Learning Parameters of Stochastic Radio Channel Models from Summaries

Estimating parameters of stochastic radio channel models based on new measurement data is an arduous task usually involving multiple steps such as multipath extraction and clustering. We propose two different machine learning methods, one based on approximate Bayesian computation (ABC) and the other on deep learning, for fitting stochastic channel models to data directly. The proposed methods make use of easy-to-compute summary statistics of measured data instead of relying on extracted multipath components. Moreover, the need for post-processing of the extracted multipath components is omitted. Taking the polarimetric propagation graph model as an example stochastic model, we present relevant summaries and evaluate the performance of the proposed methods on simulated and measured data. We find that the methods are able to learn the parameters of the model accurately in simulations. Applying the methods on 60 GHz indoor measurement data yields parameter estimates that generate averaged power delay profile from the model that fits the data

Applying the finite-difference time-domain to the modelling of large-scale radio channels

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy (PhD)Finite-difference models have been used for nearly 40 years to solve electromagnetic problems of heterogeneous nature. Further, these techniques are well known for being computationally expensive, as well as subject to various numerical artifacts. However, little is yet understood about the errors arising in the simulation of wideband sources with the finitedifference time-domain (FDTD) method. Within this context, the focus of this thesis is on two different problems. On the one hand, the speed and accuracy of current FDTD implementations is analysed and increased. On the other hand, the distortion of numerical pulses is characterised and mitigation techniques proposed. In addition, recent developments in general-purpose computing on graphics processing units (GPGPU) have unveiled new methods for the efficient implementation of FDTD algorithms. Therefore, this thesis proposes specific GPU-based guidelines for the implementation of the standard FDTD. Then, metaheuristics are used for the calibration of a FDTD-based narrowband simulator. Regarding the simulation of wideband sources, this thesis uses first Lagrange multipliers to characterise the extrema of the numerical group velocity. Then, the spread of numerical Gaussian pulses is characterised analytically in terms of the FDTD grid parameters. The usefulness of the proposed solutions to the previously described problems is illustrated in this thesis using coverage and wideband predictions in large-scale scenarios. In particular, the indoor-to-outdoor radio channel in residential areas is studied. Furthermore, coverage and wideband measurements have also been used to validate the predictions. As a result of all the above, this thesis introduces first an efficient and accurate FDTD simulator. Then, it characterises analytically the propagation of numerical pulses. Finally, the narrowband and wideband indoorto-outdoor channels are modeled using the developed techniques

The automatic placement of multiple indoor antennas using Particle Swarm Optimisation

In this thesis, a Particle Swarm Optimization (PSO) method combined with a ray propagation method is presented as a means to optimally locate multiple antennas in an indoor environment. This novel approach uses Particle Swarm Optimisation combined with geometric partitioning. The PSO algorithm uses swarm intelligence to determine the optimal transmitter location within the building layout. It uses the Keenan-Motley indoor propagation model to determine the fitness of a location. If a transmitter placed at that optimum location, transmitting a maximum power is not enough to meet the coverage requirements of the entire indoor space, then the space is geometrically partitioned and the PSO initiated again independently in each partition. The method outputs the number of antennas, their effective isotropic radiated power (EIRP) and physical location required to meet the coverage requirements. An example scenario is presented for a real building at Loughborough University and is compared against a conventional planning technique used widely in practice

Terahertz Wireless Channels: A Holistic Survey on Measurement, Modeling, and Analysis

Terahertz (0.1-10 THz) communications are envisioned as a key technology for sixth generation (6G) wireless systems. The study of underlying THz wireless propagation channels provides the foundations for the development of reliable THz communication systems and their applications. This article provides a comprehensive overview of the study of THz wireless channels. First, the three most popular THz channel measurement methodologies, namely, frequency-domain channel measurement based on a vector network analyzer (VNA), time-domain channel measurement based on sliding correlation, and time-domain channel measurement based on THz pulses from time-domain spectroscopy (THz-TDS), are introduced and compared. Current channel measurement systems and measurement campaigns are reviewed. Then, existing channel modeling methodologies are categorized into deterministic, stochastic, and hybrid approaches. State-of-the-art THz channel models are analyzed, and the channel simulators that are based on them are introduced. Next, an in-depth review of channel characteristics in the THz band is presented. Finally, open problems and future research directions for research studies on THz wireless channels for 6G are elaborated.Comment: to appear in IEEE Communications Surveys and Tutorial

On the effect of varying spectral conditions on amorphous silicon solar cell performance

