SOFTT 2026 (Symposium on Future Telecommunication Technologies) is an academic conference held in Yogyakarta, Indonesia on 2026-11-30. The paper submission deadline is 2026-07-20. Acceptance notifications are sent on 2026-09-20.
The 2026 8th Symposium on Future Telecommunication Technologies (SOFTT) provide a forum for researchers, academicians, professionals, and students from various engineering fields and with cross-disciplinary interests Artificial Intelligence and Communications Technology to interact and disseminate information on the latest developments.
Prospective authors are invited to submit original technical papers for presentation at the conference and publication in the conference proceedings. Accepted papers will be submitted for possible inclusion into IEEE Xplore.
We invites papers, original & unpublished work from individuals active in the broad theme of the conference. Potential topics include, but are not limited to:
Research Topics
1. Fundamental B5G
Channel Coding for 5G, 5.5G and 6G
Molecular, Biological and Multi-Scale Communications
Reconfigurable Intelligent Surfaces (RIS)
NOMA for 5G, 5.5G and 6G
Orbital Angular Momentum (OAM) and Free Space Optics
Intelligent Reflecting Surfaces and Meta-materials
Non-Terrestrial Networks (NTN): HAPS, LEO, and direct-to-device
THz, mmWave and Optical Wireless Communications
Semantic Communication Models and Theories
Semantic Communication with Federated Learning
MIMO, Massive MIMO, and Beamforming Advances
AI-Driven Channel Coding for 6G
RIS-Assisted Deep Learning for Beamforming
THz Imaging and Sensing
2. Key Enablers
Aerial Communication (UAV, drones, air-to-ground)
Backhaul and Fronthaul Communication for 6G
Smart-Grid Communication and IoT Connectivity
Big Data and Cloud Computing
Edge and Fog Computing
Software Defined Networking (SDN) and Network Slicing
Network Virtualization and Open RAN
Ethics, Privacy, and Regulation in Future Telecommunication
Green Communication and Energy Harvesting Networks
Satellite-Terrestrial Integration and Hybrid Networks
AI and Machine Learning for Communication Networks
Cognitive Radio and Spectrum Sharing with AI
Blockchain Technology for Secure Communication Systems
Quantum Communication and Quantum Key Distribution (QKD)
Quantum Error Correction Codes (QECC) for Future Networks
Post-Quantum Cryptography and Physical Layer Security
Edge AI for IoT and Smart-Grids
Blockchain-Based Spectrum Sharing
Quantum Machine Learning for Network Security
Green AI for Energy-Efficient Networks
General Engineering
3. Implementation
Emergency Communication and Disaster Resilience Networks
Agricultural Communication and Smart Farming Technologies
Future Technologies in E-Health and Remote Monitoring
Communication in Cyber-Physical Systems and Industrial IoT
Vehicular Communication and V2X Systems
Smart City Communication Infrastructure
Human-Centric Networking and BCI-Enabled Communications
Remote Sensing and Environmental Monitoring Networks
Computer Vision for V2X Systems
YOLO-Based Object Detection in UAV Networks
AI-Powered Remote Health Monitoring
Cybersecurity for Smart City Infrastructure
4. Signal Processing
Advanced Signal Processing in Wireless Communication
Advanced Multimedia Signal Processing
Advanced Artificial Intelligence and IoT Applications
AI-Driven Semantic and Intent-Based Networking
Federated Learning in Wireless Systems
Signal Processing for THz, mmWave, and RIS
Quantum Signal Processing and Quantum Coding
Compressive Sensing and Sparse Signal Processing
Signal Intelligence and Detection Techniques
Speech, Audio, and Image Signal Processing for Communications
Multimodal and Cross-modal Signal Processing
Cryptography and Information Hiding
Deep Learning for mmWave Signal Reconstruction
Image/Video Compression for 6G Multimedia
Federated Learning for Privacy-Preserving Signal Processing
VLSI Architectures for AI-Driven Signal Processing
Biomedical Signal Processing for Telemedicine
AI-Enhanced Medical Image Compression and Transmission
Neural Signal Processing for Brain-Computer Interfaces (BCI)
Terahertz (THz) Imaging for Biomedical Applications
Machine Learning and Deep Learning Applications
아직 댓글이 없습니다.