Neuromorphic Computing and Reinforcement Learning for Optimizing 5G Network Performance

DOI

10.2991/978-94-6463-754-0_23How to use a DOI?

Keywords

Neuromorphic models; Reinforcement learning; Neuron model; Network optimization

Abstract

The research facilitates the combination of neuromorphic computing with reinforcement learning (RL) for optimizing 5G network performance. By simulating neuron models using the Brian2 framework and employing a deep Q-network (DQN) for network optimization, we demonstrate improvements in energy efficiency, latency, and throughput in a simulated 5G environment. The results highlight the potential of neuromorphic models for efficient network operation and optimization.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Palanivel Rajan
AU  - M. G. Rajendrakumar
PY  - 2025
DA  - 2025/06/30
TI  - Neuromorphic Computing and Reinforcement Learning for Optimizing 5G Network Performance
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 249
EP  - 257
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_23
DO  - 10.2991/978-94-6463-754-0_23
ID  - Rajan2025
ER  -