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Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication

- Proceedings of MDCWC 2020

Über Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication

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.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9789811602887
  • Einband:
  • Gebundene Ausgabe
  • Seitenzahl:
  • 643
  • Veröffentlicht:
  • 29. Mai 2021
  • Ausgabe:
  • 12021
  • Abmessungen:
  • 155x235x0 mm.
  • Gewicht:
  • 1154 g.
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Beschreibung von Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication

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.

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