Data Mining - Concepts and Techniques.

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

Channel models are important tools to evaluate the performance of new concepts in mobile communications. However, there is a tradeoff between complexity and accuracy. In this paper, we extend the popular Wireless World Initiative for New Radio (WINNER) channel model with new features to make it as realistic as possible. Our approach enables more realistic evaluation results at an early stage of algorithm development. The new model supports 3-D propagation, 3-D antenna patterns, time evolving channel traces of arbitrary length, scenario transitions and variable terminal speeds. We validated the model by measurements in a coherent LTE advanced testbed in downtown Berlin, Germany. We then reproduced the same scenario in the model and compared several channel parameters (delay spread, path gain, K-factor, geometry factor and capacity). The results match very well and we can accurately predict the performance for an urban macro-cell setup with commercial high-gain antennas. At the same time, the computational complexity does not increase significantly and we can use all existing WINNER parameter tables. These artificial channels, having equivalent characteristics as measured data, enable virtual field trials long before prototypes are available.

QuaDRiGa: A 3-D Multi-Cell Channel Model With Time Evolution for Enabling Virtual Field Trials

This paper presents a 3-D statistical channel impulse response (IR) model for urban line of sight (LOS) and non-LOS channels developed from 28- and 73-GHz ultrawideband propagation measurements in New York City, useful in the design of 5G wireless systems that will operate in both the ultra-high frequency/microwave and millimeter-wave (mmWave) spectrum to increase channel capacities. A 3GPP-like stochastic IR channel model is developed from measured power delay profiles, angle of departure, and angle of arrival power spectra. The extracted statistics are used to implement a channel model and simulator capable of generating 3-D mmWave temporal and spatial channel parameters for arbitrary mmWave carrier frequency, signal bandwidth, and antenna beamwidth. The model presented here faithfully reproduces realistic IRs of measured urban channels, supporting air interface design of mmWave transceivers, filters, and multi-element antenna arrays.

3-D Millimeter-Wave Statistical Channel Model for 5G Wireless System Design

Understanding ultra-wide-band (UWB) propagation channels is a prerequisite for UWB system design as well as communication-theoretic and information-theoretic investigations. This paper surveys the fundamental properties of UWB channels, pointing out the differences to conventional channels. If the relative bandwidth is large, the propagation processes, and therefore path loss and shadowing, become frequency-dependent, and the well-known wide-sense stationary uncorrelated scattering model is not applicable anymore. If the absolute bandwidth is large, the shape of the impulse responses as well as the fading statistics change. This paper also describes methods for measuring UWB channels and extracing channel parameters. Throughout this paper, the relationship between channel properties and other areas of UWB research are pointed out.

Ultra-Wide-Band Propagation Channels

We present a solution to the problem of identifying clusters from MIMO measurement data in a data window, with a minimum of user interaction. Conventionally, visual inspection has been used for the cluster identification. However this approach is impractical for a large amount of measurement data. Moreover, visual methods lack an accurate definition of a "cluster" itself. We introduce a framework that is able to cluster multi-path components (MPCs), decide on the number of clusters, and discard outliers. For clustering we use the K-means algorithm, which iteratively moves a number of cluster centroids through the data space to minimize the total difference between MPCs and their closest centroid. We significantly improve this algorithm by following changes: (i) as the distance metric we use the multi- path component distance (MCD), (ii) the distances are weighted by the powers of the MPCs. The implications of these changes result in a definition of a "cluster" itself that appeals to intuition. We assess the performance of the new algorithm by clustering real-world measurement data from an indoor big hall environment.

A Framework for Automatic Clustering of Parametric MIMO Channel Data Including Path Powers

This paper discusses over-the-air (OTA) test setup for multiple-input-multiple-output (MIMO) capable terminals with emphasis on channel modelling. The setup is composed of
a fading emulator, an anechoic chamber, and multiple probes. Creation of a propagation environment inside an anechoic chamber requires unconventional radio channel modelling, namely, a specific mapping of the original models onto the probe antennas. We introduce two novel methods to generate fading emulator channel coefficients; the prefaded signals synthesis and the plane wave synthesis. To verify both methods we present a set of simulation results. We also show that the geometric description is a prerequisite for the original channel model.

/pdf/channel-modelling-for-multiprobe-over-the-air-mimo-testing-2qhv6ypxd5.pdf

Channel Modelling for Multiprobe Over-the-Air MIMO Testing

A testing system comprises a noise source (500) coupled to at least two antenna elements (102 to 116). The noise source (500) forms a total noise power on the basis of a total signal power received by the emulator (118), a gain of at least one antenna-specific channel (504) between the emulator (118) and the antenna elements (102 to 116), and a desired signal-to-noise ratio. The noise source (500) transmits noise at the total noise power from the at least two antenna elements (102 to 116) to the device under test (100) wirelessly.

Over-the-air test

The problem of identifying clusters from MIMO measurement data is addressed. Conventionally, visual inspection has been used for cluster identification, but this approach is impractical for a large amount of measurement data. For automatic clustering, the multipath component distance (MCD) is used to calculate the distance between individual multipath components estimated by a channel parameter estimator, such as SAGE. This distance is implemented in the well-known KMeans clustering algorithm. To demonstrate the effectiveness of the choice made, the performance of the MCD and the Euclidean distance were compared by clustering synthetic data generated by the 3GPP spatial channel model (SCM). Using the MCD significantly improved clustering performance.

Improving clustering performance using multipath component distance

This paper presents a joint clustering-and-tracking framework to identify time-variant cluster parameters for geometry-based stochastic MIMO channel models. The method uses a Kalman filter for tracking and predicting cluster positions, a novel consistent initial guess procedure that accounts for predicted cluster centroids, and the well-known KPowerMeans algorithm for cluster identification. We tested the framework by applying it to two different sets of MIMO channel measurement data, indoor measurements conducted at 2.55 GHz and outdoor measurements at 300 MHz. The results from our joint clustering-and-tracking algorithm provide a good match with the physical propagation mechanisms observed in the measured scenarios.

/pdf/tracking-time-variant-cluster-parameters-in-mimo-channel-70o0wv53ok.pdf

Jukka-Pekka Nuutinen

Papers

A Framework for Automatic Clustering of Parametric MIMO Channel Data Including Path Powers

Channel Modelling for Multiprobe Over-the-Air MIMO Testing

Over-the-air test

Improving clustering performance using multipath component distance

Tracking Time-Variant Cluster Parameters in MIMO Channel Measurements