Abstract
In this work, we propose a novel integrated sensing and communication (ISAC) framework for connected and autonomous vehicles (CAVs), which incorporates digital semantic communication (SemCom) to achieve both reliable communication and accurate sensing. Within this framework, the transmitting vehicle extracts semantic symbols from the source data and transmits them over orthogonal frequency division multiplexing (OFDM) sub-carriers, while simultaneously utilizing echo signals for radar-based environmental sensing. To achieve reliable SemCom, the transmitter must jointly optimize the quantization bitwidth for semantic symbols, the modulation order, the power allocation across semantic symbol dimensions, and the transmit beamforming strategy. These optimizations must also consider sensing performance, leading to a tradeoff between radar sensing and task-oriented SemCom. To address this joint optimization problem, we decompose it into three subproblems and develop corresponding solutions: 1) a hierarchical constrained proximal policy optimization (H-CPPO) algorithm to determine the quantization bitwidth, modulation order, and power allocation under frequency-flat channels, 2) a joint beamforming strategy to optimize the dual-function radar-SemCom transmit beamforming vector, and 3) a semantic importance-based signal-to-noise ratio (SNR) matching strategy that effectively adapts the optimal power allocation obtained under frequency-flat conditions to fading channels with random gains. Simulation results on a road image segmentation task show that, the proposed SemCom scheme achieves near-optimal segmentation accuracy while reducing radar beamforming error by up to 82% compared to the conventional digital system using the same quadrature phase shift keying (QPSK) modulation.