Researchers at the Public University of Navarre (NUP/UPNA) have developed a system to help assess how much wireless networks suffer from interference. A 3D Ray Launching simulation tool serves as its core. The device can analyze electromagnetic wave propagation, also known as radio propagation, through space in interior environments, such as homes, buildings, and shopping centers.
With this system, researchers can accurately narrow down the best location for networking wireless devices. This carries a host of optimizations, especially concerning deployment cost, transmission speed, and energy consumption.
The new system also offers power estimates in large-scale environments, and generates radio planning unique to each area.
According to the team, “There are other simulation tools that provide estimates of radio propagation, but 3D Ray Launching has not only been developed in its entirety at the NUP/UPNA, it also provides accurate results in a very short simulation time if we compare it with other similar methods.”
The research showed that wireless sensor networks and communication systems will face the rising issue of accurate interference assessment. The data particularly holds merit with the emergence of 5G.
“With the arrival of 5G communications systems and the Internet of Things, there are expected to be 50,000 million wireless devices across the world by the year 2020, an average of eight per person. Knowing the distribution of radiated output by a transmitter in a specific scenario enables optimal radio planning to be made,” according to the researchers.
The team is also targeting the electromagnetic noise from electronic devices, estimated with a hybrid testing method. The hybrid technique still has the same focus—offer better deployment plans for wireless communication networks where these devices are present.
“For example, a baby monitor with a camera may interfere with the receiving of the WiFi signal that we have at home; and the microwave oven may cause severe interference in ZigBee wireless communication used, above all, in the deployment of sensor networks,” according to the research team.