beitrag zur Entwicklung linearer Sende- und Empfangstechniken fuer MIMO-Technologie mit mehreren Nutzern. Zunaechst stellen wir ein Framework zur Approximation des Datendurchsatzes in Broadcast-MIMO-Kanaelen mit mehreren Nutzern vor. In diesem Framework nehmen wir das lineare Vorkodierverfahren regularisierte Blockdiagonalisierung (RBD) an. Durch den Vergleich von Dirty Paper Coding (DPC) und linearen Vorkodieralgorithmen (z.B. Zero Forcing (ZF) und Blockdiagonalisierung (BD)) ist es uns moeglich, untere und obere Schranken fuer den Unterschied bezueglich Datenraten und bezueglich Leistung zwischen beiden anzugeben. Im Weiteren entwickeln wir einen Algorithmus fuer koordiniertes Beamforming (Coordinated Beamforming, CBF), dessen Loesung sich in geschlossener Form angeben laesst. Dieser CBF-Algorithmus basiert auf der SeDJoCo-Transformation und loest bisher vorhandene Probleme im Bereich CBF. Im Anschluss schlagen wir einen iterativen CBF-Algorithmus namens FlexCoBF (flexible coordinated beamforming) fuer MIMO-Broadcast-Kanaele mit mehreren Nutzern vor. Im Vergleich mit bis dato existierenden iterativen CBF-Algorithmen kann als vielversprechendster Vorteil die freie Wahl der linearen Sende- und Empfangsstrategie herausgestellt werden. Das heisst, jede existierende Methode der linearen Vorkodierung kann als Sendestrategie genutzt werden, waehrend die Strategie zum Empfangsbeamforming frei aus MRC oder MMSE gewaehlt werden darf. Im Hinblick auf Szenarien, in denen Mobilfunkzellen in Clustern zusammengefasst sind, erweitern wir FlexCoBF noch weiter. Hier wurde das Konzept der koordinierten Mehrpunktverbindung (Coordinated Multipoint (CoMP) transmission) integriert. Zuletzt stellen wir drei Moeglichkeiten vor, Kanalzustandsinformationen (Channel State Information, CSI) unter verschiedenen Kanalumstaenden zu erlangen. Die Qualitaet der Kanalzustandsinformationen hat einen starken Einfluss auf die Guete des Uebertragungssystems. Die durch unsere neuen Algorithmen erzielten Verbesserungen haben wir mittels numerischer Simulationen von Summenraten und Bitfehlerraten belegt.In order to combat interference and exploit large multiplexing gains of the multi-antenna systems, a particular interest in spatial division multiple access (SDMA) techniques has emerged. Linear precoding techniques, as one of the SDMA strategies, have obtained more attention due to the fact that an increasing number of users and antennas involved into the existing and future mobile communication systems requires a simplification of the precoding design. Therefore, this thesis contributes to the design of linear transmit and receive strategies for multi-user MIMO broadcast channels in a single cell and clustered multiple cells. First, we present a throughput approximation framework for multi-user MIMO broadcast channels employing regularized block diagonalization (RBD) linear precoding. Comparing dirty paper coding (DPC) and linear precoding algorithms (e.g., zero forcing (ZF) and block diagonalization (BD)), we further quantify lower and upper bounds of the rate and power offset between them as a function of the system parameters such as the number of users and antennas. Next, we develop a novel closed-form coordinated beamforming (CBF) algorithm (i.e., SeDJoCo based closed-form CBF) to solve the existing open problem of CBF. Our new algorithm can support a MIMO system with an arbitrary number of users and transmit antennas. Moreover, the application of our new algorithm is not only for CBF, but also for blind source separation (BSS), since the same mathematical model has been used in BSS application.Then, we further propose a new iterative CBF algorithm (i.e., flexible coordinated beamforming (FlexCoBF)) for multi-user MIMO broadcast channels. Compared to the existing iterative CBF algorithms, the most promising advantage of our new algorithm is that it provides freedom in the choice of the linear transmit and receive beamforming strategies, i.e., any existing linear precoding method can be chosen as the transmit strategy and the receive beamforming strategy can be flexibly chosen from MRC or MMSE receivers. Considering clustered multiple cell scenarios, we extend the FlexCoBF algorithm further and introduce the concept of the coordinated multipoint (CoMP) transmission. Finally, we present three strategies for channel state information (CSI) acquisition regarding various channel conditions and channel estimation strategies. The CSI knowledge is required at the base station in order to implement SDMA techniques. The quality of the obtained CSI heavily affects the system performance. The performance enhancement achieved by our new strategies has been demonstrated by numerical simulation results in terms of the system sum rate and the bit error rate

Ondas milimétricas e MIMO massivo para otimização da capacidade e cobertura de redes heterogeneas de 5G

