Dynamic Channel Estimation and Adaptive Network Slicing using CNN-LSTM

DOI

10.2991/978-94-6463-718-2_39How to use a DOI?

Keywords

Channel Estimation; CNN-LSTM Models; Network Slicing; Channel Quality Index; Dynamic Allocation; Modulation Schemes; 5G Optimization

Abstract

The rising complexity of 5G MIMO necessitates efficient channel estimation and resource allocation strategies to be able to assure high-quality communications in such environments. Traditionally, table-driven MCS and static network slicing approaches tend to fail dynamically during adaptive channel changes. The challenge is addressed using a CNN-LSTM architecture for dynamic channel estimation, predicting Channel Quality Index (CQI) and the optimal MCS by exploiting both spatial and temporal features. A new algorithm for dynamic slice allocation is proposed, slicing based on channel quality; it considers high-priority users with greater CQI while meeting their slice requirements. The system improves the modulation flexibility of slices, increases the efficiency of slicing, and achieves better utilization of high-quality channels. Comparative analysis shows better performance over the traditional methods. With dynamic estimation and slicing together, it maximizes throughput as well as reliability in systems while furthering resource management within 5G networks. Its applications are more significant in very dynamic channel conditions.

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  - K. Motheeswaran
AU  - S. Harshavartanan
AU  - Ezhilarasi
PY  - 2025
DA  - 2025/05/23
TI  - Dynamic Channel Estimation and Adaptive Network Slicing using CNN-LSTM
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 451
EP  - 463
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_39
DO  - 10.2991/978-94-6463-718-2_39
ID  - Motheeswaran2025
ER  -