| Autor: |
Braga, Iran M., Cavalcante, Eduardo de O., Fodor, Gabor, Silva, Yuri C. B., e Silva, Carlos F. M., Freitas, Walter C. |
| Zdroj: |
IEEE Communications Letters; Dec2020, Vol. 24 Issue 12, p2809-2813, 5p |
| Abstrakt: |
Maximizing the rate in multiple input single output (MISO) systems using distributed algorithms is an important task that typically incurs high computational cost. In this work, we propose two deep Q-learning-based user scheduling schemes to solve the beamforming problem of sum-rate maximization with per base station power constraints in multicell MISO scenarios. The two key features of the proposed algorithms are that they are executed in a distributed fashion and are robust with respect to channel state information (CSI) errors. Simulation results show that in the presence of CSI errors the proposed schemes outperform state-of-the-art algorithms both in terms of average spectral efficiency and execution time. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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