Off-grid Near-field Narrowband Channel Estimate with Grouping Strategy

With the rise of 6G technology, extremely large-scale multiple-input-multiple-output will be a cornerstone in future communication systems, featuring base stations equipped with hundreds or even thousands of antennas. Additionally, terahertz and millimeter wave bands are extensively utilized in 6G due to their superior performance. According to electromagnetic theory, as the antenna aperture and frequency increase, the Rayleigh distance also increases, thereby extending the near-field region. However, accurately estimating the near-field narrowband channel is extremely challenging, requiring consideration of both the recovery distance and angle dimensions simultaneously. To address these challenges, we propose a Particle Swarm optimization Associated Segmented Polar Domain Multiple Iterative Mesh Weighting algorithm for near-field narrowband channel estimation. The proposed method groups the subcarriers, indirectly improving the system’s resistance to interference in the channel estimation phase. Simulaton results show that our method has improved accuracy compared to other commonly used segmentation methods, such as K-means and average segmentation.