In recent years, we have seen a rapid increase in the number of `smart' wireless devices, including smartphones and tablets, Wi-Fi networks and everyday objects connected to the internet. This has caused the demand for radio spectrum to increase dramatically, driven by the growing quantitiesof data transmitted over the internet by these devices. In Europe alone,the European Commission estimated in 2017 that the total value of servicesthat depend on radio spectrum is at least 200 billion euros annually. Therefore,it is crucial that new technologies should aim to increase the eciency with which the radio spectrum is being used. As such, researchers aim to model the medium between the transmitting antenna and the receiving antenna as best as possible. This medium is called the radio channel, and it is responsible for all the changes of the characteristics of a wireless signal as it propagates from the transmitter to the receiver. Due to several interactions with physical objects in the environment, a multitude of signals will arrive at the receiver, subject to dierent propagation echanisms such as reection, diraction, and scattering. This phenomenon is called multipath propagation, and the result thereof is that dierent propagation paths will have dierent characteristics. The aim of channel modeling is thus to make a mathematical representation of the eects of the communication channel through which the wireless signals are propagating. By using an accurate channel model, we can provide a realistic assessment of the overall performance in the design of applications and communication systems, and optimize link performances and data rates.
To overcome the limited availability of the radio frequency spectrum, Multiple-Input Multiple-Output (MIMO) systems have emerged as the most promising technology to multiply the capacity of a radio link. MIMO systems use multiple antennas at the transmitting and receiving stations, exploiting the multipath propagation in an environment. An observation of the MIMO radio channel can be modeled as the superposition of a deterministic part (the Specular Multipath Components (SMC)), a stochastic part (Dense Multipath Components (DMC)), and additive measurement noise.
The SMC consists of a number of plane waves with well dened parameters in the dimensions in which the radio channel is expressed. They can be described by their Angle of Departure (AoD) from which they are emitted xxxii English summary by the transmitter, the Angle of Arrival (AoA) from which they are incident at the receiver, and the Time-delay of Arrival (ToA) they have encountered, proportional to their traveled path length from transmitter to receiver. The DMC originates from distributed diuse scattering of the electromagnetic waves on electrically small and rough surfaces. As their contribution to the radio channel is often non-coherent, meaning that their phase is not a deterministic quantity, they can only be described in a stochastic manner.
This means that they contain some kind of randomness, such that we typically model them by means of the covariance matrix of the residual signal components, which is the remainder of the radio channel after removal of the SMC. The DMC are typically modeled both in the frequency domain, and the angular domain. The inclusion of DMC in channel models is often lacking in literature nowadays, although it has already been shown that their contribution to the total capacity of the MIMO radio channel can be quite signicant.
In this work, we will focus on stochastic empirical channel models, meaning that we will derive channel models consisting of the coherent contributions of the SMC, with the inclusion of the non-coherent contributions of the DMC, through experimental results based on channel sounding measurement campaigns in specic environments. We will do this with a focus on Ultra-Wideband (UWB) communication systems, characterized by their ability to transmit pulses with a very low power density in a large frequency band, ranging from 3.1 GHz to 10.6 GHz. As such, this allows these systems to share the radio spectrum with other applications, after which their combination with a MIMO antenna conguration vastly increases the capacity of these communication systems. Chapter 1 explains these concepts in more detail.
Therefore, we will examine the importance of accounting for DMC in conventional channel models in Chapter 2, which covers an analysis of the DMC contributions in an oce environment, a laboratory environment, and a large industrial hall. In this chapter, we will analyze both their frequency and polarization-dependencies, and we will check the validity of the DMC assumption for higher frequency bands. Afterwards, Chapter 3 presents the design of a newly developed multipath estimation algorithm, allowing for the frequency-wise analysis of propagation paths throughout the UWB frequency band. Subsequently, this algorithm is used in Chapter 4, where it is applied in a novel localization framework, employing a riangulation method using the geometrical properties of the propagation paths such as the aforementioned AoD, AoA and ToA. Whereas these chapters all focused on the analysis of DMC in the frequency domain, we will go a step further in Chapter 5, where we analyze the DMC in the angular domain. To facilitate this, the maximum likelihood estimation of the DMC parameters is extended from the conventional unimodal DMC assumption to a multimodal DMC assumption. As such, we are able to characterize diuse scattering from multiple angles in an environment. The validity of our approach is tested English summary xxxiii by means of the generation of synthetic radio channels, and is evaluated in an indoor hall environment. Next, Chapter 6 concerns an analysis of the delay-Doppler characteristics of the radio channel in a university hall. This environment is analyzed during several short and long breaks in-between classes, so that the Doppler characteristics could be analyzed in the timedelay domain as a function of the occupational density of the hall (i.e., as a function of the amount of people present in it). Finally, Chapter 7 concludes this book with a summary of the accomplished work, and proposes some opportunities for future research.