Ray-Tracing vs. 3GPP TDL: Power Delay Profile Analysis in Outdoor-to-Indoor and Indoor Channels

3rd Generation Partnership Project (3GPP) Technical Report (TR) 38.901 channel models (Releases 15-19) are widely used for physical-layer design and system-level evaluation in dense urban outdoor-to-indoor (O2I) and indoor environments. These models capture ensemble-averaged channel statistics but do not account for site-specific geometry. In this paper, we compare Power Delay Profiles (PDPs) derived from a deterministic ray-tracing model (Remcom Wireless InSite software) with those from the 3GPP TR 38.901 Tapped Delay Line (TDL) channel models. This comparative analysis is performed using a dense urban O2I scenario and a representative single-story indoor layout modeled in Washington, D.C., under matched link-distance and Non-Line-of-Sight (NLOS) conditions. All Wireless InSite PDPs are power-normalized to enable comparison of relative multipath delay structure. We evaluate root-mean-square (RMS) delay spread, mean excess delay, effective maximum delay, and Kullback-Leibler (KL) distribution divergence. Results indicate that 3GPP TDL models generally exhibit longer delay spreads and often fail to capture deterministic, site-specific features such as late-arriving energy and irregular spikes. While TDL models can approximate primary channel features in some cases, their reliance on ensemble-averaged statistics rather than geometry limits their representation of fine multipath structures. We conclude that while 3GPP TDL models are suitable for large-scale system evaluation, deterministic or hybrid approaches are more appropriate for site-specific physical-layer design.

A Machine Learning Framework for Large-Scale Static Wireless Mesh Networks

This paper presents a system design methodology for a large-scale static wireless mesh network for 155 commercial off-the-shelf (COTS) radio nodes at fixed infrastructure sites in a challenging island environment. The architecture consists of approximately ten 15-node clusters, each with designated primary and secondary gateway nodes to support inter-cluster communication. A structured, multi-stage planning methodology was developed to guide network design. Site-specific radio frequency (RF) path loss predictions were generated using Remcom's Wireless InSite ray-tracing platform, incorporating terrain, buildings, and dense foliage effects. To optimize connectivity under physical-layer and operational constraints, spectral embedding combined with balanced k-means clustering was applied to partition the nodes into clusters of comparable size. A link budget analysis determined the maximum tolerable path loss under waveform and hardware constraints, defining the connectivity threshold used in the clustering framework. This work integrates deterministic RF propagation modeling with constrained clustering optimization to provide a scalable framework for planning static wireless mesh networks in complex geographic environments. Node mobility and higher-layer networking protocols were outside the scope of this study.