Research and development of mobile communications systems require a detailed analysis and evaluation of novel technologies to further enhance spectral efficiency, connectivity and reliability. Due to the exponentially increasing demand of mobile broadband data rates and challenging requirements for latency and reliability, mobile communications specifications become increasingly complex to support ever more sophisticated techniques. For this reason, analytic analysis as well as measurement based investigations of link level methods soon encounter feasibility limitations. Therefore, computer aided numeric simulation is an important tool for investigation of wireless communications standards and is indispensable for analysis and developing future technologies. In this contribution, we introduce the Vienna 5G Link Level Simulator, a Matlab-based link level simulation tool to facilitate research and development of 5G and beyond mobile communications. Our simulator enables standard compliant setups according to 4G Long Term Evolution, 5G new radio and even beyond, making it a very flexible simulation tool. Offered under an academic use license to fellow researchers it considerably enhances reproducibility in wireless communications research. We give a brief overview of our simulation platform and introduce unique features of our link level simulator in more detail to outline its versatile functionality.
Accurate LL simulations require sophisticated channel models that realistically represent practically relevant propagation environments. Since 5G introduces novel PHY technologies, such as, full-dimension MIMO and transmission in the MMW band, also channel models need to be updated and revised. The modular implementation structure of our simulator supports easy integration of dedicated wireless channel models and emulators, such as @cite_49 or @cite_54 @cite_9 .
@cite_9 @cite_54 @cite_49
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The rapid evolution of nowadays wireless communication systems constitutes a challenge for the versatile and flexible evaluation of transmissions under different scenarios. In order to evaluate the current fourth generation (4G) and future fifth generation (5G) communication techniques, a modular link-level simulator was developed at the GTEC group of the University of A Coruna. In this paper, the structure of the simulator as well as how to use it are presented in detail. The simulator allows for performing link layer simulations both for Orthogonal Frequency-Division Multiplexing (OFDM) and Filter Bank Multicarrier (FBMC) signals. It includes fully-functional transmitter and receiver implementations, as well as different channel models and functions for processing the obtained results. Moreover, we show also that the simulator is fully integrated with the GTEC testbed, enabling not only for performing simulation-based evaluations, but also considering over-the-air measurements. Hence, the GTEC 5G Simulator constitutes a valuable tool for evaluating new communication systems in a versatile and flexible way. Some exemplary results obtained both by simulations and through measurements are included. This paper presents details and applications of a novel channel simulation software named NYUSIM, which can be used to generate realistic temporal and spatial channel responses to support realistic physical-and link-layer simulations and design for fifth-generation (5G) cellular communications. NYUSIM is built upon the statistical spatial channel model for broadband millimeter-wave (mmWave) wireless communication systems developed by researchers at New York University (NYU). The simulator is applicable for a wide range of carrier frequencies (500 MHz to 100 GHz), radio frequency (RF) bandwidths (0 to 800 MHz), antenna beamwidths (7° to 360° for azimuth and 7° to 45° for elevation), and operating scenarios (urban microcell, urban macrocell, and rural macrocell), and also incorporates multiple-input multiple-output (MIMO) antenna arrays at the transmitter and receiver. This paper also provides examples to demonstrate how to use NYUSIM for analyzing MIMO channel conditions and spectral efficiencies, which show that NYUSIM is an alternative and more realistic channel model compared to the 3rd Generation Partnership Project (3GPP) and other channel models for mmWave bands. In millimeter wave (mm-wave) massive multiple-input multiple-output (MIMO) systems, acquiring accurate channel state information is essential for efficient beamforming (BF) and multiuser interference cancellation, which is a challenging task since a low signal-to-noise ratio is encountered before BF in large antenna arrays. The mm-wave channel exhibits a 3-D clustered structure in the virtual angle of arrival (AOA), angle of departure (AOD), and delay domain that is imposed by the effect of power leakage, angular spread, and cluster duration. We extend the approximate message passing (AMP) with a nearest neighbor pattern learning algorithm for improving the attainable channel estimation performance, which adaptively learns and exploits the clustered structure in the 3-D virtual AOA-AOD-delay domain. The proposed method is capable of approaching the performance bound described by the state evolution based on vector AMP framework, and our simulation results verify its superiority in mm-wave systems associated with a broad bandwidth. For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.