Cloud-based vehicular networks is a new paradigm to improve the vehicular services through distributing computation tasks between remote clouds and local vehicular terminals To further reduce the latency and the transmission cost of the computation offloading, we propose a cloud-based Mobile Edge Computing (MEC) offloading framework in vehicular networks In the framework, efficient computation offloading strategies are designed through a contract theoretic approach We obtain the optimal feasible contracts that maximize the benefit of the MEC service provider while enhancing the utilities of the vehicles Furthermore, considering the resource limitation of the MEC server and the latency tolerance of the computation tasks, we propose a contract-based computation resource allocation scheme Numerical results show that our proposed scheme greatly enhances the utility of the MEC service provider

Delay constrained offloading for Mobile Edge Computing in cloud-enabled vehicular networks

Recently, to address the astonishing capacity requirement of 5G, researchers are investigating the possibility of combining different technologies with ultra-dense networks (UDNs). However, the ultra-dense deployment of small cells in the coverage area of conventional macrocells known as UDNs introduces new technical challenges such as severe interference, unfairness in radio resource sharing, unnecessary handover, a significant increase in energy consumption, and degraded quality-of-service (QoS). To overcome these challenges and achieve the performance requirements in 5G, there is a need to combine UDNs with other 5G enabling technologies and then, design intelligent management techniques for better performance of the overall networks. Hence, in this paper, we present a comprehensive survey on different generations of wireless networks, 5G new radio (NR) standards, 5G enabling technologies and the importance of combining UDNs with other 5G technologies. Also, we present an extensive overview of the recent advances and research challenges in intelligent management techniques and backhaul solutions in the last five years for the combination of UDNs and other enabling technologies that offers the visions of 5G. We summarise the mathematical tools widely exploited in solving these problems and the performance metrics used to evaluate the intelligent management algorithms. Moreover, we classify various intelligent management algorithms according to the adopted enabling technologies, benefits, challenges addressed, mathematical tools and performance metrics used. Finally, we summarise the open research challenges, provide design guidelines and potential research directions for the development of intelligent management techniques and backhaul solutions for the combination of UDNs and other 5G technologies.

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Combination of Ultra-Dense Networks and Other 5G Enabling Technologies: A Survey

To obtain better bandwidth and performance, the fifth generation (5G) cellular network is proposed to implement new-generation cellular mobile communications for new applications such as the internet of things, big data, smart city and so on. However, due to multiple/dense cellular network structures and high data rate, the 5G cellular network holds high inter-cell interference (ICI) and lower energy efficiency. The soft frequency reuse (SFR) is introduced to reduce the inter-cell interference in multiple cellular networks (such as 5G cellular networks) with the orthogonal frequency division multiplexing in base stations. Then, we investigate the energy-efficient resource allocation problem in the 5G cellular network with SFR. To coordinate the ICI among adjacent cells, we introduce the interference pricing factor into the utility function. The energy-efficient resource allocation problem is described as a Stackelberg game model. Because the sub-carrier assignment in the optimization process is an integer program which is very hard to be solved, we make a relaxation for the integer variable in the model and propose an iteration algorithm to obtain the Stackelberg game equilibrium solution. Simulation results show that the proposed method is feasible and promising.

Stackelberg game-based energy-efficient resource allocation for 5G cellular networks

Due to their environment friendliness, electric vehicles (EVs) are anticipated to form a considerable fraction of vehicles for transportation in smart cities. It is essential to design an electricity charging scheme that takes the utilities of both the charging stations and the EVs into consideration. However, the self-interested nature of the EVs together with the information asymmetry between the energy demand and supply sides makes the design a significant challenge. In this paper, we propose a queuing network-based model to characterize the charging process of the multiple EVs in a renewable energy-aided charging station. Based on the model, we adopt a contract theoretic approach to design an optimal charging policy in an information asymmetry scenario. Furthermore, we propose the new contract-based charging rate assignment and admission control schemes that maximize the utility of the charging station under certain charging constraints. To derive the optimal contract, we present a two-step iterative algorithm and prove its convergence. We evaluate the proposed schemes based on the IEEE 69-bus distribution test system. Results indicate that the contract-based charging schemes can effectively benefit both the charging stations and the EVs and concurrently improve the load level of the smart grid.

Optimal Charging Schemes for Electric Vehicles in Smart Grid: A Contract Theoretic Approach

In vehicular networks, Mobile Edge Computing (MEC) is applied to meet the offloading demand from vehicles. However, the mobility of vehicles may increase the offloading delay and even reduce the success rate of offloading, because vehicles may access another road side unit (RSU) before finishing offloading. Therefore, an offloading algorithm with low time complexity is required to make the offloading decision quickly. In this paper, we put forward an efficient offloading algorithm based on Support Vector Machine (SVMO) to satisfy the fast offloading demand in vehicular networks. The algorithm can segment a huge task into several sub-tasks through a weight allocation method according to available resources of MEC servers. Then each sub-task is decided whether it should be offloaded or executed locally based on SVMs. As the vehicle moves through several MEC servers, sub-tasks are allocated to them by order if they are offloaded. Each server ensures the sub-task can be processed and returned in time. Our proposed algorithm generate training data through Decision Tree. The simulation results show that the SVMO algorithm has a high decision accuracy, converges faster than other algorithms and has a small response time.

