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.

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.

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.

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.

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.