Sensing-Communication Co-Design for UAV Swarm-Assisted Vehicular Network in Perspective of Doppler

Zhu, Qian; Liu, Rongke; Wang, Zijie; Liu, Qirui; Chen, Changwen

doi:10.1109/tvt.2023.3315868

Cited by 3 publications

(1 citation statement)

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“…Once the absolute geographical coordinates of individual nodes are obtained, the absolute geographical coordinates of all nodes can be determined [17]. However, In complex electromagnetic battlefield environments, radio signals may be affected by factors such as high-intensity interference [18], channel attenuation [19], multipath effects, and Doppler effects [20], leading to significant errors in distance valued and even unobtainable distance values between some nodes. In this scenario, the obtained distance matrix of the swarm deviates significantly from the actual distance matrix.…”

Section: Introductionmentioning

confidence: 99%

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

Di,

Guo

2024

Aerospace

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In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation.

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Section: Introductionmentioning

confidence: 99%

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

Di,

Guo

2024

Aerospace

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Joint UAV Deployment and Precoder Optimization for Multicasting and Target Sensing in UAV-Assisted ISAC Networks

Abdissa Bayessa,

Chai,

Liang

et al. 2024

IEEE Internet Things J.

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Federated Reinforcement Learning for Collaborative Intelligence in UAV-Assisted C-V2X Communications

Gupta,

Fernando

2024

Drones

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This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial vehicle (UAV). Different sensors in vehicles capture different types of data, contributing to data heterogeneity. C-V2X communication networks impose additional communication overhead in order to converge to a global model when the sensor data are not independent-and-identically-distributed (non-i.i.d.). Consequently, the training time for local model updates also varies considerably. Using FRL, we accelerated this convergence by minimizing communication rounds, and we delayed it by exploring the correlation between the data captured by various vehicles in subsequent time steps. Additionally, as UAVs have limited battery power, processing of the collected information locally at the vehicles and then transmitting the model hyper-parameters to the UAVs can optimize the available power consumption pattern. The proposed FRL algorithm updates the global model through adaptive weighing of Q-values at each training round. By measuring the local gradients at the vehicle and the global gradient at the UAV, the contribution of the local models is determined. We quantify these Q-values using nonlinear mappings to reinforce positive rewards such that the contribution of local models is dynamically measured. Moreover, minimizing the number of communication rounds between the UAVs and vehicles is investigated as a viable approach for minimizing delay. A performance evaluation revealed that the FRL approach can yield up to a 40% reduction in the number of communication rounds between vehicles and UAVs when compared to gross data offloading.

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Sensing-Communication Co-Design for UAV Swarm-Assisted Vehicular Network in Perspective of Doppler

Cited by 3 publications

References 50 publications

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

Joint UAV Deployment and Precoder Optimization for Multicasting and Target Sensing in UAV-Assisted ISAC Networks

Federated Reinforcement Learning for Collaborative Intelligence in UAV-Assisted C-V2X Communications

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