Spiking Neural Networks for Resource Allocation in UAV-Enabled Wireless Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913975894016000 |
|---|---|
| author | Kouvakis, Vasileios Trevlakis, Stylianos E. Arapakis, Ioannis Boulogeorgos, Alexandros-Apostolos A. |
| author_facet | Kouvakis, Vasileios Trevlakis, Stylianos E. Arapakis, Ioannis Boulogeorgos, Alexandros-Apostolos A. |
| contents | This work presents a new spiking neural network (SNN)-based approach for user equipment-base station (UE-BS) association in non-terrestrial networks (NTNs). With the introduction of UAV's in wireless networks, the system architecture becomes heterogeneous, resulting in the need for dynamic and efficient management to avoid congestion and sustain overall performance. The presented framework compares two SNN-based optimization strategies. Specifically, a top-down centralized approach with complete network visibility and a bottom-up distributed approach for individual network nodes. The SNN is based on leak integrate-and-fire neurons with temporal components, which can perform fast and efficient event-driven inference. Realistic ray-tracing simulations are conducted, which showcase that the bottom-up model attains over 90\% accuracy, while the top-down model maintains 80-100\% accuracy. Both approaches reveal a trade-off between individually optimal solutions and UE-BS association feasibility, thus revealing the effectiveness of both approaches depending on deployment scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03279 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spiking Neural Networks for Resource Allocation in UAV-Enabled Wireless Networks Kouvakis, Vasileios Trevlakis, Stylianos E. Arapakis, Ioannis Boulogeorgos, Alexandros-Apostolos A. Signal Processing This work presents a new spiking neural network (SNN)-based approach for user equipment-base station (UE-BS) association in non-terrestrial networks (NTNs). With the introduction of UAV's in wireless networks, the system architecture becomes heterogeneous, resulting in the need for dynamic and efficient management to avoid congestion and sustain overall performance. The presented framework compares two SNN-based optimization strategies. Specifically, a top-down centralized approach with complete network visibility and a bottom-up distributed approach for individual network nodes. The SNN is based on leak integrate-and-fire neurons with temporal components, which can perform fast and efficient event-driven inference. Realistic ray-tracing simulations are conducted, which showcase that the bottom-up model attains over 90\% accuracy, while the top-down model maintains 80-100\% accuracy. Both approaches reveal a trade-off between individually optimal solutions and UE-BS association feasibility, thus revealing the effectiveness of both approaches depending on deployment scenarios. |
| title | Spiking Neural Networks for Resource Allocation in UAV-Enabled Wireless Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2508.03279 |