A measurement-based fading model for wireless personal area networks

Personal area networks (PANs) are wireless communications systems with high data rates but small coverage area. PAN propagation channels differ from the well-explored propagation channels of wide-area networks due to several reasons: (i) the distances are typically very small, (ii) the antenna arrangements can be quite different, and (iii) the influence from human presence in the environment is different. The current paper presents results of a channel measurement campaign, where measurements are conducted over distances of 1-10 m using several multi-antenna devices, combined to create different PAN scenarios. For each measured Tx-Rx separation, channel realizations are obtained by small spatial movements of the antenna devices, and by rotating the persons holding the devices. From the results, we draw two main conclusions: (i) The small-scale amplitude statistics, analyzed as the variations over a small sampling area and frequency subchannels, cannot be described in a satisfactory way using only the Rayleigh or Ricean distributions, rather a mixed distribution, the generalized gamma distribution, is more suitable; (ii) it is advantageous to distinguish between two types of large-scale fading: body shadowing (due to the orientation of the person holding the device) and shadowing due to surrounding objects (lateral movement). We also define and parameterize a complete statistical model for all fading

Measurement-Based Modeling of Wireless Propagation Channels - MIMO and UWB

Future wireless systems envision higher speeds and more reliable services but at the same time face challenges in terms of bandwidth being a limited resource. Two promising techniques that can provide an increased throughput without requiring additional bandwidth allocation are multiple-input multiple-output (MIMO) systems and ultra-wideband (UWB) systems. However, the performance of such systems is highly dependent on the properties of the wireless propagation channel, and an understanding of the channel is therefore crucial in the design of future wireless systems. Examples of such systems covered by this thesis are wireless personal area networks (papers I and II), vehicle-to-vehicle communications (paper III), board-to-board communications inside computers (paper IV) and sensor networks for industrial applications (paper V). Typically, channel models are used to evaluate the performance of different transmission and reception schemes. Channel modeling is the focus of this thesis, which contains a collection of papers that analyze and model the behavior of MIMO and UWB propagation channels. Paper I investigates the fading characteristics of wireless personal area networks (PANs), networks that typically involve human influence close to the antenna terminals. Based on extensive channel measurements using irregular antenna arrays, typical properties of PAN propagation channels are discussed and a model for the complete fading of a single link is presented. Paper II extends the model from paper I to a complete MIMO channel model. The paper combines the classical LOS model for MIMO with results from paper I by prescribing different fading statistics and mean power at the different antenna elements. The model is verified against measurement data and the paper also provides a parameterization for an example of a PAN scenario. Paper III presents a geometry-based stochastic MIMO model for vehicle-to-vehicle communications. The most important propagation effects are discussed based on the results from extensive channel measurements, and the modeling approach is motivated by the non-stationary behavior of such channels. The model distinguishes between diffuse contributions and those stemming from interaction with significant objects in the propagation channel, and the observed fading characteristics of the latter are stochastically accounted for in the model. Paper IV gives a characterization of UWB propagation channels inside desktop computer chassis. By studying measurement results from two different computers, it is concluded that the propagation channel only shows minor differences for different computers and positions within the chassis. It is also found out that the interference power produced by the computer is limited to certain subbands, suggesting that multiband UWB systems are more suitable for this type of applications. Paper V describes a UWB channel model based on the first UWB measurements in an industrial environment. Analyzing results from two different factory halls, it is concluded that energy arrives at the receiver in clusters, which motivates the use of a classical multi-cluster model to describe the channel impulse response. Parts of the results from this paper were also used as input to the channel model in the IEEE 802.15.4a UWB standardization work. In summary, the work within this thesis leads to an increased understanding of the behavior of wireless propagation channels for MIMO and UWB systems. By providing three detailed simulation models, two for MIMO and one for UWB, it can thus contribute to a more efficient design of the wireless communications systems of tomorrow

RMS delay spread vs. coherence bandwidth from 5G indoor radio channel measurements at 3.5 GHz band

Our society has become fully submersed in fourth generation (4G) technologies, setting constant connectivity as the norm. Together with self-driving cars, augmented reality, and upcoming technologies, the new generation of Internet of Things (IoT) devices is pushing the development of fifth generation (5G) communication systems. In 5G architecture, increased capacity, improved data rate, and decreased latency are the objectives. In this paper, a measurement campaign is proposed; we focused on studying the propagation properties of microwaves at a center frequency of 3.5 GHz, commonly used in 5G cellular networks. Wideband measurement data were gathered at various indoor environments with different dimensions and characteristics. A ray-tracing analysis showed that the power spectrum is dominated by the line of sight component together with reflections on two sidewalls, indicating the practical applicability of our results. Two wideband parameters, root mean square delay spread and coherence bandwidth, were estimated for the considered scenarios, and we found that they are highly dependent on the physical dimension of the environment rather than on furniture present in the room. The relationship between both parameters was also investigated to provide support to network planners when obtaining the bandwidth from the delay spread, easily computed by a ray-tracing tool

A Deep Learning Approach to Location- and Orientation-aided 3D Beam Selection for mmWave Communications

Position-aided beam selection methods have been shown to be an effective approach to achieve high beamforming gain while limiting the overhead and latency of initial access in millimeter wave (mmWave) communications. Most research in the area, however, has focused on vehicular applications, where the orientation of the user terminal (UT) is mostly fixed at each position of the environment. This paper proposes a location- and orientation-based beam selection method to enable context information (CI)-based beam alignment in applications where the UT can take arbitrary orientation at each location. We propose three different network structures, with different amounts of trainable parameters that can be used with different training dataset sizes. A professional 3-dimensional ray tracing tool is used to generate datasets for an IEEE standard indoor scenario. Numerical results show the proposed networks outperform a CI-aided benchmark such as the generalized inverse fingerprinting (GIFP) method as well as hierarchical beam search as a non-CI-based approach. Moreover, compared to the GIFP method, the proposed deep learning-based beam selection shows higher robustness to different line-of-sight blockage probability in the training and test datasets and lower sensitivity to inaccuracies in the position and orientation information.Comment: 30 pages, 12 figure. This article was submitted to IEEE Transactions on Wireless Communications on Oct 11 202