Deep Reinforcement Learning Based Resource Allocation for V2V Communications

2019/02/04 by Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang +1 · 850 citations
Computer Science · Engineering · #Advanced Data and IoT Technologies #Artificial intelligence #Computer network #Computer science #Distributed computing #Reinforcement learning #Resource allocation #Resource management (computing) #Software-Defined Networks and 5G #Vehicular Ad Hoc Networks (VANETs)

paper · pdf · doi:10.1109/tvt.2019.2897134

published in IEEE Transactions on Vehicular Technology 68(4), 3163-3173 (Institute of Electrical and Electronics Engineers)

openalex publication_date 2019/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to both unicast and broadcast scenarios. According to the decentralized resource allocation mechanism, an autonomous “agent,” a V2V link or a vehicle, makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Since the proposed method is decentralized, it incurs only limited transmission overhead. From the simulation results, each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure communications.

Citations

• ImageNet classification with deep convolutional neural networks

Cited by

• 6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities
• Optimizing QoS in HD Map Updates: Cross-Layer Multi-Agent With Multi-Task and Mixed-Dependence (MTMD)
• Deep Reinforcement Learning-Aided Strategies for Big Data Offloading in Vehicular Networks
• A-LAMP: Agentic LLM-Based Framework for Automated MDP Modeling and Policy Generation
• Active Visual Perception: Opportunities and Challenges
• Deep Neural Network Based Resource Allocation for V2X Communications
• Learning-Based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks
• 20 Years of Evolution from Cognitive to Intelligent Communications
• Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks
• Value-Decomposition Networks based Distributed Interference Control in Multi-platoon Groupcast
• Deep Learning for MIMO Channel Estimation: Interpretation, Performance, and Comparison
• Dynamic Power Allocation and Virtual Cell Formation for Throughput-Optimal Vehicular Edge Networks in Highway Transportation
• Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networks
• Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective
• On Spatial-Provenance Recovery in Wireless Networks with Relaxed-Privacy Constraints
• Semantic Communications for Speech Recognition
• An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation
• Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges
• Environment-Adaptive Multiple Access for Distributed V2X Network: A Reinforcement Learning Framework
• Digital Twin Channel-Enabled Online Resource Allocation for 6G: Principle, Architecture and Application
• AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
• To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks
• MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments
• DDPG-based Resource Management for MEC/UAV-Assisted Vehicular Networks
• Accelerating Generalized Benders Decomposition for Wireless Resource Allocation
• Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communications
• Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services
• Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing
• Zeroth-order Deterministic Policy Gradient
• Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach
• Decentralized Inference with Graph Neural Networks in Wireless Communication Systems
• Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs
• Deep Reinforcement Learning for Multi-Agent Power Control in Heterogeneous Networks
• Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning
• Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Acceleration
• Unsupervised Resource Allocation with Graph Neural Networks
• Learn to Allocate Resources in Vehicular Networks
• Adaptive ABAC Policy Learning: A Reinforcement Learning Approach
• Learn to Compress CSI and Allocate Resources in Vehicular Networks
• Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach
• Understanding Deep MIMO Detection
• Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs
• Joint Optimization of Spectrum and Energy Efficiency Considering the C-V2X Security: A Deep Reinforcement Learning Approach
• Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach
• Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learning
• Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks
• Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey. [europepmc]
• Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning. [europepmc]
• Approximate reinforcement learning to control beaconing congestion in distributed networks. [europepmc]
• Design of a SIMO Deep Learning-Based Chaos Shift Keying (DLCSK) Communication System. [europepmc]
• Deep Reinforcement Learning-Based Resource Allocation for Cellular Vehicular Network Mode 3 with Underlay Approach. [europepmc]
• Deep Reinforcement Learning Multi-Agent System for Resource Allocation in Industrial Internet of Things. [europepmc]
• Automatic composition of Guzheng (Chinese Zither) music using long short-term memory network (LSTM) and reinforcement learning (RL). [europepmc]
• Deep Reinforcement Learning Based Resource Allocation for D2D Communications Underlay Cellular Networks. [europepmc]
• Joint Deep Reinforcement Learning and Unsupervised Learning for Channel Selection and Power Control in D2D Networks. [europepmc]
• Survey of Reinforcement-Learning-Based MAC Protocols for Wireless Ad Hoc Networks with a MAC Reference Model. [europepmc]
• A deep reinforcement learning-based wireless body area network offloading optimization strategy for healthcare services. [europepmc]
• Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications. [europepmc]
• A Power Allocation Scheme for MIMO-NOMA and D2D Vehicular Edge Computing Based on Decentralized DRL. [europepmc]
• Intelligent Resource Allocation for V2V Communication with Spectrum-Energy Efficiency Maximization. [europepmc]
• LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning. [europepmc]
• Resource-aware DBSCAN-based re-clustering in hybrid C-V2X/DSRC vehicular networks. [europepmc]
• Reuse Distance-Aided Resource Selection Mechanisms for NR-V2X Sidelink Communication. [europepmc]
• Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks. [europepmc]
• Quality of Service-Aware Multi-Objective Enhanced Differential Evolution Optimization for Time Slotted Channel Hopping Scheduling in Heterogeneous Internet of Things Sensor Networks. [europepmc]
• Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning. [europepmc]
• Research on Computation Offloading and Resource Allocation Strategy Based on MADDPG for Integrated Space-Air-Marine Network. [europepmc]
• Dynamic Allocation of C-V2X Communication Resources Based on Graph Attention Network and Deep Reinforcement Learning. [europepmc]
• State-Aware Resource Allocation for V2X Communications. [europepmc]
• Towards Dynamic V2X Infrastructure: Joint Deployment and Optimization of 6DMA-Enabled RSUs. [europepmc]
• A hierarchical neuromorphic multi agent framework for energy aware and secure 6G resource optimization using Neuro6G agent. [europepmc]

Related

• Deep Reinforcement Learning based Resource Allocation for V2V Communications
• Deep Reinforcement Learning for Resource Allocation in V2V Communications
• Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications
• Decentralized Deep Reinforcement Learning for Delay-Power Tradeoff in Vehicular Communications
• Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems