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- W3176933038 abstract "In this paper, we present an interference prediction strategy employing deep learning approach for improved performance of the device-to-device (D2D) communication in LTE-based network. The deep learning-enabled autonomous channel selection in D2D communication is envisaged to help improve the system reliability and transmission accuracy. A long short-term memory (LSTM) model is proposed to predict the interference level at a channel using time sequence data, which consists of the past interference values at that particular channel. Further, weighted exponential averaging is performed over output sequence generated through regression layer of LSTM module. This phase provides the level of the interference over the channel for one time step ahead and allows D2D user to select the best suitable channel for next message transmission. The proposed strategy of interference predictor is applied for resource allocation and interference management for in-band underlay D2D communication system. It is shown through numerical results that integration of the weighted averaging with deep learning LSTM module provides significant improvement in the interference prediction, which in turn results in enhanced performance in terms of the normalized mean square error, packet error rate and system reliability." @default.
- W3176933038 created "2021-07-05" @default.
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- W3176933038 date "2021-01-01" @default.
- W3176933038 modified "2023-09-23" @default.
- W3176933038 title "Deep Learning-Enabled Interference Management in LTE-D2D Communication" @default.
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- W3176933038 doi "https://doi.org/10.1007/978-981-16-1295-4_2" @default.
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