Near-field integrated sensing and communication (ISAC) can deliver the high spatial resolution and transmission capability with the shared spectrum and hardware. Due to the partial overlap between communication scatterers and radar targets, the sensing information can provide valuable priors to enhance the channel estimation while fusing the two heterogeneous modalities remain challenging. To address this problem, a Cross-Attention Transformer based Channel Estimation Neural Network (CAT-CENet) is developed, which includes a communication pilot branch generating the the Key and Value features and a sensing information branch generating the Query feature. By elaborating the three-module structure, CAT-CENet can focus on features of overlapped targets automatically without need of identifying them in advance. The modality contribution is theoretically analyzed based on the Shapley value to verify the cross-attention gain achieved by CAT-CENet. Simulation results show that CAT-CENet outperforms the state-of-the-art schemes, especially with the higher overlapping proportion, and is robust to the model pruning.
Near-field localization has attracted significant attention in recent years due to the move toward higher frequencies and extremely large aperture arrays, which expand the near-field region and bring many sources into it. This implies that antenna arrays can be used to localize n…
With the rapid development of integrated sensing and communication (ISAC) as a key enabler for future wireless networks, ensuring the security of both communication and sensing functions has become increasingly important. Current secure ISAC studies focus restrictively either on…
Attention-based neural estimators achieve strong channel-estimation accuracy, but the computational cost of global attention over the time-frequency resource grid grows quadratically with the number of subcarriers, and these estimators are typically tied to a single resource allo…
Integrated sensing and communication (ISAC) at terahertz (THz) frequencies enables ultra-high-resolution perception while facing a key limitation: highly directional THz beams cannot illuminate extended targets within a single beam. Conventional solutions rely on sequential beam…
Extremely large-scale multiple-input multiple-output (XL-MIMO) is crucial for next-generation communication systems. In practice, the deployment of non-square uniform planar arrays (UPAs) fundamentally alters wavefront characteristics and induces anisotropic beamfocusing capabili…
Data-aided channel estimation enhances spectral efficiency by reusing detected symbols as virtual pilots. In this process, selecting only reliable symbols is crucial to prevent misdetected symbols from corrupting the channel estimate. However, conventional methods rely exclusivel…