This paper investigates the mixed-timescale resource allocation problem in satellite-terrestrial integrated networks (STIN). Moreover, the multi-access edge computing (MEC) technology and millimeter wave (mmWave) with rich spectrum resource are merged into the STIN to improve the network performance. A network utility maximization problem characterized by the achievable rate and backhaul reduction is formulated under the constraints of the maximum caching capacity, transmission power of mmWave small-cell base stations (SBSs) and quality of service (QoS) for Internet of Things (IoT) devices, where the caching placement, power allocation and user-SBS association are jointly optimized. In order to tackle this mixed-integer nonlinear programming (MINLP) problem, we decompose the original problem into the long-term caching placement subproblem, and short-term power allocation and user-SBS association subproblems. Then, a multi-agent deep reinforcement learning (MADRL)-based independent proximal policy optimization (IPPO) algorithm is proposed to solve the short-term user-SBS association subproblem. Meanwhile, the linear programming (LP) is used to solve the long-term caching placement subproblem. Furthermore, we derive the closed-form solution of the short-term power allocation subproblem through the Karush-Kuhn-Tucker (KKT) conditions. Simulation results are carried out to validate the effectiveness and scalability of the proposed joint approach.

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.

In this paper, we propose a semantic communication framework facilitated by multi-access edge computing (MEC)assisted satellite-air-ground integrated networks (SAGIN), which comprises of LEO satellites, unmanned aerial vehicles (UAVs), and macro-cell base stations (MBSs). Considering the limited wireless resources and diversified quality of service (QoS) requirements of semantic tasks, an optimization problem with the goal of minimizing system cost in terms of the task latency and energy consumption is formulated. In order to address the mixed-integer nonlinear programming (MINLP) problem, we propose an alternating optimization algorithm that tackles UAV deployment sub-problem with the successive convex approximation (SCA) method, task offloading, semantic compression, power allocation and computation resource allocation optimization with deep reinforcement learning (DRL)-based multi-agent proximal policy optimization (MAPPO) method. Simulation results demonstrate that our proposed algorithm outperformes other reinforcement learning algorithms, i.e., about 5.12%, 23.72% and 35.64% over PPO, DDPG, and A2C, respectively.

With the blossom of Internet of Things (IoT) services and applications, the big data volumes raised by the large number of IoT devices have posed great burden on the traditional terrestrial networks. Considering the advantages of multi-access edge computing (MEC) and reconfigurable intelligent surface (RIS), this paper investigates the MEC and RIS-assisted airground IoT networks, where the joint resource allocation problem is formulated to minimize the system energy consumption. To handle the proposed nonconvex optimization problem, we decompose it into three subproblems, i.e., the coded caching placement problem, the phase shift problem and the joint multi-user association and power allocation problem. Then, we propose a deep reinforcement learning (DRL)-based Proximal Policy Optimization (PPO) algorithm to solve the joint multiuser association and power allocation problem. Moreover, the CVX technique and exhaustive search method are respectively adopted to solve the coded caching placement problem and the phase shift problem. Simulation results demonstrate that our proposed algorithm outperforms the benchmark schemes.

In order to support colourful multimedia services with strict quality-of-service (QoS) requirements of user equipments (UEs), the space-air-ground integrated networks (SAGIN) can be taken as a promising approach to enhance network capacity. Among them, millimeter wave (mmWave) and edge caching promise to significantly improve the SAGIN performance due to the advantage in rich bandwidth resource and low latency, respectively. In this paper, we investigate the joint caching and resource allocation for multimedia services in SAGIN, where multimedia content requests can be simultaneously served by multiple access points (APs). Considering the delay-constraint of multimedia services, we then formulate a mixed-integer non-linear programming (MINLP) problem aiming at minimizing the service delay, which involves jointly optimizing coded caching (CC), power allocation (PA) and UEs-to-APs association (UA). We propose to find the optimal solution by employing an alternating iteration optimization framework. The optimal CC and PA problems are firstly addressed by utilizing convex optimization technology. Then, two many-to-many swap matching algorithms are developed to slove the UA subproblem effectively. Numerical results demonstrate that our proposed algorithms can substantially reduce the service delay over other benchmarks.

Recent years have witnessed the rising popularity of urban vehicular crowd sensing (UVCS) systems that leverage drivers' mobile devices equipped with on-board sensors for various urban sensing tasks. Because of the importance of ensuring satisfactory spatio-temporal sensing coverage in such UVCS systems, most existing work has focus on designing efficient scheduling mechanisms to maximize the task completion rate under drivers' traveling constraints. Different from prior work, we propose Hector, a joint trajectory scheduling and incentive mechanism for spatio-temporal UVCS systems, which concentrates on capturing the interactive effects between scheduling and incentive mechanisms. Technically, we first reduce the dimensions of the original scheduling problem by mapping it into an augmented set cover problem with spatio-temporal constraints. Then, based on reverse combinatorial auctions, we design Hector, whose incentive mechanism with the presence of uncertain future trajectory information makes scheduling and compensation decisions in real-time. Specifically, Hector is truthful, individual rational and computationally efficient. Furthermore, the social cost yielded by Hector is close-to-optimal, and the approximation ratio is Hm. The advantageous properties of Hector are verified by both rigorous theoretical analysis and extensive simulations based on the real world datasets in the Chinese city Shenzhen which consists of 726,000 taxi trajectories.