Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet

Shehzad, Muhammad Karam; Rose, Luca; Azam, M. Furqan; Assaad, Mohamad

doi:10.1109/globecom48099.2022.10001362

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…In neural network prediction algorithms, the Autoregressive Neural Network (AR-Net) [22] has been successfully applied to time series forecasting in various fields [23][24][25], making it a feasible method for predicting the performance degradation trend of gas turbines. This paper proposes a hybrid model-based prediction method for gas turbine performance degradation using support vector regression (SVR) and AR-Net.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data-Driven Approach for Predicting the Performance Degradation of a Gas Turbine

Dai,

Zhang,

Luo

2024

Energies

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Gas turbines operate under harsh conditions of high temperature and pressure for extended periods, inevitably experiencing performance degradation. Predicting the performance degradation trend of gas turbines and optimizing planned maintenance cycles are crucial for the economic and safety aspects of gas turbine operation. In this study, a novel data-driven approach for predicting gas turbine performance degradation is proposed. Initially, gas turbine operating data are augmented using a mechanism model. Subsequently, a data-driven performance model is constructed based on support vector regression (SVR) and gas turbine operational characteristics, enabling real-time calculation of performance degradation indicators. Building on this, an Autoregressive Neural Network (AR-Net) is employed to construct a model for predicting the trend of performance degradation. The proposed method is applied to predict performance degradation caused by fouling in the compressor of a gas turbine. Comparative analysis with three other performance degradation prediction methods indicates that the proposed approach accurately identifies the performance degradation trend of gas turbines, determining the optimal maintenance timing. This holds significant importance for the condition-based maintenance of gas turbines.

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Section: Introductionmentioning

confidence: 99%

A Novel Data-Driven Approach for Predicting the Performance Degradation of a Gas Turbine

Dai,

Zhang,

Luo

2024

Energies

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“…In [12] metalearning is used to improve the prediction accuracy with only a few fine-tuning samples. In [7], [13]- [15] recurrent neural networks (RNNs) are used for CSI prediction. In particular, due to the ability of RNNs to incorporate the typical dynamics of time series data, they represent a valid alternative to AR models for time series forecasting.…”

Section: Introductionmentioning

confidence: 99%

Reverse Ordering Techniques for Attention-Based Channel Prediction

Rizzello,

Böck,

Joham

et al. 2024

IEEE Open J. Signal Process.

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Channel state information (CSI) is crucial for enhancing the performance of wireless systems by allowing to adjust the transmission strategies based on the current channel conditions. However, obtaining precise CSI is difficult because of the fast-changing channel conditions caused by multi-path fading. An inaccurate CSI hinders the performance of various adaptive wireless systems, highlighting the need for channel prediction techniques to effectively mitigate the drawbacks of outdated CSI. Conventional methods typically depend on assumptions regarding user velocity or require knowledge of the Doppler frequency. In contrast to existing approaches, we aim for a more robust and practical solution by training neural networks without making any assumptions about user velocity, relying solely on noisy channel observations during training. Specifically, we adapt both the sequence-to-sequence with attention (Seq2Seqattn) and transformer models for channel prediction. Additionally, a new technique called reverse positional encoding is introduced in the transformer model to improve the robustness of the model against varying sequence lengths. Similarly, the encoder outputs of the Seq2Seq-attn model are reversed prior to the application of attention mechanisms. By means of simulations, we show that these proposed techniques enable the models to effectively capture relationships within sequences of channel snapshots without increasing the complexity. Importantly, this capability remains robust across varying sequence lengths, representing a substantial improvement over existing methodologies.

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“…NP and LSTM-CNN 13 enhance the seasonality analysis performance for a satellite and PV solar plant. A hybrid framework combining RNN and NP 14 achieved better accuracy for channel predictors problem forecasting for a real-time dataset obtained from Nokia Bell-Labs. Default NP also has achieved the best forecasting performance for the COVID-19 problem 15 compared to Random Forest and Poisson distribution models.…”

mentioning

confidence: 99%

Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet

Dash,

Giri,

Mallik

et al. 2024

Sci Rep

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In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP’s efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases.

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Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet

Cited by 7 publications

References 34 publications

A Novel Data-Driven Approach for Predicting the Performance Degradation of a Gas Turbine

A Novel Data-Driven Approach for Predicting the Performance Degradation of a Gas Turbine

Reverse Ordering Techniques for Attention-Based Channel Prediction

Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet

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