# CUDA-Optimized GPU Acceleration of 3 GPP 3D Channel Model Simulations for 5G Network Planning
## 2 Related Work  
The field-testing and validation of wireless systems is expensive both in terms of equipment cost and time to market. This step can be replaced by fast and accurate software-based models with high repeatability. Several channel simulators have been reported in the literature [13; 14; 15; 16; 17]. [PERSON] et al. [13] proposed a geometry-based channel model simulator for the link and physical layers. [PERSON] et al. [14] proposed ground reflection components to the existing geometry-based channel models. [PERSON] et al. [15] presented a model simulator for spatially consistent channel realizations using pedestrian measurements for human blockage. [PERSON] et al. [17] presented a simulator with support for dual mobility. General-purpose CPU-based channel simulators are either too simple to accurately replicate the propagation environment or too computationally complex to produce meaningful results in a reasonable time. Channel model accelerators minimize simulation time. Acceleration technologies include application-specific integrated circuits, massively parallel GPUs, and FPGAs. Several hardware-based channel accelerators have been reported in the literature. The A GSCM emulator on FPGA, which considers only discrete time segments was introduced [18]. For the an FPGA implementation of the a 3 GPP 3D channel model, a variety of high level synthesis (HLS)-based optimizations are discussed, which are required to achieve acceleration [19]. A CUDA-based multipath fading accelerator has been proposed [20], as well as another wireless channel emulator [21]. It lacks complex-valued channel coefficient emulation, which reduces accuracy. [PERSON] and [PERSON] [22] emulated a scalable wireless channel architecture on a cluster of 64 FPGAs, but its high hardware cost limits its application. Recently, [PERSON] et al. [23] proposed a technique to reduce the complexity of the 3 GPP channel model for 5G NR by reducing the number of sub-paths, thus reducing the computational cost, but it analyzes only a subset of wireless channel propagation characteristics, limiting its application. We investigate various optimization techniques for the effective deployment of a 3D GSCM channel for frequencies from 0.5 GHz to 100 GHz as proposed by ETSI [12] for GPU platforms. Despite its computational complexity, this model simulates the propagation environment more accurately. The goal is to maximize the planning quality of 5G mobile networks by leveraging the generality and accuracy of the 3 GPP channel model given in TR.38.901 [12]. The channel model was designed for CPU platforms, then optimized for NVIDIA GPUs using CUDA-based techniques. We developed a GPU-based simulator that is more accurate than state-of-the-art programs and delivers data to network designers quickly. When larger antenna arrays and user mobility are included, CPU-oriented 5G simulation stack executions can take days or weeks. The accelerated channel model is integrated into our implementation of the 5G simulation stack using a socket-based client/server architecture for shared use by multiple network planners. Our acceleration efforts have resulted in a remarkable 240\(\times\) increase in speed, allowing the simulation to be completed in hours instead of weeks.