Efficient CKM Exchange via Semantic Communications

Channel knowledge map (CKM) is a novel technique for achieving environment-aware wireless communication and sensing. CKM exchange among different base stations (BSs) is needed to achieve efficient BS cooperations. This paper proposes an efficient CKM exchange method based on semantic communication to facilitate the utilization of CKM at dynamic users. The proposed semantic communication framework achieves the tradeoff between the bandwidth consumption and reconstruction quality of CKM. Compared to traditional image compression and transmission methods, the semantic communication approach demonstrates higher stability and reliability in CKM exchange, especially under low SNR region. Besides, semantic communication based on deep learning can also effectively mitigate the measurement errors associated with CKM reconstruction. Simulation results show that under 4 times compression ratio, the proposed method outperforms traditional methods in terms of stability and can effectively transmit severely corrupted data, recovering CKMs close to the original values.