Raghavendran, TS and Ahmed, ST and Narendra, NC
(2026)
NeuroChaos-inspired edge intelligence for resource allocation in 5G/6G vehicular networks.
Preprints.
Full text not available from this repository.
| Abstract: |
5G Advanced and 6G V2X Environments require intelligence that is real-time, resource-light and robust across high-density vehicle mobility, non-stationary channels and hard ware constrained edge nodes. Existing AI-driven resource man agement approaches — including CNNs, LSTMs, GNNs, Deep Reinforcement Learning, Transformers, and Federated Learning — have limitations, such as high computational overhead, large data requirements, poor generalization under topology churn, and catastrophic forgetting under continuous retraining. These limitations render them ill-suited for deployment, especially at the level of radio units. To address this issue, this paper proposes NeuroChaos-V2X, a chaos-inspired edge intelligence framework that integrates vehicle mobility, wireless channel, and edge computing states into a unified multi-domain state vector. A Neu roChaos Feature Transformation module, employing GLS and Logistic-map neurons, converts raw network measurements into chaos-derived descriptors — firing time, firing rate, energy, and entropy — without requiring deep architectures or large training datasets. These features drive a unified NeuroChaos Decision Engine supporting edge server selection, task offloading, resource prioritization, and handover management simultaneously. The framework’s low-sample learning capability, CPU-only footprint, and sensitivity to small state perturbations make it inherently suited to resource-constrained V2X edge tiers. This position paper establishes the conceptual foundation and motivates experimental validation against GNN, DRL, and Transformer baselines in future 6G-V2X simulation environments. |
| Item Type: |
Journal Paper
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| Subjects: |
School of Natural and Engineering Sciences > Complex Systems |
| Divisions: |
Schools > Natural Sciences and Engineering |
| Date Deposited: |
23 Oct 2026 11:03 |
| Last Modified: |
23 Oct 2026 11:03 |
| Official URL: |
https://www.preprints.org/manuscript/202610.0953 |
| Related URLs: |
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| Funders: |
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| Projects: |
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| DOI: |
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| URI: |
http://eprints.nias.res.in/id/eprint/3487 |
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