Name
Abstract | ||
|---|---|---|
This paper compares classical algorithms for adaptive coding and modulation (ACM) with an approach based on artificial intelligence (AI). The proposed approach utilizes an online random regression forest (ORRF) to predict time series of signal to noise ratio (SNR) values aiding the ACM switching decisions. The evaluation of the ACM algorithms is based on two years of Q/V-band channel data recorded at the ground station in Graz using the Alphasat experimental Q/V-band payload. The results indicate that the ORRF based approach could outperform the classical approaches in terms of spectral efficiency, and parameterization of the ORRF is simpler and needs less knowledge of the channel properties as the discussed classical ACM approaches. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ASMS/SPSC48805.2020.9268889 | 2020 10th Advanced Satellite Multimedia Systems Conference and the 16th Signal Processing for Space Communications Workshop (ASMS/SPSC) |
Keywords | DocType | ISSN |
Adaptive coding and modulation,Q/V-band,machine learning,artificial intelligence | Conference | 2329-7093 |
ISBN | Citations | PageRank |
978-1-7281-5795-5 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Ebert | 1 | 0 | 2.03 |
| Werner Bailer | 2 | 328 | 47.96 |
| Joel Flavio | 3 | 0 | 0.34 |
| Karin Plimon | 4 | 0 | 2.03 |
| martin winter | 5 | 3 | 2.21 |