Pilot Optimization and Channel Estimation Scheme for Semantic Communication: A Framework for Edge Intelligence

The semantic communication system has become one of the promising communication technologies to support high data-intensive artificial intelligence (AI) applications and services such as meta-verse, 3D maps, and so on for achieving low communication overhead. Unlike traditional communication system, accurate channel estimation is a vital issue in semantic wireless communication since a semantic transmitter is required to send the core meaning of a message rather than an entire bit streams for the receiver. Thus, designing a semantic communication framework is challenging due to the dependencies of the semantic encoder and decoder over the orthogonal frequency division multiplexing (OFDM) setting. Therefore, first, this work designs a holistic semantic communication system model that is composed of a semantic encoder, a 3GPP-defined cluster delay line (CDL) wireless channel model, and a semantic decoder for AI services. Second, this paper proposes a semantic communication framework for AI services, 1) a masked autoencoder (MAE)-based channel estimation, and 2) a hierarchical reinforcement learning (HRL)-based pilot allocation method. Third, the proposed semantic communication framework is trained in an end-to-end manner, combined with OFDM layers, considering the image reconstruction and the performance of vision AI applications. Finally, the proposed MAE-based channel estimation and HRL-based pilot allocation RL agent are integrated into the semantic communication framework. Finally, Experimental results show that the proposed semantic framework demonstrates up to a 21.25% performance improvement in image segmentation tasks. Furthermore, the proposed channel estimator and pilot allocator also show higher channel estimation accuracy compared to existing channel estimators and pilot allocation methods.