Projects per year
We consider machine learning for intra cell beam handovers in mmWave 5GNR systems by leveraging Channel Charting (CC). We develop a base station centric approach for predicting the Signal-to-Noise-Ratio (SNR) of beams. Beam SNRs are predicted based on measured signal at the BS without the need to exchange information with UEs. In an offline training phase, we construct a beam-specific dimensionality reduction of Channel State Information (CSI) to a low-dimensional CC, annotate the CC with beam-wise SNRs and then train SNR predictors for different target beams. In the online phase, we predict target beam SNRs. K-nearest neighbors, Gaussian Process Regression and Neural Network based prediction are considered. Based on SNR difference between the serving and target beams a handover can be decided. To evaluate the efficiency of the proposed framework, we perform simulations for a street segment with synthetically generated CSI. SNR prediction accuracy of average root mean square error less than 0.3 dB is achieved.
|Title of host publication||2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)|
|Number of pages||6|
|Publication status||Published - 28 Jul 2021|
|MoE publication type||A4 Article in a conference publication|
|Event||European Conference on Networks and Communications - Porto, Portugal|
Duration: 8 Jun 2021 → 11 Jun 2021
|Name||European conference on networks and communications|
|Conference||European Conference on Networks and Communications|
|Period||08/06/2021 → 11/06/2021|
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WINDMILL: Integrating Wireless Communication ENgineering and MachIne Learning
Tirkkonen, O., Salami, D., Sigg, S. & Kazemi, P.
01/01/2019 → 30/06/2023
Project: EU: MC
Radio Network Optimization for Heterogeneous Machine Connectivity
Tirkkonen, O., Al-Tous, H., Vehkalahti, R., Ponnada, T., Kazemi, P., Heikkilä, E., Pllaha, T. & Garau Burguera, P.
01/01/2019 → 31/12/2022
Project: Academy of Finland: Other research funding