A Meta Multi-Objective Reinforcement Learning Framework For Non-Orthogonal, Age-Optimized Information Dissemination in Vehicular ‎Networks

  • Authors

    • Dr. Vullam Nagagopiraju Professor, Department of CSE, Chalapathi Institute of Engineering and Technology, ‎Guntur
    • U. S. B. K. Mahalaxmi Department of Electronics and Communication Engineering, Aditya University, Surampalem, ‎Andhra Pradesh, India
    • Bathula Prasanna Kumar Associate Professor, Department of CSE- Data Science, KKR & KSR Institute ‎of Technology and Sciences, Guntur, Andhra Pradesh, India
    • Dr. Suresh Betam Assistant Professor, Department of CSE, KL Deemed to be University, Vaddeswaram, ‎Andhra Pradesh, India
    • Manasa Bandlamudi Assistant Professor, Information Technology, RVR & JC College of Engineering, Guntur, Andhra ‎Pradesh, India
    • Dr. Aktar Geeta Bhimrao Assistant Professor, G H Raisoni College of Engineering and Management, Pune
    • Rohini Rajesh Swami Devnikar Assistant Professor, G H Raisoni College of Engineering and Management, Pune
    • Dr. Sarala Patchala Associate Professor, Department of Electronics and Communication Engineering, ‎KKR & KSR Institute of Technology and Sciences, Guntur, Andhra Pradesh, India
    • Srija Gundapaneni Assistant Professor, Computer Science and Engineering-IoT, RVR & JC College of Engineering, ‎ Andhra Pradesh, India
    https://doi.org/10.14419/hkfqf643

    Received date: May 23, 2025

    Accepted date: August 6, 2025

    Published date: September 8, 2025

  • Reinforcement; Multi-Objective; Vehicular Network; Multi; Meta Objective

  • Abstract

    This paper studies how to send fresh and efficient information in vehicular networks. A roadside unit ‎‎(RSU) updates vehicles about different events. The goal is to reduce the delay in receiving information ‎while also saving power during transmission. The system uses an innovative way of transmitting data. ‎It sends multiple messages at the same time using superposition. Vehicles cancel unwanted signals with ‎a technique called successive interference cancellation (SIC). This method helps to improve the efficiency of communication. The problem is complex because two objectives must be optimized. One is to ‎minimize the delay in information updates, and the other is to minimize the power needed to send ‎updates. This is a multi-objective problem that is difficult to solve using traditional methods. To ‎address this challenge, the paper uses reinforcement learning (RL). A deep Q-network (DQN) decides the ‎best way to decode messages, while a deep deterministic policy gradient (DDPG) model determines ‎the optimal power allocation. Each learning model trains separately for different cases, which increases ‎computational time and effort. Instead of training models separately, the paper proposes a meta-‎learning approach. This helps estimate good solutions quickly without the need for retraining every ‎time. The meta-model adapts with small updates, saving significant time and computational resources. ‎Simulation results demonstrate that the proposed method outperforms older approaches. It reduces ‎training time while still achieving high efficiency. Moreover, it provides a better balance between ‎formation freshness and power consumption. These improvements make it highly suitable for real-time ‎data sharing in vehicular networks. This research has practical implications for enhancing road safety ‎and smart transportation systems. By optimizing data dissemination it contributes to the development ‎of more reliable and efficient vehicular communication networks‎.

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  • How to Cite

    Nagagopiraju, D. V. . ., Mahalaxmi , U. S. B. K. ., Kumar , B. P. ., Betam, D. S. . ., Bandlamudi , M. ., Bhimrao , D. A. G. ., Devnikar , R. R. S. ., Patchala , D. S. ., & Gundapaneni , S. . (2025). A Meta Multi-Objective Reinforcement Learning Framework For Non-Orthogonal, Age-Optimized Information Dissemination in Vehicular ‎Networks. International Journal of Basic and Applied Sciences, 14(5), 268-281. https://doi.org/10.14419/hkfqf643