[hpc-announce] AIxNET 2026 - Call for Papers - Due Date: June 20th, 2026

[qkun at ieee.org]

[Apologies if you got multiple copies of this email. If you'd like to opt out of these announcements, information on how to unsubscribe is available at the bottom of this email.]

********************************************************************

CALL FOR PAPERS – AIxNET 2026

International Conference on Interconnected AI and NETworks 2026

Nov. 23-25, 2026 – Paris, France

https://urldefense.us/v3/__https://aixnet.dnac.org/__;!!G_uCfscf7eWS!YXPlCHx9g6WaN8qbL2MOPUYQOrvbECpQ0ueQrjShPI1ZQzvN7kxkKzqa22GK7bOkFm0XXysn3ubzfM1ebQ$ 

********************************************************************

Important dates

Paper submission deadline: June 20, 2026

Paper Acceptance Notification: September 15, 2026

Camera ready: October 5, 2026

Conference Date: November 23-25, 2026

Networks are entering an era where both classical ML and emerging generative and agentic AI are transforming end-to-end networking—from intent capture to closed-loop control across RAN, Core, transport, and edge/cloud. AIxNET welcomes contributions that advance algorithms, architectures, protocols, evaluations, and safeguards for trustworthy, explainable, and safe-to-operate AI-driven networking. We particularly encourage rigorous comparative studies across control layers (SMO/intent vs near-RT vs lower-layer control), and the release of open datasets and artifacts to help the community build together.

AIxNET is intending to build a stimulating, open, dynamic, and friendly forum to co-create the future and spark collaborations across teams. The conference will be a unique opportunity to gather academic and industry research on this crucial topic for 2030 networks. Expect interactive sessions, demos, and time for discussion.

Topics of Interest include, but are not limited to:

1. Agentic AI: from Human Intent to Action Autonomy
   - Networked "xLM" challenges: Intent capture/parsing/policy synthesis at SMO and service layers, use of Large, Small or Machine Language Models (LLM, SLM, MLM)
   - Hierarchical/heterogeneous agents spanning non-RT and near-RT control (e.g., O-RAN RIC), Core CNFs, and edge resources
   - Agentic 6G functions
   - Interconnection and collaboration between AI agents
   - Tool and protocols for network-facing agents (e.g., MCP-enabled clients/servers), conflict resolution, safe rollbacks

2. New paradigms for networking: from Classical ML to xLM-based Control at Scale
   - Supervised/unsupervised/self-supervised learning for prediction, anomaly detection, resource allocation, QoE optimization
   - ML and LLM techniques for scheduling, slicing, mobility, energy saving; cross-domain orchestration across RAN/Core/transport for B5G and 6G
   - Programmable data planes (P4/eBPF) and SDN control plane with ML-in-the-loop; NWDAF-enabled analytics
   - Challenges for access networks and edge networking, use of alternative models, SLM, TRM
   - Architecture and framework for agentic AI networking; Data collection and labeling

3. Machine Learning and Artificial Intelligence for the Physical Layer
   - AI/ML for PHY layer optimization: new air interfaces, waveforms, modulation and coding techniques
   - AI/ML-augmented next generation multiple access techniques (SDMA, NOMA, RSMA)
   - Physical layer AI/ML techniques for Massive MIMO; Cell-free and distributed massive MIMO; Massive, Ultra-Massive, extreme, and fluid MIMO
   - Integrated sensing and communication (ISAC)
   - AI/ML-based waveform design techniques tailored for emerging multi-antenna solutions, including different RIS architectures, XL-MIMO, pinching antennas, moveable antennas, fluid antennas, etc.
   - ML for Terahertz and Millimeter Wave communications
   - AI/ML for wireless/optical/satellite networks

4. Open, Programmable and AI-Native Radio Access Network and nodes
   - Intelligent radio resource management and spectrum allocation
   - Self-organizing networks (SON) and autonomous network management
   - AI-native air interface design for 6G
   - Autonomous satellite network management and orchestration
   - AI for LEO/MEO/GEO constellation optimization; Integrated terrestrial-satellite network intelligence
   - AI and ML for optical systems and networks; Optical network control and management; elastic optical networks and software-defined optics; Digital twins for optical network planning and optimization

5. Comparative Designs Across Layers: SMO/Intent vs Near-RT vs Lower-Layer Control
   - Side-by-side evaluations of top-down (intent-driven) vs bottom-up (local) autonomy
   - Responsibility split across SMO policies, RIC xApps/rApps, Core functions, device/edge controllers
   - Stability, latency and safety; arbitration under competing objectives (QoE, energy, cost, SLAs)
   - Cross-layer observability, auditability, and explainability methodologies

6. Explainability and trustworthiness: Bias and Functional Safety
   - Human in the loop supervision and autonomy levels for safe operations
   - Explainability for operator oversight (pre/post methods, rationales, provenance, accountability logs)
   - Security and governance for AI-operated changes (access control, authorization, verification, compliance-by-design)
   - Possible Bias sources and mitigation (data, prompts, tools, policies); fairness in resource allocation and service admission
   - Trust, safety and ethical considerations in generative and agentic AI networking

7. Evaluation, Benchmarks, Open Datasets, and experimentations
   - Public datasets/benchmarks for RAN/Core/transport/edge; simulated vs real testbeds
   - Evaluation methodology and built of meaningful KPIs (e.g., relying on MTTR, SLO, energy-QoE trade-offs...)
   - Network performance metric in generative and agentic AI communication systems
   - Digital twins, experimentation platforms, and testbeds for generative and agentic AI networking
   - Reproducible pipelines, artifact sharing, and insightful negative results, robustness to drift
   - Sustainability and cost modeling (e.g., compute budgets, edge vs cloud placement)

Paper Submission:

Authors are invited to submit original contributions that have not been published or submitted for publication elsewhere. Papers should be prepared using the IEEE 2-column conference style and are limited to 8 pages including references for regular papers, 5 pages including references for short papers and 2-4 pages for Demos/Positions. Papers must be submitted electronically in PDF format through EDAS at: https://urldefense.us/v3/__https://edas.info/N35032__;!!G_uCfscf7eWS!YXPlCHx9g6WaN8qbL2MOPUYQOrvbECpQ0ueQrjShPI1ZQzvN7kxkKzqa22GK7bOkFm0XXysn3uZLSrSiog$ 

Open artifacts are encouraged: release code/data/measurement scripts when possible; otherwise provide high-fidelity synthetic surrogates or detailed reproduction recipes. Comparative studies must clearly state the targeted control layer(s) and report stability/latency/safety metrics alongside performance.

For accepted papers to be included on AIxNEt 2026 proceedings, at least one author must register at the Author rate and papers must be presented in-person at the conference by a registered co-author.

Technical Program Co-Chairs:

Sahar Hoteit (Paris-Saclay University, France)
Chiara Contoli (University of Urbino, Italy)

General Co-Chairs:

Stefano Secci (CNAM, France)
Emmanuel Bertin (Orange Innovation, France)

--
Dr. Kun Qiu
Assistant Professor
Institute of Space Internet
Fudan University, China
qkun at fudan.edu.cn
qkun at ieee.org