Efficient Millimeter-Wave Beam Prediction: A Wi-Fi Sensing-Assisted Deep Learning Approach

With the development of 5G and future 6G communication technology, millimeter-wave communication (MMWC) has attracted widespread attention because it can provide a wide frequency band and high data transmission rate. However, the effective application of MMWC is challenged by the severe path loss and environmental sensitivity caused by its high-frequency characteristics. In addition, most of the existing beam prediction methods are limited by excessive communication resource overhead and low prediction accuracy. To tackle these problems, this article proposes for the first time to use Wi-Fi channel state information (CSI) to assist beam prediction. By combining ubiquitous and low-communication-resource-cost Wi-Fi sensing technology with deep learning technology, an efficient deep learning model called dynamic channel attention convolutional neural network (DCACNN) is designed. This model aims to reduce the computational complexity and high resource overhead while improving prediction accuracy. Then, a real-world dataset based on millimeter-wave (mmWave) power and CSI is made. Finally, extensive experiments are conducted to demonstrate the proposed method’s potential for efficient and reliable beam prediction in dynamic environments. Specifically, the prediction accuracy can be achieved up to 94.8%, 86.81%, and 70.18% under quiet, normal, and noisy environments, respectively. The results showcase the exceptional predictive accuracy and superior environmental adaptability of the proposed deep learning model compared to other deep learning models.