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.

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.

This paper considers a wireless-powered massive multi-input multi-output (MIMO) aided multi-way amplify-andforward (AF) relay network, where a relay equipped with massive antennas charges users through energy beamforming and assists with wireless information transmission. For this system we propose a novel energy-efficient resource allocation scheme for the global energy efficiency (GEE) optimization. In particular, we firstly derive an accurate closed-form expression of GEE when zero-forcing transceivers are employed in the considered system. Secondly, based on this analytical expression, we formulate a resource allocation optimization problem for the GEE maximization by jointly optimizing power and time allocation, subject to limited transmit power, time duration, and minimum quality-of-service (QoS) constraints. Due to the complexity of this nonconvex optimization, a two-layer iterative algorithm is proposed to address the original GEE maximization problem. Numerical results demonstrate the accuracy of our theoretical analysis and the effectiveness of the derived algorithms.

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.

Ground networks are facing severe challenges posed by the conflict between the increasing number of terminals and limited coverage and network resources. The space-air-ground integrated network (SAGIN) is a promising solution due to its global coverage and multi-dimensional heterogeneous resources. In this paper, we investigate a scenario, where the terminals are served by networks in SAGIN with hybrid orthogonal multiple access (OMA)/non-OMA (NOMA) transmission. According to the network characteristics in SAGIN, such as communication link, movement, and communication resources, we build up an utility function. We further formulate the problem of joint hybrid OMA/NOMA mode selection and resource allocation to maximize the system utility. Due to the non-convexity of the formulated problem, we decompose the original problem into two subproblems, which can be solved by the successive convex approximation, Lagrange dual method, and deep Qnetwork (DQN) learning, respectively. Moreover, to guarantee the constraints, an efficient reward function is further designed in DQN. Finally, simulation results show that compared with the benchmark schemes, the proposed algorithm can achieve better performance in terms of the achievable sum rate, the average achievable rate, and outage probability. Compared with the existing algorithms, the proposed algorithm can always facilitate terminals to flexibly select the suitable transmission mode according to the characteristics of the connected network, achieving better system 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 unmanned aerial vehicles (UAVs) and the high altitude platforms (HAPs). In this paper, 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 non-convex 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: the UAV trajectory design and IoRT device power control subproblem, and the partial task offloading and resource allocation subproblem. To address these two subproblems, we apply the basic concepts of the multi-agent 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.