Channel Charting-based Radio Resource Management
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School of Electrical Engineering |
Doctoral thesis (article-based)
| Defence date: 2025-03-17
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Authors
Date
2025
Major/Subject
Mcode
Degree programme
Language
en
Pages
108 + 59
Series
Aalto University publication series Doctoral Theses, 44/2025
Abstract
5th Generation (5G) cellular networks are designed to deliver unparalleled performance in mobile environments with three promises: i) increased capacity, ii) ultra-reliable and low-latency connections, and iii) a massive number of connected devices. Achieving these ambitious goals necessitates the integration of novel technologies into networks. Millimeter-wave (mmWave) communications stand out as a key enabler for achieving ultrahigh data rates and low latency, leveraging the substantial bandwidth available at these high frequencies. Beamforming techniques have been extensively employed in the mm-wave bands to alleviate the path loss of mm- wave radio links. However, several challenges must be overcome, primarily associated with the high overhead of finding suitable beams. This thesis addresses key challenges in beam management for 5G and mmWave communication systems through the application of Channel Charting (CC) and Machine Learning (ML) techniques. CC is a self-supervised method that maps the collected high dimensional Channel State Information (CSI) at a Base Station (BS) into a low dimensional space which represents pseudo positions of User Equipment (UEs) in the radio environment. The low dimensional space preserves the local geometry of the UEs meaning that nearby UEs in real space are close to each other on the CC. A CC-based framework is designed where in an offline training phase, CCs are constructed and annotated with Signal-to-Noise Ratio (SNR)s of neighboring cells/beams. ML algorithms are used to predict the SNR of a user at neighboring cells/beams from its transmission in a massive Multiple Input Multiple Output (mMIMO) cellular system. By predicting the signal quality of neighboring stations without UE assistance, the protocol overhead for handover decisions can be reduced. Both standalone and non-standalone 5G system deployments are considered and the best beam prediction is investigated. Beam tracking based on CC is investigated and results show that at a very low beam-search overhead one can leverage a CCto-SNR mapping in order to track strong beams between the UEs and the BS. As the fundamental building block of the framework proposed in this thesis, CC necessitates enhancements in its construction to enable versatile applications across different scenarios. To address this, a CSI feature has been devised aimed at mitigating the influence of small-scale fading. This improvement empowers the framework to yield robust predictions even with low spatial sampling density. Additionally, a low complexity Out-of-Sample (OOS) algorithm has been developed, which boasts reduced computational requirements compared to conventional OoS algorithms, making it a more efficient choice for practical implementations.Description
Supervising professor
Tirkkonen, Olav, Prof., Aalto University, Department of Information and Communications Engineering, FinlandThesis advisor
Al-Tous, Hanan, Dr., Aalto University, Department of Information and Communications Engineering, FinlandKeywords
channel charting, beam management, SNR prediction
Other note
Parts
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[Publication 1]: Hanan Al-Tous, Parham Kazemi and Olav Tirkkonen. Coordinated Uplink Precoding for Spatially Consistent mmWave Channel Covariance Measurements. In IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, pp. 1-5, May 2020.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202009255531DOI: 10.1109/SPAWC48557.2020.9154333 View at publisher
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[Publication 2]: Parham Kazemi, Hanan Al-Tous, Christoph Studer, and Olav Tirkkonen. SNR Prediction in Cellular Systems based on Channel Charting. In IEEE International Conference on Communications and Networking (ComNet), Hammamet, Tunisia, pp. 1-8, October 2020.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202102262040DOI: 10.1109/ComNet47917.2020.9306087 View at publisher
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[Publication 3]: Tushara Ponnada, Parham Kazemi, Hanan Al-Tous, Ying-Chang Liang, and Olav Tirkkonen. Best Beam Prediction in Non-Standalone mmWave Systems. In European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Porto, Portugal, pp. 532-537, June 2021.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202109028846DOI: 10.1109/EuCNC/6GSummit51104.2021.9482504 View at publisher
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[Publication 4]: Parham Kazemi, Tushara Ponnada, Hanan Al-Tous, Ying-Chang Liang, and Olav Tirkkonen. Channel Charting Based Beam SNR Prediction. In European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Porto, Portugal, pp. 72-77, June 2021.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202210196019DOI: 10.1109/VTC2022-Spring54318.2022.9860709 View at publisher
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[Publication 5]: Parham Kazemi, Hanan Al-Tous, Christoph Studer, and Olav Tirkkonen. Beam SNR Prediction Using Channel Charting. IEEE Transactions on Vehicular Technology , 72, 11, pp. 13130-13145, May 2023.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202306143841DOI: 10.1109/TVT.2023.3275280 View at publisher
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[Publication 6]: Parham Kazemi, Hanan Al-Tous, Christoph Studer, and Olav Tirkkonen. Channel Charting Assisted Beam Tracking. In IEEE Vehicular Technology Conference: (VTC-Spring), Helsinki, Finland, pp. 1-5, June 2022.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202210196019DOI: 10.1109/VTC2022-Spring54318.2022.9860709 View at publisher