Integrated Sensing and Semantic Communication with Adaptive Source-Channel Coding

TL;DR

This work addresses integrated sensing and semantic communication (ISSC) for 6G by introducing Adaptive Source-Channel Coding (ASCC) that jointly optimizes SemCom coding rate and transmit beamforming to support both semantic transmission and sensing. It models end-to-end semantic distortion as a sum of source and channel components and derives a data-driven regression form $D_o(R_s, ho_b)$, together with a Hybrid Cramér-Rao Bound (HCRB) for target localization under imperfect time synchronization. An optimization framework minimizes $D_o$ under HCRB, channel-use, and power constraints, solved via alternating optimization combining Successive Convex Approximation (SCA) and Fractional Programming (FP). Simulation results show that ASCC outperforms a deep joint source-channel coding baseline with water-filling and zero-forcing, especially in low-SNR regimes, and highlight robustness to time synchronization errors. The approach offers a practical path to jointly configure SemCom and radar-like sensing in next-generation wireless systems.

Abstract

Semantic communication has emerged as a new paradigm to facilitate the performance of integrated sensing and communication systems in 6G. However, most of the existing works mainly focus on sensing data compression to reduce the subsequent communication overheads, without considering the integrated transmission framework for both the SemCom and sensing tasks. This paper proposes an adaptive source-channel coding and beamforming design framework for integrated sensing and SemCom systems by jointly optimizing the coding rate for SemCom task and the transmit beamforming for both the SemCom and sensing tasks. Specifically, an end-to-end semantic distortion function is approximated by deriving an upper bound composing of source and channel coding induced components, and then a hybrid Cramér-Rao bound (HCRB) is also derived for target position under imperfect time synchronization. To facilitate the joint optimization, a distortion minimization problem is formulated by considering the HCRB threshold, channel uses, and power budget. Subsequently, an alternative optimization algorithm composed of successive convex approximation and fractional programming is proposed to address this problem by decoupling it into two subproblems for coding rate and beamforming designs, respectively. Simulation results demonstrate that our proposed scheme outperforms the conventional deep joint source-channel coding -water filling-zero forcing benchmark.

Figures (4)

• Figure 1: Framework of the ISSC System.
• Figure 2: E2E distortion as a function of $\rho_b$ under different $R_s$.
• Figure 3: MS-SSIM comparison under different schemes
• Figure 4: HCRB comparison under different schemes