Internet of Things Devices (IoTDs) in remote areas connect to the Internet through Low Earth Orbit (LEO) satellites. The transmission of massive IoTDs data volumes leveraging satellites faces a critical challenge, as constrained bandwidth onboard significantly impacts the performance of time-sensitive applications. Moreover, due to the limited number of satellite-ground links and the capacity of satellite access devices, it is challenging to serve all IoTDs within the satellite network coverage. To tackle these challenges, we propose a dual-layer network architecture that integrates Satellite Internet of Things (SIoT) and Unmanned Aerial Vehicles (UAVs), where UAVs serve as transmission units and satellites serve as computing units to overcome the limitations of conventional SIoT. In the dual-layer network architecture, a dual-layer routing problem is formulated to ensure the rapid transmission of tasks, and the problem is further decomposed into UAV layer routing and LEO satellite layer routing. At the UAV layer, we propose a multitask concurrent routing strategy based on task segmentation according to path bandwidth to reduce link load and improve transmission efficiency. At the LEO satellite layer, an integrated computation-transmission routing strategy is introduced to mitigate onboard bandwidth constraints through computational capabilities, thereby reducing transmitted data volume and significantly enhancing transmission efficiency. We transform the problem into a Markov Decision Process (MDP) for each layer and solve the problem using Deep Reinforcement Learning (DRL). To enhance the performance, we introduce improvements to the route algorithm. In the UAV layer, we introduce channel state averaging to reduce algorithmic complexity. In the LEO layer, we employ Prioritized Trajectory Replay (PTR) to improve learning efficiency, while a loss constraint is introduced to enhance training stability. Simulation results demonstrate that the proposed algorithm outperforms other algorithms in terms of convergence performance and overall delay.

The deployment of Internet of remote things (IoRT) devices in remote areas with insufficient communication infrastructure can employ the unmanned aerial vehicles (UAVs) for data collection. The previous works only considered the IoRT devices information coverage probability, but ignored the IoRT devices energy coverage probability in the UAV networks. This letter analyzes the joint information-energy coverage probability performance in UAV networks with hybrid energy harvesting (EH). Firstly, the closed-form expressions of the information coverage probability and the energy coverage probability are derived by utilizing the Laplace transform and the Campbell theorem, respectively. On this basis, the closed-form expression of the joint information-energy coverage probability is derived by the law of large numbers (LLN). Finally, the numerical results confirm the validity of the joint information-energy coverage probability performance.

The integration of mobile edge computing (MEC) and air-ground integrated network is viewed as a crucial technology for Internet of Remote Things (IoRT) devices. It provides widespread service coverage and allows the tasks of IoRT devices to be executed by the uncrewed aerial vehicles (UAVs) and the high altitude platforms (HAPs). In this article, we investigate a joint partial task offloading, resource allocation, and UAV trajectory design problem to minimize the total task offloading delay of all IoRT devices in the air-ground integrated MEC network. Given that the problem is nonconvex and hard to solve by the traditional methods, we convert it into a Markov decision process (MDP) and leverage the deep reinforcement learning method to address it. Considering the complexity of the MDP grows with the number of the IoRT devices and the UAVs increasing, the primal problem is decomposed into two subproblems: 1) the UAV trajectory design and IoRT device power control subproblem, and 2) the partial task offloading and resource allocation subproblem. To address these two subproblems, we apply the basic concepts of the multiagent deep deterministic policy gradient (MADDPG) and the independent proximal policy optimization (IPPO) methods, respectively. Additionally, we introduce the enhanced prioritized experience replay and noise value to improve both the convergence performance and rate. This leads to the development of the MADDPG-improved prioritized experience replay (MADDPG-IPER) algorithm and noise value-IPPO (NV-IPPO) algorithm. Based on the solution of these two subproblems, a joint partial task offloading, resource allocation, and UAV trajectory design (JPTORAUTD) algorithm is proposed. Simulation results present that the proposed JPTORAUTD algorithm outperforms other benchmark algorithms in terms of reducing the total task offloading delay.

