Titlebook: Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication; Proceedings of MDCWC E. S. Gopi Conference proce

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Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication978-981-16-0289-4Series ISSN 1876-1100 Series E-ISSN 1876-1119

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LSTM Network for Hotspot Prediction in?Traffic Density of Cellular Network depends on numerous factors like time, location, number of mobile users connected and so on. It exhibits spatial and temporal relationships. However, only certain regions have higher data rates, known as hotspots. A hotspot is defined as a circular region with a particular centre and radius where t

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Generative Adversarial Network and?Reinforcement Learning to Estimate Channel Coefficientsincreasing need to guarantee accuracy. There is little value in large data rates if the channel state information (CSI) is subject to frequent contamination. In the context of massive MIMO systems, error in decoding the signal is introduced mainly due to two key factors: (i) intercell interference (

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Novel Method of Self-interference Cancelation in Full-Duplex Radios for 5G Wireless Technology Usingnique, same set of frequency channels is used for simultaneous uplink and downlink signal transmissions and hence is termed as full-duplex (FD) communications or full-duplex radios. However, a major shortcoming of this technique is the presence of self-interference (SI), which arises due to the pres