OTFS Transceiver Design and Sparse Doubly-Selective CSI Estimation in Analog and Hybrid Beamforming Aided mmWave MIMO Systems

Abstract

Orthogonal time frequency space (OTFS) waveform based millimeter wave (mmWave) MIMO systems are capable of achieving high data rates in high-mobility scenarios. Hence, transceivers are designed for both analog beamforming (AB) and hybrid beamforming (HB), where we commence by deriving the delay-Doppler (DD)-domain input-output relationship considering a delay-Doppler-angular domain channel model. Subsequently, a novel two-stage procedure is developed for transmit beamformer (TBF)/ precoder (TPC) and receiver combiner (RC) design, and for estimating the DD-domain's equivalent channel state information (CSI). The key feature of the proposed framework is that the RF TBF/ TPC and RC design maximizes the directional beamforming gains. It is also demonstrated that the low-dimensional baseband CSI of the DD-domain becomes sparse for mmWave-AB MIMO OTFS systems, and blocksparse for mmWave-HB MIMO OTFS systems. Subsequently, Bayesian learning (BL) and block-sparse BL (BS-BL) solutions are developed for improved CSI estimation. We also derive the Bayesian Cramer-Rao lower bounds (BCRLB) for benchmarking the mean-squared-error (MSE) of the CSI estimates. Finally, our simulation results demonstrate the improved efficacy of the proposed transceiver designs and confirm the enhanced CSI estimation performance of the BL-based schemes over other competing sparse signal recovery schemes.

Profuse Channel Estimation and Signal Detection Techniques for Orthogonal Time Frequency Space in 6G Epoch: A Survey

“…Moreover, to achieve enhanced data rates in OTFS, millimeter wave is exploited for its large bandwidth but the transceiver design and CE is a challenging task with MIMO systems. In par with this, analog and hybrid beam-forming transceivers are designed to enhance the QoS in [61]. Initially, the input-output relation is established for analog and hybrid transceivers and then Bayesian learning and block-sparse Bayesian learning techniques are aided for achieving the CE accuracy.…”

Cell-Free Massive MIMO Meets OTFS Modulation

“…Note that in our work, we assume these covariance matrices are perfectly known. This assumption is reasonable since these matrices can be accurately estimated by following, for example, the Bayesian learning (BL)based framework proposed in [33]. The BL-based approach iteratively estimates the elements of the covariance matrix using the well-established expectation maximization procedure and is shown to be a very accurate estimation method [33].…”