Cellular networks assisted by flexibly placed high-maneuverability unmanned aerial vehicles (UAVs) have attracted virtual interests recently. In this paper, the utility maximization problem is investigated to determine how to improve the performance of multi-UAV enabled software-defined cellular networks (SDCNs) with wireless backhaul. The formulated problem jointly optimizes the three dimensional (3D) UAV placement, user scheduling and association, and spectrum resource allocation. The proposed problem is intractable since it is a mixed-integer combined non-convex problem. Thus, an efficient distributed alternating maximization (AM) iterative algorithm is developed to solve the proposed problem. Then, the original optimization problem is decomposed into three subproblems that are solved alternatively via the successive convex optimization (SCO) technique and the modified alternating direction method of multipliers (ADMM) in the proposed algorithm. The theoretical analysis and the simulation results confirm the convergence performance of the proposed algorithm. The extensive numerical results substantiate the superiority of the proposed algorithm, which significantly increases the throughput and utility of the overall users relative to the traditional overlaid ground base station (GBS) and UAV structure and other benchmark methods. The maximal throughput gain is as large as 74.9% on average for all users, in contrast to other benchmark schemes.

An unmanned aerial vehicle (UAV) swarm together with a large-scale heterogeneous Internet of Things (IoT) network consisting of macrocells and energy-constrained IoT transmitters (IoT-Ts) is investigated. The UAVs are utilized as flying robot swarms that intelligently transfer energy to the energy-constrained IoT-Ts on the ground. Each IoT-T has an associated IoT device (IoT-D) that is placed at a fixed distance in a random direction. The transmission probability of the energy-constrained IoT-Ts is derived by considering one-slot charging and two-slot charging according to three dimensional (3D) locations, respectively. The coverage probability of each type of IoT-D is investigated. The energy efficiency is derived by considering the transmission power of the active IoT-Ts and the effect of the association biasing factor, and the energy efficiency is also maximized by deploying the optimal density of IoT-Ts. Simulation results are examined to validate the accuracy of our theoretical analysis. Results illustrate the insightful effects of the network parameters, and the helpful guidelines for practical UAV swarms and IoT system design.

The explosive growth of mobile data traffic and the scarcity of available licensed spectrum make resource allocation in heterogeneous networks a critical issue. A distributed resource allocation algorithm for software defined cellular networks (SDCN) for future 5G networks is proposed. The adoption of integrated femto-WiFi small cells is used to alleviate spectrum shortage, by permitting simultaneous access to both the licensed bands (via cellular interface) and unlicensed bands (via WiFi interface). A weighted utility maximization problem is formulated to optimize resource allocation, utilizing the software defined network controller’s global view. A fully distributed solution based on the weighted utility maximization optimizes resource allocation, keeping the interference from small cells to macrocells below predefined thresholds. The proposed algorithm considers the sDevices, which have both cellular and WiFi interfaces, and the wDevices which have WiFi-only interfaces. Numerical simulations substantiate the superiority of the proposed resource allocation algorithm, which increases significantly the average throughput and average utility of all devices, compared with traditional and current methods. Throughput gains as large as 41.6% in spectral efficiency for the average of all sDevices and wDevices are achieved by the new designs.

Industrial Internet mobile edge computing (MEC) deploys edge servers near base stations to bring computing resources to the edge of industrial networks to meet the energy-saving requirements of Industrial Internet terminal devices. This paper considers a wireless MEC system in an intelligent factory that has multiple edge servers and mobile smart industrial terminal devices. In this paper, the terminal device has the choice of either offloading the task in whole or in part to the edge server, or performing it locally. Through combined optimization of the task offload ratio, number of subcarriers, transmission power, and computing frequency, the system can achieve minimum total energy consumption. A computing offloading and resource allocation approach that combines federated learning (FL) and deep reinforcement learning (DRL) is suggested to address the optimization problem. According to the simulation results, the proposed algorithm displays fast convergence. Compared with baseline algorithms, this algorithm has significant advantages in optimizing the performance of energy consumption.

The allocation of communication and computational resources in cell-free mobile edge computing (CF-MEC) networks is investigated in this study. Under the constraints of maximum transmission delay, offloading rate of user equipment (UE), and power control factors, a CF network for urban scenarios is established and an optimization problem aimed at minimizing the weighted sum of delay and energy consumption is proposed. Due to the frequent signaling interactions between the access point (AP) and central processing unit (CPU) of CF networks, as well as the complexity and non-convexity of the proposed problem, an algorithm of scalable cell-free federated deep deterministic policy gradient (CFF-DDPG) has been proposed to solve the original problem. Firstly, dynamic clustering of all APs is implemented to enhance the scalability of the CF network. Secondly, a federated approach is adopted to adapt to the CF network, aiming to reduce the complexity of signaling interactions between APs and the CPU. Specifically, the deep deterministic policy gradient (DDPG) algorithm is utilized for local training at the UE, followed by federated averaging at the CPU of the CF network. Simulation results show that the proposed CFF-DDPG algorithm provides significant performance improvement over the three baseline algorithms, in addition to reducing the total cost by 78% compared to small-cell MEC, and can effectively enhance the scalability of traditional cell free networks.