Generating CKM Using Others' Data: Cross-AP CKM Inference With Deep Learning

Channel knowledge map (CKM) is a promising paradigm shift towards environment-aware communication and sensing by providing location-specific prior channel knowledge before real-time operation. Although CKM is particularly appealing for dense networks such as cell-free networks, it remains a challenge to efficiently generate CKMs. For a dense network where existing access points (APs) have already built their respective CKMs, it will be useful to construct CKMs of potentially new APs with only their location information. The generation of CKMs across APs can help dense networks achieve convenient CKM initialization, environment-aware new AP deployment, and cost-effective CKM updates. For APs in the same region that share the same physical environment, there is a natural correlation between their CKMs. Therefore, by mining the implicit correlation between location-specific channel knowledge, cross-AP CKM inference can be realized using data from other APs. This paper proposes a cross-AP inference method to generate CKMs of potentially new APs with deep learning. The location of the target AP is fed into the UNet model in combination with the channel knowledge of other existing APs, and supervised learning is performed based on the channel knowledge of the target AP. Based on the trained UNet and the channel knowledge of the existing APs, the CKM inference of the potentially new AP can be generated across APs. The generation results of the inferred CKM validate the feasibility and effectiveness of cross-AP CKM inference with other APs' channel knowledge.