• Intelligent cloud / edge / cloud-edge collaborative communications
• ML-driven resource allocation (spectrum, power, computation, bandwidth)
• Green and energy-aware intelligent communications (sustainable network design)
• Intelligent software-defined radios (SDR) and flexible communication architectures
• Intelligent massive MIMO, beamforming, and dynamic antenna configuration
• Cooperative and distributed coding & signal processing for intelligent networks
• Wireless sensor networks (WSN) and IoT: ML-optimized data transmission
• Satellite and radar communications: intelligent signal processing and anti-interference
• Cognitive radio networks: ML-based spectrum sensing and sharing
• Federated learning (FL) and distributed learning for secure communication systems
• Multi-modality learning for cross-media communication (text, image, audio, video)
• Pre-trained models and transfer learning in intelligent communications
• Generative AI for communication content generation and error correction
• ML-driven network security, privacy preservation, and trust management

• Industrial IoT (IIoT) communication optimization for smart factories
• ML-driven predictive maintenance via real-time industrial data transmission
• Intelligent communication in collaborative robotics and automated production lines

• Intelligent remote teaching communication platforms (adaptive bandwidth allocation)
• ML-based personalized learning: communication optimization for educational data
• Secure transmission of educational big data and cross-institutional collaboration
• AI-driven interactive communication tools

• Smart governance communication platforms
• Secure communication for e-government
• AI-driven traffic and transportation communication
• Environmental monitoring: ML-based communication for sensor networks

• Intelligent unmanned systems (vehicles, drones): communication and adaptive sensing
• Accessible communication: ML-optimized solutions for people with disabilities
• ML-based semantic communication and cultural adaptation
• Digital archaeology: communication optimization for remote sensing data
• AI-assisted preservation of endangered languages: intelligent speech and text communication
• ML-based analysis of humanistic communication patterns

• Datasets and evaluation metrics for ML-empowered intelligent communications
• Low-resource language processing in cross-lingual communication
• Edge AI for resource-constrained communication devices
• Quantum machine learning for next-generation secure communications
• Ethical and regulatory issues of AI-driven intelligent communications

Accepted and presented posters and demos will be submitted for publication by Springer and made available through SpringerLink Digital Library.

Posters and demos will be part of the EAI MLICOM 2026 Conference Proceedings in a non-indexed Annex section.

Poster and demo submissions should be minimum 4 pages in length, and be formatted according to the Springer LNICST Authors’ Kit. Paper titles must begin with the prefix “Poster:” or “Demo:”. 

Please note that a shorter version of already published work is not suitable for this session. Authors willing to present a poster or a demo will submit a short paper describing their poster or demo. We expect most works to include a short introduction, a description of the employed methods and, if applicable, results.

Papers must be formatted using the Springer LNICST Authors’ Kit.

Instructions and templates are available from Springer’s LNICST homepage:

• LaTeX2e Proceedings Templates (zip)
• Microsoft Word Proceedings Templates (zip)

Please make sure that your paper adheres to the format as specified in the instructions and templates.

When uploading the camera-ready copy of your paper, please be sure to upload both:

• a PDF copy of your paper formatted according to the above templates, and
• an archive in .ZIP file, containing LaTeX or Word source material prepared according to the above guidelines.