# CUDA-Optimized GPU Acceleration of 3 GPP 3D Channel Model Simulations for 5G Network Planning
## 1 Introduction  
Mobile communications have become an increasingly important part of modern life. They make it possible to perform routine tasks such as making video calls while traveling or staying in touch with family and the office from anywhere in the world [1; 2; 3; 4]. The process of digitization began in the early 1990s with the introduction of second-generation cellular technologies [5]. Cellular technologies are undergoing continuous service development to improve customer experience and provide a higher level of service. The number of mobile devices such as smartphones, tablets, Internet of Things (IoT) devices, and laptops are expected to reach around 28.5 billion networked devices by the end of 2023 [6]. This increases the demand for higher throughput, lower latency, and higher quality of service, which are the main drivers for the introduction of the fifth-generation (5G) wireless communication standard [7]. The radio link is by far the most important part of any cellular technology. Its simulation must closely take into account propagation in real-world environments in order to predict and optimize network coverage and minimize post-deployment on-field measurements.  
Several channel models for massive multiple-input multiple-output (MIMO)-based communication systems have been reported in the most recent literature [8; 9; 10; 11; 12]. The International Telecommunication Union (ITU) has defined the requirements for the InternationalMobile Telecommunications 2020 (IMT-2020) channel model with support for enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC) and massive machine-type communications (mMTC) [8].  
The COST 2100 model [9] is a geometry-based stochastic model (GSCM) for MIMO channels with cluster power and delays derived from fixed geometry and therefore has limited support for dual mobility propagation scenarios. [PERSON] et al. [10] presented a quasi-deterministic channel model where the channel impulse response (CIR) is a combination of a few strong quasi-deterministic rays, flashing rays, and weak random rays. METIS [11] is a ray-tracing based channel model that supports propagation scenarios such as blocking, diffraction, specular reflection, and spherical wave propagation.  
However, existing channel model simulators are either too simple to accurately replicate the propagation environment or too computationally expensive to produce meaningful results in a reasonable amount of time. In addition, most existing simulators are designed for central processing unit (CPU) platforms, which have limited parallelism and throughput. Therefore, there is a need for efficient and accurate channel model simulators that can run on parallel platforms such as graphics processing units (GPUs), which offer high performance and scalability. This article proposes a GPU-based hardware acceleration for the 3 rd Generation Partnership Project (3 GPP) three-dimensional (3D) channel model, which is a highly parameterized and realistic channel model for fifth-generation new radio (5G NR) networks, and shows that the proposed GPU accelerator can significantly improve the simulation speed and accuracy over a CPU-based C++ model, and also has higher single-precision performance than a previously designedfield programmable gate array (FPGA)-based accelerator.  
The rest of the paper is organized as follows. Section 2 discusses previous related work. Section 3 discusses the importance of 3D channel modeling in MIMO communication techniques and briefly describes the 3 GPP channel model. Section 4 presents an introduction to GPU and compute unified device architecture (CUDA) programming, followed by optimization methods for GPU-based hardware acceleration and its key benefits. The architecture of the proposed accelerator is outlined in Section 5, and the impact of various optimizations on the final result is analyzed in Section 6. Finally, conclusions are drawn in Section 7.