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Due to the non-ideality of analog components, transceivers experience highlevels of hardware imperfections, like in-phase and quadrature imbalance (IQI),which manifests itself as the mismatches of amplitude and phase between the Iand Q branches. Unless proper mitigated, IQI has an important and negativeimpact on the reliability and efficiency of high-frequency and high-data-ratesystems, such as terahertz wireless networks. Recognizing this, the currentpaper presents an intelligent transmitter (TX) and an intelligent receiver (RX)architecture that by employing machine learning (ML) methodologies is capableto fully-mitigate the impact of IQI without performing IQI coefficientsestimation. They key idea lies on co-training the TX mapper's and RX demapperin order to respectively design a constellation and detection scheme that takesaccounts for IQI. Two training approaches are implemented, namely: i)conventional that requires a considerable amount of data for training, and ii)a reinforcement learning based one, which demands a shorter dataset incomparison to the former. The feasibility and efficiency of the proposedarchitecture and training approaches are validated through respective MonteCarlo simulations.

Training Terahertz Wireless Systems to Battle I/Q Imbalance