The Role of AI and Machine learning (M.L) in Radio Frequency ( RF) Thermal phase noise and nonlinearity losses transceiver system

Abstract

The implementations of more realistic fade channel may be incorporate here. The MIMOs system is not always symmetric, the numbers of antenna in transmissions and in receptions can be different and the idea of do a studies with asymmetric matric should be consider in additional investigation. The  first and generic studies has been done by use a generic amplifiers, the next steps in that fields will be choosing a real devices, places it in the systems and analyzed its behaviors. This  paper was implementthe wireless sensor network  has been studying ML-based modeling algorithms to find a middle-ground. ML algorithms have become faster toexecuteand, more importantly, more radio data measurements have become available with the increased deployment of wireless devices. In this survey,weexplore the recent advancementsand for the standards 802.11a which work at 5.2 GHz, the it can be interests to checks the effects of changed the frequencies or use other standards. For high frequency is conceivable that numerous drawbacks can arises, but for low value might be certain improvements could be realized applied in unsupervised Machine learning (M.L) benchmarking MLbiases, The ML in practice, as summarized the key insights from each article and embed these insights into the respective theme.