Machine Learning-Based Spectrum Utilization Prediction for Dynamic Spectrum Sharing
| dc.contributor.advisor | Dereje Hailemariam (PhD) | |
| dc.contributor.author | Tewodros Abebe | |
| dc.date.accessioned | 2023-12-14T14:48:17Z | |
| dc.date.available | 2023-12-14T14:48:17Z | |
| dc.date.issued | 2023-09 | |
| dc.description.abstract | Dynamic Spectrum Sharing (DSS) is a promising technology for improving the performance of heterogeneous wireless networks. DSS allows Fifth Generation New Radio (5G NR) to be deployed in the same frequency bands as Fourth Generation (4G) Long Term Evolution (LTE), which can help to increase cell capacity and improve the overall network performance. Machine Learning (ML) can be used to improve the efficiency of DSS by helping to predict future spectrum utilization and allocate resources accordingly. ML algorithms can be trained on historical data to identify patterns in spectrum usage and learn the behavior of different users. This information can then be used to make predictions about future spectrum utilization and allocate resources accordingly, in a way that minimizes interference and maximizes throughput. This work proposes an ML-based approach to dynamically distribute spectrum resources between 4G LTE and 5G NR users in a way that meets the traffic requirements of each user and optimizes link-level performance at varying Signal-to-Noise Ratio (SNR) points. Two ML models, namely, Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) models are developed for the spectrum utilization prediction. The Resource Element (RE)-level rate-matching DSS technique is evaluated by using 24-hour sample data from ML prediction results. This thorough assessment encompasses measuring throughput, and spectral efficiency at various SNR points. The proposed model’s performance is compared with that of the static spectrum-sharing technique. The results show that the CNN algorithm-based model can be the best input for the DSS controller to distribute spectrum resources for both technologies optimally. The model can predict the next 6 hours eNodeB (eNB) spectrum utilization with an Root Mean Square Error (RMSE) value of 1.3. Based on the prediction results, the average 4G LTE and 5G NR throughput per day is 8.7 and 107.7216 Mbps, respectively. Furthermore, the overall cell spectral efficiency is increased to 5.82 bits/sec/Hz. LTE performance is not affected by DSS when compared to an existing non-sharing network and Static Spectrum Sharing (SSS). However, NR experiences 32.54% of performance degradation. The proposed ML-based DSS technique can significantly improve the performance of DSS by dynamically allocating spectrum resources to LTE and 5G NR users. The CNN algorithm-based model is shown to be the best model for spectrum utilization prediction. | |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/949 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Dynamic Spectrum Sharing (DSS), Machine Learning (ML), Spectrum Utilization, Long Term Evolution (LTE), and Fifth Generation (5G) | |
| dc.title | Machine Learning-Based Spectrum Utilization Prediction for Dynamic Spectrum Sharing | |
| dc.type | Thesis |