Today's Long Term Evolution Advanced (LTE-A) networks cannot support the exponential growth in mobile traffic forecast for the next decade. By 2020, according to Ericsson, 6 billion mobile subscribers worldwide are projected to generate 46 exabytes of mobile data traffic monthly from 24 billion connected devices, smartphones and short-range Internet of Things (IoT) devices being the key prosumers. In response, 5G networks are foreseen to markedly outperform legacy 4G systems. Triggered by the International Telecommunication Union (ITU) under the IMT-2020 network initiative, 5G will support three broad categories of use cases: enhanced mobile broadband (eMBB) for multi-Gbps data rate applications; ultra-reliable and low latency communications (URLLC) for critical scenarios; and massive machine type communications (mMTC) for massive connectivity. Among the several technology enablers being explored for 5G, millimeter-wave (mmWave) communication, massive MIMO antenna arrays and ultra-dense small cell networks (UDNs) feature as the dominant technologies. These technologies in synergy are anticipated to provide the 1000_ capacity increase for 5G networks (relative to 4G) through the combined impact of large additional bandwidth, spectral efficiency (SE) enhancement and high frequency reuse, respectively. However, although these technologies can pave the way towards gigabit wireless, there are still several challenges to solve in terms of how we can fully harness the available bandwidth efficiently through appropriate beamforming and channel modeling approaches. In this thesis, we investigate the system performance enhancements realizable with mmWave massive MIMO in 5G UDN and cellular infrastructure-to-everything (C-I2X) application scenarios involving pedestrian and vehicular users. As a critical component of the system-level simulation approach adopted in this thesis, we implemented 3D channel models for the accurate characterization of the wireless channels in these scenarios and for realistic performance evaluation. To address the hardware cost, complexity and power consumption of the massive MIMO architectures, we propose a novel generalized framework for hybrid beamforming (HBF) array structures. The generalized model reveals the opportunities that can be harnessed with the overlapped subarray structures for a balanced trade-o_ between SE and energy efficiently (EE) of 5G networks. The key results in this investigation show that mmWave massive MIMO can deliver multi-Gbps rates for 5G whilst maintaining energy-efficient operation of the network.As redes LTE-A atuais não são capazes de suportar o crescimento exponencial de tráfego que está previsto para a próxima década. De acordo com a previsão da Ericsson, espera-se que em 2020, a nível global, 6 mil milhões de subscritores venham a gerar mensalmente 46 exa bytes de tráfego de dados a partir de 24 mil milhões de dispositivos ligados à rede móvel, sendo os telefones inteligentes e dispositivos IoT de curto alcance os principais responsáveis por tal nível de tráfego. Em resposta a esta exigência, espera-se que as redes de 5a geração (5G) tenham um desempenho substancialmente superior às redes de 4a geração (4G) atuais. Desencadeado pelo UIT (União Internacional das Telecomunicações) no âmbito da iniciativa IMT-2020, o 5G irá suportar três grandes tipos de utilizações: banda larga móvel capaz de suportar aplicações com débitos na ordem de vários Gbps; comunicações de baixa latência e alta fiabilidade indispensáveis em cenários de emergência; comunicações massivas máquina-a-máquina para conectividade generalizada. Entre as várias tecnologias capacitadoras que estão a ser exploradas pelo 5G, as comunicações através de ondas milimétricas, os agregados MIMO massivo e as redes celulares ultradensas (RUD) apresentam-se como sendo as tecnologias fundamentais. Antecipa-se que o conjunto destas tecnologias venha a fornecer às redes 5G um aumento de capacidade de 1000x através da utilização de maiores larguras de banda, melhoria da eficiência espectral, e elevada reutilização de frequências respetivamente. Embora estas tecnologias possam abrir caminho para as redes sem fios com débitos na ordem dos gigabits, existem ainda vários desafios que têm que ser resolvidos para que seja possível aproveitar totalmente a largura de banda disponível de maneira eficiente utilizando abordagens de formatação de feixe e de modelação de canal adequadas. Nesta tese investigamos a melhoria de desempenho do sistema conseguida através da utilização de ondas milimétricas e agregados MIMO massivo em cenários de redes celulares ultradensas de 5a geração e em cenários 'infraestrutura celular-para-qualquer coisa' (do inglês: cellular infrastructure-to-everything) envolvendo utilizadores pedestres e veiculares. Como um componente fundamental das simulações de sistema utilizadas nesta tese é o canal de propagação, implementamos modelos de canal tridimensional (3D) para caracterizar de forma precisa o canal de propagação nestes cenários e assim conseguir uma avaliação de desempenho mais condizente com a realidade. Para resolver os problemas associados ao custo do equipamento, complexidade e consumo de energia das arquiteturas MIMO massivo, propomos um modelo inovador de agregados com formatação de feixe híbrida. Este modelo genérico revela as oportunidades que podem ser aproveitadas através da sobreposição de sub-agregados no sentido de obter um compromisso equilibrado entre eficiência espectral (ES) e eficiência energética (EE) nas redes 5G. Os principais resultados desta investigação mostram que a utilização conjunta de ondas milimétricas e de agregados MIMO massivo possibilita a obtenção, em simultâneo, de taxas de transmissão na ordem de vários Gbps e a operação de rede de forma energeticamente eficiente.Programa Doutoral em Telecomunicaçõe