System-level electromagnetic (EM) simulation is becoming an increasingly important technology for the design of next-generation smart cities, transportation, and global communications systems. These sectors demand innovative, automated tools for EM simulation.

Let’s step inside the world of EM simulation to understand the scale of smart city complexity it needs to address today. For example, EM plays an important role in determining antenna placement. The simulation must consider all the antennas, how they interact with each other, and how they interact with their surroundings, including potential interference from other on-board or nearby communications systems.

Another example is the push toward self-driving cars, a communications application that requires EM analysis for internal communications of critical vehicle functions such as steering, motor control, and braking, along with vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communications services.

More systems are coming online that need to communicate. It used to be that a car would have a global positioning system (GPS). Now, this has evolved to GPS coupled with radars and communications between vehicles. New vehicles are likely to have V2V communication whereby vehicles can exchange sensor information. One vehicle may notice an obstacle and transmit that information for other vehicles to pick up. Or V2X where vehicles can communicate directly with traffic lights, for example. Of course, if all vehicles support this type of communication, then traffic lights won’t be needed because the vehicles could synchronize between themselves.

In these advanced cases, communication link reliability becomes critical, particularly when there’s no line of sight. An intersection with skyscrapers at each corner and no clear line of communication means the vehicle must rely on communication signals that bounce off the buildings. Analyzing how these signals propagate and how to control them is a complex challenge that needs to be solved.

5G cellular communications with its push toward millimeter wave frequencies is another increasingly complex area. Millimeter band electromagnetic waves don’t travel as far and are easily obstructed by buildings, trees, vehicles, and even pedestrians. At lower frequencies, they can penetrate through buildings and bounce around corners. This means antenna placement for base stations becomes more critical. It also explains why 5G networks typically rely on small cells for adequate coverage that can be mounted on existing structures like light posts and traffic signals.

Beamforming might be used to track a pedestrian. With traditional antennas, energy radiates in all directions. For millimeter wave 5G networks, the energy needs to be concentrated and it’s challenging to figure out where to direct the beam. The introduction of Reconfigurable Intelligence Surfaces (RIS) adds yet another layer of complexity. Operators must simultaneously and in real-time configure both the beamforming antenna and the RIS. They must determine the direction of the focused beam while also providing instant updates for RIS configuration changes. These changes to the RIS can be extremely energy-efficient by only toggling diodes on and off.

Looking ahead, 6G with frequencies ranging from 28 to 300 gigahertz will pose new challenges. Semiconductor systems capable of handling these high frequencies are under development. Traditional simulation methods, like component-level SPICE models, lack the required accuracy for such high-frequency applications.

In summary, EM simulation is an indispensable tool for solving complex computational problems in smart city, transportation, and communications systems. The growing complexity and high-frequency nature of these systems make it imperative to develop more advanced EM simulation tools.