Channel Knowledge Map Construction: Recent Advances and Open Challenges

TL;DR

This paper reviews recent methods for constructing channel knowledge maps (CKMs) in wireless communications, covering classical, AI-based, and neural radiance field approaches, and discusses future research challenges.

Contribution

It provides a comprehensive overview of recent advances in CKM construction techniques and outlines open challenges and future directions in the field.

Findings

Classical interpolation methods have limitations in practical deployment.

AI techniques enable feature-based CKM construction from environmental data.

Neural radiance fields can create high-fidelity CKMs from sparse measurements.

Abstract

Channel knowledge map (CKM) has emerged as a pivotal technology for environment-aware wireless communications and sensing, which provides a priori location-specific channel knowledge to facilitate network optimization. Efficient CKM construction is an important technical problem for its effective implementation. This article provides a comprehensive overview of recent advances in CKM construction. First, we examine classical interpolation-based CKM construction methods, highlighting their limitations in practical deployments. Next, we explore image processing and generative artificial intelligence (AI) techniques, which leverage feature extraction to construct CKMs based on environmental knowledge. Furthermore, we present emerging wireless radiance field (WRF) frameworks that exploit neural radiance fields or Gaussian splatting to construct high-fidelity CKMs from sparse measurement data. Finally, we outline various future research directions in real-time and cross-domain CKM construction, as well as cost-efficient deployment of CKMs.