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<p>With an emphasis on improving energy efficiency (EE) and lowering power consumption of rapidly growing connected vehicles and infrastructures, Vehicle-to-Everything (V2X) communication is emerging as a fundamental element in the development of smart cities. This paper introduces an innovative reinforcement learning (RL)-based method for dynamic resource allocation within 5G-enabled V2X networks, focusing on EE and minimizing power consumption. The suggested framework adeptly modifies transmission power, and spectrum allocation in real-time, responding to fluctuating traffic patterns and network demands. By facilitating ongoing learning and decision-making, the RL system guarantees optimal resource utilization while preserving high-quality service and low-latency communication. Q-learning is employed to dynamically regulate power levels in urban vehicular scenarios, taking Doppler shift, user mobility, and changing traffic conditions into account. Experimental evaluations demonstrate a substantial decrease in power consumption and an improvement in network efficiency providing a sustainable solution for smart mobility initiatives, promoting the advancement of greener, more reliable, and energy-efficient urban transportation systems.</p>

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Reinforcement learning based resource allocation scheme for vehicular communication in 5G networks for smart cities

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月2日

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  • S. Brindha,
  • P. P. Shehila Nasreen,
  • Paresh Sagar,
  • Subhra Sankha Sarma

摘要

With an emphasis on improving energy efficiency (EE) and lowering power consumption of rapidly growing connected vehicles and infrastructures, Vehicle-to-Everything (V2X) communication is emerging as a fundamental element in the development of smart cities. This paper introduces an innovative reinforcement learning (RL)-based method for dynamic resource allocation within 5G-enabled V2X networks, focusing on EE and minimizing power consumption. The suggested framework adeptly modifies transmission power, and spectrum allocation in real-time, responding to fluctuating traffic patterns and network demands. By facilitating ongoing learning and decision-making, the RL system guarantees optimal resource utilization while preserving high-quality service and low-latency communication. Q-learning is employed to dynamically regulate power levels in urban vehicular scenarios, taking Doppler shift, user mobility, and changing traffic conditions into account. Experimental evaluations demonstrate a substantial decrease in power consumption and an improvement in network efficiency providing a sustainable solution for smart mobility initiatives, promoting the advancement of greener, more reliable, and energy-efficient urban transportation systems.