An Efficient Offloading Algorithm Based on Support Vector Machine for Mobile Edge Computing in Vehicular Networks

This paper focuses on resource allocation in heterogeneous Ultra Dense small-cell Networks (UDNs), in which massive overlaid small cells are under the coverage of a macro cell. In UDN, both co-tier and cross-tier interference need to be taken into account. When increasing the deployment density of Small-cell Base Stations (SBSs) and the unreasonable energy usage, it results in serious interference and degrades the user's satisfaction, which encourages us to agree on maximizing the total system Energy Efficiency (EE). This problem is non-convex and cannot be solved within polynomial time. Therefore, an efficient hierarchical Resource Allocation (RA) algorithm with reduced computational complexity is proposed to decompose the EE optimization problem into two sub-optimization problems: Sub-Channel Allocation (SCA) process and Power Allocation (PA) process. To solve these two processes, thereafter, we employ a uniform pricing scheme to reduce the feedback overhead and build the PA problem into a two-stage Stackelberg game, where the Macro-cell Base Station (MBS) acts as a follower and SBSs are leaders. The obtained simulation results show that the proposed hierarchical RA algorithm and the two-stage game can achieve a higher EE and a lower computation complexity.

Energy-efficient resource allocation strategy in ultra dense small-cell networks: A Stackelberg game approach

Vehicle-to-Grid (V2G) is a promising paradigm to alleviate energy supply and demand imbalance of the grid. To further improve the power transmission efficiency, in this paper, we propose a cloudlet-based Vehicle-to-Vehicle (V2V) energy exchange framework. In the framework, the Energy Switch Center (ESC) serves as a trading broker, which purchases electricity from discharging vehicles and then resells it to the charging ones without energy transmission on the grid. The energy trading process is modeled in a contract theoretic approach. We derive the optimal feasible contracts which maximize the profit of the ESC. Furthermore, we systematically study the practical scenario where both the charging demand and renewable energy supplement are random variables, and propose a practical optimal contract-based electricity purchase scheme. Simulation results show that the proposed scheme can efficiently increase the profit of the ESC than the other mechanisms.

Optimal energy exchange schemes in smart grid networks: A contract theoretic approach

In the next generation wireless communication networks, a large portion of operational expenditures (OPEX) has to be paid for energy consumption. Millions of base stations consume more energy in order to support increasing dramatically data traffic and diverse application services. Energy harvesting is becoming a promising technology to minimize the traditional power demand and thus reduce the OPEX. However, energy management and scheduling in conventional heterogeneous cellular network is greatly challenged. In this paper, we first introduce a hierarchical framework for the design and analysis of heterogeneous radio access network (H-C-RAN) with energy harvesting, which is composed of heterogeneous internet service provider (ISP) and remote radio heads (RRH) powered by energy harvesting facilities. The proposed hierarchical framework is feasible and tractable to utilize a market economical model which can work efficiently by utilizing energy sharing and energy trading among different ISPs. Moreover, we design user association scheme for D2D-assisted based on energy storage capacity and traffic load status of each small cell. Inspired by the fact that the energy harvesting rate, selection of user association and channel state vary in different time scale, we also propose a multiple time-scale energy scheduling strategy by modeling this issue using multitime scale Markov decision processes (MMDP). Simulation experiments indicate that the proposed strategies are able to significantly reduce the energy cost.

Multiple time-scale energy scheduling with energy harvesting aided heterogeneous cloud radio access networks

In the future wireless heterogeneous network, it is significantly critical to rationally and efficiently utilize network communication resource for satisfying the diversification and differentiation of user and service requirements. In this paper, we propose a user-centric wireless access and service matching scheme in heterogeneous networks with multiple radio access technologies (Multi-RATs). We model the interaction between user requirements and the quality of cloud services as the Markov decision process with Multi-RATs. The optimal matching scheme is derived by an iteration algorithm. To reduce time complexity, we further present a heuristic matching algorithm for determining whether the current stationary scheme is optimal in an off-line. Moreover, the difference between the current stationary scheme and the optimal matching scheme is derived by an upper bound. Performance evaluation demonstrates the achievable quality of experience by employing the two algorithms.

Joint multi-RATs and cloud-service matching scheme in wireless heterogeneous networks

This paper focuses on heterogeneous dense small-cell networks (Dense-SCNs), consisting of a macro cell base station (MBS) and multiple small cell base stations (SBSs). In the Dense-SCNs, the MBS is a centralized energy trading center and SBSs receive the required energy resource from the trading center. Due to the SBSs' randomly deployment, resulting in the diversity of energy acquisition price, we have an incentive to agree on maximizing the throughput-benefit per unit energy cost, which problem is proved to be non-convex and cannot be solved within polynomial time. Thus, we propose an algorithm to reduce the computation complexity by decomposing the optimization problem into clustering strategy and iterative resource allocation method. The obtain simulation results show that the proposed adaptive clustering strategy considering both interference and minimum data rate demand can alleviate the negative impact of the multiple strong interference SBSs and balance dynamic traffic load by the selection of appropriate weight factor. What is more, translating a non-convex optimization problem into a series of convex optimization problems and linear problems, the proposed iterative resource allocation method can achieve a better performance of the benefit and computation complexity.

Liang Xu

Papers

Energy-efficient resource allocation strategy in ultra dense small-cell networks: A Stackelberg game approach

Optimal energy exchange schemes in smart grid networks: A contract theoretic approach

Multiple time-scale energy scheduling with energy harvesting aided heterogeneous cloud radio access networks

Joint multi-RATs and cloud-service matching scheme in wireless heterogeneous networks

A Cluster-Based Resource Allocation Strategy with Energy Harvesting in Dense Small-Cell Networks