TY - GEN
T1 - Comparative Study of a Deterministic Adaptive Beamforming Technique with Neural Network Implementations
AU - Mallioras, Ioannis
AU - Zaharis, Zaharias D.
AU - Lazaridis, Pavlos
AU - Yioultsis, Traianos V.
AU - Kantartzis, Nikolaos V.
AU - Chochliouros, Ioannis P.
N1 - Funding Information:
Acknowledgements. This research was supported by the European Union, partially through the Horizon 2020 Marie Skłodowska-Curie Innovative Training Networks Programme “Mobility and Training for beyond 5G Ecosystems (MOTOR5G)” under grant agreement no. 861219, and partially through the Horizon 2020 Marie Skłodowska-Curie Research and Innovation Staff Exchange Programme “Research Collaboration and Mobility for Beyond 5G Future Wireless Networks (RECOMBINE)” under grant agreement no. 872857.
Funding Information:
This research was supported by the European Union, partially through the Horizon 2020 Marie Skłodowska-Curie Innovative Training Networks Programme “Mobil-ity and Training for beyond 5G Ecosystems (MOTOR5G)” under grant agreement no. 861219, and partially through the Horizon 2020 Marie Skłodowska-Curie Research and Innovation Staff Exchange Programme “Research Collaboration and Mobility for Beyond 5G Future Wireless Networks (RECOMBINE)” under grant agreement no. 872857.
Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.
PY - 2022/6/17
Y1 - 2022/6/17
N2 - Future wireless networks depend on the development of new mechanisms that can increase the efficiency of the network. Antenna array adaptive beamforming (ABF) is an antenna operation that can be significantly improved with the use of machine learning. In this paper, a deterministic beamforming technique is compared with two different types of neural networks (NNs). These are the non-linear autoregressive network with exogenous inputs (NARX) and the recurrent NN (RNN) with long short-term memory (LSTM) units. To train the NNs, we produce a dataset using the minimum variance distortionless algorithm (MVDR) applied to a realistic antenna array. Using grid search, we find the best architecture for both NNs. Then, we train the final models and evaluate them by comparing their accuracy to that of the MVDR algorithm. We demonstrate how the use of NNs is preferable to that of deterministic algorithms as they appear to maintain high accuracy while having a much lower response time than that of deterministic algorithms. The RNN with LSTM units is the most promising out of the two NN models as it achieves higher accuracy with a slightly shorter training time.
AB - Future wireless networks depend on the development of new mechanisms that can increase the efficiency of the network. Antenna array adaptive beamforming (ABF) is an antenna operation that can be significantly improved with the use of machine learning. In this paper, a deterministic beamforming technique is compared with two different types of neural networks (NNs). These are the non-linear autoregressive network with exogenous inputs (NARX) and the recurrent NN (RNN) with long short-term memory (LSTM) units. To train the NNs, we produce a dataset using the minimum variance distortionless algorithm (MVDR) applied to a realistic antenna array. Using grid search, we find the best architecture for both NNs. Then, we train the final models and evaluate them by comparing their accuracy to that of the MVDR algorithm. We demonstrate how the use of NNs is preferable to that of deterministic algorithms as they appear to maintain high accuracy while having a much lower response time than that of deterministic algorithms. The RNN with LSTM units is the most promising out of the two NN models as it achieves higher accuracy with a slightly shorter training time.
KW - Adaptive beamforming
KW - Antenna array
KW - Long short-term memory (LSTM)
KW - Neural network (NN)
KW - Non-linear autoregressive network with exogenous inputs (NARX)
KW - Recurrent neural network (RNN)
UR - http://www.scopus.com/inward/record.url?scp=85133226074&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-08341-9_9
DO - 10.1007/978-3-031-08341-9_9
M3 - Conference contribution
AN - SCOPUS:85133226074
SN - 9783031083402
VL - 652
T3 - IFIP Advances in Information and Communication Technology
SP - 98
EP - 107
BT - Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops - MHDW 2022, 5G-PINE 2022, AIBMG 2022, ML@HC 2022, and AIBEI 2022, Proceedings
A2 - Maglogiannis, Ilias
A2 - Iliadis, Lazaros
A2 - Macintyre, John
A2 - Cortez, Paulo
PB - Springer, Cham
T2 - 11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022
Y2 - 17 June 2022 through 20 June 2022
ER -
