Volume 2 (2021), Issue 4 – AI and machine learning solutions in 5G andfuture networks
Challenge Organizers’ Editorial
Editorial Board
Table of Contents
List of Abstracts
GRAPH‑NEURAL‑NETWORK‑BASED DELAY ESTIMATIONFOR COMMUNICATION NETWORKSWITH HETEROGENEOUS SCHEDULING POLICIES
     1. INTRODUCTION
     2. RELATED WORK
     3. SETTING
     4. PROPOSED SOLUTION
     5. CONCLUSION
     ACKNOWLEDGMENTS
     REFERENCES
     AUTHORS
SITE‑SPECIFIC MILLIMETER‑WAVE COMPRESSIVE CHANNEL ESTIMATIONALGORITHMS WITH HYBRID MIMO ARCHITECTURES
     1. INTRODUCTION
     2. SYSTEM MODEL
     3. MLGS‑SBL
     4. PCSBL‑DDT
     5. PC‑OMP
     6. NUMERICAL RESULTS
     7. CONCLUSION
     ACKNOWLEDGEMENTS
     REFERENCES
     AUTHORS
ENHANCED SHARED EXPERIENCES IN HETEROGENEOUS NETWORK WITH GENERATIVE AI
     1. INTRODUCTION
     2. BACKGROUND
     3. RELATED WORK
     4. ARCHITECTURAL DESIGN OF SPEECHDRIVEN VIDEO SYNTHESIS
     5. LOSSES
     6. QUALITY OF EXPERIENCE (QOE)
     7. EXPERIMENTS
     8. PSYCHOPHYSICAL ASSESSMENT
     9. TURING TEST
     10. CONCLUSIONS AND FUTURE WORK
     REFERENCES
     AUTHORS
A DYNAMIC Q‑LEARNING BEAMFORMING METHOD FOR INTER‑CELL INTERFERENCEMITIGATION IN 5G MASSIVE MIMO NETWORKS
     1. INTRODUCTION
     2. RELATED WORK
     3. SYSTEM MODEL
     4. THE PROPOSED REINFORCEMENTLEARNING ASSISTED BEAMFORMING
     5. SIMULATIONS AND DISCUSSIONS
     6. CONCLUSION
     REFERENCES
     AUTHORS
NETXPLAIN: REAL‑TIME EXPLAINABILITY OFGRAPH NEURAL NETWORKS APPLIED TO NETWORKING
     1. INTRODUCTION
     2. BACKGROUND
     3. RELATED WORK
     4. PRELIMINARIES
     5. NETXPLAIN: PROPOSED EXPLAINABILITYMETHOD
     6. EVALUATION
     7. DISCUSSION ON POSSIBLE APPLICATIONS
     8. CONCLUSIONS
     ACKNOWLEDGEMENT
     REFERENCES
     AUTHORS
MACHINE LEARNING FOR PERFORMANCE PREDICTION OF CHANNEL BONDING INNEXT‑GENERATION IEEE 802.11 WLANS
     1. INTRODUCTION
     2. CHANNEL BONDING IN NEXTGENERATIONWLANS
     3. MACHINE LEARNING SOLUTIONS FORTHROUGHPUT PREDICTION
     4. PERFORMANCE EVALUATION
     5. DISCUSSION
     ACKNOWLEDGEMENT
     REFERENCES
     AUTHORS
AI-BASED NETWORK TOPOLOGY OPTIMIZATION SYSTEM
     1. INTRODUCTION
     2. LSTM Model for Traffic Forecasting
     3. Network Topology Analysis for Optimization
     4. CONCLUSION
     REFERENCES
     AUTHORS
APPLYING MACHINE LEARNING IN NETWORK TOPOLOGY OPTIMIZATION
     1. Background
     2. Solution
     3. Results and Discussion
     REFERENCES
     AUTHORS
ANALYSIS ON ROUTE INFORMATION FAILURE IN IP CORE NETWORKS BY NFV‑BASED TEST ENVIRONMENT
     1. INTRODUCTION
     2. RELATED WORK
     3. METHODOLOGY
     4. EXPERIMENTATION AND EVALUATION
     5. CONCLUSION
     6. FUTURE WORK
     REFERENCES
     AUTHORS
SIMULATION OF MACHINE LEARNING‑BASED 6G SYSTEMSIN VIRTUAL WORLDS
     1. INTRODUCTION
     2. 6G SIMULATION IN VIRTUAL WORLDS
     3. IMPROVEMENTS ON RAYMOBTIME METHODOLOGY
     4. CAVIAR SIMULATION RESULTS
     5. CONCLUSIONS
     REFERENCES
     AUTHORS
Index of Authors