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

TL;DR

This paper introduces a deep learning-based method to generate channel knowledge maps for new access points in dense networks by leveraging existing APs' data, enhancing environment-aware communication.

Contribution

It proposes a novel cross-AP CKM inference approach using UNet to predict CKMs of new APs from existing AP data, addressing a key challenge in dense network deployment.

Findings

Cross-AP CKM inference is feasible and effective.

The UNet model accurately predicts CKMs for new APs.

The method improves initial CKM generation and deployment efficiency.

Abstract

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 communication. Although CKM is particularly appealing for dense networks such as cell-free networks, it remains a challenge to efficiently generate CKMs in dense networks. For a dense network with CKMs of existing access points (APs), it will be useful to efficiently generate CKMs of potentially new APs with only AP location information. The generation of inferred CKMs across APs can help dense networks achieve convenient initial CKM generation, environment-aware AP deployment, and cost-effective CKM updates. Considering that different APs in the same region share the same physical environment, there exists a natural correlation between the channel knowledge of different APs. 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.