# Event-Triggered Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency V2X Communications

arXiv: https://arxiv.org/abs/2407.13947 · HTML: https://mlsanity.com/paper/2407.13947/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 17 of 17)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning](https://mlsanity.com/paper/1802.06480/) (2018)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications](https://mlsanity.com/paper/2002.05485/) (2020)
- [Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC)](https://mlsanity.com/paper/1810.06938/) (2018)
- [Event-triggered Learning](https://mlsanity.com/paper/1904.03042/) (2019)
- [Joint Power and Blocklength Optimization for URLLC in a Factory Automation Scenario](https://mlsanity.com/paper/1911.13050/) (2019)
- [Resource Allocation for Secure URLLC in Mission-Critical IoT Scenario](https://mlsanity.com/paper/1911.13154/) (2019)
- [Optimized Age of Information Tail for Ultra-Reliable Low-Latency Communications in Vehicular Networks](https://mlsanity.com/paper/1912.07981/) (2019)
- [5G NR-V2X: Towards Connected and Cooperative Autonomous Driving](https://mlsanity.com/paper/2009.03638/) (2020)
- [Multivariate Extreme Value Theory Based Channel Modeling for Ultra-Reliable Communications](https://mlsanity.com/paper/2401.05833/) (2024)
- [Joint Blocklength and Location Optimization for URLLC-enabled UAV Relay Systems](https://mlsanity.com/paper/1901.05413/) (2019)
- [Joint Frame Design and Resource Allocation for Ultra-Reliable and Low-Latency Vehicular Networks](https://mlsanity.com/paper/2002.07341/) (2020)
- [ISAC-Enabled V2I Networks Based on 5G NR: How Much Can the Overhead Be Reduced?](https://mlsanity.com/paper/2301.12787/) (2023)
- [Multivariate Extreme Value Theory Based Rate Selection for Ultra-Reliable Communications](https://mlsanity.com/paper/2401.05171/) (2024)

## Built on by (most cited first, 3 of 3)

- [Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation](https://mlsanity.com/paper/2501.13552/) (2025)
- [A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications](https://mlsanity.com/paper/2506.09512/) (2025)
- [Collaborative Channel Access and Transmission for NR Sidelink and Wi-Fi Coexistence over Unlicensed Spectrum](https://mlsanity.com/paper/2501.17878/) (2025)

## Abstract (arXiv)

Future 6G-enabled vehicular networks face the challenge of ensuring ultra-reliable low-latency communication (URLLC) for delivering safety-critical information in a timely manner. Existing resource allocation schemes for vehicle-to-everything (V2X) communication systems primarily rely on traditional optimization-based algorithms. However, these methods often fail to guarantee the strict reliability and latency requirements of URLLC applications in dynamic vehicular environments due to the high complexity and communication overhead of the solution methodologies. This paper proposes a novel deep reinforcement learning (DRL) based framework for the joint power and block length allocation to minimize the worst-case decoding-error probability in the finite block length (FBL) regime for a URLLC-based downlink V2X communication system. The problem is formulated as a non-convex mixed-integer nonlinear programming problem (MINLP). Initially, an algorithm grounded in optimization theory is developed based on deriving the joint convexity of the decoding error probability in the block length and transmit power variables within the region of interest. Subsequently, an efficient event-triggered DRL-based algorithm is proposed to solve the joint optimization problem. Incorporating event-triggered learning into the DRL framework enables assessing whether to initiate the DRL process, thereby reducing the number of DRL process executions while maintaining reasonable reliability performance. Simulation results demonstrate that the proposed event-triggered DRL scheme can achieve 95% of the performance of the joint optimization scheme while reducing the DRL executions by up to 24% for different network settings.