Joint Semantic Communication and Target Sensing for 6G Communication System
Yinchao Yang, Mohammad Shikh-Bahaei, Zhaohui Yang, Chongwen Huang, Wei Xu, Zhaoyang Zhang
TL;DR
This work tackles secure downlink integrated sensing and semantic communication (ISSC) by jointly optimizing transmit beamforming and semantic extraction to maximize the sum semantic secrecy rate (SSR) under QoS, power, and sensing constraints. The transmitted signal is $\mathbf{x} = \mathbf{W}\mathbf{c} + \mathbf{R}\mathbf{z}$, where $\mathbf{c}$ contains semantic messages and $\mathbf{z}$ encodes sensing information; semantic performance is measured by $S_k = \frac{1}{\rho_k} \log_2(1+\gamma_k)$ with semantic leakage captured by SSR$_k = [S_k - \max_l S_{l|k}]^+$, and the secrecy rate is further influenced by a BLEU-based semantic similarity bound. The authors develop an alternating optimization algorithm leveraging SDR and first-order Taylor expansions to iteratively update $\{\mathbf{W}_k, \mathbf{R}_l, \rho_k, \lambda_k\}$, achieving tractable convex subproblems (Steps 1–4) and recovering rank-one solutions via Gaussian randomization. Numerical results demonstrate that the proposed ISSC design improves the worst-case SSR while maintaining comparable sensing performance (as seen in MUSIC spectra) relative to baselines, highlighting its practical potential for secure, spectrum-efficient ISAC systems with semantic considerations.
Abstract
This paper investigates the secure resource allocation for a downlink integrated sensing and communication system with multiple legal users and potential eavesdroppers. In the considered model, the base station (BS) simultaneously transmits sensing and communication signals through beamforming design, where the sensing signals can be viewed as artificial noise to enhance the security of communication signals. To further enhance the security in the semantic layer, the semantic information is extracted from the original information before transmission. The user side can only successfully recover the received information with the help of the knowledge base shared with the BS, which is stored in advance. Our aim is to maximize the sum semantic secrecy rate of all users while maintaining the minimum quality of service for each user and guaranteeing overall sensing performance. To solve this sum semantic secrecy rate maximization problem, an iterative algorithm is proposed using the alternating optimization method. The simulation results demonstrate the superiority of the proposed algorithm in terms of secure semantic communication and reliable detection.