Aaltodoc Repository :: Browsing by Author "Yang, Bin"

Browsing by Author "Yang, Bin"

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Ahead-Me Coverage (AMC): On Maintaining Enhanced Mobile Network Coverage for UAVs
(2023-01-11) Hellaoui, Hamed; Yang, Bin; Taleb, Tarik; Manner, Jukka; Department of Information and Communications Engineering; Internet technologies; Chuzhou University; University of Oulu
This paper proposes the concept of Ahead-Me Cov-erage (AMC) aiming to get the coverage of a cellular network ahead of the mobile users for maintaining enhanced Quality- of-Service (QoS) in cellular-connected unmanned aerial vehicle (UAV) networks. In such networks, each base station (BS) with an intelligent logic can automatically tilt the direction of its radio antennas based on the trajectory of UAV s. For this purpose, we first formulate AMC as an integer optimization problem for maximizing the minimum transmission rate of UAVs by jointly optimizing the angles of the different radio antenna, the resource allocation and the selection of the appropriate serving BS for the UAVs throughout their path. For this complex optimization problem, we then propose a solution based on Deep Reinforcement Learning (DRL) to solve it. Under this solution, we adopt a multi-heterogeneous agent-based approach (MHA-DRL) including two types of agents, namely the UAV agents and the BS agents. Each agent implements an Advantage Actor Critic (A2C) to learn optimal policies. Specifically, the BS agents aim to tilt their antennas to get ahead of the UAV s throughout their mobility, and the UAV agents target selecting the appropriate serving BSs along with resource allocation. Performance evaluations are presented to validate the effectiveness of the proposed approach.
Analyses of Impact of Needle Surface Properties on Estimation of Needle Absorption Spectrum: Case Study with Coniferous Needle and Shoot Samples
(2016-07) Yang, Bin; Knyazikhin, Yuri; Lin, Yi; Yan, Kai; Chen, Chi; Park, Taejin; Choi, Sungho; Mottus, Matti; Rautiainen, Miina; Myneni, Ranga B.; Yan, Lei; Department of Built Environment; Department of Radio Science and Engineering; Geoinformatics; Boston University; Peking University; Beijing Normal University; University of Helsinki
Leaf scattering spectrum is the key optical variable that conveys information about leaf absorbing constituents from remote sensing. It cannot be directly measured from space because the radiation scattered from leaves is affected by the 3D canopy structure. In addition, some radiation is specularly reflected at the surface of leaves. This portion of reflected radiation is partly polarized, does not interact with pigments inside the leaf and therefore contains no information about its interior. Very little empirical data are available on the spectral and angular scattering properties of leaf surfaces. Whereas canopy-structure effects are well understood, the impact of the leaf surface reflectance on estimation of leaf absorption spectra remains uncertain. This paper presents empirical and theoretical analyses of angular, spectral, and polarimetric measurements of light reflected by needles and shoots of Pinus koraiensis and Picea koraiensis species. Our results suggest that ignoring the leaf surface reflected radiation can result in an inaccurate estimation of the leaf absorption spectrum. Polarization measurements may be useful to account for leaf surface effects because radiation reflected from the leaf surface is partly polarized, whereas that from the leaf interior is not.
Deep Ensemble Learning based GPS Spoofing Detection for Cellular-Connected UAVs
(IEEE, 2022-12-15) Dang, Yongchao; Benzaid, Chafika; Yang, Bin; Taleb, Tarik; Shen, Yulong; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Xidian University; University of Oulu; Chuzhou University
Unmanned Aerial Vehicles (UAVs) are an emerging technology in the 5G and beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the open-source simulator can use commercial software-defined radio tools to generate fake GPS signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations at the same time. In this paper, we propose a novel deep ensemble learning-based, mobile network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between base stations and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multi-layer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.
Mode Selection and Cooperative Jamming for Covert Communication in D2D Underlaid UAV Networks
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-03) Yang, Bin; Taleb, Tarik; Fan, Yuanyuan; Shen, Shikai; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Chuzhou University; Kunming University of Science and Technology
The integration of unmanned aerial vehicle (UAV) networks and device-to-device (D2D) communications is expected to provide ubiquitous connectivity and high-speed rates for sensitive information transmission in future wireless networks. However, the traditional cryptography and physical layer security techniques still cannot prevent adversaries from knowing the existence of information transmission such that they further launch attacks on transmitters and receivers. Covert communication can offer an even stronger level of security via hiding the information transmission process of wireless networks. In this article, we first integrate D2D communications into UAV networks, and then investigate the fundamental issues of mode selection and cooperative jamming for covert communication in such networks, aiming to provide a powerful security solution to support widespread securi-ty-sensitive applications of such networks. To this end, we propose two promising D2D underlaid UAV network architectures, whereby each UAV acts as either a flying BS or an aerial UE. Then, we propose a covert communication strategy by combining mode selection and cooperative jamming, where mode selection allows each user equipment to adaptively switch between half-du-plex and full-duplex communication modes, and cooperative jamming means that idle D2D pairs inject interference to confuse adversaries. The goal of the proposed strategy is to enhance covert capacity performance (i.e., the maximum channel rate) while maintaining a high detection error probability at adversaries in the promising network architectures. Numerical results are presented to evaluate our strategy of mode selection and cooperative jamming, and to illustrate performance gains in terms of covert capacity and detection error probability in these two network architectures. Finally, a vision is discussed for our future research in D2D underlaid UAV networks.
