Indoor Localization Using 6G Time-Domain Feature and Deep Learning

Accurate indoor localization is essential for Internet of Things (IoT) systems and autonomous navigation in the 6G communication system. However, achieving precision in environments affected by signal multipath effects and interference remains a challenge for 6G communication systems. We employ a Residual Neural Network (ResNet) augmented with channel and spatial attention mechanisms to enhance indoor localization performance using time-domain data. Through extensive experimentation, our models, when equipped with an attention mechanism, can achieve accurate location under 20% interference. Numerical results show that the ResNet with a Channel Local Attention Block (CLAB) can reduce the localization error by about 12% even when the interference is high. Similarly, the ResNet with a Spatial Local Attention Block (SLAB) can also improve the localization accuracy. While a ResNet combining both CLAB and SLAB can reduce the position error to about 7 cm.