  The Space-Air-Ground Integrated Network (SAGIN), crucial to the advancement
of sixth-generation (6G) technology, plays a key role in ensuring universal
connectivity, particularly by addressing the communication needs of remote
areas lacking cellular network infrastructure. This paper delves into the role
of unmanned aerial vehicles (UAVs) within SAGIN, where they act as a control
layer owing to their adaptable deployment capabilities and their intermediary
role. Equipped with millimeter-wave (mmWave) radar and vision sensors, these
UAVs are capable of acquiring multi-source data, which helps to diminish
uncertainty and enhance the accuracy of decision-making. Concurrently, UAVs
collect tasks requiring computing resources from their coverage areas,
originating from a variety of mobile devices moving at different speeds. These
tasks are then allocated to ground base stations (BSs), low-earth-orbit (LEO)
satellite, and local processing units to improve processing efficiency. Amidst
this framework, our study concentrates on devising dynamic strategies for
facilitating task hosting between mobile devices and UAVs, offloading
computations, managing associations between UAVs and BSs, and allocating
computing resources. The objective is to minimize the time-averaged network
cost, considering the uncertainty of device locations, speeds, and even types.
To tackle these complexities, we propose a deep reinforcement learning and
perception-aided online approach (DRL-and-Perception-aided Approach) for this
joint optimization in SAGIN, tailored for an environment filled with
uncertainties. The effectiveness of our proposed approach is validated through
extensive numerical simulations, which quantify its performance relative to
various network parameters.
