Research on the Application of 5G Technology in UAV Data Link

Yan, Kai; Ma, Linfei; You-gen, Zhang

doi:10.1109/itaic49862.2020.9339133

Cited by 8 publications

(6 citation statements)

References 1 publication

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“…6. We have considered the number of users as (10,15,20,25,30) with DDQN model and shown comparison with DQN, Q-Learning and Random algorithms. The performance with 10 users is better than the other because of low dense network environment.…”

Section: Simulation Results and Parameter Analysismentioning

confidence: 99%

“…12. The number of base stations we considered as (2,4,6,8,10,12,14). The number of user can be dynamic which can be change with interval of time, we have simulated the computation time with numbers of users as 10, 15 and 20.…”

Section: Performance Comparisonsmentioning

confidence: 99%

“…However, the ground communication systems may suffer from equipment aging, human damage and natural corrosion which, affects the reliability and stability. In case of sudden breakdown situation, the base station (BS) may act as immobile with poor flexibility, which is not suitable for to meet the quality of experience (QoE) requirements of emergency communication systems [10]. The BS of wireless communication is main powered like cluster head (CH) in the D2D network.…”

Section: Introductionmentioning

confidence: 99%

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Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning

2022

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The Internet of Things (IoT) application scenarios is becoming extensive due to the quick evolution of smart devices with fifth-generation (5G) network slicing technologies and hence IoT becoming significantly important beyond fifth-generation (B5G) networks. However, communication with IoT devices is more sensitive in disasters because the network depends on the main power supply and devices are fragile. In this paper, we consider Unmanned Aerial Vehicles (UAV) as a flying base station (BS) for the emergency communication system with 5G mMTC Network Slicing to improve the quality of service. The UAV-assisted mMTC creates a base station selection method with the aim of maximizing the system energy efficiency. Then, the system model is reduced into the stochastic optimization based problem using Markov Decision Process (MDP) theory. We propose a Dueling-Deep-Q-Networks (DDQN) based approach based on Reinforcement Learning (RL) technique for maximization of energy efficiency to solve the resource allocation problem. We compare the proposed model with DQN and Q-Learning models and found that the proposed DDQN based model performs better for resource allocation in terms of low transmission power and maximum energy efficiency.

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Section: Simulation Results and Parameter Analysismentioning

confidence: 99%

Section: Performance Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning

2022

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“…To overcome these limitations, it is crucial to connect drones to cellular networks to enable BVLoS capabilities. While integrating drones with 5G offers several advantages, it also poses certain challenges [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks

Warrier,

Al-Rubaye,

Inalhan

et al. 2023

Aerospace

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Integrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G) networks presents a significant challenge due to network interference. UAVs’ high altitude and propagation conditions increase vulnerability to interference from neighbouring 5G base stations (gNBs) in the downlink direction. This paper proposes a novel deep reinforcement learning algorithm, powered by AI, to address interference through power control. By formulating and solving a signal-to-interference-and-noise ratio (SINR) optimization problem using the deep Q-learning (DQL) algorithm, interference is effectively mitigated, and link performance is improved. Performance comparison with existing interference mitigation schemes, such as fixed power allocation (FPA), tabular Q-learning, particle swarm optimization, and game theory demonstrates the superiority of the DQL algorithm, where it outperforms the next best method by 41.66% and converges to an optimal solution faster. It is also observed that, at higher speeds, the UAV sees only a 10.52% decrease in performance, which means the algorithm is able to perform effectively at high speeds. The proposed solution effectively integrates UAVs with 5G networks, mitigates interference, and enhances link performance, offering a significant advancement in this field.

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“…Due to the mobility and rapid deployment of unmanned aerial vehicles, the application scenarios of UAVs are becoming more and more abundant in recent years, such as surveillance, disaster relief, and provision of cellular coverage [1][2][3][4]. e popularity of 5G technology has further accelerated the deployment process of UAVs in various applications [5][6][7][8]. UAVs can assist ground nodes in the air for data transmission, responsible for the relay and forwarding of transmitted data [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Downlink Cofrequency Interference Analysis of Vehicles and UAV Network in Ka Band

Wang

Zhang

Wang

2022

Wireless Communications and Mobile Computing

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With the rapid development of radio and computer technologies, the application scenarios of UAV carrying computer flight control systems are becoming more and more abundant. UAV can form a collaborative network with ground IoV (Internet of Vehicle) to improve the link quality of vehicle signals in some environments with poor network connectivity. For UAV and ground vehicles that are far apart, satellites can assist in the transmission of data between them. UAVs are allowed to be included in the satellite communication network in the Ka band in the 2015 World Radiocommunication Conference. In order to avoid the interference with existing communication services in the Ka band, it is necessary to limit the flying height of the UAV. This article will study the frequency compatibility between UAV and Internet of Vehicles in the same-frequency band. First, model the propagation environment of the UAV transmitting signal is modeled and the path loss of it is calculated, including the basic transmission loss in free space of the signal and the atmospheric propagation loss when the signal passes through the atmosphere. Then the radiation pattern of the receiving antenna is modeled, which is a 4 × 2 antenna array in this article, and the total antenna gain is obtained by adding the element gain and the array gain. Finally, the interference between UAV and Internet of Vehicles is simulated, calculating the interference and noise ratio of UAV to the Internet of Vehicles under different pointing angles between the interference and the interfered antenna and different elevation angles from UAV to the GSO satellite. Finally, the safe separation distance to protect the Internet of Vehicles from interference is got.

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Research on the Application of 5G Technology in UAV Data Link

Cited by 8 publications

References 1 publication

Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning

Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning

AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks

Downlink Cofrequency Interference Analysis of Vehicles and UAV Network in Ka Band

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