An opto-electrical modelling platform has been designed to model the effects of illumination spectra on amorphous silicon solar cells of different i-layer thickness and degradation state. The illumination spectra, which were investigated in this work, are solar simulator spectra and solar spectra recorded outdoors at CREST, Loughborough. These spectra are used to probe the effect of spectral variation on a-Si:H solar cell performance and its co-dependence with the state of the device. For the case of indoor evaluation of performance of a-Si:H solar cells, it is shown that the performance of the device remains relative to the illumination source of the solar simulator. Spectra with Average Photon Energy (APE) higher than AM1.5G tend to overestimate the performance parameters (JSC, MPP, VOC) of the device, while spectra with APE lower than AM1.5G tend to underestimate the values of the performance parameters of the device. The maximum power deviation of most class-A solar simulators is less than 1% of the actual STC values, but the performance deviation may arise up to 4% for the case of LED light sources. It is suggested to apply voltage dependant corrections to the J-V characteristics, whenever the spectral mismatch between the illumination spectra and AM1.5G is significant. The effects of outdoor spectral variation on the performance of a-Si:H solar cells has been investigated. The results show that light intensity is primarily responsible for a- Si:H outdoor performance. The APE of the outdoor spectra is also identified a significant factor for the variation of performance. The magnitude of maximum power deviations due to APE changes is in the range of ±3% as compared to power output of the device under the AM1.5G spectrum. The percentage of performance variation to STC differed for a-Si:H solar cells of different i-layer thickness and level of degradation. Specifically devices with thicker i-layer, which have suffered degradation, are prone to performance variations. Finally, the energy yield and the performance ratio of amorphous silicon solar cells were reviewed in respect to outdoor spectral changes. The performance ratio is a useful method for cases where prediction of power output is necessary. However, it is suggested that PV modules should be rated on the basis of their annual energy yield, when possible

Intelligent GNSS Positioning using 3D Mapping and Context Detection for Better Accuracy in Dense Urban Environments

Conventional GNSS positioning in dense urban areas can exhibit errors of tens of meters due to blockage and reflection of signals by the surrounding buildings. Here, we present a full implementation of the intelligent urban positioning (IUP) 3D-mapping-aided (3DMA) GNSS concept. This combines conventional ranging-based GNSS positioning enhanced by 3D mapping with the GNSS shadow-matching technique. Shadow matching determines position by comparing the measured signal availability with that predicted over a grid of candidate positions using 3D mapping. Thus, IUP uses both pseudo-range and signal-to-noise measurements to determine position. All algorithms incorporate terrain-height aiding and use measurements from a single epoch in time. Two different 3DMA ranging algorithms are presented, one based on least-squares estimation and the other based on computing the likelihoods of a grid of candidate position hypotheses. The likelihood-based ranging algorithm uses the same candidate position hypotheses as shadow matching and makes different assumptions about which signals are direct line-of-sight (LOS) and non-line-of-sight (NLOS) at each candidate position. Two different methods for integrating likelihood-based 3DMA ranging with shadow matching are also compared. In the position-domain approach, separate ranging and shadow-matching position solutions are computed, then averaged using direction-dependent weighting. In the hypothesis-domain approach, the candidate position scores from the ranging and shadow matching algorithms are combined prior to extracting a joint position solution. Test data was recorded using a u-blox EVK M8T consumer-grade GNSS receiver and a HTC Nexus 9 tablet at 28 locations across two districts of London. The City of London is a traditional dense urban environment, while Canary Wharf is a modern environment. The Nexus 9 tablet data was recorded using the Android Nougat GNSS receiver interface and is representative of future smartphones. Best results were obtained using the likelihood-based 3DMA ranging algorithm and hypothesis-based integration with shadow matching. With the u-blox receiver, the single-epoch RMS horizontal (i.e., 2D) error across all sites was 4.0 m, compared to 28.2 m for conventional positioning, a factor of 7.1 improvement. Using the Nexus tablet, the intelligent urban positioning RMS error was 7.0 m, compared to 32.7 m for conventional GNSS positioning, a factor of 4.7 improvement. An analysis of processing and data requirements shows that intelligent urban positioning is practical to implement in real-time on a mobile device or a server. Navigation and positioning is inherently dependent on the context, which comprises both the operating environment and the behaviour of the host vehicle or user. No single technique is capable of providing reliable and accurate positioning in all contexts. In order to operate reliably across different contexts, a multi-sensor navigation system is required to detect its operating context and reconfigure the techniques accordingly. Specifically, 3DMA GNSS should be selected when the user is in a dense urban environment, not indoors or in an open environment. Algorithms for detecting indoor and outdoor context using GNSS measurements and a hidden Markov model are described and demonstrated