On Supporting Multiservices in UAV-Enabled Aerial Communication for Internet of Things
(IEEE, 2023-08-01) Hellaoui, Hamed; Bagaa, Miloud; Chelli, Ali; Taleb, Tarik; Yang, Bin; Department of Communications and Networking; Department of Information and Communications Engineering; Mobile Network Softwarization and Service Customization; University of South-Eastern Norway; Chuzhou University; University of Oulu
Multiservices are of fundamental importance in unmanned aerial vehicle (UAV)-enabled aerial communications for the Internet of Things (IoT). However, the multiservices are challenging in terms of requirements and use of shared resources such that the traditional solutions for a single service are unsuitable for the multiservices. In this article, we consider a UAV-enabled aerial access network for ground IoT devices, each of which requires two types of services, namely, ultrareliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB), measured by transmission delay and effective rate, respectively. We first consider a communication model that accounts for most of the propagation phenomena experienced by wireless signals. Then, we derive the expressions of the effective rate and the transmission delay, and formulate each service type as an optimization problem with the constraints of resource allocation and UAV deployment to enable multiservice support for the IoT. These two optimization problems are nonlinear and nonconvex and are generally difficult to be solved. To this end, we transform them into linear optimization problems, and propose two iterative algorithms to solve them. Based on them, we further propose a linear program algorithm to jointly optimize the two service types, which achieves a tradeoff of the effective rate and the transmission delay. Extensive performance evaluations have been conducted to demonstrate the effectiveness of the proposed approach in reaching a tradeoff optimization that enhances the two services.
Performance, Fairness and Tradeoff in UAV Swarm Underlaid mmWave Cellular Networks with Directional Antennas
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-04) Yang, Bin; Taleb, Tarik; Shen, Yulong; Jiang, Xiaohong; Yang, Weidong; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Xidian University; Future University Hakodate; Henan University of Technology
Unmanned aerial vehicle (UAV) swarm connected to millimeter wave (mmWave) cellular networks is emerging as a new promising solution to provide ubiquitous high-speed and long distance wireless communication services for supporting various applications. To satisfy different quality of service (QoS) requirements in future large-scale applications of such networks, this article investigates the rate performance, fairness and their tradeoff in the networks with directional antennas in terms of sum-rate maximization, fairness index maximization, max-min fair rate and proportional fairness. We first consider a more realistic mmWave 3D directional antenna array model for UAVs and base station (BS), where the antenna gain depends on the radiation angle of the antenna array. Based on this antenna array model, we formulate the performance, fairness and their tradeoff as four constrained optimization problems, and propose corresponding iterative algorithm to solve these problems by jointly optimizing elevation angle, azimuth angle and height of antenna array at BS in the downlink transmission scenario. Furthermore, we also explore them in uplink transmission scenario, where the interference issue among links is carefully considered. Finally, according to the sum rate, minimum rate and fairness index under each optimization problem, numerical results are provided to illustrate the impacts of network parameters on the performance, fairness and their tradeoff, and also to reveal new findings under both downlink and uplink transmission scenarios, respectively.
Seamless Replacement of UAV-BSs Providing Connectivity to the IoT
(2023-01-11) Hellaoui, Hamed; Yang, Bin; Taleb, Tarik; Manner, Jukka; Department of Communications and Networking; Internet technologies; Chuzhou University; University of Oulu
This paper considers the scenario of Unmanned Aerial Vehicles (UAVs) acting as flying base stations (UAV-BSs) to provide network connectivity to ground Internet of Things (IoT) devices. More precisely, we investigate the issue where a UAV-BS needs to be replaced by a new one in a seamless way. First, we formulate the issue as an optimization problem aiming to maximize the minimum transmission rate of the served IoT devices during the UAV-BS replacement process. This is translated into jointly optimizing the trajectory of the source UAV-BS (the one to be replaced) and the target UAV-BS (the replacing one), while pushing the IoT devices to seamlessly transfer their connections to the target UAV-BS. We therefore consider a target replacement zone where the UAV-BS replacement can happen, along with IoT connections transfer. Furthermore, we propose a solution based on Deep Reinforcement Learning (DRL). More precisely, we introduce a Multi-Heterogeneous Agent-based approach (MHA-DRL), where two types of agents are considered, namely the UAV-BS agents and the IoT agents. Each agent implements a DQN (Deep Q-Learning) algorithm, where UAV-BS agents learn optimal policies to perform replacement while IoT agents learn optimal policies to transfer their connections to the target UAV-BS. The conducted performance evaluations show that the proposed approach can achieve near optimal optimization.
