LSTM-Based Power Delay Profile Predictions for Intra-Bus Wireless Propagation

Longlshort-term memory (LSTM) is a deep learning model that can capture long-term dependencies of wireless channel models and is highly adaptable to short-term changes in a wireless environment. This paper proposes a simple LSTM model to predict the channel transfer function (CTF) for a given transmitter-receiver location inside a bus for the 60 GHz millimetre wave band. The average error of the derived power delay profile (PDP) taps, obtained from the predicted CTFs, was less than 10% compared to the ground truth.

Framework for Indoor Wireless Propagation Modeling Through Wireless Insite

Multipaths, reflections, diffractions, and material interactions complicate indoor wireless propagation modelling. More than 80% of wireless data is consumed indoors; hence, planning successful deployments and maximizing network performance depends on accurate propagation modelling of indoor environments. This work explains a complete framework for indoor wireless propagation modelling via ray tracing simulation in a step-by-step manner. The ray tracing simulations are conducted with Wireless Insite, a proprietary electromagnetic propagation software, whereas SketchUp is used at the input side for layout construction from the field measurements, and MATLAB is used at the output side for portraying channel model parameters such as power delay profile (PDP). A whole floor of the authors' department is modelled, and different transmitter-receiver locations were tested for possible use cases such as coverage hole prediction.

A Deep Learning Approach to Multipath Component Detection in Power Delay Profiles

Power Delay Profile (PDP) plays a crucial role in wireless communications, providing information on multipath propagation and signal strength variations over time. Accurate detection of peaks within PDP is essential to identify dominant signal paths, which are critical for tasks such as channel estimation, localization, and interference management. Traditional approaches to PDP analysis often struggle with noise, low resolution, and the inherent complexity of wireless environments. In this paper, we evaluate the application of traditional and modern deep learning neural networks to reconstruction-based anomaly detection to detect multipath components within the PDP. To further refine detection and robustness, a framework is proposed that combines autoencoders and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. To compare the performance of individual models, a relaxed F1 score strategy is defined. The experimental results show that the proposed framework with transformer-based autoencoder shows superior performance both in terms of reconstruction and anomaly detection.

Microdiversity and Vegetation Influence on Forward Scattering at 60 GHz and 80 GHz

Understanding the impact of vegetation and small-scale antenna movements on signal propagation is important for the design and optimization of high-frequency wireless communication systems. This paper presents an experimental study analyzing signal propagation at 60 GHz and 80 GHz in the presence of vegetation, with a focus on forward scattering and microdiversity effects. A controlled measurement campaign was conducted in an indoor environment, where the influence of a potted plant placed in the line-of-sight (LOS) path between the transmitter and receiver was investigated. The study examines the effects of antenna micro-shifts on the channel impulse response (CIR), highlighting variations in received power due to small positional changes of the antennas. The results indicate that the 80 GHz band exhibits higher sensitivity to micro-movements compared to the 60 GHz band, leading to greater fluctuations in received power.

Angularly-Resolved 3D Foliage Modeling and Measurements at 60 and 80 GHz: From Stochastic Geometry to Deterministic Channel Characterization

In this paper, we show a stochastic approach to generate a 3D model of a foliage, which is then used for deterministic ray-tracing channel modeling. This approach is verified by a measurement campaign at 60 and 80 GHz with 2 GHz bandwidth. The wireless channel is characterized by path-loss and RMS delay spread and we show the angular dependency of those parameters when the receiver is placed on a half-circle around the tree. Besides electromagnetic material properties, the 3D model is characterized by several interpretable parameters, including tree volume, leaf size, leaf density, and the tree crown shape parameter.

Stochastic 3-D Foliage Modeling at 80 GHz: Experimental Validation and Ray-Tracing Simulations

A stochastic modeling methodology for 3-D foliage is presented, aimed at enhancing ray-tracing simulations. The model supports adjustable stochastic geometry, density, and shape to capture variability in foliage structures. The model is validated through experimental measurements of representative vegetation. The influence of foliage density and size on path loss and root mean square delay spread is analyzed to demonstrate the applicability of the model in the 80 GHz frequency band.

