Abstract
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 the need for 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 may limit 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.
Highlights
Recent developments have paved the way towards 5th generation (5G) cellular networks and enhanced Wireless Local Area Network (WLAN) designs, to address the traffic demands of the 2020 digital society [2]
The Institute of Electrical and Electronics Engineers (IEEE) has developed amendments to 802.11 networks, namely 802.11ad and 802.11ay [4], which operate at millimeter wave (mmWave). 3rd Generation Partnership Project (3GPP) NR carrier frequency can be as high as 52.6 GHz for Release 15, while IEEE 802.11ad and 802.11ay exploit the unlicensed spectrum at 60 GHz [4]
Stochastic Spatial Channel Models (SCMs), e.g., [22] for 3GPP NR, characterize the channel as a combination of random variables fitted from real-world measurements, providing a more realistic assessment of the mmWave network performance compared to their analytical counterparts [23], for measurements at sub-6 GHz
Summary
The very short wavelength makes it practical to build large antenna arrays (e.g., with hundreds of elements) and establish highly directional communications, boosting the network performance through beamforming and spatial diversity [7] Despite these promising characteristics, propagation at mmWaves raises several challenges for the design and performance of the whole protocol stack [8]. Stochastic Spatial Channel Models (SCMs), e.g., [22] for 3GPP NR, characterize the channel as a combination of random variables fitted from real-world measurements, providing a more realistic assessment of the mmWave network performance compared to their analytical counterparts [23], for measurements at sub-6 GHz. Still, the stochastic nature of these models may prevent researchers from evaluating the impact of the channel dynamics in specific environments, and may respond poorly to the need of accurately characterizing the spatio-temporal evolution of the channel Multi Path Components (MPCs) [24].
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