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Xiaobin Xu

Bio: Xiaobin Xu is an academic researcher from Beijing University of Technology. The author has contributed to research in topics: Resource allocation & Resource management. The author has an hindex of 5, co-authored 10 publications receiving 111 citations.

Papers
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Journal ArticleDOI
TL;DR: A two-stage joint optimization algorithm is conceived for solving both the optimal UAV deployment and the cyclic UAV recharging and reshuffling strategy, and the results quantify the efficiency of the proposed algorithm.
Abstract: Unmanned aerial vehicles (UAVs) may be used for providing seamless network coverage in urban areas for improving the performance of conventional cellular networks. Given the predominantly line-of-sight channel of drones, UAV-aided seamless coverage becomes particularly beneficial in case of emergency situations. However, a single UAV having a limited cruising capability is unable to provide seamless long-term coverage, multiple drones relying on sophisticated recharging and reshuffling schemes are necessary. In this context, both the positioning and the flight strategy directly affect the efficiency of the system. Hence, we first introduce a novel UAV energy consumption model, based on which an energy-efficiency-based objective function is derived. Second, we propose an energy-efficient rechargeable UAV deployment strategy optimized under a seamless coverage constraint. Explicitly, a two-stage joint optimization algorithm is conceived for solving both the optimal UAV deployment and the cyclic UAV recharging and reshuffling strategy. Our simulation results quantify the efficiency of our proposed algorithm.

116 citations

Journal ArticleDOI
TL;DR: NetworkAI is presented, an intelligent architecture for self-learning control strategies in software defined networking networks that employs deep reinforcement learning and incorporates network monitoring technologies, such as the in-band network telemetry, to dynamically generate control policies and produces a near optimal decision.
Abstract: The past few years have witnessed a wide deployment of software defined networks facilitating a separation of the control plane from the forwarding plane. However, the work on the control plane largely relies on a manual process in configuring forwarding strategies. To address this issue, this paper presents NetworkAI, an intelligent architecture for self-learning control strategies in software defined networking networks. NetworkAI employs deep reinforcement learning and incorporates network monitoring technologies, such as the in-band network telemetry to dynamically generate control policies and produces a near optimal decision. Simulation results demonstrated the effectiveness of NetworkAI.

64 citations

Journal ArticleDOI
TL;DR: In this paper, a data collection system considering security and energy efficiency is proposed for UAV-assisted IoT, where the UAVs use charging coins to exchange charging time to reduce energy consumption.
Abstract: With the rapid development of Internet of Things (IoT), more and more applications focus on the detection of unmanned areas. With the assistance of unmanned aerial vehicle (UAV), IoT devices are able to access the network via aerial base stations. These UAV-assisted IoT applications still face security and energy challenges. The open environment of IoT applications makes the application easy to encounter external invasion. Limited energy of UAV results in the limited lifetime of network access. To address these challenges, researches on IoT security and energy efficiency are becoming hotspots. Nevertheless, in the UAV continuous coverage scenario, there is still an enormous potential to improve the security and efficiency of data collection in IoT applications. In this article, blockchain is introduced into the scene of UAV-assisted IoT, and a data collection system considering security and energy efficiency is proposed. In this system, UAV, as an edge data collection node, provides a long-term network access for IoT devices through regular cruises with recharging. By forwarding data and recording transactions, UAVs get charging coins as rewards. UAVs use charging coins to exchange charging time. UAV swarm builds distributed ledgers based on blockchain to resist the invasion of malicious UAV. In order to reduce energy consumption, this article designs an adaptive linear prediction algorithm. Through this algorithm, IoT devices upload prediction model instead of original data to greatly reduce in-network transmissions. Simulation results show that the proposed system can effectively improve the security and efficiency of data collection.

