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.

To satisfy the differentiated service requirements of delay-sensitive and computing-intensive tasks, it is urgent to efficiently allocate limited network resources to improve content distribution in cloud-edge environments. In this paper, we proposes a task partition-based intelligent offloading scheme to optimize resource allocation in cache-assisted cloud-edge cooperation environments. Specifically, we formulate the task partition-based optimal computation offloading problem as a latency minimization model in the cache-aided cloud-edge collaboration system. A new deep reinforcement learning (DRL) algorithm is designed to make optimal subtask offloading and resource allocation decisions based on current network state information, improving resource utilization and network delay. Simulation results demonstrate that the proposed model achieves lower-latency content delivery than the existing popular models in cache-enabled cloud-edge cooperation networks, and fast converges.

In the Internet of Things (IoT) for smart healthcare applications, sensors collect a vast amount of healthcare data, while coverage significantly affects the Quality of Service (QoS). In wireless sensor networks (WSNs), the QoS as well as the network lifetime are dramatically degraded with the increment of coverage holes, especially in large-scale hybrid WSNs (LS-HWSNs) where big data are collected by thousands of sensors distributed in a wide monitored area. In a LS-HWSN, two crucial problems, i.e., covering the wide area without coverage holes and designing an energy-efficient manner for dispatching mobile sensors to repair coverage holes, need to be solved. We study the problems from the cutting point of confident information coverage hole repairing (CICHR). To this end, based on the confident information coverage (CIC) model, a CIC hole predicting (CICHP) algorithm, centralized energy-efficient repairing (CEER) algorithm, and distributed energy-efficient repairing (DEER) algorithm are developed. The CICHP algorithm can predict the prior information of CIC holes (CICHs) by using the period-by-period energy consumption information of sensor nodes. Based on the prior information of CICHs, two repairing algorithms: 1) CEER and 2) DEER can schedule mobile sensors to repair CICHs beforehand. Simulation results show that the proposed algorithms can significantly improve the QoS and extend the network lifetime of LS-HWSNs.

This work investigates the energy-efficient resource allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission in cell-free massive multiple-input multiple-output (MIMO) systems, where each user equipment (UE) performs wireless information and power transfer simultaneously. To begin with, the achievable data rates for multicast and unicast services are derived in closed form, as well as the received radio frequency (RF) power at each UE. Based on the analytical results, a nonsmooth and nonconvex optimization problem for energy efficiency (EE) maximization is formulated, which is however a challenging fractional programming problem with complex constraints. To suit the massive access setting, a first-order algorithm is developed to find both initial feasible point and the nearly optimal solution. Moreover, an accelerated algorithm is designed to improve the convergence speed. Numerical results demonstrate that the proposed first-order algorithms can achieve almost the same EE as that of second-order approaches yet with much lower computational complexity, which provides insight into the superiority of the proposed algorithms for massive access in cell-free massive MIMO systems.

This work investigates energy-efficient power allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission of cell-free massive multiple-input multiple-output systems. In particular, the achievable data rates for multicast and unicast services are derived. Based on the analytical results, a nonsmooth and nonconvex optimization problem for energy efficiency (EE) maximization is formulated, which is however a challenging fractional programming problem. For ease of mathematical tractability, the smooth and successive convex approximations are exploited to transform the original optimization problem into a sequence of quasi-concave problems and, then, Dinkelbach's method is applied to solve the resultant problems. Simulation results demonstrate that the LDM-based transmission achieves higher EE than orthogonal multiplexing schemes.

Non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology for the fifth generation (5G) networks due to its high spectrum efficiency (SE). As green radio which focuses on energy efficiency (EE) becomes an inevitable trend, energy efficient design is becoming more and more important. In this paper, we focus on energy efficient resource allocation problem in multi-user downlink NOMA system with the aim to optimize subchannel assignment and power allocation to maximize the system EE. We propose a novel low-complexity suboptimal subchannel assignment algorithm and obtain the optimal power allocation coefficients among subchannel multiplexed users. To further improve the system EE, unequal power allocation across subchannels (UPAAS) scheme including an optimal solution and a suboptimal Dinkelbach-like algorithm is studied. Simulation results show the effectiveness of our proposed resource allocation algorithms.

We investigate power allocation optimization for global energy efficiency (GEE) maximization in the massive multiple-input multiple-output technique aided multi-pair one-way decode-and-forward relay systems. Assuming that the minimum mean-square error channel estimator and zero-forcing transceivers are employed at the relay, we first derive an accurate closed-form expression of the GEE of this complex system. Based on our analytical results, a non-convex power allocation optimization problem with the objective of GEE maximization is formulated under specific quality-of-service (QoS) and transmit power constraints. To solve this challenging problem, the successive convex approximation technique is invoked to transform the original optimization problem into a concave fractional programming problem, which is then efficiently solved by Dinkelbach's method and by the Charnes-Cooper transformation-based method. In addition, as a special case, the GEE maximization problem under the assumption of using the equal power allocation strategy at both the source users and the relay is also considered. Simulation results demonstrate the accuracy of our analytical results and the effectiveness of the proposed algorithms. Furthermore, the impact of several important system parameters (i.e., the QoS constraint, the transmit power constraints at both the source users and the relay, as well as the quality of channel estimation) on the maximum GEE achieved by the proposed algorithms is also illustrated.

In this paper, a generalized low-complexity beamspace approach is proposed for two-dimensional localization of incoherently distributed sources with a uniform cylindrical array (UCyA) in large scale/massive multiple-input multiple-output (MIMO) systems. The received signal vectors in the antenna-element space are transformed into the beamspace by employing beamforming vectors. As a beneficial result, the total dimensions of the received signal vectors are significantly reduced. In addition, it is shown that the error introduced by the transformation decreases as the number of UCyA antennas increases. The UCyA is composed of multiple uniform circular arrays (UCAs), and the beamspace array response matrices of adjacent UCAs are linearly related. Then, the linear relation is exploited to estimate the nominal elevation direction-of-arrivals (DOAs) directly and the nominal azimuth DOAs based on a low-complexity search algorithm. In contrast, the linear relation in the traditional approach is based on approximations and the associated search algorithm is more complicated. Numerical results demonstrate that the proposed approach outperforms the existing approach in terms of both performance and complexity in the context of massive MIMO systems.