English

Constructing Knowledge Map for MIMO-OFDM Clustered Channel Estimation

Signal Processing 2026-02-27 v1

Abstract

Channel knowledge map (CKM) exploits environ-ment information to assist channel estimation during communi-cation. For clustered channels, which represent a typical type ofwireless propagation environment, there has been no researchdevoted to designing an appropriate CKM to enhance theirestimation. To exploit environment information for clusteredchannel, improve channel estimation accuracy and reduce pilotoverhead, we propose ClusterCKM, a CKM providing the rangeof clustered multipath parameters for any pair of transmitter-receiver links in the region of interest. Firstly, we construct Clus-terCKM through estimating the spatial range of scatterer clustersfrom historical channel information. From these spatial range ofscatterer clusters, ClusterCKM infers the range of multipathparameters for the target link. Furthermore, a ClusterCKM-based channel estimation algorithm is developed to utilize theparameter range provided by ClusterCKM. Simulation resultsshow that, more accurate channel estimation can be achievedand pilot overhead can also be reduced by ClusterCKM and theClusterCKM-based estimation algorithm.

Keywords

Cite

@article{arxiv.2602.22746,
  title  = {Constructing Knowledge Map for MIMO-OFDM Clustered Channel Estimation},
  author = {Heling Zhang and Xiujun Zhang and Xiaofeng Zhong and Shidong Zhou},
  journal= {arXiv preprint arXiv:2602.22746},
  year   = {2026}
}

Comments

Accepted for presentation at IEEE ICC 2026

R2 v1 2026-07-01T10:53:30.402Z