Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication

A significant innovation for future indoor wireless networks is the use of the mmWave frequency band. However, an important challenge comes from the restricted propagation conditions in this band, which necessitates the use of beamforming and associated beam management procedures, including, for instance, beam tracking or beam prediction. A possible solution to the beam management problem is to use artificial-intelligence-based procedures to learn the hidden spatial propagation patterns of the channel and to use this knowledge to predict the best beam directions. In this paper, we present a deep-neural-network-based method that has memory that can be used to predict the best reception directions for moving users. The best direction is the highest expected signal level at the next moment. The resulting method allows for a user-side antenna management system. The result was evaluated using three different metrics, thus detailing not only its predictive ability, but also its usability.

[1]  Ayodeji Olalekan Salau,et al.  Multiband Millimeter Wave Phased Array Antenna Design for 5G Communication , 2022, 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT).

[2]  Ayodeji Olalekan Salau,et al.  Interference mitigation technique for self optimizing Picocell indoor LTE-A networks , 2022, Telecommunication Systems.

[3]  L. Csurgai-Horváth,et al.  Classification of Indoor Environment in Neural Network Controlled FR2-band Communication , 2022, 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET).

[4]  C. Sarris,et al.  Toward Physics-Based Generalizable Convolutional Neural Network Models for Indoor Propagation , 2022, IEEE Transactions on Antennas and Propagation.

[5]  A. Pikrakis,et al.  Deep Learning-Based Indoor Localization Using Multi-View BLE Signal , 2022, Sensors.

[6]  Y. You,et al.  Online learning-based beam and blockage prediction for indoor millimeter-wave communications , 2022, ICT Express.

[7]  L. Csurgai-Horváth,et al.  Indoor User Movement Simulation with Markov Chain for Deep Learning Controlled Antenna Beam Alignment , 2021, 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET).

[8]  Ayodeji Olalekan Salau,et al.  Genetic Algorithm Based Optimum Finger Selection for Adaptive Minimum Mean Square Error Rake Receivers Discrete Sequence-CDMA Ultra-Wide Band Systems , 2021, Wireless Personal Communications.

[9]  Arp'ad L'aszl'o Makara,et al.  Measurement-based Indoor Beam Alignment Utilizing Deep Learning , 2021, 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME).

[10]  G. Pedersen,et al.  Dielectric Properties of Common Building Materials for Ultrawideband Propagation Studies [Measurements Corner] , 2020, IEEE Antennas and Propagation Magazine.

[11]  Ta-Sung Lee,et al.  BsNet: A Deep Learning-Based Beam Selection Method for mmWave Communications , 2019, 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall).

[12]  Yi Wang,et al.  New Radio (NR) and its Evolution toward 5G-Advanced , 2019, IEEE Wirel. Commun..

[13]  László Csurgai-Horváth,et al.  Indoor Propagation Measurements for 5G Networks , 2018, 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP).

[14]  Emil Björnson,et al.  Massive MIMO in Sub-6 GHz and mmWave: Physical, Practical, and Use-Case Differences , 2018, IEEE Wireless Communications.

[15]  Jiaheng Wang,et al.  Codebook Design for Beam Alignment in Millimeter Wave Communication Systems , 2017, IEEE Transactions on Communications.

[16]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[17]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[18]  Alexandre B. Tsybakov,et al.  Introduction to Nonparametric Estimation , 2008, Springer series in statistics.

[19]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[20]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[21]  L. Csurgai-Horváth,et al.  Improved Model for Indoor Propagation Loss in the 5G FR2 Frequency Band , 2021, Infocommunications journal.

[22]  Andrew L. Maas Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .