Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication Proceedings of MDCWC 2020 /
| 団体著者: | |
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| その他の著者: | |
| 要約: | XIX, 643 p. 387 illus., 304 illus. in color. text |
| 言語: | 英語 |
| 出版事項: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2021.
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| 版: | 1st ed. 2021. |
| シリーズ: | Lecture Notes in Electrical Engineering,
749 |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/978-981-16-0289-4 |
| フォーマット: | 電子媒体 図書 |
目次:
- Deep Learning to Predict the Number of Antennas in a Massive MIMO Setup based on Channel Characteristics
- Optimal Design of Fractional Order PID Controller for AVR System using Black Widow Optimization (BWO) Algorithm
- LSTM Network for Hotspot Prediction in Traffic Density of Cellular Network
- Generative Adversarial Network and Reinforcement Learning to Estimate Channel Coefficients
- Self-Interference Cancellation in Full-duplex Radios for 5G Wireless Technology using Neural Network
- Dimensionality Reduction of KDD-99 using Self-perpetuating Algorithm
- Energy Efficient Neigbour Discovery using Bacterial Foraging Optimization (BFO) Technique for Asynchronous Wireless Sensor Networks
- LSTM based Outlier Detection Method for WSNs
- An Improved Swarm Optimization Algorithm based Harmonics Estimation and Optimal Switching Angle Identification
- A Study of Ensemble Methods for Classification.