OTFS Transceivers Design using Deep Neural Networks

Abstract

Next generation wireless systems are envisioned to provide a variety of services with a wide range of performance requirements. Particularly, demand for high-mobility use cases involving high-speed trains, UAVs/drones, and aeroplanes is increasing. Also, wireless spectrum in the millimeter wave band (e.g., 28-60 GHz) is used to meet the growing bandwidth requirements. Communication in high-mobility and high-carrier frequency scenarios is challenging as it involves high Doppler shifts. Widely used modulation schemes such as orthogonal frequency division multiplexing (OFDM) perform poorly in such high-Doppler scenarios. Orthogonal time frequency space (OTFS) is a recently proposed modulation scheme which is robust to high Doppler shifts. It operates in the delay-Doppler domain and converts a high-Doppler channel into an almost static channel. In this thesis, we focus on the design of OTFS transceivers using deep neural networks (DNNs). The key contributions in the thesis can be summarized into three parts: 1) design of a low-complexity DNN architecture for OTFS signal detection, 2) design of a multi-DNN architecture for delay-Doppler channel training and detection, along with IQ imbalance (IQI) compensation, and 3) bit error rate (BER) analysis of OTFS in the presence of imperfect channel state information (CSI).

First, we consider a DNN architecture in which each information symbol multiplexed in the delay-Doppler (DD) grid is associated with a separate DNN. The considered symbol-level DNN has fewer parameters to learn compared to a full DNN that takes into account all symbols in an OTFS frame jointly, and therefore has less complexity. When the noise model deviates from the standard i.i.d. Gaussian model (e.g., non-Gaussian noise with t-distribution) the proposed symbol-DNN detection is found to outperform maximum-likelihood (ML) detection, because of the ability of the DNN to learn the distribution. A similar performance advantage is observed in MIMO-OTFS systems where the noise across multiple received antennas are correlated.

Next, we propose a multi-DNN transceiver architecture for DD channel training and detection, along with 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 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.

Finally, we analyze the effect of imperfect CSI on the BER performance of OTFS. We carry out the BER analysis when a mismatched ML detector is used, i.e., when an estimated channel matrix is used for detection in place of the true channel matrix. We derive an exact expression for the pairwise error probability (PEP) using the characteristic function of the decision statistic. Using the PEP, an upper bound on the BER is obtained. Our results show that the BER bound is tight at high SNR values. We also obtain the decision rule for the true ML detector in the presence of imperfect CSI, which takes into account the channel estimation error statistics. We quantify the performance gap between the true ML detector and the mismatched ML detector through simulations.