Comparison of 60 GHz and 80 GHz Vehicle-to-Vehicle Channels Using Delay and Doppler Characteristics

The aim of this paper is to provide a comparison of channel characteristics for vehicle-to-vehicle (V2V) communication at 60 GHz and 80 GHz frequency bands in a high-mobility scenario where two vehicles pass each other in opposite directions. The study is based on measurements of the time-varying channel impulse response capturing the behavior of multi-path propagation during vehicle motion. By directly comparing these two frequency bands under identical measurement conditions, we attempt to quantify the differences in power delay profile, root mean square (RMS) delay spread, RMS Doppler spread, and intervals (regions) of stationarity in time domain. The results show that these bands do not differ significantly, but the 80 GHz band exhibits somewhat greater RMS delay spread and RMS Doppler spread when calculated over the entire delay-Doppler spectrum, and conversely exhibits shorter stationarity regions. However, the characteristics of the measurement setup in the two bands and their influence on comparative measurements must be considered. In particular, attention must be paid to the impact of antennas.

Vehicle-to-Vehicle Millimeter-Wave Channel Characterization at 60 and 80 GHz

This paper presents results from a vehicle-to-vehicle channel measurement campaign conducted in the millimeter-wave (MMW) frequency bands at center frequencies of 60GHz and 80GHz, each with a bandwidth of 2GHz. The measurements were performed in a dynamic oncoming-vehicle scenario using a time-domain channel sounder with high-resolution data acquisition. Power delay profiles were extracted to study the temporal evolution of multipath components, and the root mean square (RMS) delay spread was analyzed to characterize the temporal dispersion of the channel. The results demonstrate differences between the two frequency bands. At 60GHz, the RMS delay spread is well approximated by a Gaussian distribution with a higher median value, while at 80GHz it follows a lognormal distribution with a lower median. Furthermore, the number of resolvable multipath components was found to be nearly twice as high at 60\,GHz compared to 80GHz, highlighting the impact of antenna beamwidth and frequency-dependent propagation mechanisms.

Millimeter Wave Path Loss for Diverse Antenna Patterns in Outdoor Environment

Empirical path loss models are defined for a specific antenna system used during measurements and characterized by a particular radiation pattern and main lobe beam width. In this paper, we propose a novel approach to modifying such a model to estimate path loss for antenna systems with different radiation patterns and beam widths. This method is based on a multi-elliptical propagation model, enabling a more flexible adaptation of the path loss model. The paper presents the general concept of the proposed method and numerical study results demonstrating the influence of the antenna pattern shape and its beam width on path loss estimation.

Channel impulse response peak clustering using neural networks

This paper introduces an approach to process channel sounder data acquired from Channel Impulse Response (CIR) of 60GHz and 80GHz channel sounder systems, through the integration of Long Short-Term Memory (LSTM) Neural Network (NN) and Fully Connected Neural Network (FCNN). The primary goal is to enhance and automate cluster detection within peaks from noised CIR data. The study initially compares the performance of LSTM NN and FCNN across different input sequence lengths. Notably, LSTM surpasses FCNN due to its incorporation of memory cells, which prove beneficial for handling longer series.Additionally, the paper investigates the robustness of LSTM NN through various architectural configurations. The findings suggest that robust neural networks tend to closely mimic the input function, whereas smaller neural networks are better at generalizing trends in time series data, which is desirable for anomaly detection, where function peaks are regarded as anomalies.Finally, the selected LSTM NN is compared with traditional signal filters, including Butterworth, Savitzky-Golay, Bessel/Thomson, and median filters. Visual observations indicate that the most effective methods for peak detection within channel impulse response data are either the LSTM NN or median filter, as they yield similar results.