TY - GEN
T1 - Power control in massive MIMO networks using transfer learning with deep neural networks
AU - Ahmadi, Neda
AU - Mporas, Iosif
AU - Papathanasopoulos, Anastasios
AU - Kourtessis, Pandelis
AU - Senior, John
N1 - Organising Body: The Institute of Electrical and Electronics Engineers (IEEE)
PY - 2022/11/2
Y1 - 2022/11/2
N2 - Power control (PC) plays a crucial role in massive multiple-input-multiple-output (mMIMO) networks. There are several heuristic algorithms, like the weighted mean square error (WMMSE) algorithm, used to optimise the PC. In order these algorithms to perform the power allocation they require high computational power. In this paper, we address this problem through the application of machine learning (ML)-based algorithms as they can produce close to optimal solutions with a very low computational complexity. We propose the use of transfer learning with deep neural networks (TLDNN) under the objective of maximising the sum spectral efficiency (SE). The evaluation results demonstrate that the TLDNN approach outperforms the deep neural network (DNN) based PC and is twice faster than the WMMSE based PC.
AB - Power control (PC) plays a crucial role in massive multiple-input-multiple-output (mMIMO) networks. There are several heuristic algorithms, like the weighted mean square error (WMMSE) algorithm, used to optimise the PC. In order these algorithms to perform the power allocation they require high computational power. In this paper, we address this problem through the application of machine learning (ML)-based algorithms as they can produce close to optimal solutions with a very low computational complexity. We propose the use of transfer learning with deep neural networks (TLDNN) under the objective of maximising the sum spectral efficiency (SE). The evaluation results demonstrate that the TLDNN approach outperforms the deep neural network (DNN) based PC and is twice faster than the WMMSE based PC.
KW - Computer science and informatics
U2 - 10.1109/CAMAD55695.2022.9966903
DO - 10.1109/CAMAD55695.2022.9966903
M3 - Conference contribution
SN - 9781665461306
T3 - IEEE International Workshop on Computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD
SP - 89
EP - 93
BT - Proceedings of 2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
PB - Institute of Electrical and Electronics Engineers
CY - Piscataway, U.S.
T2 - IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
Y2 - 2 November 2022 through 3 November 2022
ER -