Chengchao Liang

Wireless Network Virtualization: A Survey, Some Research Issues and Challenges

Since wireless network virtualization enables abstraction and sharing of infrastructure and radio spectrum resources, the overall expenses of wireless network deployment and operation can be reduced significantly. Moreover, wireless network virtualization can provide easier migration to newer products or technologies by isolating part of the network. Despite the potential vision of wireless network virtualization, several significant research challenges remain to be addressed before widespread deployment of wireless network virtualization, including isolation, control signaling, resource discovery and allocation, mobility management, network management and operation, and security as well as non-technical issues such as governance regulations, etc. In this paper, we provide a brief survey on some of the works that have already been done to achieve wireless network virtualization, and discuss some research issues and challenges. We identify several important aspects of wireless network virtualization: overview, motivations, framework, performance metrics, enabling technologies, and challenges. Finally, we explore some broader perspectives in realizing wireless network virtualization.

Information-centric network function virtualization over 5g mobile wireless networks

Wireless network virtualization and information-centric networking (ICN) are two promising techniques in software-defined 5G mobile wireless networks. Traditionally, these two technologies have been addressed separately. In this paper we show that integrating wireless network virtualization with ICN techniques can significantly improve the end-to-end network performance. In particular, we propose an information- centric wireless network virtualization architecture for integrating wireless network virtualization with ICN. We develop the key components of this architecture: radio spectrum resource, wireless network infrastructure, virtual resources (including content-level slicing, network-level slicing, and flow-level slicing), and informationcentric wireless virtualization controller. Then we formulate the virtual resource allocation and in-network caching strategy as an optimization problem, considering the gain of not only virtualization but also in-network caching in our proposed information-centric wireless network virtualization architecture. The obtained simulation results show that our proposed information-centric wireless network virtualization architecture and the related schemes significantly outperform the other existing schemes.

Wireless virtualization for next generation mobile cellular networks

With wireless virtualization, the overall expenses of mobile cellular network deployment and operation can be significantly reduced by abstracting and sharing infrastructure and radio spectrum resources. Moreover, wireless virtualization can provide easier migration to newer products or technologies by isolating part of the network. Despite the potential vision of wireless virtualization, several significant research challenges remain. In this article, we provide a brief survey on some of the work that has been done on wireless virtualization in cellular networks. We also present the motivations and business models of wireless virtualization. Furthermore, we present a framework that enables wireless virtualization. In addition, we discuss a number of challenges that need to be addressed for the deployment of wireless virtualization in next generation mobile cellular networks.

Software-Defined Device-to-Device (D2D) Communications in Virtual Wireless Networks With Imperfect Network State Information (NSI)

Software-defined networking (SDN) and network function virtualization (NFV) are a promising system architecture and control mechanism for future networks. Although some works have been done on wireless SDN and NFV, recent advancements in device-to-device (D2D) communications are largely ignored in this novel framework. In this paper, we study the integration of D2D communication in the framework of SDN and NFV. An inherent challenge in supporting software-defined D2D is the imperfectness of network state information, including channel state information (CSI) and queuing state information, in virtual wireless (QSI) networks. To address this challenge, we formulate the resource sharing problem in this framework as a discrete stochastic optimization problem and develop discrete stochastic approximation algorithms to solve this problem. Such algorithms can reduce the computational complexity compared with exhaustive search while achieving satisfactory performance. Both the static wireless channel and time-varying channels are considered. Extensive simulations show that users can benefit from both wireless network virtualization and software-defined D2D communications, and our proposed scheme can achieve considerable performance gains in both system throughput and user utility under practical network settings.

Virtual Resource Allocation in Information-Centric Wireless Networks with Virtualization

Wireless network virtualization and information-centric networking (ICN) are two promising technologies in next-generation wireless networks. Traditionally, these two technologies have been addressed separately. In this paper, we show that jointly considering wireless network virtualization and ICN is necessary. Specifically, we propose an information-centric wireless network virtualization framework for enabling wireless network virtualization and ICN. Then, we formulate the virtual resource allocation and in-network caching strategy as an optimization problem, considering not only the revenue earned by serving the end users but the cost-of-leasing infrastructure as well. In addition, with recent advances in distributed convex optimization, we develop an efficient alternating direction method of multipliers (ADMM)-based distributed virtual resource allocation and in-network caching scheme. Simulation results are presented to show the effectiveness of the proposed scheme.

Distributed resource allocation in virtualized wireless cellular networks based on ADMM

In next generation wireless mobile networks, network virtualization will become an important key technology. In this paper, we firstly propose a resource allocation scheme for enabling efficient resource allocation in wireless network virtualization. Then, we formulate the resource allocation strategy as an optimization problem, considering not only the revenue earned by serving end users of virtual networks, but also the cost of leasing infrastructure from infrastructure providers. In addition, we develop an efficient alternating direction method of multipliers (ADMM)-based distributed virtual resource allocation algorithm in virtualized wireless networks. Simulation results are presented to show the effectiveness of the proposed scheme.

Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing

Mobile edge computing has risen as a promising technology for augmenting the computational capabilities of mobile devices. Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic. The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively. In order to jointly tackle these issues in wireless cellular networks with mobile edge computing, we formulate the computation offloading decision, resource allocation and content caching strategy as an optimization problem, considering the total revenue of the network. Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way. Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers-based algorithm to solve the optimization problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters.

Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing

Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with MEC. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using the graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

Optimization of cache-enabled opportunistic interference alignment wireless networks: A big data deep reinforcement learning approach

Both caching and interference alignment (IA) are promising techniques for future wireless networks. Nevertheless, most of existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, we propose a novel big data reinforcement learning approach in this paper. Deep reinforcement learning is an advanced reinforcement learning algorithm that uses deep Q network to approximate the Q value-action function. Deep reinforcement learning is used in this paper to obtain the optimal lA user selection policy in cache-enabled opportunistic lA wireless networks. Simulation results are presented to show the effectiveness of the proposed scheme.

Virtual resource allocation in information-centric wireless virtual networks

Wireless network virtualization and information-centric networking (ICN) are two promising technologies in next generation wireless networks. Traditionally, these two technologies have been addressed separately. In this paper, we show that jointly considering wireless network virtualization and ICN is necessary. Specifically, we propose an information-centric wireless network virtualization framework for enabling wireless network virtualization and ICN. Then, we formulate the virtual resource allocation and in-network caching strategy as an optimization problem, considering not only the revenue earned by serving end users but also the cost of leasing infrastructure. In addition, with recent advances in distributed convex optimization, we develop an efficient distributed method based on alternating direction method of multipliers (ADMM)-based to solve virtual resource allocation and in-network caching scheme. Simulation results are presented to show the effectiveness of the proposed scheme.