CUDA-Optimized GPU Acceleration of 3GPP 3D Channel Model Simulations for 5G Network Planning

Simulation of massive multiple-input multiple-output (MIMO) channel models is becoming increasingly important for testing and validation of fifth-generation new radio (5G NR) wireless networks and beyond. However, simulation performance tends to be limited when modeling a large number of antenna elements combined with a complex and realistic representation of propagation conditions. In this paper, we propose an efficient implementation of a 3rd Generation Partnership Project (3GPP) three-dimensional (3D) channel model, specifically designed for graphics processing unit (GPU) platforms, with the goal of minimizing the computational time required for channel simulation. The channel model is highly parameterized to encompass a wide range of configurations required for real-world optimized 5G NR network deployments. We use several compute unified device architecture (CUDA)-based optimization techniques to exploit the parallelism and memory hierarchy of the GPU. Experimental data show that the developed system achieves an overall speedup of about 240× compared to the original C++ model executed on an Intel processor. Compared to a design previously accelerated on a datacenter-class field programmable gate array (FPGA), the GPU design has 33.3 % higher single precision performance, but for 7.5 % higher power consumption. The proposed GPU accelerator can provide fast and accurate channel simulations for 5G NR network planning and optimization

FPGA Acceleration of 3GPP Channel Model Emulator for 5G New Radio

The channel model is by far the most computing intensive part of the link level simulations of multiple-input and multiple-output (MIMO) fifth-generation new radio (5G NR) communication systems. Simulation effort further increases when using more realistic geometry-based channel models, such as the three-dimensional spatial channel model (3D-SCM). Channel emulation is used for functional and performance verification of such models in the network planning phase. These models use multiple finite impulse response (FIR) filters and have a very high degree of parallelism which can be exploited for accelerated execution on Field Programmable Gate Array (FPGA) and Graphics Processing Unit (GPU) platforms. This paper proposes an efficient re-configurable implementation of the 3rd generation partnership project (3GPP) 3D-SCM on FPGAs using a design flow based on high-level synthesis (HLS). It studies the effect of various HLS optimization techniques on the total latency and hardware resource utilization on Xilinx Alveo U280 and Intel Arria 10GX 1150 high-performance FPGAs, using in both cases the commercial HLS tools of the producer. The channel model accuracy is preserved using double precision floating point arithmetic. This work analyzes in detail the effort to target the FPGA platforms using HLS tools, both in terms of common parallelization effort (shared by both FPGAs), and in terms of platform-specific effort, different for Xilinx and Intel FPGAs. Compared to the baseline general-purpose central processing unit (CPU) implementation, the achieved speedups are 65X and 95X using the Xilinx UltraScale+ and Intel Arria FPGA platform respectively, when using a Double Data Rate (DDR) memory interface. The FPGA-based designs also achieved ~3X better performance compared to a similar technology node NVIDIA GeForce GTX 1070 GPU, while consuming ~4X less energy. The FPGA implementation speedup improves up to 173X over the CPU baseline when using the Xilinx UltraRAM (URAM) and High-Bandwidth Memory (HBM) resources, also achieving 6X lower latency and 12X lower energy consumption than the GPU implementation

Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

Accuracy-Complexity Tradeoff Analysis and Complexity Reduction Methods for Non-Stationary IMT-A MIMO Channel Models

open access journalHigh-mobility wireless communication systems have attracted growing interests in recent years. For the deployment of these systems, one fundamental work is to build accurate and efficient channel models. In high-mobility scenarios, it has been shown that the standardized channel models, e.g., IMT-Advanced (IMT-A) multiple-input multiple-output (MIMO) channel model, provide noticeable longer stationary intervals than measured results and the wide-sense stationary (WSS) assumption may be violated. Thus, the non-stationarity should be introduced to the IMT-A MIMO channel model to mimic the channel characteristics more accurately without losing too much efficiency. In this paper, we analyze and compare the computational complexity of the original WSS and non-stationary IMT-A MIMO channel models. Both the number of real operations and simulation time are used as complexity metrics. Since introducing the nonstationarity to the IMT-A MIMO channel model causes extra computational complexity, some computation reduction methods are proposed to simplify the non-stationary IMT-A MIMO channel model while retaining an acceptable accuracy. Statistical properties including the temporal autocorrelation function, spatial cross-correlation function, and stationary interval are chosen as the accuracy metrics for verifications. It is shown that the tradeoff between the computational complexity and modeling accuracy can be achieved by using these proposed complexity reduction methods