Lei Yang , Juan A. Fraire , Kanglian Zhao , Ruhai Wang , Wenfeng Li , Hong Yang
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Optimizing deep-space DTN congestion control via deep reinforcement learning
This paper introduces an innovative congestion control mechanism for delay/disruption-tolerant networking (DTN) within deep-space communication systems, leveraging the nuanced capabilities of deep reinforcement learning (DRL). This approach significantly departs from traditional methods, addressing the unique challenges of deep-space data transmissions. The proposed DRL-based strategy demonstrates a superior balance of critical factors, including transmission delay, energy efficiency, and data reception integrity. We assess our approach through meticulous simulation and comparison with established benchmark schemes. The findings underscore the mechanism’s enhanced performance metrics, positing it as an appealing solution in the evolving landscape of non-terrestrial networking.
期刊介绍:
Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.