The Need
The increasing adoption of mmWave technologies in 5G, IoT, and other advanced communication networks highlights a critical need for cost-effective, efficient, and scalable antenna calibration solutions. Traditional calibration methods rely on expensive laboratory setups and specialized equipment, creating barriers to widespread deployment. Manufacturers and operators require a solution that reduces costs, minimizes hardware dependencies, and ensures high-quality performance across a wide range of mmWave applications.

The Technology
The "Self-Calibration Technique for mmWave Antenna Arrays Based on Everyday Communication Behaviors" leverages natural communication activities to calibrate antenna arrays without the need for specialized equipment or external sensors. This innovative methodology extracts relative gains among antenna elements, bypassing the need for complex setup processes and prior knowledge of the Angle of Departure (AoD). The algorithm automatically selects calibration datasets to optimize accuracy, making the process both efficient and autonomous. Experimental validation demonstrates that even outdated or uncalibrated platforms can achieve superior Signal-to-Noise Ratio (SNR) performance with consistent communication instances.

Commercial Applications

• 5G network infrastructure for mobile communications
• Wireless Local Area Networks (WLAN) in residential and commercial settings
• Vehicle-to-Everything (V2X) communication systems for connected and autonomous vehicles
• Internet of Things (IoT) devices in smart cities and industrial environments
• Emerging 6G communication technologies

Benefits/Advantages

• Cost-Effective: Eliminates the need for costly laboratory setups and specialized equipment.
• Easy Deployment: Operates without requiring hardware modifications or external sensor assistance.
• High Efficiency: Delivers performance comparable to prior methods after only 700 communication instances.
• Scalable: Suitable for both new and existing mmWave infrastructures, facilitating large-scale deployment.
• Autonomous: Includes an algorithm for automatic dataset selection, ensuring seamless and accurate calibration.