Channel Modeling For The AI Age: Inside Anuraag Bodi’s Breakthrough Wireless Tools

The networks supporting wireless technologies like 5G and the upcoming 6G face a problem most people never see: predicting how radio signals will behave in the physical world.

This is the role of wireless channel modeling, the process of determining how signals travel, bounce, scatter, and fade in real environments. The accuracy of these models affects everything

from keeping a phone call connected to ensuring a self-driving car receives the right sensor data at the right time.

The challenge is that most models in use today aren’t well-suited for the architecture of modern life. They rely heavily on statistical assumptions that treat all environments as uniform and predictable, when in reality they’re anything but. In a world moving toward high-frequency, ultra-reliable, low-latency connections, those types of rigid systems can lead to performance gaps and even system outages.

This is what wireless researcher Anuraag Bodi is currently addressing. Instead of leaning on probabilities, he develops models that adjust to physical reality so wireless systems can be more than just data carriers and can get a precise sense of their actual surroundings.

A Researcher Focused On Building Adaptive Networks

With dual master’s degrees in electrical engineering and computer science, Anuraag Bodi works as a researcher for a major technology company, collaborating with agencies to streamline and strengthen their wireless processes. His focus goes beyond improving speed — he aims to make networks more capable of interpreting the world around them.

“Channel modeling is more than predicting signal strength; it’s about understanding how the environment itself communicates,” he says. “By combining AI, sensing, and physics, we are building communication systems that can more accurately and closely understand their world.”

This guiding principle has led Bodi to develop a range of tools that give engineers and researchers a highly accurate view of how signals behave in real-world conditions.

From Signal Paths To A Digital Twin: The Context-Aware Channel Sounder

One example of Bodi’s work in action is his development of the Context-Aware Channel Sounder, which rethinks one of wireless engineering’s standard tools. Traditional channel sounders measure the electromagnetic properties of an environment, but they rarely connect those measurements to the specific physical features causing them.

This technicality turns into a real limitation when dealing with what engineers call multipath components (MPCs) — the reflected or scattered copies of a signal created when it bounces off surfaces or passes around obstacles. These copies are common in real-world environments and can interfere with one another, but tracing them back to their exact origin is notoriously difficult.

“In realistic wireless environments, accurately identifying which environmental objects cause specific signal reflections is key to building physically meaningful channel models — yet remains extremely challenging with traditional methods,” he explains.

Bodi’s sounder solves this by combining electromagnetic data with a detailed digital twin of the environment. Using LiDAR (light detection and ranging) technology, cameras, positional sensors, and neural networks, it can link a signal’s behavior directly to its physical cause and pinpoint where each signal reflection or scatter originated (whether from a building wall, a passing vehicle, or a tree canopy).

This level of precision benefits the entire wireless ecosystem. Hardware companies can use these models to fine-tune their devices and architectural designs, carriers can apply them in network planning, and protocol designers can rely on them for accurate simulations.

By revealing the exact causes of interference, the sounder supports targeted solutions, improving reliability for applications from self-driving cars to phone service in high-demand urban areas.

Bringing Physics Into Play: The Electromagnetic Ray Tracer

To improve accuracy even further, Bodi developed a custom electromagnetic ray tracer, a physics-based simulation tool that models how radio waves interact with real environments. This technology considers geometry, material properties, and the laws of physics to predict signal behavior.

Ray tracing is widely recognized in theory but rarely used in most technical systems, often due to its computational cost. Bodi’s approach aims to solve that by providing a more flexible model that combines sensor-informed data with physics-based calculations to keep simulations more realistic.

This level of modeling can be particularly beneficial for applications like beamforming, where a signal is directed toward a specific receiver rather than broadcast in all directions. This system

can improve quality and reduce interference, but finding the optimal pattern for each device is computationally demanding.

By pairing the ray tracer with the outputs from the Context-Aware Channel Sounder, researchers can test beamforming strategies, evaluate protocol performance, and look into more specific launch options without having to commit to costly field trials.

“This tool enhances the accuracy of channel predictions and supports the design and testing of advanced wireless systems,” he says.

Laying The Groundwork For Smarter Networks

Bodi has also contributed at the broader ecosystem level through his involvement in the NextG Channel Model Alliance, participating in national efforts to standardize the modeling of next-generation wireless channels. He has also engaged with wider initiatives dedicated to studying and supporting the evolution of 5G and early 6G standards, particularly in the area of channel modeling.

These efforts help guide how new wireless technologies take shape, informing the technical standards they follow and the best practices that companies across the industry adopt.

As wireless systems continue to be integrated among difficult and intensive physical territory, Anuraag Bodi is building networks that understand the world around them.

BDG Media newsroom and editorial staff were not involved in the creation of this content.