Semantic communication is emerging as an effective means of facilitating intelligent and context-aware communication for next-generation communication systems. In this paper, we propose a novel metric called Age of Incorrect Semantics (AoIS) for the transmission of video frames over multiple-input multiple-output (MIMO) channels in a monitoring system. Different from the conventional age-based approaches, we jointly consider the information freshness and the semantic importance, and then formulate a time-averaged AoIS minimization problem by jointly optimizing the semantic actuation indicator, transceiver beamformer, and the semantic symbol design. We first transform the original problem into a low-complexity problem via the Lyapunov optimization. Then, we decompose the transformed problem into multiple subproblems and adopt the alternative optimization (AO) method to solve each subproblem. Specifically, we propose two efficient algorithms, i.e., the successive convex approximation (SCA) algorithm and the low-complexity zero-forcing (ZF) algorithm for optimizing transceiver beamformer. We adopt exhaustive search methods to solve the semantic actuation policy indicator optimization problem and the transmitted semantic symbol design problem. Experimental results demonstrate that our scheme can preserve more than 50% of the original information under the same AoIS compared to the constrained baselines.

Existing semantic communications have exhibited satisfactory performance in many tasks, but secure image transmission has not been adequately investigated. In this paper, we propose a novel secure semantic image transmission (SSIT) framework over multiple-input single-output (MISO) wiretap channels. To enhance image transmission security for a legitimate semantic user (SU) while interfering with the eavesdropper (Eve), a type of beneficial semantic noise map, which is determined by both source and SU channel states, is produced by a semantic noise-aided conditional variational autoencoder (SN-CVAE) in an unsupervised manner. Furthermore, to improve the secure image reconstruction quality, we propose an efficient transmit beamformer optimization algorithm and leverage the constrained stochastic successive convex approximation (CSSCA) to solve the optimization problem. Numerical results demonstrate that our method effectively protects image information from eavesdroppers while ensuring high-fidelity image reconstruction at the legitimate receiver.

Joint source-channel coding (JSCC) is a promising paradigm for next-generation communication systems, particularly in challenging transmission environments. In this paper, we propose a novel standard-compatible JSCC framework for the transmission of images over multiple-input multiple-output (MIMO) channels. Different from the existing end-to-end AI-based DeepJSCC schemes, our framework consists of learnable modules that enable communication using conventional separate source and channel codes (SSCC), which makes it amenable for easy deployment on legacy systems. Specifically, the learnable modules involve a preprocessing-empowered network (PPEN) for preserving essential semantic information, and a precoder & combiner-enhanced network (PCEN) for efficient transmission over a resource-constrained MIMO channel. We treat existing compression and channel coding modules as non-trainable blocks. Since the parameters of these modules are non-differentiable, we employ a proxy network that mimics their operations when training the learnable modules. Numerical results demonstrate that our scheme can save more than 29% of the channel bandwidth, and requires lower complexity compared to the constrained baselines. We also show its generalization capability to unseen datasets and tasks through extensive experiments.