Abstract
To address the challenges of Internet of Things (IoT) device diversity and media service heterogeneity in human and machine-type communications, a predominant approach in sixth-generation (6G) networks and beyond is to serve diversified IoT devices by differentiated services. In this paper, a satellite-terrestrial IoT framework with multi-access edge computing (MEC) is investigated for two types of heterogeneous services, data-intensive and computation-intensive service. In our proposed framework, MEC and millimeter wave (mmWave) communication are jointly considered to optimize data- and computation-intensive services, guaranteeing the rate, delay and energy requirements of diversified IoT devices. From the viewpoint of heterogeneous services, we formulate a joint resource allocation problem, in which quality of experience (QoE) of diversified IoT devices are recognized as system utility. Specifically, service offloading, power allocation and computation resource allocation are jointly considered. Since the optimized problem is nonconvex, necessary problem reformulations are conducted to transfer the original problem to convex problems. Furthermore, an alternating iterative method based on deep reinforcement learning (DRL) and CVX technique is adopted to obtain the sub-optimal solution with low computation complexity. Finally, extensive simulations are conducted with different system parameter configurations to verify the effectiveness of our proposed scheme.