  In this paper, we present a deep neural network (DNN) based transceiver
architecture for delay-Doppler (DD) channel training and detection of
orthogonal time frequency space (OTFS) modulation signals along with IQ
imbalance (IQI) compensation. The proposed transceiver learns the DD channel
over a spatial coherence interval and detects the information symbols using a
single DNN trained for this purpose at the receiver. The proposed transceiver
also learns the IQ imbalances present in the transmitter and receiver and
effectively compensates them. The transmit IQI compensation is realized using a
single DNN at the transmitter which learns and provides a compensating
modulation alphabet (to pre-rotate the modulation symbols before sending
through the transmitter) without explicitly estimating the transmit gain and
phase imbalances. The receive IQI imbalance compensation is realized using two
DNNs at the receiver, one DNN for explicit estimation of receive gain and phase
imbalances and another DNN for compensation. Simulation results show that the
proposed DNN-based architecture provides very good performance, making it as a
promising approach for the design of practical OTFS transceivers.
