Simplified Ray Tracing for the Millimeter Wave Channel: A Performance Evaluation

Millimeter-wave (mmWave) communication is one of the cornerstone innovations of fifth-generation (5G) wireless networks, thanks to the massive bandwidth available in these frequency bands. To correctly assess the performance of such systems, however, it is essential to have reliable channel models, based on a deep understanding of the propagation characteristics of the mmWave signal. In this respect, ray tracers can provide high accuracy, at the expense of a significant computational complexity, which limits the scalability of simulations. To address this issue, in this paper we present possible simplifications that can reduce the complexity of ray tracing in the mmWave environment, without significantly affecting the accuracy of the model. We evaluate the effect of such simplifications on link-level metrics, testing different configuration parameters and propagation scenarios.Comment: 6 pages, 6 figures, 1 table. This paper has been accepted for presentation at ITA 2020. (c) 2020 IEEE. Please cite it as: M. Lecci, P. Testolina, M. Giordani, M. Polese, T. Ropitault, C. Gentile, N. Varshney, A. Bodi, M. Zorzi, "Simplified Ray Tracing for the Millimeter Wave Channel: A Performance Evaluation," Information Theory and Applications Workshop (ITA), San Diego, US, 202

Accuracy vs. Complexity for mmWave Ray-Tracing: A Full Stack Perspective

The millimeter wave (mmWave) band will provide multi-gigabits-per-second connectivity in the radio access of future wireless systems. The high propagation loss in this portion of the spectrum calls for the deployment of large antenna arrays to compensate for the loss through high directional gain, thus introducing a spatial dimension in the channel model to accurately represent the performance of a mmWave network. In this perspective, ray-tracing can characterize the channel in terms of Multi Path Components (MPCs) to provide a highly accurate model, at the price of extreme computational complexity (e.g., for processing detailed environment information about the propagation), which limits the scalability of the simulations. In this paper, we present possible simplifications to improve the trade-off between accuracy and complexity in ray-tracing simulations at mmWaves by reducing the total number of MPCs. The effect of such simplifications is evaluated from a full-stack perspective through end-to-end simulations, testing different configuration parameters, propagation scenarios, and higher-layer protocol implementations. We then provide guidelines on the optimal degree of simplification, for which it is possible to reduce the complexity of simulations with a minimal reduction in accuracy for different deployment scenarios.Comment: 31 pages, 14 figures, 1 table. This paper has been submitted to IEEE for publication. Copyright IEEE 2020. Please cite it as: Mattia Lecci, Paolo Testolina, Michele Polese, Marco Giordani, Michele Zorzi, "Accuracy vs. Complexity for mmWave Ray-Tracing: A Full Stack Perspective.'

Scalable and Accurate Modeling of the Millimeter Wave Channel

Communication at millimeter wave (mmWave) frequencies is one of the main novelties introduced in the 5th generation (5G) of cellular networks. The opportunities and challenges associated with such high frequencies have stimulated a number of studies that rely on simulation for the evaluation of the proposed solutions. The accuracy of simulations largely depends on that of the channel model, but popular channel models for mmWaves, such as the Spatial Channel Models (SCMs), have high computational complexity and limit the scalability of the scenarios. This paper profiles the implementation of a widely-used SCM model for mmWave frequencies, and proposes a simplified version of the 3GPP SCM that reduces the computation time by up to 12.5 times while providing essentially the same distributions of several metrics, such as the Signal-to-Interference-plus-Noise Ratio (SINR) in large scale scenarios. We also give insights on the use cases in which using a simplified model can still yield valid results