This is a collection of application examples written by Remcom’s engineers. They demonstrate how Remcom’s software can be used to solve challenges related to 5G and MIMO use cases such as MIMO and array design, 5G urban small cells, fixed wireless access, indoor WiFi and mmWave, beamforming, and more.
This presentation demonstrates a new predictive capability for simulating massive MIMO antennas and beamforming in dense urban propagation environments. Remcom's unique approach allows us to predict the signal-to-interference-plus-noise ratio (SINR) at specific device locations and the actual physical beams formed using these techniques, including unintentional distortions caused by pilot contamination.
To keep up with rising demand and new technologies, the wireless industry is researching a wide array of solutions for 5G, including Massive MIMO. Remcom’s Wireless InSite provides an efficient method to predict channel characteristics for large-array MIMO antennas in complex multipath environments.
This presentation demonstrates how the 3D ray tracing code in Wireless InSite can accurately predict received power coverage even in a multi-room environment containing many walls and different materials types. In order to verify the accuracy of the code, the floor plan of Remcom’s business offices was modeled in the software with a WiFi antenna and a third party tool was used to create a coverage plot of the received power throughout several of the suites.
Uncertainty in structure geometry is a fundamental limitation of ray-tracing methods when simulating urban propagation. We present a hybrid approach using ray-tracing methods and empirically derived loss factors to incorporate the effect of unknown interior layouts. This approach is compared with a more typical empirical implementation to demonstrate the benefits of hybridization.
Heterogeneous, mobile wireless networks are becoming increasingly difficult to validate for operational use. Presented is an approach to reduce the run-time of these high fidelity simulations by constructing precise results based on adjacent ray-paths from a lower resolution simulation. Speed and accuracy trade-offs are presented for this approach in typical urban scenarios, demonstrating its effectiveness in meeting the growing needs of wireless channel emulation.
Accurately characterizing the propagation of RF signals in tunnels is important for rescue, safety, and military purposes. The material composition of the tunnel, the tunnel shape and size, obstructions, and tunnel bends present challenges. In this paper we use Wireless InSite to analyze how tunnel diameter and shape affect the propagation characteristics.
This paper presents results from sea to land propagation using Wireless InSite. The effort explores the effects of various elements in the scene and how they impact the results. The various elements in the scene include the ships out at sea, the ships docked, the docks themselves, the buildings around the dock area, and the material properties of each.
Significant improvements in the quality and reliability of indoor WLAN communications are claimed for devices with MIMO technology applying 802.11n standards, which allow users to achieve a theoretical data rate up to 300-600 Mbps on a single transmission. This paper presents an analysis of a commercial 802.11n MIMO 2×3 dual band (2.4 and 5 GHz) system focusing on the operational throughput performance over an indoor environment for Line of Sight (LOS) and Non Line of Sight (NLOS) scenarios.
The moving window finite difference time domain (MWFDTD) method is used to analyze propagation between low to the ground antennas commonly used in wireless unattended ground sensor networks. The propagation path loss at 300 MHz is computed for several terrains exhibiting different degrees of roughness.
For predictions of vehicle to vehicle communications,convoy communications, and Improvised Electronic Device (IED) detection/defense operations, Remcom's Wireless InSite Real Time (RT) provides a very rapid propagation prediction capability in urban environments. Previous models are either empirical and inaccurate but fast, or deterministic and high-fidelity but slow. The RT Module takes the best of both.