Multicast Capacity for VANETs With Directional Antenna and Delay Constraint Under Random Walk Mobility Model

Ren, Jiajie; Zhang, Guanglin; Li, Demin

doi:10.1109/access.2017.2683718

Cited by 18 publications

(8 citation statements)

References 22 publications

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“…In this section, we formulate our ESN algorithm for predicting the movement of users. A variety of mobility models have been utilized in [39,40]. However, in these mobility models, the direction of each user's movement tends to be uniformly distributed among left, right, forward and backward, which does not fully reflect the real movement of users.…”

Section: Echo State Network Algorithm For Prediction Of Users' Mmentioning

confidence: 99%

Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach

Liu

Chen

et al. 2019

IEEE Trans. Veh. Technol.

323

197

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A novel framework is proposed for the trajectory design of multiple unmanned aerial vehicles (UAVs) based on the prediction of users' mobility information. The problem of joint trajectory design and power control is formulated for maximizing the instantaneous sum transmit rate while satisfying the rate requirement of users. In an effort to solve this pertinent problem, a three-step approach is proposed which is based on machine learning techniques to obtain both the position information of users and the trajectory design of UAVs. Firstly, a multi-agent Qlearning based placement algorithm is proposed for determining the optimal positions of the UAVs based on the initial location of the users. Secondly, in an effort to determine the mobility information of users based on a real dataset, their position data is collected from Twitter to describe the anonymous usertrajectories in the physical world. In the meantime, an echo state network (ESN) based prediction algorithm is proposed for predicting the future positions of users based on the real dataset. Thirdly, a multi-agent Q-learning based algorithm is conceived for predicting the position of UAVs in each time slot based on the movement of users. In this algorithm, multiple UAVs act as agents to find optimal actions by interacting with their environment and learn from their mistakes. Additionally, we also prove that the proposed multi-agent Q-learning based trajectory design and power control algorithm can converge under mild conditions. Numerical results are provided to demonstrate that as the size of the reservoir increases, the proposed ESN approach improves the prediction accuracy. Finally, we demonstrate that throughput gains of about 17% are achieved.

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Section: Echo State Network Algorithm For Prediction Of Users' Mmentioning

confidence: 99%

Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach

Liu

Chen

et al. 2019

IEEE Trans. Veh. Technol.

323

197

View full text Add to dashboard Cite

show abstract

“…A beamforming antenna (BA) is an antenna technique in which the antenna pattern can be focused on a specific region. Signal quality is improved (obtained a higher signal to noise ratio or SNR) in the concentrated beam using BA, which eventually enhances the throughput 52 . BA is more relevant to the VANET deployment as the vehicle's orientations are not too random (orientation is only changed when vehicles change their routes).…”

Section: Some Solutions and Future Directionsmentioning

confidence: 99%

Spectrum sensing challenges & their solutions in cognitive radio based vehicular networks

Hossain

Noor

Azzuhri

et al. 2021

Int J Communication

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Summary Vehicular ad hoc network (VANET) has been established to mitigate road collisions and traffic congestion and provide infotainment facilities to users. Allocated channels to VANET, which is the DSRC (Dedicated Short‐Range Communication), are not adequate for the full implementation of VANET. Cognitive radio (CR) can be used to alleviate this issue. CR is a programmable and intelligent radio system capable of reaching various frequency ranges. CR needs to conduct spectrum sensing to get these bands. A vehicle fitted with a CR can sense the licensed spectrum to locate the vacant spectrum (which is not used by any licensed user) when the DSRC is wholly occupied. Compared to other CR networks, VANET faces specific additional difficulties related to spectrum sensing, such as periodic topological shifts due to high‐speed mobility, multipath fading and shadowing issues, and heterogeneous quality of service (QoS) specifications. This paper explains these problems in depth. All the problems and issues were explored from the perspective of the CR focused on VANET. Probable ideas and directions to overcome these problems have also been presented. We have provided a conceptual framework for spectrum sensing. The framework resolves a variety of issues and concerns that are discussed in the paper. It gives much better results than conventional sensing techniques.

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“…Before discussing the movement of UAV-BSs, the mobility model of UEs needs to be discussed first. The random walk model [15] is chosen as the UE mobility model in this paper, but other models can be easily included. The moving direction of UEs are uniformly distributed among left, right, forward, backward and staying still.…”

Section: Designmentioning

confidence: 99%

Deep Q-Network Based Dynamic Movement Strategy in a UAV-Assisted Network

Zhong

Huo

Dong

et al. 2020

Preprint

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Unmanned aerial vehicle (UAV)-assisted communications is a promising solution to improve the performance of future wireless networks, where UAVs are deployed as base stations for enhancing the quality of service (QoS) provided to ground users when traditional terrestrial base stations are unavailable or not sufficient. An effective framework is proposed in this paper to manage the dynamic movement of multiple unmanned aerial vehicles (UAVs) in response to ground user mobility, with the objective to maximize the sum data rate of the ground users. First, we discuss the relationship between the air-to-ground (A2G) path loss (PL) and the location of UAVs. Then a deep Q-network (DQN) based method is proposed to adjust the locations of UAVs to maximize the sum data rate of the user equipment (UE). Finally, simulation results show that the proposed method is capable of adjusting UAV locations in a real-time condition to improve the QoS of the entire network.Index Terms-Unmanned aerial vehicle (UAV), UAV-assisted network, reinforcement learning, user equipment (UE), quality of service (QoS). I. INTRODUCTIONT HE unprecedented demand for high-quality wireless communications has fueled the evolution of wireless technologies and communications networks. The unmanned aerial vehicle (UAV)-assisted network where UAVs are deployed and function as aerial base stations to assist the terrestrial base stations is an effective complementary solution to emergency wireless service recovery after natural disasters or infrastructure damage [1]. Also, in Internet of Things (IoT) networks, UAVs can be used as aerial base stations to collect data from ground devices, in which building a complete cellular infrastructure is not affordable [2]. The authors in [3] proposed a multi-layer UAV network model for UAV-enabled 5G and beyond applications. Despite advantages such as flexibility, mobility, cost and time efficiency in UAVassisted networks, one key design challenge is to determine the move strategy for UAVs. Since in realistic situations, the environment where UAVs are deployed is highly dynamic, it is critical for UAVs to adjust its locations regularly to cope with varying conditions. Furthermore, utilizing machine learning techniques for the UAV communication recently has seen unprecedented growing popularity [4].

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Multicast Capacity for VANETs With Directional Antenna and Delay Constraint Under Random Walk Mobility Model

Cited by 18 publications

References 22 publications

Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach

Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach

Spectrum sensing challenges & their solutions in cognitive radio based vehicular networks

Deep Q-Network Based Dynamic Movement Strategy in a UAV-Assisted Network

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