[Paper] Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey

Source: arXiv - 2601.03181v1

Overview

The paper surveys how foundation models (FMs)—large, pre‑trained AI systems that can be adapted to many tasks—can be harnessed for wireless network management. By focusing on multi‑modal data (e.g., radio measurements, traffic logs, images, and textual metadata), the authors argue that FM‑driven agents could simultaneously understand context, predict network behavior, and make real‑time control decisions.

Key Contributions

• Comprehensive taxonomy of FM‑enabled wireless tasks, split into prediction (e.g., traffic forecasting, channel quality estimation) and control (e.g., resource allocation, handover management).
• Analysis of multi‑modal contextual understanding, showing how combining radio, visual, and textual cues can improve situational awareness in networks.
• Survey of existing datasets (e.g., OpenRAN, 5G‑AI, Wi‑Fi trace collections) and discussion of data‑centric challenges unique to wireless domains.
• Review of methodological pipelines for building wireless‑specific FMs, covering pre‑training, modality alignment, and fine‑tuning strategies.
• Identification of open research challenges, such as model scalability, privacy‑preserving training, and real‑time inference on edge hardware.

Methodology

The authors adopt a literature‑review approach:

1. Scope definition – they delimit the survey to works that explicitly integrate foundation models (e.g., GPT‑style language models, CLIP‑style vision‑language models, or multimodal transformers) into wireless networking problems.
2. Classification – papers are grouped by the type of task (prediction vs. control) and by the modalities they exploit (radio‑only, radio + visual, radio + text, etc.).
3. Dataset mapping – each surveyed work is linked to publicly available datasets, highlighting gaps where data are scarce or not multimodal.
4. Methodological synthesis – common pipelines are extracted (large‑scale pre‑training on generic data → modality‑specific adapters → domain‑specific fine‑tuning).
5. Critical analysis – the authors discuss performance trends, computational trade‑offs, and the readiness of these approaches for deployment.

Results & Findings

• Multi‑modal FMs consistently outperform single‑modal baselines on prediction tasks such as traffic load forecasting and channel state prediction, especially when visual context (e.g., camera feeds of a base‑station site) is available.
• For control tasks, prompt‑based FM agents can generate near‑optimal scheduling or beam‑forming decisions after modest fine‑tuning, reducing the need for handcrafted rule sets.
• Dataset scarcity is a bottleneck: only a handful of large‑scale, multimodal wireless datasets exist, limiting the ability to pre‑train truly general models.
• Inference latency remains a challenge on edge devices; however, techniques like model pruning, quantization, and knowledge distillation show promise for meeting real‑time constraints.
• The survey reveals a trend toward “foundation‑as‑service” where network operators can query a central FM (via APIs) for both analytics and control commands.

Practical Implications

• Network operators can accelerate AI adoption by leveraging off‑the‑shelf FMs and focusing effort on domain‑specific fine‑tuning rather than building models from scratch.
• Edge‑cloud orchestration: a central FM can process heavy multimodal data (e.g., city‑wide camera feeds) and push distilled policies to edge nodes, enabling smarter RAN slicing and dynamic spectrum sharing.
• Reduced OPEX: automated prediction of traffic spikes and proactive resource allocation can lower over‑provisioning and improve QoS without manual tuning.
• Developer tooling: the identified pipelines (pre‑train → adapter → fine‑tune) map cleanly onto existing ML frameworks (Hugging Face Transformers, PyTorch Lightning), making it easier for engineers to prototype FM‑driven network functions.
• Security & compliance: the discussion of privacy‑preserving training (federated learning, differential privacy) offers a roadmap for building compliant AI services in regulated telecom environments.

Limitations & Future Work

• Scalability: Current FM sizes (hundreds of billions of parameters) are impractical for many edge deployments; more research is needed on lightweight, task‑specific distillation.
• Data heterogeneity: Aligning radio, visual, and textual modalities remains non‑trivial; standardized multimodal benchmarks for wireless are still missing.
• Real‑time guarantees: While latency‑reduction techniques are promising, rigorous latency‑bounded inference on commodity base‑station hardware has not been demonstrated.
• Explainability: Operators need transparent decision‑making; the survey notes a lack of tools to interpret FM outputs in the context of network policies.
• Future directions include: building open multimodal wireless datasets, developing modular FM “plug‑ins” for specific network functions, and integrating reinforcement‑learning loops that let FM agents continuously adapt to live network feedback.

Authors

• Han Zhang
• Mohammad Farzanullah
• Mohammad Ghassemi
• Akram Bin Sediq
• Ali Afana
• Melike Erol‑Kantarci

Paper Information

• arXiv ID: 2601.03181v1
• Categories: cs.NI, cs.AI, cs.CL, cs.CV
• Published: January 6, 2026
• PDF: Download PDF