Abstract
With the development of the fifth generation (5G) and sixth generation (6G) networks, wireless networks are beginning accomplish persistent large scale obtainment, and communication. Modern cellular technology and the developing new era are both seen favourably for the smart grid. The 5G and 6G networks are being developed and prepared for deployment by the mobile industry. The development of IoT and other intelligent automation applications is being significantly fueled by the growing wireless networks, which are becoming more widely accessible. Network densification, fast throughput, precise location, and energy efficiency criteria will be increasingly demanding in future wireless communications. One of the core areas of wireless networking research in the future will be how to increase productivity while reducing expenses. Approaching this goal in a way that allows for the ability to learn from experience is crucial. Transfer learning (TL) promotes new activities and domains to pick up knowledge from more seasoned tasks and domains so that new tasks may be completed more quickly and effectively. The connection and similarity information between various jobs in several domains of wireless communications can assist TL conserve energy and increase efficiency. Applying TL to upcoming 6G communications is thus a very important subject.
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