UAV Offloading: Spectrum Trading Contract Design for UAV-Assisted Cellular Networks

Hu, Zhiwen; Zheng, Zijie; Song, Lingyang; Wang, Tao; Li, Xiaoming

doi:10.1109/twc.2018.2854598

Cited by 68 publications

(28 citation statements)

References 27 publications

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“…Besides, existing incentive mechanisms with high complexity and centralized control may not be suitable for UAV networks when considering the energy constraint and distributed features of UAV networks. Recently, Hu et al in [19] investigated the use of contract theory to formulate the spectrum trading problem between the macro base station manager and the UAV operators to encourage the macro base station manager to lease its owned bandwidth to the UAVs. However, there are significant security and privacy challenges for such peer-to-peer (P2P) spectrum trading in UAV-assisted cellular networks for the following reasons.…”

Section: B Related Workmentioning

confidence: 99%

Blockchain-Based Secure Spectrum Trading for Unmanned-Aerial-Vehicle-Assisted Cellular Networks: An Operator’s Perspective

Qiu

Grace

Ding

et al. 2020

IEEE Internet Things J.

166

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Unmanned aerial vehicles (UAVs) are envisioned to be widely deployed as an integral component in the next generation cellular networks, where spectrum sharing between the aerial and terrestrial communication systems will play an important role. However, there exist significant security and privacy challenges due to the untrusted broadcast features and wireless transmission of the UAV networks. This paper endeavors to resolve the security issues through proposing a novel privacypreserving secure spectrum trading and sharing scheme based on blockchain technology. Specifically, from the operator's perspective, a pricing-based incentive mechanism is firstly introduced, in which a primary mobile network operator (MNO) leases its owned spectrum to a secondary UAV network in exchange for some revenue from the UAV operators. To address the potential security issues, a spectrum blockchain framework is then proposed to illustrate detailed operations of how the blockchain helps to improve the spectrum trading environment. Under this framework, a Stackelberg game is formulated to jointly maximize the profits of the MNO and the UAV operators considering uniform and non-uniform pricing schemes. Security assessment and numerical results confirm the security and efficiency of our schemes for spectrum sharing in UAV-assisted cellular networks.

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Section: B Related Workmentioning

confidence: 99%

Blockchain-Based Secure Spectrum Trading for Unmanned-Aerial-Vehicle-Assisted Cellular Networks: An Operator’s Perspective

Qiu

Grace

Ding

et al. 2020

IEEE Internet Things J.

166

View full text Add to dashboard Cite

show abstract

“…In [10], M. J. Marcus pointed out that while UAV technologies were attracting growing attention, more efficient and effective protocols and methods of spectrum resource management are needed to satisfy the requirements of emerging communication applications. From this perspective, numerous research investigated problems of communication resource optimization in the UAVinvolved communication systems [11]- [14]. However, most research considered the cross-system spectrum resource allocation between UAVs and other communication systems such as cellular systems.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions

Wang

Chen

et al. 2020

IEEE Access

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The unmanned swarm system (USS) has been seen as a promising technology, and will play an extremely important role in both the military and civilian fields such as military strikes, disaster relief and transportation business. As the ''nerve center'' of USS, the unmanned swarm communication system (USCS) provides the necessary information transmission medium so as to ensure the system stability and mission implementation. However, challenges caused by multiple tasks, distributed collaboration, high dynamics, ultra-dense and jamming threat make it hard for USCS to manage limited spectrum resources. To tackle with such problems, the machine learning (ML) empowered intelligent spectrum management technique is introduced in this paper. First, based on the challenges of the spectrum resource management in USCS, the requirement of spectrum sharing is analyzed from the perspective of spectrum collaboration and spectrum confrontation. We found that suitable multi-agent collaborative decision making is promising to realize effective spectrum sharing in both two perspectives. Therefore, a multi-agent learning framework is proposed which contains mobile-computing-assisted and distributed structures. Based on the framework, we provide case studies. Finally, future research directions are discussed. INDEX TERMS Unmanned swarm system, spectrum sharing, machine learning, multi-agent learning, game theory.

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“…Due to the limited energy of UAVs, their deployment must be optimized to minimize the transmit power while satisfying the data rate and illumination requirements of users. Since the area of aerial cells is small and ground users served by UAVs are static, as done in [22]- [24], we do not consider the mobility energy consumption of the UAVs.…”

Section: A Transmission Modelmentioning

confidence: 99%

Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs

Wang

Chen

Yang

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

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In this paper, the problem of optimizing the deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities is studied. In the studied model, the UAVs can predict the illumination distribution of a given service area and determine the user association with the UAVs to simultaneously provide communications and illumination. However, ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs.Therefore, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem, which jointly optimizes UAV deployment, user association, and power efficiency while meeting the illumination and communication requirements of users. To solve this problem, an algorithm that combines the machine learning framework of gated recurrent units (GRUs) with convolutional neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. Based on the prediction of illumination distribution, the optimization problem becomes nonconvex and is then solved using a low-complexity, iterative physical relaxation algorithm.The proposed algorithm can find the optimal UAV deployment and user association to minimize the total transmit power. Simulation results using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 64.6% reduction in total transmit power compared to a conventional optimal UAV deployment that does not consider the illumination Y. Wang and T. Luo are with the Beijing 2 distribution and user association. The results also show that UAVs must hover at areas having strong illumination, thus providing useful guidelines on the deployment of VLC-enabled UAVs. Index TermsVisible light communication, unmanned aerial vehicles, drones, machine learning, gated recurrent units, convolutional neural networks, energy efficiency. I. INTRODUCTIONDeploying unmanned aerial vehicles (UAVs) as flying base stations (BSs) for wireless networking is a flexible and cost-effective approach to providing on-demand communications [2]-[6]. However, for tomorrow's ultra dense wireless networks that encompass a large number of ground BSs, UAVs may not have enough radio frequency (RF) resources to service ground users. Moreover, UAVs deployed as aerial BSs using RF will interfere with ground devices, hence significantly affecting the performance of the ground network. In addition, the limited energy will restrict the applicability of UAVs using RF resource to provide high-speed communication services for ground users. These challenges can be addressed by equipping UAVs with visible light communication (VLC) capabilities [7]. Indeed, VLC has recently attracted attention due to its large license-free bandwidth and high energy efficiency. For instance, a VLC system that uses light-emitting diodes (LEDs) to t...

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UAV Offloading: Spectrum Trading Contract Design for UAV-Assisted Cellular Networks

Cited by 68 publications

References 27 publications

Blockchain-Based Secure Spectrum Trading for Unmanned-Aerial-Vehicle-Assisted Cellular Networks: An Operator’s Perspective

Blockchain-Based Secure Spectrum Trading for Unmanned-Aerial-Vehicle-Assisted Cellular Networks: An Operator’s Perspective

Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions

Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs

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