TY - GEN
T1 - Power Control in massive MIMO Networks Using Transfer Learning with Deep Neural Networks
AU - Ahmadi, Neda
AU - Mporas, Iosif
AU - Papazafeiropoulos, Anastasios
AU - Kourtessis, Pandelis
AU - Senior, John
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
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 - deep neural network
KW - machine learning
KW - massive MIMO
KW - power control
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85144035004&partnerID=8YFLogxK
U2 - 10.1109/CAMAD55695.2022.9966903
DO - 10.1109/CAMAD55695.2022.9966903
M3 - Conference contribution
AN - SCOPUS:85144035004
T3 - IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
SP - 89
EP - 93
BT - 2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 27th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022
Y2 - 2 November 2022 through 3 November 2022
ER -