Indoor wireless communications and applications

Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter

Doctoral Thesis: Massive MIMO in Real Propagation Environments

Mobile communications are now evolving towards the fifth generation (5G). In the near future, we expect an explosive increase in the number of connected devices, such as phones, tablets, sensors, connected vehicles and so on. Much higher data rates than in today's 4G systems are required. In the 5G visions, better coverage in remote regions is also included, aiming for bringing the current "4 billion unconnected" population into the online world. There is also a great interest in "green communications", for less energy consumption in the ICT (information and communication technology) industry. Massive MIMO is a potential technology to fulfill the requirements and visions. By equipping a base station with a large number, say tens to hundreds, of antennas, many terminals can be served in the same time-frequency resource without severe inter-user interference. Through "aggressive" spatial multiplexing, higher data rates can be achieved without increasing the required spectrum. Processing efforts can be made at the base station side, allowing terminals to have simple and cheap hardware. By exploiting the many spatial degrees of freedom, linear precoding/detection schemes can be used to achieve near-optimal performance. The large number of antennas also brings the advantage of large array gain, resulting in an increase in received signal strength. Better coverage is thus achieved. On the other hand, transmit power from base stations and terminals can be scaled down to pursue energy efficiency. In the last five years, a lot of theoretical studies have been done, showing the extraordinary advantages of massive MIMO. However, the investigations are mainly based on theoretical channels with independent and identically distributed (i.i.d.) Gaussian coefficients, and sometimes assuming unlimited number of antennas. When bringing this new technology from theory to practice, it is important to understand massive MIMO behavior in real propagation channels using practical antenna arrays. Not much has been known about real massive MIMO channels, and whether the claims about massive MIMO still hold there, until the studies in this thesis were done. The thesis study connects the "ideal" world of theory to the "non-ideal" reality. Channel measurements for massive MIMO in the 2.6 GHz band were performed, in different propagation environments and using different types of antenna arrays. Based on obtained real-life channel data, the studies include • channel characterization to identify important massive MIMO properties, • evaluation of propagation conditions in real channels and corresponding massive MIMO performance, • channel modeling for massive MIMO to capture the identified channel properties, and • reduction of massive MIMO hardware complexity through antenna selection. The investigations in the thesis conclude that massive MIMO works efficiently in real propagation environments. The theoretical advantages, as observed in i.i.d. Rayleigh channels, can also be harvested in real channels. Important propagation effects are identified for massive MIMO scenarios, including channel variations over large arrays, multipath-component (MPC) lifetime, and 3D propagation. These propagation properties are modeled and included into the COST 2100 MIMO channel model as an extension for massive MIMO. The study on antenna selection shows that characteristics in real channels allow for significant reductions of massive MIMO complexity without significant performance loss. As one of the world's first research work on massive MIMO behavior in real propagation channels, the studies in this thesis promote massive MIMO as a practical technology for future communication systems

Massive MIMO in Real Propagation Environments: Do All Antennas Contribute Equally?

Massive MIMO can greatly increase both spectral and transmit-energy efficiency. This is achieved by allowing the number of antennas and RF chains to grow very large. However, the challenges include high system complexity and hardware energy consumption. Here we investigate the possibilities to reduce the required number of RF chains, by performing antenna selection. While this approach is not a very effective strategy for theoretical independent Rayleigh fading channels, a substantial reduction in the number of RF chains can be achieved for real massive MIMO channels, without significant performance loss. We evaluate antenna selection performance on measured channels at 2.6 GHz, using a linear and a cylindrical array, both having 128 elements. Sum-rate maximization is used as the criterion for antenna selection. A selection scheme based on convex optimization is nearly optimal and used as a benchmark. The achieved sum-rate is compared with that of a very simple scheme that selects the antennas with the highest received power. The power-based scheme gives performance close to the convex optimization scheme, for the measured channels. This observation indicates a potential for significant reductions of massive MIMO implementation complexity, by reducing the number of RF chains and performing antenna selection using simple algorithms.Comment: Submitted to IEEE Transactions on Communication

Hybrid ray-tracing/FDTD method for human exposure evaluation of a massive MIMO technology in an industrial indoor environment

This paper presents a numerical approach for massive multiple-input multiple-output (MIMO) human exposure assessment. It combines ray-tracing for the estimation of the wireless channel and the finite-difference time-domain method to simulate the exposure of a realistic human phantom. We apply it to estimate the exposure in a model of an industrial indoor environment with a single massive MIMO base station (BS). The exposure scenarios include line-of-sight and non-line-of-sight propagation with the BS using equal gain transmission precoding at 3.5 GHz. Calculated channel parameters are discussed in comparison with the data available in the literature. The exposure in the phantom's head is evaluated in terms of the peak-spatial specific absorption rate averaged over a 10-g cube and referenced to the free-space time-averaged Poynting vector magnitude at the same location

Frequency Domain Backoff for Continuous Beamforming Space Division Multiple Access on Massive MIMO Wireless Backhaul Systems

This paper newly proposes a frequency domain backoff scheme dedicated to continuous beamforming space division multiple access (CB-SDMA) on massive antenna systems for wireless entrance (MAS-WE). The entrance base station (EBS) has individual base band signal processing units for respective relay stations (RSs) to be accommodated. EBS then continuously applies beamforming weight to transmission/reception signals. CB-SDMA yields virtual point-to-point backhaul link where radio resource control messages and complicated multiuser scheduling are not required. This simplified structure allows RSs to work in a distributed manner. However, one issue remains to be resolved; overloaded multiple access resulting in collision due to its random access nature. The frequency domain backoff mechanism is introduced instead of the time domain one. It can flexibly avoid co-channel interference caused by excessive spatial multiplexing. Computer simulation verifies its superiority in terms of system throughput and packet delay

