Name
Abstract | ||
|---|---|---|
Recent literature demonstrated promising results of Long-Term Evolution (LTE) deployments over unlicensed bands when coexisting with Wi-Fi networks via the Duty-Cycle (DC) approach. However, it is known that performance in coexistence is strongly dependent on traffic patterns and on the duty-cycle ON–OFF rate of LTE. Most DC solutions rely on static coexistence parameters configuration, hence real-life performance in dynamically varying scenarios might be affected. Advanced reinforcement learning techniques may be used to adjust DC parameters towards efficient coexistence, and we propose a Q-learning Carrier-Sensing Adaptive Transmission mechanism which adapts LTE duty-cycle ON–OFF time ratio to the transmitted data rate, aiming at maximizing the Wi-Fi and LTE-Unlicensed (LTE-U) aggregated throughput. The problem is formulated as a Markov decision process, and the Q-learning solution for finding the best LTE-U ON–OFF time ratio is based on the Bellman’s equation. We evaluate the performance of the proposed solution for different traffic load scenarios using the ns-3 simulator. Results demonstrate the benefits from the adaptability to changing circumstances of the proposed method in terms of Wi-Fi/LTE aggregated throughput, as well as achieving a fair coexistence. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11235-018-00535-7 | Telecommunication Systems |
Keywords | Field | DocType |
LTE, LTE-LAA, LTE-U, Wi-Fi, Q-Learning | Adaptability,Time ratio,Traffic load,Computer science,Q-learning,Markov decision process,Real-time computing,Data rate,Throughput,Reinforcement learning | Journal |
Volume | Issue | ISSN |
71 | 4 | 1572-9451 |
Citations | PageRank | References |
2 | 0.38 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro M. de Santana | 1 | 2 | 0.38 |
| Vicente A. de Sousa Jr. | 2 | 92 | 9.52 |
| Fuad M. Abinader Jr. | 3 | 162 | 15.49 |
| José M. de C. Neto | 4 | 2 | 0.38 |