Lingxia Wang

Bio: Lingxia Wang is an academic researcher from Xidian University. The author has contributed to research in topics: Heterogeneous network & User experience design. The author has an hindex of 4, co-authored 12 publications receiving 40 citations.

Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework

Abstract: Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage of multiple radio access technologies (RATs), such as WiFi, UMTS, LTE, and WiMAX. However, the dense environment of multi-RATs challenges the network selection because of the more frequent and complex decision process along with increased complexity. Introducing artificial intelligence to ultra-dense heterogeneous networks can improve the way we address network selection today, and can execute efficient and intelligent network selection. Whereas, there still exist difficulties to be noted. On one hand, the contradiction between real-time communications and time-consuming learning is exacerbated, which can result in slow convergence. On the other hand, the black-box learning mode suffers from oscillation due to the diversity of multi-RATs, which can result in arbitrary convergence. In this paper, we propose a model-driven framework with a joint off-line and on-line way, which is able to achieve fast and optimal network selection through an alliance of machine learning and game theory. Further, we implement a distributed algorithm at the user side based on the proposed framework, which can reduce the number of frequent switching, increase the possibility of gainful switching, and provide the individual service. The simulation results confirm the performance of the algorithm in accelerating convergence rate, boosting user experience, and improving resource utilization.

Autonomous Traffic Offloading in Heterogeneous Ultra-Dense Networks Using Machine Learning

Abstract: The scarcity of network resources and the contention between resources and traffic volume have been the most critical network performance bottlenecks due to the booming growth of various applications in mobile Internet and Internet of Things. Consequently, effectively matching traffic with resources is of great importance and poses significant challenges. The 5G mobile communications networks will be heterogeneous, dense, and smart with various resources autonomously matching with the traffic demands. Although various traffic offloading schemes have been extensively investigated, applications in 5G present new characteristics such as interference-awareness on licensed or unlicensed bands, autonomous spectrum utilization, and delay-tolerant or delay sensitive traffic. In this article, we focus on HUDNs, which comprise dense small cells on the licensed band, WiFi AP on the unlicensed band, D2D communications, and V2V communications coexisting together to address the ever-increasing performance demands on both the user and network sides. We first summarize the recent research findings in this area and the technical challenges. We further present emerging traffic offloading frameworks and discuss the implementation issues including traffic offloading from virtualization, user-centric caching, and network selection in V2V communications. Furthermore, we propose an autonomous traffic offloading scheme based on big data and machine learning and also highlight future research directions.

User Oriented Resource Management With Virtualization: A Hierarchical Game Approach

Abstract: The explosive advancements in mobile Internet and Internet of Things challenge the network capacity and architecture. The ossification of wireless networks hinders the further evolution toward the fifth generation of mobile communication systems. Ultra-dense small cell networks are considered as a feasible way to meet high-capacity demands. Meanwhile, ultra-dense small cell network virtualization also exploits an insightful perspective for the evolution because of its superiority, such as diversity, flexibility, low cost, and scalability. In this paper, we specify the necessity of resource management in virtualized ultra-dense small cell networks through a mapping and management architecture and consider the problem of user-oriented virtual resource management. Then, we model the virtual resource management problem as a hierarchical game and obtain the closed-form solutions for spectrum, power, and price, respectively. Furthermore, we propose a customer-first (CF) algorithm that characterizes the user-oriented service of virtualization, and analyze its convergence. Simulation results present the effectiveness of the proposed CF algorithm.

Content distribution method based on D2D and service unloading

Abstract: The invention discloses a content distribution method based on D2D and service unloading, which is applied to a fifth-generation mobile communication system, and belongs to the field of wireless communication. The method comprises the following steps of (1) constructing a popular content set; (2) constructing a user preference vector; (3) constructing a user cluster preference vector; (4) randomlyselecting a user cluster; (5) constructing a trade-off model of an unloading rate and energy consumption; (6) distributing popular contents; (7) calculating remaining storage space size; (8) judgingwhether the remaining storage space size is larger than 0 or not; (9) selecting a to-be-distributed content cluster; (10) judging whether the content distribution of all user clusters is completed ornot; and (11) completing the content distribution of all users. Compared with the traditional content distribution method, the method has the advantages that the high service unloading rate of a basestation is guaranteed while the energy consumption is considered, and the user experience quality is improved.