Spectrum Sharing for Secrecy Performance Enhancement in D2D-Enabled UAV Networks
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020-11-01) Yang, Bin; Taleb, Tarik; Wu, Zhenqiang; Ma, Lisheng; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Shaanxi Normal University; Chuzhou University
With the assistance of device-to-device (D2D) communications, unmanned aerial vehicle (UAV) networks are anticipated to support widespread applications in fifth generation (5G) and beyond wireless systems, by providing seamless coverage, flexible deployment, and high channel rate. However, the networks face significant security threats from malicious eavesdroppers due to the inherent broadcast and openness nature of wireless channels. To ensure secure communications of such networks, physical layer security is a promising technique, which utilizes the randomness and noise of wireless channels to enhance secrecy performance. This article investigates physical layer security performance via spectrum sharing in D2D-enabled UAV networks. We first present two typical network architectures where each UAV serves as either a flying base station or an aerial user equipment. Then, we propose a spectrum sharing strategy to fully exploit interference incurred by spectrum reuse for improving secrecy performance. We further conduct two case studies to evaluate the spectrum sharing strategy in these two typical network architectures, and also show secrecy performance gains compared to traditional spectrum sharing strategy. Finally, we discuss some future research directions in D2D-enabled UAV networks.
Sum Rate and Max-Min Rate for Cellular-Enabled UAV Swarm Networks
(IEEE, 2023-01) Yang, Bin; Dang, Yongchao; Taleb, Tarik; Shen, Shikai; Jiang, Xiaohong; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Kunming University of Science and Technology; Future University Hakodate; Chuzhou University; University of Oulu
This paper investigates the fundamental rate performances in the highly promising cellular-enabled unmanned aerial vehicle (UAV) swarm networks, which can provide ubiquitous wireless connectivity for supporting various Internet of things (IoT) applications. We first provide the formulations for the sum rate maximization and max-min rate, which are two nonlinear optimization problems subject to the constraints of UAV transmit power and antenna parameters at base station (BS). For the sum rate maximization problem, we propose an iterative algorithm to solve it utilizing the Karush-Kuhn-Tucker (KKT) condition. For the max-min rate problem, we transform it to an equivalent conditional eigenvalue problem based on the nonlinear Perron-Frobenius theory, and thus design an iterative algorithm to obtain the solution of such problem. Finally, numerical results are presented to indicate the effect of some key parameters on the rate performances in such networks.
Towards using Deep Reinforcement Learning for Connection Steering in Cellular UAVs
(2021) Hellaoui, Hamed; Yang, Bin; Taleb, Tarik; Department of Communications and Networking; Department of Bioproducts and Biosystems; Mobile Network Softwarization and Service Customization
This paper investigates the fundamental connection steering issue in cellular-enabled Unmanned Aerial Vehicles (UAVs), whereby a UAV steers the cellular connection across multiple Mobile Network Operators (MNOs) for ensuring enhanced Quality-of-Service (QoS). We first formulate the issue as an optimization problem for minimizing the maximum outage probability. This is a nonlinear and nonconvex problem that is generally difficult to be solved. To this end, we propose a new approach for solving the optimization problem based on Deep Reinforcement Learning (DRL), considering two important reinforcement learning algorithms (i.e., Deep Q-Learning (DQN) and Advantage Actor Critic (A2C)). Simulation results show that under the proposed approach, the UAVs can make optimal decisions to select the most suitable connection with MNOs for achieving the minimization of the maximum outage probability. Furthermore, the results also show that in our new approach, the A2C-based algorithm is better than the DQN-based one, especially when the number of MNOs increases, while the DQN-based algorithm can be executed in a shorter time.
Traffic Flow Modeling for UAV-Enabled Wireless Networks
(2020) Abada, Abderrahmane; Yang, Bin; Taleb, Tarik; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Department of Communications and Networking
This paper investigates traffic flow modeling issue in multi-services oriented unmanned aerial vehicle (UAV)-enabled wireless networks, which is critical for supporting future various applications of such networks. We propose a general traffic flow model for multi-services oriented UAV-enable wireless networks. Under this model, we first classify the network services into three subsets: telemetry, Internet of Things (IoT), and streaming data. Based on the Pareto distribution, we then partition all UAVs into three subgroups with different network usage. We further determine the number of packets for different network services and total data size according to the packet arrival rate for the nine segments, each of which represents one map relationship between a subset of services and a subgroup of UAVs. Simulation results are provided to illustrate that the number of packets and the data size predicted by our traffic model can well match with these under a real scenario.
