SC3-MDRA: A New Approach to Coordinating Bi-Level Age of Information in AAV-Enabled 6G Integrated Networks

6G confronts a paradigm shift towards integrated sensing, caching, computation and communication (SC³) networks, designed to render comprehensive information services and support diversified applications, in which Age of information (AoI) serves as a pivotal metric for evaluating the data freshness during the end-to-end information service procedure. However, existing works mainly focus on single-level AoI modeling, which fails to maintain fresh information in heterogeneous network infrastructure including autonomous aerial vehicles (AAVs) and ground access points (APs). Therefore, in this paper, we propose a AAV-enabled integrated SC³ network model with bi-level AoI concept, where a AAV exploits common signals to collect sensory information from targets whilst updating the cached items of APs. Thereafter, we formulate a long-term optimization problem to coordinate bi-level AoI by jointly scheduling target sensing and caching updates, together with AAV trajectory and beamforming design. To tackle this intractable problem, we develop a deep reinforcement learning-based solution named SC³ multi-domain resource allocation (SC³-MDRA). This algorithm innovatively incorporates hindsight experience replay and sharpness-aware minimization to overcome sparse reward as well as enhance policy adaptivity, thereby making immediate decisions in response to dynamic AoI status. Additionally, SC³-MDRA allocates computation and bandwidth resources of APs for effectively delivering information to requesting users. Experimental results reveal that the proposed SC³-MDRA outperforms baseline methods in terms of both learning convergence and system overall performance. Besides, the tradeoff between information freshness and AAV energy consumption is delineated.