With the unprecedented development of smart mobile devices, e.g., Internet of Things devices and smart phones, various computation-intensive applications are explosively increasing in ultra-dense networks (UDNs). Mobile edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of smart mobile devices and decrease service latency for computation-intensive applications. With the benefits of network function virtualization, MEC can be integrated with cloud radio access network (C-RAN) in UDNs for computation and communication cooperation. However, with stochastic computation task arrivals and time-varying channel states, it is challenging to offload computation tasks online with energy-efficient computation and radio resource management. In this paper, we investigate the task offloading and resource allocation problem in MEC-enable dense C-RAN, aiming at optimizing network energy efficiency. A stochastic mixed-integer nonlinear programming problem is formulated to jointly optimize the task offloading decision, elastic computation resource scheduling, and radio resource allocation. To tackle the problem, Lyapunov optimization theory is introduced to decompose the original problem into four individual subproblems which are solved by convex decomposition methods and matching game. We theoretically analyze the tradeoff between energy efficiency and service delay. Extensive simulations evaluate the impacts of system parameters on both energy efficiency and service delay. Simulation results also validate the superiority of the proposed task offloading and resource allocation scheme in dense C-RAN.

Technological evolutions in the automobile industry, especially the development of connected and autonomous vehicles, have granted vehicles more computing, storage, and sensing resources. The necessity of efficient utilization of these resources leads to the vision of vehicular cloud computing (VCC), which can offload the computing tasks from the edge or remote cloud to enhance the overall efficiency. In this paper, we study the problem of computation offloading through the vehicular cloud (VC), where computing missions from edge cloud (EC) can be offloaded and executed cooperatively by vehicles in VC. Specifically, computing missions are further divided into computing tasks with interdependency and executed in different vehicles in the VC to minimize the overall response time. To characterize the instability of computing resources resulting from the high vehicular mobility, a mobility model focusing on vehicular dwell time is utilized. Considering the heterogeneity of vehicular computing capabilities and the inter-dependency of computing tasks, we formulate an optimization problem for task scheduling, which is NP-hard. For low complexity, a modified genetic algorithm (MGA) based scheduling scheme is designed where integer coding is used rather than binary coding, and relatives are defined and employed to avoid infeasible solutions. In addition, a task load based stability analysis of the VCC system is presented for the cases where some vehicles within the VC are offline. Numerical results demonstrate that the proposed scheme can significantly improve the utilization of computing resources while guaranteeing low latency and system stability.

This paper presents a comprehensive literature review on applications of economic and pricing theory for resource management in the evolving fifth generation (5G) wireless networks. The 5G wireless networks are envisioned to overcome existing limitations of cellular networks in terms of data rate, capacity, latency, energy efficiency, spectrum efficiency, coverage, reliability, and cost per information transfer. To achieve the goals, the 5G systems will adopt emerging technologies such as massive multiple-input multiple-output, mmWave communications, and dense heterogeneous networks. However, 5G involves multiple entities and stakeholders that may have different objectives, e.g., high data rate, low latency, utility maximization, and revenue/profit maximization. This poses a number of challenges to resource management designs of 5G. While the traditional solutions may neither efficient nor applicable, economic and pricing models have been recently developed and adopted as useful tools to achieve the objectives. In this paper, we review economic and pricing approaches proposed to address resource management issues in the 5G wireless networks including user association, spectrum allocation, and interference and power management. Furthermore, we present applications of economic and pricing models for wireless caching and mobile data offloading. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to the 5G wireless networks.

With the advantages of high mobility and flexible deployment, Unmanned Aerial Vehicle (UAV) combines with Mobile Edge Computing (MEC) is a promising technology. When dynamic Terminal Users (TUs) offload tasks to UAVs, eavesdroppers may eavesdrop on the channel information. The offloading decisions, trajectory plannings of UAVs and resource allocation with the objective of high-capacity secure communication is a challenging problem. In this paper, we design a multi-UAVs MEC system, where the original region is divided into several sub-regions and TUs offload tasks to UAVs which provide computing services for these TUs. Meanwhile, A joint optimization problem of offloading decision, resource allocation and trajectory planning is formulated, where TUs move with the Gauss-Markov random model. In addition, the Base Station (BS) emits jamming signals to evade the eavesdropping of offloading information from eavesdroppers. The goal of the optimization problem is to maximize the TUs’ minimum secure calculation capacity, and a Joint Dynamic Programming and Bidding (JDPB) algorithm is proposed to solve it. The Successive Convex Approximation (SCA) and Block Coordinate Descent (BCD) algorithms are used to handle the resource allocation and trajectory planning problems, and the bidding method is used to address the task offloading decision problem. Simulation results show that JDPB has better performance and better robustness under different parameter settings than other schemes.

As an important application scenario of the Industrial Internet of Things (IIoT), the Internet of Vehicles can significantly improve road safety, improve traffic management efficiency and improve people's travel experience. Due to the high dynamics of the Internet of Vehicles environment, traditional resource optimization technologies cannot meet the requirements of Internet of Vehicles for dynamic communication, computing and storage resources optimization management, and artificial intelligence algorithms can adaptively obtain dynamic resource allocation schemes through self-learning. Therefore, adopting artificial intelligence techniques to optimize dynamic resource of Internet of Vehicles is the research focus of this paper. This paper first models the Internet of Vehicles resource allocation problem as a Semi-Markov Decision Process which introduces a resource reservation strategy and a secondary resource allocation mechanism. Then the reinforcement learning algorithm is used to solve the model. Thereafter, it theoretically analyzes the joint optimization of computing and communication resources, models it as a hierarchical architecture, and uses Hierarchical Reinforcement Learning (HRL) to obtain the optimal system resource allocation plan. Finally, the results of simulation experiments show that the dynamic resource allocation scheme of Internet of Vehicles based on reinforcement learning in this paper greatly improve resource utilization and user Quality of Experience (QoE) with guaranteeing system Quality of Service (QoS) compared with the traditional greedy algorithm.

Vehicular ad hoc network (VANET) plays a promising role to support diverse intelligent transportation system (ITS) applications in 5G networks, namely 5G-VANET. There are three types of communication modes in 5G-VANET: cellular mode, reuse mode and dedicated mode, i.e., vehicle users (VUEs) communicates with each other using the cellular network spectrum directly, in an underlay sharing way, and utilizing the allocated dedicated spectrum, respectively. However, how to dynamically share the multi-mode spectrum to optimize the network performance (i.e., network throughput) in 5G-VANET is a challenging task due to the high dynamic VANET environment and network resource heterogeneity. In this paper, we propose a dynamic Stackelberg pricing game enabled multi-mode spectrum sharing solution in 5G-VANET. In specific, we develop an access price strategy for different spectrum sharing modes considering the cellular BS's revenue and whole network throughput, while VUEs can select communication modes in a distributed way and dynamically change the selections through an evolutionary game. Through testing different traffic scenarios generated by SUMO, we demonstrate the effectiveness of the proposed algorithm. Specifically, the proposed algorithm can improve the total transmission rate of VANET by at least 20% compared with the random selection method.