On the Impact of Antenna Array Geometry on Indoor Wideband Massive MIMO Networks

Multi-user massive multiple-input-multiple-output (massive MIMO) will play a key role in future wireless communication networks. Since spatial channel diversity is the fundamental merit of this technique, high channel correlation may significantly restrict its abilities. This study investigates the impact of channel correlation on a prototyped massive MIMO network with the objective to identify an antenna array geometry which has reduced mutual coupling and channel correlation. To this end, a highly efficient directional wideband single antenna element was designed for the antenna arrays and the user equipments (UEs). The designed array geometry is tested in an experimental indoor wideband massive MIMO setup. Important system parameters, such as channel correlation, power delay profile, and average received power from the UEs, are studied by analyzing the measured channel data. Furthermore, system-level simulations and network capacity calculations are performed based on the measured channel data to evaluate the performance of the prototyped antenna arrays. A regular array was also fabricated and used for benchmarking comparison. Moreover, a power control algorithm is introduced for the uplink, which was shown to improve the network capacity by up to 3 dB. The results demonstrate that the introduced antenna array outperforms the uniform antenna array in terms of mutual coupling and channel capacity

Analysis of the sum rate for massive MIMO using 10 GHz measurements

Orientador: Gustavo FraidenraichTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho apresenta um conjunto de contribuições para caracterização e modelagem de canais reais de rádio abordando aspectos relacionados com as condições favoráveis de propagação para sistemas massive MIMO. Discutiremos como caracterizar canais de rádio em um ambiente real, processamento de dados e análise das condições favoráveis de propagação. Em uma segunda parte, focamos na determinação teórica de alguns aspectos da tecnologia de massive MIMO utilizando propriedades de distribuições matriciais Wishart. Inicialmente, apresentamos uma contribuição sobre a aplicação do algoritmo ESPRIT, para estimar parâmetros de um conjunto de dados multidimensional. Obtivemos dados por varredura em frequência de um Analisador Vetorial de Rede e os adaptamos para o algoritmo ESPRIT. Mostramos como remover a influência do ganho de padrão de antenas e como utilizar um gerador de modelo de canal baseado nas medidas reais de canal de rádio. As medidas foram feitas na frequência de 10.1 GHz com largura de faixa de 500 MHz. Utilizando um gerador de modelo de canal, fomos além do universo das simulações por distribuições Gaussianas. Introduzimos o conceito de propagação favorável e analisamos condições de linha-de-visada usando arranjos lineares uniformes e arranjos retangulares uniformes de antena. Como novidade da pesquisa, mostramos os benefícios de explorar um número extra de graus de liberdade devido à escolha dos formatos de arranjo de antenas e ao aumento do número de elementos. Esta propriedade é observada ao analisarmos a distribuição dos autovalores de matrizes Gramianas. Em seguida, estendemos o mesmo raciocínio para as matrizes de canal geradas a partir de informações reais e verificamos se as propriedades ainda permaneceriam válidas. Na segunda parte deste trabalho, incluímos mais de uma antena no terminal móvel e calculamos a probabilidade de indisponibilidade para várias configurações de antenas e número arbitrário de usuários. Esboçamos inicialmente a formulação para a informação mútua e, em seguida, calculamos os resultados exatos em uma situação com dois usuários e duas antenas, tanto na estação base (EB) como nos terminais de usuário(TU). Visto que as formulações para a derivação exata dos casos com mais antenas e mais usuários mostrou-se muito intrincada, propusemos uma aproximação Gaussiana para simplificar o problema. Esta aproximação foi validada por simulações Monte Carlo para diferentes relações sinal/ruídoAbstract: This thesis presents a set of contributions for channel modeling and characterization of real radio channels delineating aspects related with the favorable propagation for massive MIMO systems. We will discuss about how to proceed for characterizing radio channels in an real environment , data processing, and analysis of favorable conditions. In a second part, we focused on determination of some theoretical aspects of the Massive MIMO technology using properties of Wishart distribution matrices. We initially present a contribution on the application of ESPRIT algorithm for estimating a multidimensional set of measured data. We have obtained data by frequency sweep carried out by a vector network analyzer(VNA) and adapted it to fit in the ESPRIT algorithm. We show how to remove antenna pattern gain using virtual antenna arrays and how to use a channel model generator based on radio channel measurements of real environments. The measurements were conducted at the frequency of 10.1 GHz and 500 MHz bandwidth. By using a channel model generator, we have explored beyond the simulation of Gaussian Distributions. We will introduce the concept of favorable propagation and analyze the line-of-sight conditions using ULA and URA array shapes. As a research novelty, we will show the benefits of exploiting an extra degree of freedom due to the choice of the antenna shapes and amount of antenna elements. We observe these properties through the distribution of the Gramian Matrices. Next, we extend the same rationale to channel matrices generated from real channels and we verify that the properties are still valid. In a second part of the research work, we included more than one antenna in the mobile terminals and calculated the outage probability for several antenna configurations and arbitrary number users. We introduce a formulation for mutual information and then we calculate exact results in a case with two users with two antennas in both Base Station (BS) and User Terminals (UT). Since the formulations to the exact derivation for cases with more antennas and users seems to be intricate, we propose a Gaussian approximation solution to simplify the problem. We validated this approximation with Monte Carlo simulations for different signal-to-noise ratiosDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia Elétrica248416/2013-8CNPQCAPE