The integration of vehicle edge computing (VEC) and air-ground integrated network is considered as a key technology to achieve autonomous driving. It exploits the ubiquitous service coverage and enables tasks to be offloaded to various components, such as high-altitude platform (HAP), unmanned aerial vehicle (UAV), and roadside unit (RSU). In this article, we address the challenge of minimizing the overall task offloading delay in the air-ground integrated VEC network through a joint multicomputation equipment selection and multidimensional resource allocation (JCESRA) problem. Considering the nonconvexity inherent in the problem, we employ the fundamental idea of the block coordinate descent (BCD) method to tackle it. Initially, we exclude the HAP and decompose the primal problem into three subproblems: 1) low-altitude computation equipment selection; 2) joint bandwidth and computation resource allocation; and 3) UAV trajectory design. The first subproblem, which involves integer programming, is solved by using the many-to-one matching method. Meanwhile, we utilize the CVX and successive convex approximation (SCA) method to solve the last two subproblems, respectively. Considering the matching externality, we utilize the coalition game method to deal with it. Based on the solutions of the three subproblems, the JCESRA algorithm without considering the HAP has been proposed. Subsequently, we consider the HAP into the problem. Because the task offloading decision and computation resource allocation of the HAP problem can be viewed as a knapsack problem, we utilize the dynamic programming method to solve it. Because some tasks are offloaded to the HAP, there are some redundant computation resources in UAVs and RSU. We reallocate the computation resources of UAVs and RSU to further reduce the task offloading delay. At last, we present the complete JCESRA algorithm. The simulation results unequivocally indicate that the proposed JCESRA algorithm outperforms other algorithms by significantly reducing the task offloading delay.

Compared with terrestrial network, the air-ground integrated network consisting of unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) offers the advantages of large coverage, high capacity, and seamless connection. Therefore, the air-ground integrated network can provide effective communication services for the Internet of remote things (IoRT). In order to reduce the end-to-end (e2e) packet delay and avoid network congestion of the two-hop network, we investigate a joint packet scheduling, resource allocation, and UAV trajectory design problem, with the objective of minimizing the average packet queue delay from HAP to IoRT devices in the air-ground integrated network. This problem is non-convex and difficult to solve by the traditional methods. In order to solve this problem, we reformulate it into a Markov decision process (MDP) firstly. And then, considering there are continuous and discrete hybrid action spaces in the MDP, we separate the primal action spaces into two sub-action spaces, and utilize the basic idea of multi-agent deep deterministic policy gradient (MADDPG) and multi-agent double deep Q network (MADDQN) methods to solve them, respectively. After that, in order to improve the stability, convergence rate and learning efficiency, we introduce the basic idea of adaptive prioritized experience replay, and propose a hybrid MADDPG-adaptive prioritized experience replay (MADDPG-APER) algorithm. Simulation results show that the proposed algorithm can reduce the average packet queue delay compared with other benchmark algorithms.

Wireless train communication network (WLTCN) is a promising technology for intelligent rail vehicles. It is responsible for bearer of train control services (TCS) and passenger information services (PIS), with the latter mainly referring to multimedia services. The two services have notably different quality of service (QoS) requirements from traditional telecommunication services. To realize multiple services co-existence bearer with different quality of service (QoS) requirements in a single network, we propose a radio access network (RAN) slicing framework to fully utilize the bandwidth resource within a WLTCN in this paper. Based on the characteristics of TCS and PIS, the communications models for the two services are proposed. Next, the slicing strategy problem is formulated as the system bandwidth minimization problem, and then it is transformed into an equivalent problem as the non-convexity. We proposed a dual-decomposition based bandwidth allocation (DBA) algorithm to derive the closed-form expressions for the optimal resource allocation. Simulation results show that the proposed slicing strategy enables WLTCN to meet the QoS requirements for TCS and PIS with minimal bandwidth consumption.

