UAV Anti-Jamming Communications With Power and Mobility Control

Wang, Haichao; Ding, Guoru; Chen, Jin; Zou, Yulong; Gao, Feifei

doi:10.1109/twc.2022.3228265

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Data transmission rate from k th vehicle to the UAV in i th transmission window σ 2 Power spectral density of Gaussian noise p ui…”

Section: Symbol Definitionmentioning

confidence: 99%

“…In the realm of autonomous driving and intelligent transportation systems (ITSs), the sixth generation (6G) cellular vehicle-to-everything (C-V2X) networks face exponential growth in data transmission requirements [1]. Consequently, unmanned aerial vehicle (UAV)-assisted C-V2X networks are being investigated to meet high throughput, ultra reliability, and low latency requirements [2]. The high mobility and line of sight (LoS) channel characteristics of UAVs offer much-needed performance improvement to C-V2X networks [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment

Fernando,

Gupta

2024

Drones

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The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions.

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“…Data transmission rate from k th vehicle to the UAV in i th transmission window σ 2 Power spectral density of Gaussian noise p ui…”

Section: Symbol Definitionmentioning

confidence: 99%