Researchers at Florida Atlantic Center for Connected Autonomy and Artificial Intelligence (CA-AI) have developed a method to reduce electromagnetic interference in wireless communications. This interference is a significant issue in dense Internet of Things and AI robotic environments. The team’s algorithm fine-tunes multiple-input multiple-output (MIMO) links, crucial in modern wireless systems like Wi-Fi and cellular networks. The method optimizes transmission in space and time, potentially creating clearer communication channels. The research, led by Dimitris Pados, Ph.D., was published in the journal of the Institute of Electrical and Electronics Engineers (IEEE) and highlighted in “Nature Reviews.”
Overcoming Interference in Machine-to-Machine Communication
Researchers at the Florida Atlantic Center for Connected Autonomy and Artificial Intelligence (CA-AI) have developed a novel method to mitigate the issue of electromagnetic interference in machine-to-machine communications. This interference, a byproduct of wireless communications, presents significant challenges in densely populated Internet of Things (IoT) and AI robotic environments. As the demand for high-speed data rates continues to grow, the need to address this interference becomes increasingly critical.
The team at CA-AI, in collaboration with the FAU Institute for Sensing and Embedded Network Systems Engineering (I-SENSE), has devised an algorithmic solution that dynamically fine-tunes multiple-input multiple-output (MIMO) links. These links are a fundamental component of modern wireless systems, including Wi-Fi and cellular networks. The researchers’ method, a first of its kind, optimizes wireless waveforms to navigate crowded frequency bands, potentially paving the way for clearer communication channels.
A Game-Changing Approach to Wireless Communication
The researchers’ approach, recently published in a special issue of the Institute of Electrical and Electronics Engineers (IEEE) journal and highlighted in “Nature Reviews,” demonstrates how their algorithmic method shapes wireless waveforms to navigate crowded frequency bands. By optimizing transmission in both space and time, this algorithm could potentially create pristine communication channels.
In field demonstrations, the researchers dynamically optimized MIMO wireless waveform shapes over a given frequency band to manage and avoid interference in machine-to-machine communications. The effectiveness of this method was demonstrated in real-world scenarios where interference is a common problem.
Groundbreaking Research in Machine Learning and Communication
“We have laid the conceptual and practical groundwork for machines equipped with multiple antennas to autonomously determine the most effective waveform shapes in both time and space domains for communication within a designated frequency band, even among extremely challenging interference and disturbances,” said Dimitris Pados, Ph.D., senior author, professor, director of the CA-AI and a fellow of I-SENSE in the Department of Electrical Engineering and Computer Science. “By employing dynamic waveform machine learning in tandem across space and time, we believe that we have found a solution to mitigating electromagnetic interference.”
The researchers conducted extensive simulations to validate the efficacy of this method against a variety of interference scenarios, from near-field to far-field and in both light and dense interference scenarios. These simulations highlighted the ability of the optimized waveforms, particularly joint space-time optimization, to maintain clear communications in extreme mixed-interference environments.
The Importance of Reliable Communication in Autonomous Systems
“In the realm of autonomous systems and machine-to-machine communications, secure, reliable and ‘clean’ communications are paramount, underscoring the importance of this breakthrough research at Florida Atlantic,” said Stella Batalama, Ph.D., dean, FAU College of Engineering and Computer Science. “In the midst of chaos in modern communication, this innovative research offers a very promising avenue to address interference challenges in machine-to-machine communications where there are high volumes of devices and multiple networks.”
The study’s co-authors include Sanaz Naderi, first author and graduate research assistant; George Sklivanitis, Ph.D., Schmidt Research Associate Professor and I-SENSE fellow; within the FAU College of Engineering and Computer Science; and Elizabeth Serena Bentley, Ph.D.; Joseph Suprenant; and Michael J. Medley, Ph.D., all with the United States Air Force Research Laboratory.
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