In this work, we investigate the transmission and offloading strategy in the nonorthogonal multiple access (NOMA)-enabled fog computing system for the Internet of Things (IoT). We aim to minimize the total energy consumption of the IoT system while satisfying the latency requirements. Due to the energy minimization problem is a mixed-integer nonlinear programming, we decompose the problem into two subproblems for different optimizing variables, i.e., fog node selection and resource allocation subproblems, and propose a multinode collaboration transmission and computation (MCTC) algorithm. Specifically, the fog node selection subproblem can be transformed into the assignment problem, which is constructed as a bipartite graph to obtain the node selection strategy. For the resource allocation subproblem, we propose an iterative algorithm to obtain the offloading workload, duration allocation, and computation resource. Simulation results are provided, which demonstrate that the proposed algorithm outperforms the other strategies by 56.88% at least.

Recently, in order to extend the computation capability of smart mobile devices (SMDs) and reduce the task execution delay, mobile edge computing (MEC) has attracted considerable attention. In this paper, a stochastic optimization problem is formulated to maximize the system utility and ensure the queue stability, which subjects to the power, subcarrier, SMDs, and MEC server computation resource constraints by jointly optimizing congestion control and resource allocation. With the help of the Lyapunov optimization method, the primal problem is transformed into five subproblems including the system utility maximization subproblem, SMD congestion control subproblem, SMD computation resource allocation subproblem, joint power and subcarrier allocation subproblem, and MEC server scheduling subproblem. Since the first three subproblems are all single variable problems, the solutions can be obtained directly. The joint power and subcarrier allocation subproblem can be efficiently solved by utilizing alternating and time‐sharing methods. For the MEC server scheduling subproblem, an efficient algorithm is proposed to solve it. By solving the five subproblems at each slot, we propose a delay‐aware task congestion control and resource allocation (DTCCRA) algorithm to solve the primal problem. Theoretical analysis shows that the proposed DTCCRA algorithm can achieve the system utility and execution delay trade‐off. Compared with the intelligent heuristic (IH) algorithm, when the control parameter V increases from 106 to 107, the total backlogs are decreased by 5.03% and the system utility is increased by 3.9% on average for the extensive performance by using the proposed DTCCRA algorithm.

Adaptive hierarchical modulation is an effective technique to improve the spectral efficiency for multi-service transmission in high-speed railway (HSR) communication systems. Many efforts have been devoted to studying this technique with the perfect channel state information (CSI) assumption. However, the fast time-varying fading channels and feedback delay lead that the perfect CSI cannot be obtained in HSR scenario. Therefore, the performance of the existing non-robust hierarchical modulation schemes degrades severely. This paper proposes a robust adaptive hierarchical modulation scheme, which can bear multiple services in one symbol with different QoS requirements. The closed form bit error rate (BER) expressions of the robust hierarchical modulation scheme are derived, then the adaptive transmission strategy of the HSR communication system is designed. Finally, the performance of the robust hierarchical modulation scheme is evaluated by extensive simulations. Comparing with the uniform modulation scheme, the overall spectral efficiency increases by 15% when the train is at 500 km/h.

Ultra dense networks are a promising technique to achieve increasing 1000 times data rate requirements of the fifth generation (5G) wireless communications. However, due to the ‘tidal effect’ of mobile Internet traffic, the energy efficiency of the 5G communication system decreases dramatically, especially in off-peak hour, such as midnight. The software-defined wireless networks (SDWN) architecture provides a solution to reduce the energy consumption via cells management. To facilitate wide usage of cells management in SDWN, we consider in this paper the problem of joint ultra dense cells management and resource allocation, with the objective to minimize the network power consumption while guaranteeing the quality of service requirements of users and the coverage rate requirement. The problem is formulated as a mixed-integer nonlinear programming problem. To deal with this problem, we utilize the sparse characteristics of the beamforming vector and the method of reweighted l1 norm to approximate l0 norm. Because the coverage rate requirement constraint is still non-convex, we propose an iterative algorithm to derive the lower bound of the problem, and then propose a heuristic iterative algorithm to obtain a practical solution. Simulation results confirm that minimizing the total network power consumption results in sparse network topologies, and some of the small cells are inactivated when possible. The performance of the proposed heuristic algorithm is only increased by 13.09% compared with the lower bound, while the target signal-to-interference-plus-noise ratio grow from 0 to 8 dB. And the proposed heuristic algorithm can achieve good performance compared with the conventional sparsity-based algorithm.

