TY - GEN
T1 - Multi-Antenna Tuning Simulation Platform by Deep Reinforcement Learning
AU - Zhao, Ying
AU - Zhang, Keqiao
AU - Han, Rui
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Recently, communication technology is highly developed. The communication convenience that people enjoy is relying on a large number of base station antenna devices set up by major operating companies. When the parameters of the antennas in local area are adjusted reasonably, the Reference Signal Receiving Power (RSRP), Signal to Interference plus Noise Ratio (SINR) and other related indicators in the region will be at a reasonable level to ensure the communication quality of users. However, the number of antennas is huge, and manual adjustment of various parameters is bound to cost a lot of money and time. Therefore, a multi-antenna simulation platform is built in this paper, which applies reinforcement learning to self-learn the parameters of the antennas, and learns an optimal antenna tuning policy. Finally, the results are migrated to real antenna scenarios, which saves the cost of antenna adjustment and has high economic value. This paper proposed a method that apply multi-agent reinforcement learning technology to the multi-antenna tuning scene, and achieved good results in the simulation scene.
AB - Recently, communication technology is highly developed. The communication convenience that people enjoy is relying on a large number of base station antenna devices set up by major operating companies. When the parameters of the antennas in local area are adjusted reasonably, the Reference Signal Receiving Power (RSRP), Signal to Interference plus Noise Ratio (SINR) and other related indicators in the region will be at a reasonable level to ensure the communication quality of users. However, the number of antennas is huge, and manual adjustment of various parameters is bound to cost a lot of money and time. Therefore, a multi-antenna simulation platform is built in this paper, which applies reinforcement learning to self-learn the parameters of the antennas, and learns an optimal antenna tuning policy. Finally, the results are migrated to real antenna scenarios, which saves the cost of antenna adjustment and has high economic value. This paper proposed a method that apply multi-agent reinforcement learning technology to the multi-antenna tuning scene, and achieved good results in the simulation scene.
KW - RSRP
KW - SINR
KW - communication technology
KW - multi-agent reinforcement learning
KW - multi-antenna simulation platform
UR - http://www.scopus.com/inward/record.url?scp=85091922108&partnerID=8YFLogxK
U2 - 10.1109/ICSIDP47821.2019.9172941
DO - 10.1109/ICSIDP47821.2019.9172941
M3 - Conference contribution
AN - SCOPUS:85091922108
T3 - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
BT - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Y2 - 11 December 2019 through 13 December 2019
ER -