Multi-Task Learning for mmWave Transceiver Beam Prediction
Rigorous and reliable alignment of narrow transceiver beams is a requisite for ensuring the highly directional transmission in millimeter-wave (mmWave) communications. Exhaustively testing these narrow beam pairs results in increased reference signal (RS) overhead, latency, and power consumption. In...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/11050966/ |
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| author | Muhammad Qurratulain Khan Abdo Gaber Mohammad Parvini Philipp Schulz Gerhard Fettweis |
| author_facet | Muhammad Qurratulain Khan Abdo Gaber Mohammad Parvini Philipp Schulz Gerhard Fettweis |
| author_sort | Muhammad Qurratulain Khan |
| collection | DOAJ |
| description | Rigorous and reliable alignment of narrow transceiver beams is a requisite for ensuring the highly directional transmission in millimeter-wave (mmWave) communications. Exhaustively testing these narrow beam pairs results in increased reference signal (RS) overhead, latency, and power consumption. In this paper, we propose a centralized multi-task learning (MTL) based beam prediction strategy that ensures a high success rate using measurements from a few site-specific probing beams identified via the proposed uniformly distributed beam relevance and beam significance (UDBRBS) criterion, thereby obviating the need for an exhaustive scan. Performance evaluation over 3rd Generation Partnership Project (3GPP) defined performance indicators demonstrates that the proposed method outperforms existing independent task learning (ITL) and single task learning (STL) beam prediction designs. We further argue that the proposed strategy is highly practical for implementation in fifth generation (5G)-Advanced and sixth generation (6G) communication systems. |
| format | Article |
| id | doaj-art-3d00fa6202a8437f9db4d7262c8b5979 |
| institution | Kabale University |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-3d00fa6202a8437f9db4d7262c8b59792025-08-20T03:49:46ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0165535555110.1109/OJCOMS.2025.358307411050966Multi-Task Learning for mmWave Transceiver Beam PredictionMuhammad Qurratulain Khan0https://orcid.org/0000-0001-7075-8990Abdo Gaber1Mohammad Parvini2https://orcid.org/0000-0002-1315-7635Philipp Schulz3https://orcid.org/0000-0002-0738-556XGerhard Fettweis4https://orcid.org/0000-0003-4622-1311Vodafone Chair for Mobile Communications Systems, Technische Universität Dresden, Dresden, GermanyNI Dresden R&D Dresden, National Instruments Corporation, Dresden, GermanyVodafone Chair for Mobile Communications Systems, Technische Universität Dresden, Dresden, GermanyVodafone Chair for Mobile Communications Systems, Technische Universität Dresden, Dresden, GermanyVodafone Chair for Mobile Communications Systems, Technische Universität Dresden, Dresden, GermanyRigorous and reliable alignment of narrow transceiver beams is a requisite for ensuring the highly directional transmission in millimeter-wave (mmWave) communications. Exhaustively testing these narrow beam pairs results in increased reference signal (RS) overhead, latency, and power consumption. In this paper, we propose a centralized multi-task learning (MTL) based beam prediction strategy that ensures a high success rate using measurements from a few site-specific probing beams identified via the proposed uniformly distributed beam relevance and beam significance (UDBRBS) criterion, thereby obviating the need for an exhaustive scan. Performance evaluation over 3rd Generation Partnership Project (3GPP) defined performance indicators demonstrates that the proposed method outperforms existing independent task learning (ITL) and single task learning (STL) beam prediction designs. We further argue that the proposed strategy is highly practical for implementation in fifth generation (5G)-Advanced and sixth generation (6G) communication systems.https://ieeexplore.ieee.org/document/11050966/Fifth generation (5G)-Advancedsixth generation (6G)beam management (BM)beam predictionmachine learning (ML)millimeter-wave (mmWave) |
| spellingShingle | Muhammad Qurratulain Khan Abdo Gaber Mohammad Parvini Philipp Schulz Gerhard Fettweis Multi-Task Learning for mmWave Transceiver Beam Prediction IEEE Open Journal of the Communications Society Fifth generation (5G)-Advanced sixth generation (6G) beam management (BM) beam prediction machine learning (ML) millimeter-wave (mmWave) |
| title | Multi-Task Learning for mmWave Transceiver Beam Prediction |
| title_full | Multi-Task Learning for mmWave Transceiver Beam Prediction |
| title_fullStr | Multi-Task Learning for mmWave Transceiver Beam Prediction |
| title_full_unstemmed | Multi-Task Learning for mmWave Transceiver Beam Prediction |
| title_short | Multi-Task Learning for mmWave Transceiver Beam Prediction |
| title_sort | multi task learning for mmwave transceiver beam prediction |
| topic | Fifth generation (5G)-Advanced sixth generation (6G) beam management (BM) beam prediction machine learning (ML) millimeter-wave (mmWave) |
| url | https://ieeexplore.ieee.org/document/11050966/ |
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