This paper focuses on the energy management problem of ultra dense wireless networks. The work modes of small cells are controlled by the software defined approach to reduce the system energy consumption. Considering the quality of service (QoS) requirements of users and coverage ratio requirement, the energy minimization problem are formulated as a mixedinteger nonlinear programming (MINLP) problem. To deal with this problem, we utilize the sparse character of the beamforming vector and the method of reweighted l1 norm to approximate l0 norm. Because the coverage ratio requirement constraint is still non-convex, we proposed a heuristic iterative algorithm to obtain a practical solution. Simulation results show that the proposed heuristic algorithm can achieve good performance compared with the conventional sparsity (SP) based algorithm.

To meet the increasing demand of high-data-rate services of high-speed railway (HSR) passengers, cloud radio access network (C-RAN) is a promising technique in mobile communication system of HSR. In this paper, we focus on the energy-efficient power allocation problem for providing the high- data- rate services in HSR scenario. Based on the predicted path loss characteristic of HSR, the power allocation is formulated as a non-linear fractional programming problem. The service pro- cessing delay of baseband unit (BBU) pool and radio transmission delay at radio remote units (RRUs) are considered in the services transmission delay constraint. The formulated problem is a non- convex problem, an equivalent convex problem is reformulated firstly, and then propose an iterative algorithm to obtain the optimal power allocation solution. Furthermore, we analyze the effect of small-scale fading on energy-efficiency (EE) performance of the proposed power allocation scheme. Simulation results confirm that the proposed energy-efficient power allocation policy can meet the QoS of passengers and obtain the optimal EE performance.

In this paper, we investigate the delay-aware dynamic resource management problem for multi- service transmission in high-speed railway wireless communications, with a focus on resource allocation among the services and power control along the time. By taking account of average delay requirements and power constraints, the considered problem is formulated into a stochastic optimization problem, rather than pursuing the traditional convex optimization means. Inspired by Lyapunov optimization theory, the intractable stochastic optimization problem is transformed into a tractable deterministic optimization problem, which is a mixed-integer resource management problem. By exploiting the specific problem structure, the mixed-integer resource management problem is equivalently transformed into a single variable problem, which can be effectively solved by the golden section search method with guaranteed global optimality. Finally, we propose a dynamic resource management algorithm to solve the original stochastic optimization problem. Simulation results show the advantage of the proposed dynamic algorithm and reveal that there exists a fundamental tradeoff between delay requirements and power consumption.

Robust adaptive modulation (AM) is a promising technique to improve the spectrum efficiency (SE) of high speed railway (HSR) communication system. Although robust AM transmission schemes with assumption of block fading channel have been extensively investigated, which are no longer valid in the fast time-varying channel due to the channel variation in frame duration. This paper investigates the optimum of AM transmission schemes design with different bit-error rate (BER) criteria for maximizing SE in HSR communication system. We propose a variable rate and variable power modulation scheme in a normal HSR environment, in which the closed-form expression of the power allocation policy is derived. Furthermore, considering the extract coverage requirements in a higher mobility scenario and different BER requirements, we propose two constant power and variable rate transmission schemes with probability BER constraint and average BER constraint. The effectiveness of the proposed AM transmission schemes are validated by extensive simulations. The effects of channel quality and Doppler shift on the performance of the proposed AM transmission schemes are evaluated.