Traffic Steering for Cellular-Enabled UAVs: A Federated Deep Reinforcement Learning Approach
(2023) Hellaoui, Hamed; Yang, Bin; Taleb, Tarik; Manner, Jukka; Department of Communications and Networking; Department of Information and Communications Engineering; Zorzi, Michele; Tao, Meixia; Saad, Walid; Mobile Network Softwarization and Service Customization; Internet technologies; Chuzhou University; University of Oulu
This paper investigates the fundamental traffic steering issue for cellular-enabled unmanned aerial vehicles (UAVs), where each UAV needs to select one from different Mobile Network Operators (MNOs) to steer its traffic for improving the Quality-of-Service (QoS). To this end, we first formulate the issue as an optimization problem aiming to minimize the maximum outage probabilities of the UAVs. This problem is non-convex and non-linear, which is generally difficult to be solved. We propose a solution based on the framework of deep reinforcement learning (DRL) to solve it, in which we define the environment and the agent elements. Furthermore, to avoid sharing the learned experiences by the UAV in this solution, we further propose a federated deep reinforcement learning (FDRL)-based solution. Specifically, each UAV serves as a distributed agent to train separate model, and is then communicated to a special agent (dubbed coordinator) to aggregate all training models. Moreover, to optimize the aggregation process, we also introduce a FDRL with DRL-based aggregation (DRL2A) approach, in which the coordinator implements a DRL algorithm to learn optimal parameters of the aggregation. We consider deep Q-learning (DQN) algorithm for the distributed agents and Advantage Actor-Critic (A2C) for the coordinator. Simulation results are presented to validate the effectiveness of the proposed approach.
Transfer Learning based GPS Spoofing Detection for Cellular-Connected UAVs
(2022-07-19) Dang, Yongchao; Benzaid, Chafika; Taleb, Tarik; Yang, Bin; Shen, Yulong; Department of Communications and Networking; Department of Bioproducts and Biosystems; Mobile Network Softwarization and Service Customization; Xidian University
Unmanned Aerial Vehicles (UAVs) are set to become an integral part of 5G and beyond systems with the promise of assisting cellular communications and enabling advanced applications and services, such as public safety, caching, and virtual/mixed reality-based remote inspection. However, safe and secure navigation of UAVs is a key requisite for their integration in the airspace. The GPS spoofing is one of the major security threats to remotely and autonomously controlled UAVs. In this paper, we propose a machine learning-based, mobile network-assisted UAV monitoring and control system that allows live monitoring of UAVs' locations and intelligent detection of spoofed positions. We introduce the Convolutional Neural Network (CNN) in the edge UAV Flight Controller (UFC) to locate a UAV and detect any GPS spoofing by comparing differences between the theoretical path loss computed by UFC and the corresponding path loss reported by the connected base station (BS). To reduce the detection latency as well as to increase the detection accuracy, transfer learning is leveraged to transfer the CNN knowledge between edge servers when the UAV handovers from one BS to another. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with an accuracy rate above 88% using only one BS.
Ventilation and environmental control of underground spaces: a short review
(EDP Sciences, 2019-08-13) Li, Angui; Kosonen, Risto; Melikov, Arsen; Yang, Bin; Olofsson, Thomas; Sörensen, Björn; Zhang, Linhua; Cui, Ping; Han, Ou; Department of Mechanical Engineering; Energy efficiency and systems; Xi'an University of Architecture and Technology; Danmarks Tekniske Universitet; Umeå University; Arctic University of Norway; Shandong Jianzhu University
More and more underground spaces were used in 21st century because of rapid urbanization, traffic problems, etc. Underground city, metro, tunnel, mine, industrial and agriculture engineering, civil air defence engineering need large underground spaces. Underground spaces with different thermal, ventilation and lighting environments may cause comfort, health and safety problems. Concrete problems include excessive humidity, heat transfer specialty, excessive CO caused by blockage in long distance traffic tunnels, difficulty in smoke exhaust and evacuation during fire, harmful microorganism, radioactivity pollutants, psychological problems, and so forth. Air quality control technologies for underground spaces, including ventilation technology, dehumidification technology, natural energy utilization technology, smoke extraction technology and ventilation resistance reduction technology, will be reviewed. Ventilation for smoke-proof/evacuation and ventilation will also be reviewed.

Browsing by Author "Yang, Bin"

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