On the Frequency Dependency of Radio Channel's Delay Spread: Analyses and Findings From mmMAGIC Multi-frequency Channel Sounding

This paper analyzes the frequency dependency of the radio propagation channel's root mean square (rms) delay spread (DS), based on the multi-frequency measurement campaigns in the mmMAGIC project. The campaigns cover indoor, outdoor, and outdoor-to-indoor (O2I) scenarios and a wide frequency range from 2 to 86 GHz. Several requirements have been identified that define the parameters which need to be aligned in order to make a reasonable comparison among the different channel sounders employed for this study. A new modelling approach enabling the evaluation of the statistical significance of the model parameters from different measurements and the establishment of a unified model is proposed. After careful analysis, the conclusion is that any frequency trend of the DS is small considering its confidence intervals. There is statistically significant difference from the 3GPP New Radio (NR) model TR 38.901, except for the O2I scenario.Comment: This paper has been accepted to the 2018 12th European Conference on Antennas and Propagation (EuCAP), London, UK, April 201

Propagation modelling and measurements in a populated indoor environment at 5.2 GHz

There are a number of significant radiowave propagation phenomena present in the populated indoor environment, including multipath fading and human body effects. The latter can be divided into shadowing and scattering caused by pedestrian movement, and antenna-body interaction with bodyworn or hand portable terminals [1]. Human occupants within indoor environments are not always stationary and their movement will lead to temporal channel variations that can strongly affect the quality of indoor wireless communication systems. Hence, populated environments remain a major challenge for wireless local area networks (WLAN) and other indoor communication systems. Therefore, it is important to develop an understanding of the potential and limitations of indoor radiowave propagation at key frequencies of interest, such as the 5.2 GHz band employed by commercial wireless LAN standards such as IEEE 802.11a and HiperLAN 2. Although several indoor wireless models have been proposed in the literature, these temporal variations have not yet been thoroughly investigated. Therefore, we have made an important contribution to the area by conducting a systematic study of the problem, including a propagation measurement campaign and statistical channel characterization of human body effects on line-of-sight indoor propagation at 5.2 GHz. Measurements were performed in the everyday environment of a 7.2 m wide University hallway to determine the statistical characteristics of the 5.2 GHz channel for a fixed, transverse line-of-sight (LOS) link perturbed by pedestrian movement. Data were acquired at hours of relatively high pedestrian activity, between 12.00 and 14.00. The location was chosen as a typical indoor wireless system environment that had sufficient channel variability to permit a valid statistical analysis. The paper compares the first and second order statistics of the empirical signals with the Gaussian-derived distributions commonly used in wireless communications. The analysis shows that, as the number of pedestrians within the measurement location increases, the Ricean K-factor that best fits the Cumulative Distribution Function (CDF) of the empirical data tends to decrease proportionally, ranging from K=7 with 1 pedestrian to K=0 with 4 pedestrians. These results are consistent with previous results obtained for controlled measurement scenarios using a fixed link at 5.2 GHz in [2], where the K factor reduced as the number of pedestrians within a controlled measurement area increased. Level crossing rate results were Rice distributed, considering a maximum Doppler frequency of 8.67 Hz. While average fade duration results were significantly higher than theoretically computed Rice and Rayleigh, due to the fades caused by pedestrians. A novel statistical model that accurately describes the 5.2 GHz channel in the considered indoor environment is proposed. For the first time, the received envelope CDF is explicitly described in terms of a quantitative measurement of pedestrian traffic within the indoor environment. The model provides an insight into the prediction of human body shadowing effects for indoor channels at 5.2 GHz

5G 3GPP-like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments

For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.Comment: To be published in 2016 IEEE 83rd Vehicular Technology Conference Spring (VTC 2016-Spring), Nanjing, China, May 201

Coverage prediction and optimization algorithms for indoor environments

A heuristic algorithm is developed for the prediction of indoor coverage. Measurements on one floor of an office building are performed to investigate propagation characteristics and validations with very limited additional tuning are performed on another floor of the same building and in three other buildings. The prediction method relies on the free-space loss model for every environment, this way intending to reduce the dependency of the model on the environment upon which the model is based, as is the case with many other models. The applicability of the algorithm to a wireless testbed network with fixed WiFi 802.11b/g nodes is discussed based on a site survey. The prediction algorithm can easily be implemented in network planning algorithms, as will be illustrated with a network reduction and a network optimization algorithm. We aim to provide an physically intuitive, yet accurate prediction of the path loss for different building types