57 citations

Proceedings ArticleDOI
24 Jun 2019
TL;DR: Inspired by the recent success of applying machine learning in many challenging control decision domains, such as video game, self-driving, deep deterministic policy gradient is employed for learning the optimal congestion control strategies by interacting with the underlying network environment.
Abstract: The past few years have witnessed a wide deployment of low earth orbit (LEO) satellites communications and networking. With the explosive growth of new businesses, satellite network is expected to provide global coverage and high bandwidth availability service. Toward this end, Multipath TCP(MPTCP) is a promising transport protocol to use in LEO satellites networks. MPTCP can not only achieve seamless handover, but also enhance throughput by using multiple paths transmission mechanism. However, following the improvement of the performance and scalability, it also brings unprecedented challenges for congestion control of multiple sub-flows. Especially, currently works on the congestion control largely relies on a manual process which presents a poor performance in the high-dynamic complexity network environment. Inspired by the recent success of applying machine learning in many challenging control decision domains, such as video game, self-driving, we employ deep deterministic policy gradient for learning the optimal congestion control strategies by interacting with the underlying network environment. Some simulation results demonstrated the effectiveness and feasibility of our architecture and algorithms.

14 citations

Journal ArticleDOI
TL;DR: An energy-aware multi-controller placement scheme as well as a latency-aware resource management model for the SDWN and experimental results show that the proposed schemes are conducive to reducing both the execution time and the energy consumption of each task.
Abstract: Given decoupling the control layer and the infrastructure layer, the software-defined wireless networks (SDWNs) is beneficial in terms of providing both low-latency and low-energy consumption services for mobile users, where multi-controller placement and resource management become a pair of bottlenecks. In this letter, we propose an energy-aware multi-controller placement scheme as well as a latency-aware resource management model for the SDWN. Moreover, the particle swarm optimization is invoked for solving the multi-controller placement problem, and a deep reinforcement learning algorithm-aided resource allocation strategy is conceived. Finally, experimental results show that our proposed schemes are conducive to reducing both the execution time and the energy consumption of each task.

13 citations


Cited by
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Journal ArticleDOI
TL;DR: A multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities.
Abstract: Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.

200 citations

Journal ArticleDOI
TL;DR: A UAV-enabled IoE (Ue-IoE) solution is introduced by exploiting UAVs’s mobility, in which it is shown that Ue-ioE can greatly enhance the scalability, intelligence and diversity of IoE.

134 citations

Journal ArticleDOI
26 Feb 2019
TL;DR: This paper identifies the opportunities and challenges of adaptable softwarized networks and introduces a conceptual framework for adaptations in softwarization networks, and proposes to enhance the functional primitives observation, composition, and control with data-driven decision making, e.g., machine learning modules, resulting in deep observation and control.
Abstract: Communication networks are the key enabling technology for our digital society. In order to sustain their critical services in the future, communication networks need to flexibly accommodate new requirements and changing contexts due to emerging diverse applications. In contrast to traditional networking technologies, software-oriented networking concepts, such as software-defined networking (SDN) and network function virtualization (NFV), provide ample opportunities for highly flexible network operations, enabling fast and simple adaptation of network resources and flows. This paper identifies the opportunities and challenges of adaptable softwarized networks and introduces a conceptual framework for adaptations in softwarized networks. We first explain how softwarized networks contribute to network adaptability through the functional primitives observation, composition, and control. We review the wide range of options for fine-granular observations as well as fine-granular composition and control provided by SDN and NFV. The multitude of fine-granular “tuning knobs” in adaptable softwarized networks complicates the decision making, which is the main focus of this paper. We propose to enhance the functional primitives observation, composition, and control with data-driven decision making, e.g., machine learning modules, resulting in deep observation, composition, and control. The data-driven decision making modules can learn and react to changes in the environment, e.g., new flow demands, so as to support meaningful decision making for adaptation in softwarized networks. Finally, we make the case for employing the concept of empowerment to realize truly “self-driving” networks.

88 citations

Journal ArticleDOI
TL;DR: A comprehensive survey on green UAV communications for 6G is carried out, and the typical UAVs and their energy consumption models are introduced, and several promising techniques and open research issues are pointed out.

83 citations

Journal ArticleDOI
TL;DR: This paper investigates the unmanned aerial vehicle (UAV)-aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP) and the other UAV acts as a base station (BS) collects data from numerous sensor nodes (SNs).
Abstract: In this paper, we investigate the unmanned aerial vehicle (UAV)-aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem’s hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity.

79 citations