A terminal autonomous network selection system and method for wireless heterogeneous network

Abstract: The invention belongs to the technical field of wireless communication and discloses a terminal autonomous network selection system and a method of wireless heterogeneous network. The terminal detectsand obtains service node information by using ANDSF to form a service node list of the terminal. The terminal obtains the RMI of each node in the service node list by using pilot signal measurement and carrier sensing. The terminal triggers the feature learner, inputs the measured RMI of each node to the feature learner, and outputs the measured RMI as an evaluation value of the link quality of each node. The terminal uses 802.11 u standard and ANDSF to obtain the load information of each node in the service node list. The terminal triggers the policy learner, inputs the obtained link qualityevaluation value and load information of each node to the policy learner, and outputs the obtained link quality evaluation value and load information to the policy learner to select an access node for the terminal. The terminal records the selected node and updates and records the network selection benefit. The invention overcomes the shortcomings of additional signaling overhead and delay causedby adopting server assistance or network assistance.

Issues, challenges, and research trends in spectrum management: A comprehensive overview and new vision for designing 6g networks

Abstract: With an extensive growth in user demand for high throughput, large capacity, and low latency, the ongoing deployment of Fifth-Generation (5G) systems is continuously exposing the inherent limitations of the system, as compared with its original premises. Such limitations are encouraging researchers worldwide to focus on next-generation 6G wireless systems, which are expected to address the constraints. To meet the above demands, future radio network architecture should be effectively designed to utilize its maximum radio spectrum capacity. It must simultaneously utilize various new techniques and technologies, such as Carrier Aggregation (CA), Cognitive Radio (CR), and small cell-based Heterogeneous Networks (HetNet), high-spectrum access (mmWave), and Massive Multiple-Input-Multiple-Output (M-MIMO), to achieve the desired results. However, the concurrent operations of these techniques in current 5G cellular networks create several spectrum management issues; thus, a comprehensive overview of these emerging technologies is presented in detail in this study. Then, the problems involved in the concurrent operations of various technologies for the spectrum management of the current 5G network are highlighted. The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches. Future research challenges are also discussed to highlight the necessary steps that can help achieve the desired objectives for designing 6G wireless networks.

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Abstract: Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework

Abstract: Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage of multiple radio access technologies (RATs), such as WiFi, UMTS, LTE, and WiMAX. However, the dense environment of multi-RATs challenges the network selection because of the more frequent and complex decision process along with increased complexity. Introducing artificial intelligence to ultra-dense heterogeneous networks can improve the way we address network selection today, and can execute efficient and intelligent network selection. Whereas, there still exist difficulties to be noted. On one hand, the contradiction between real-time communications and time-consuming learning is exacerbated, which can result in slow convergence. On the other hand, the black-box learning mode suffers from oscillation due to the diversity of multi-RATs, which can result in arbitrary convergence. In this paper, we propose a model-driven framework with a joint off-line and on-line way, which is able to achieve fast and optimal network selection through an alliance of machine learning and game theory. Further, we implement a distributed algorithm at the user side based on the proposed framework, which can reduce the number of frequent switching, increase the possibility of gainful switching, and provide the individual service. The simulation results confirm the performance of the algorithm in accelerating convergence rate, boosting user experience, and improving resource utilization.

On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis

Abstract: An expansion of services and unprecedented traffic growth is anticipated in future networks, aligned with the adoption of the long-awaited Fifth Generation (5G) of mobile communications To support this demand, without exposing mobile operators to the pressure of CAPEX and OPEX, 5G uses new frequency bands, and adopts promising trends, including: densification, softwarization, and autonomous management While the first technology is proposed to handle the traffic growth requirements, the softwarization and autonomous management are expected to play, in synergy, to ensure the desired trade-off between reducing the CAPEX and OPEX, while guaranteeing the quality of service (QoS) Softwarization is expected to transform the network design, from one size fits all, to more demand oriented adaptive resource allocation In this work, we focus on this point, by discussing how these technologies act in synergy towards enabling RAN sharing Particularly, we focus on how they fit into the issue of energy efficient Multi-Operator Resource Allocation (MO-RA) After a survey and classification of schemes leveraging this synergy for distinct resource allocation (RA) objectives, we present a detailed survey and qualitative classification of RA schemes with respect to energy efficiency This work presents an innovative survey, since it concentrates on multiple operators, and the enabling of Mobile Virtual Network Operators (MVNOs), which will come into play with the complete virtualization of mobile networks Based on the deep literature analysis of the different operations that can bring energy savings to MO-RA, we conclude the work with listing open challenges and future research directions