Deep Deterministic Policy Gradient Based Resource Allocation in Internet of Vehicles

Ma, Zhuo; Chen, Xin; Ma, Teng; Chen, Ying

doi:10.1007/978-981-16-0010-4_26

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the variability of QoS requirements for vehicle users, the resource allocation problem in vehicle networks has attractive research value and has received extensive attention from researchers for years [12,13]. Since the high speed movement of vehicles in IoV makes it difficult to obtain accurate and fast channel change information, Guo et al [14] obtained the time delay of V2V link in steady state based on Markov process and determine the optimal transmit power for each possible spectrum and finally allocated the spectrum resource by dichotomous matching method to maximize the system data transmission rate.…”

Section: Related Workmentioning

confidence: 99%

Energy‐Efficient Resource Allocation for NOMA‐Enabled Internet of Vehicles

Chen

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

With the rapid development of Internet of vehicles (IoV) technology, the distribution of vehicles on the highway becomes more dense and the highly reliable communication between vehicles becomes more important. Nonorthogonal multiple access (NOMA) is a promising technology to meet the multiple access volume and the high reliability communication demands of IoV. To meet the Vehicle-to-Vehicle (V2V) communication requirements, a NOMA-based IoV system is proposed. Firstly, a NOMA-based resource allocation model in IoV is developed to maximize the energy efficiency (EE) of the system. Secondly, the established model is transformed into a Markov decision process (MDP) model and a deep reinforcement learning-based subchannel and power allocation (DSPA) algorithm is designed. An event trigger block is used to reduce computation time. Finally, the simulation results show that NOMA can significantly improve the system performance compared to orthogonal multiaccess, and the proposed DSPA algorithm can significantly improve the system EE and reduce the computation time.

show abstract

Section: Related Workmentioning

confidence: 99%

Energy‐Efficient Resource Allocation for NOMA‐Enabled Internet of Vehicles

Chen

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

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

Gupta,

Fernando

2024

Drones

View full text Add to dashboard Cite

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.

show abstract

Deep Deterministic Policy Gradient Based Resource Allocation in Internet of Vehicles

Cited by 2 publications

References 13 publications

Energy‐Efficient Resource Allocation for NOMA‐Enabled Internet of Vehicles

Energy‐Efficient Resource Allocation for NOMA‐Enabled Internet of Vehicles

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

Contact Info

Product

Resources

About