Specific Bidirectional Beam Alignment for Micro-Displacement Sensing in 6G Networks: An mmWave Beamforming-Based ISAC Approach

The advent of the 6G era has positioned micro-displacement sensing as a pivotal technology for disaster prevention. Continuous displacement monitoring of deformation phenomena such as landslides and structures can facilitate early warning. Meanwhile, with the development of integrated sensing and communication (ISAC) technology, utilizing the millimeter-wave (mmWave) spectrum likely deployed in 6G networks could enhance micro-displacement sensing capabilities significantly beyond current ranging technologies based on wireless signal. This enhancement primarily derives from the larger bandwidth and shorter wavelengths characteristic of mmWave technology. However, a critical challenge remains: both the receiver and transmitter must not only perform beamforming but also ensure bidirectional beam alignment to accurately capture the phase changes of ISAC signals reflected from targets. This is crucial for micro-displacement sensing, especially when monitoring targets at greater distances and over extensive areas. To address this, we propose an effective swarm intelligence-based mmWave beam scanning algorithm that achieves the accurate measurement of ISAC signal phase changes. Additionally, we have developed an mmWave prototype based on orthogonal frequency division multiplexing (OFDM) signals and conducted field tests to validate the effectiveness of our proposed method in accurately aligning with monitoring targets and micro-displacements sensing. Given the high sensitivity of mmWave ISAC signals to target displacement changes, we also introduce a displacement estimation algorithm integrating a sparse Bayesian learning (SBL) method for continuous target displacement sensing. Field validations of our prototype system demonstrate significant improvements in displacement sensing accuracy, from centimeters to millimeters, underscoring the potential of 6G-assisted intelligent sensing technologies in applications such as disaster prevention.