Intelligent Priority-Aware Spectrum Access in 5G Vehicular IoT: A Reinforcement Learning Approach

Abstract

Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures.

Keywords: 5G; Internet of Things; priority-aware spectrum management; reinforcement learning; resource allocation; spectrum access.

Figure 1

System model of RL-PASM showing RSU-based spectrum allocation for HP (red), LP (blue), and BE (white) traffic classes.

Figure 2

Centralized RL-PASM architecture: The RSU observes the system state $(C,D,A)$ and selects an access policy based on learned priorities for HP, LP, and BE vehicular traffic.

Figure 3

Maximum Q-value trend across episodes for each of the four evaluated RL algorithms.

Figure 4

Mean throughput comparison across RL agents.

Figure 5

Mean delay per episode across all RL agents in milliseconds (ms).

Figure 6

CDF of delay across all agents.

Figure 7

Mean energy efficiency, computed as the successful transmissions per unit energy across all agents.

Figure 8

Mean Jain’s fairness index for all RL agents.

Figure 9

CDF of interruption probability for each RL agent.

Figure 10

Mean blocking probability with standard deviation across RL agents.