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.

With the rapid proliferation of intelligent internet of things (IoT) terminals, conventional terrestrial networks are increasingly strained by limited bandwidth, high latency, and constrained computation capabilities. Air-Ground integrated networks (AGIN) offer a promising solution through flexible deployment, including enhanced coverage and low latency. To enable intelligent services, we propose an air-ground multi-access edge computing (MEC) network for semantic communication. Within this network, users transmit compressed semantic task data to unmanned aerial vehicles (UAVs) and terrestrial small-cell base stations (SBSs) using a probabilistic semantic compression (PSC) technique. A joint resource optimization problem is developed to determine semantic compression, task allocation, computation resource allocation, power control, and user association, aiming to minimize the system cost. The optimization problem is then formulated as a Markov decision process (MDP) and solved by a proximal policy optimization (PPO) algorithm based on deep reinforcement learning (DRL). Simulation results show that the proposed method significantly outperforms three other DRL baselines, achieving a reduction up to 74.12% in overall latency-energy cost under diverse system configurations.

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.

Content caching (CC) plays a crucial role in improving quality of service (QoS) and mitigating the congestion of backhaul links for the Heterogenous Networks (HetNets). The accessibility of users to the base stations (BSs) seriously depends on the wireless channel capacities which can be improved by designing an efficient content delivery (CD) policy. Thereupon, a nature idea is to jointly design the CC and CD strategy to effectively utilize the limited radio resource and cache capacity of BSs. In this paper, we focus on the joint coded caching, user association (UA) and bandwidth allocation (BA) optimization problem, aiming at minimizing the overall power consumption. Particularly, we analyze energy consumption in both backhaul and access links under coded caching strategy. Then we utilize an alternative optimization algorithm to decompose the original problem into the CC and CD problems, which are solved via convex optimization, matching and bisection algorithm. Numerical results show that the proposed scheme significantly reduces power consumption compared to the benchmarks.

In order to obtain the worthwhile gain in coverage and the capacity required by future mobile services, heterogeneous networks (HetNets) are being considered as one of the most promising solutions. However, the ultra-dense deployment of small base stations (ud-SBSs) would significantly increase energy consumption (EC), which becomes particularly severe when meeting explosive growth of multimedia services. Proper user association (UA) and power allocation (PA) are both crucial to achieve desirable energy-saving performance in HetNets. In view of these, this paper investigates the joint UA and PA optimization problem for multimedia services in cache-enabled HetNets. The aim is to minimize the total power consumption under certain quality-of-service (QoS) requirement and maximum power limit. A non-convex mixed integer programming optimization problem is formulated. To solve the problem, a heuristic algorithm based on matching game is proposed. Numerical results demonstrate that the proposed algorithm yields a performance improvement in terms of the power consumption.

The large scale multiple-input multiple-output (MIMO) system with hybrid beamforming (HBF) is a promising communications technology due to its excellent tradeoff between hardware complexity and system performance. Assuming perfect channel state information is acquired, we consider a single cell downlink multi-user massive MIMO system working in a generic channel model with a hybrid structure that supports multiple streams per UE. We aim to find an analog and digital precoder/combiner that maximizes the sum-rate of the communication system. Unlike the traditional two-stage design criterion, which separately designs the analog and digital stages, our proposed criterion jointly designs two stages by trying to avoid the loss of information at each stage. When double the least number of radio frequency (RF) chains are available, we provide an asymptotically optimal solution in a massive MIMO regimen, i.e., the sum-rate of such an HBF solution could approach the channel capacity under large base station (BS) antenna arrays. A corresponding solution using the fewest RF chains is then derived. Finally, the simulation results are shown to validate the proposed schemes. Specifically, the solution with the fewest RF chains is shown to outperform the state of the art for HBF systems, even when the number of BS antennas is not very large. It should be noted that the schemes proposed in this paper have low complexity owing to their closed-form solutions.

Spatial sharing (SPSH) is one of the most important merits in 60 GHz millimeter wave (mmWave) wireless personal area networks. Multihop architecture exhibits significant potential for concurrent transmission. In this study, we investigate the SPSH problem for single-hop and multihop architecture under the physical interference model. The minimum length schedule problem of satisfying the required traffic demand with the shortest time is formulated as the optimization problem. Concurrent scheduling and routing are jointly considered in the multihop case. To reduce the complexity of the computation, the column generation algorithm is employed to derive the optimal solution. The simulation results show that multihop architecture can enhance the network throughput by 20% to 30% compared with single-hop architecture.