Semantic-Aware Resource Allocation for 6G ISAC: A DDPG-Driven Approach

The rapid evolution toward sixth-generation (6G) wireless networks introduces Integrated Sensing and Communication (ISAC) as a key enabler for intelligent and resource-efficient systems. Traditional resource allocation schemes for ISAC primarily focus on maximizing spectral efficiency, sensing accuracy, or energy efficiency. However, as networks increasingly support semantics-driven applications, the fidelity of transmitted information becomes equally critical. In this paper, we propose a semantic-aware resource allocation mechanism for 6G ISAC systems that leverages the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm. Unlike conventional approaches, our method explicitly incorporates semantic constraints into the optimization process, prioritizing semantic fidelity while jointly enhancing sensing accuracy and energy efficiency. Simulation results, benchmarked against 3GPP’s emerging 6G standards, demonstrate that the proposed mechanism achieves notable performance improvements across all three dimensions, highlighting its potential to support the next generation of intelligent, context-aware communication systems.