Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication Proceedings of MDCWC 2020 /
| Erakunde egilea: | |
|---|---|
| Beste egile batzuk: | |
| Gaia: | XIX, 643 p. 387 illus., 304 illus. in color. text |
| Hizkuntza: | ingelesa |
| Argitaratua: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2021.
|
| Edizioa: | 1st ed. 2021. |
| Saila: | Lecture Notes in Electrical Engineering,
749 |
| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.1007/978-981-16-0289-4 |
| Formatua: | Baliabide elektronikoa Liburua |
Aurkibidea:
- 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.