Collaborative Split Learning-Based Dynamic Bandwidth . . . This paper introduced a novel Collaborative Split Learning-based Dynamic Bandwidth Allocation (CSL-DBA) framework designed to meet the evolving demands of next-generation 6G-grade TDM-PON networks
A deep learning based dynamic bandwidth allocation method for . . . In the context of 5G and beyond era, the use of Time Division Multiplexed Passive Optical Network (TDM-PON) for mobile fronthaul (MFH) in centralized cloud radio access network (CRAN) has proven to be an optimal solution for addressing low-latency requirements
Deep Learning-Based Dynamic Bandwidth Allocation for Future . . . In this paper, we propose a novel DBA approach that employs deep learning to predict the bandwidth demand of end-users so that the control overhead due to the request-grant mechanism in NG-EPON is reduced, thereby increasing the bandwidth utilization
Collaborative Split Learning-Based Dynamic Bandwidth . . . Abstract Read online Dynamic Bandwidth Allocation (DBA) techniques enable Time Division Multiplexing Passive Optical Network (TDM-PON) systems to efficiently manage upstream bandwidth by allowing the centralized Optical Line Terminal (OLT) to coordinate resource allocation among distributed Optical Network Units (ONUs)
Collaborative Split Learning-Based Dynamic Bandwidth . . . This work suggests a Collaborative Split Learning-Based DBA (CSL-DBA) framework that utilizes the recently emerging Split Learning (SL) technique between the OLT and ONUs for the objective of optimizing predictive traffic adaptation and reducing communication overhead