Efficient Deep-Learning Models for Future Blockage and Beam Prediction for mmWave Systems

Vankayala, Satya Kumar; Gollapudi, Sai Krishna Santosh; Jain, Bharat; Yoon, Seungil; Mihir, K; Kumar, Swaraj; Kumar, Harish; Kommineni, Issaac

doi:10.1109/noms56928.2023.10154339

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2024

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Cited by 3 publications

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Predicting Link Failures With Online Meta-Learning Under Time-Varying Blockage Dynamics

Baek,

Seo,

Choi

2024

IEEE Wireless Commun. Lett.

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Predicting Link Failures With Online Meta-Learning Under Time-Varying Blockage Dynamics

Baek,

Seo,

Choi

2024

IEEE Wireless Commun. Lett.

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Towards Ultra-Reliable 6G: Semantics Empowered Robust Beamforming for Millimeter-Wave Networks

Raha,

Adhikary,

Gain

et al. 2024

NOMS 2024-2024 IEEE Network Operations and Management Symposium

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Multi-Link Prediction for mmWave Wireless Communication Systems Using Liquid Time-Constant Networks, Long Short- Term Memory, and Interpretation Using Symbolic Regression

Pendyala,

Patil

2024

Electronics

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A significant challenge encountered in mmWave and sub-terahertz systems used in 5G and the upcoming 6G networks is the rapid fluctuation in signal quality across various beam directions. Extremely high-frequency waves are highly vulnerable to obstruction, making even slight adjustments in device orientation or the presence of blockers capable of causing substantial fluctuations in link quality along a designated path. This issue poses a major obstacle because numerous applications with low-latency requirements necessitate the precise forecasting of network quality from many directions and cells. The method proposed in this research demonstrates an avant-garde approach for assessing the quality of multi-directional connections in mmWave systems by utilizing the Liquid Time-Constant network (LTC) instead of the conventionally used Long Short-Term Memory (LSTM) technique. The method’s validity was tested through an optimistic simulation involving monitoring multi-cell connections at 28 GHz in a scenario where humans and various obstructions were moving arbitrarily. The results with LTC are significantly better than those obtained by conventional approaches such as LSTM. The latter resulted in a test Root Mean Squared Error (RMSE) of 3.44 dB, while the former, 0.25 dB, demonstrating a 13-fold improvement. For better interpretability and to illustrate the complexity of prediction, an approximate mathematical expression is also fitted to the simulated signal data using Symbolic Regression.

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Efficient Deep-Learning Models for Future Blockage and Beam Prediction for mmWave Systems

Cited by 3 publications

References 0 publications

Predicting Link Failures With Online Meta-Learning Under Time-Varying Blockage Dynamics

Predicting Link Failures With Online Meta-Learning Under Time-Varying Blockage Dynamics

Towards Ultra-Reliable 6G: Semantics Empowered Robust Beamforming for Millimeter-Wave Networks

Multi-Link Prediction for mmWave Wireless Communication Systems Using Liquid Time-Constant Networks, Long Short- Term Memory, and Interpretation Using Symbolic Regression

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