Machine learning using neural networks in digital signal processing for RF transceivers

Communication is changing to a wireless world. Any wireless communication system needs radio frontends (transceivers) to link higher layer signals to the air interface. The increasing standard and technology diversity require transceivers with high flexibility supporting many of frequencies, standards and signal requirements. Design to cost, the demand for highest energy efficiency and MIMO systems don't ease the challenges for transceiver designs. A change in paradigm can be an opener to face these challenges for future systems. This work presents the use of neural networks for signal processing, impairment mitigation, control and optimization in transceiver systems.