In this paper, we consider an orthogonal time frequency space (OTFS) system in time-varying channels with overspread Doppler shifts, typically found in non-terrestrial multi-satellite links. The overspread Doppler shifts with magnitude greater than half of the subcarrier spacing, result in aliased Doppler shifts in the delay-Doppler (DD) domain due to the OTFS modulo operation. This makes channel estimation very challenging and the traditional channel estimation methods become ineffective. To address this challenge, we propose a DD training frame and a two-stage channel estimation method. The training frame comprises a cosine pilot signal and a pilot symbol. In the first stage of the channel estimation, the pilot symbol in the DD domain is utilized to estimate the delays, aliased Doppler shifts, and channel gains of the propagation paths. In the second stage, the received time domain signal is converted into the frequency domain to detect the peaks of all the Doppler shifts using the cosine pilot signal. Then, we present a threshold-based method to pair the estimated actual Doppler shifts with their corresponding delays and channel gains. The complexity of the proposed channel estimation is also discussed. Finally, the performance of the proposed channel estimation is validated in terms of the normalized mean square error (NMSE) and bit error rate (BER) in various scenarios.

In low latency applications and in general, for overspread channels, channel delay spread is a large percentage of the transmission frame duration. In this paper, we consider OTFS in anoverspreadchannel exhibiting a delay spread that exceeds the block duration in a frame, where traditional channel estimation (CE) fails. We propose a two-stage CE method based on a delay-Doppler (DD) training frame, consisting of a dual chirp converted from time domain and a higher power pilot. The first stage employs a DD domain embedded pilot CE to estimate the aliased delays (due to modulo operation) and Doppler shifts, followed by identifying all the underspread paths not coinciding with any overspread path. The second stage utilizes time domain dual chirp correlation to estimate the actual delays and Doppler shifts of the remaining paths. This stage also resolves ambiguity in estimating delays and Doppler shifts for paths sharing same aliased delay. Furthermore, we present a modified low-complexity maximum ratio combining (MRC) detection algorithm for OTFS in overspread channels. Finally, we evaluate performance of OTFS using the proposed CE and the modified MRC detection in terms of normalized mean square error (NMSE) and bit error rate (BER).

Orthogonal time-frequency space (OTFS) modulation shows superior performance in high-mobility wireless environments compared to orthogonal frequency division multiplexing (OFDM). In this paper, we consider maximal ratio combining (MRC) detection for an OTFS receiver with oversampling for channels with fractional delays and Doppler shifts. Specifically, we first reformulate input-output relations in delay-Doppler and delay-time domains for an oversampled OTFS receiver. Then we present a modified iterative MRC detection in both domains taking advantage of the oversampled received signal to improve error performance. The complexity of our detection method is equivalent to that of standard MRC detection scaled by the oversampling factor, while remaining much lower than message passing (MP) detection. We also develop a noise whitening approach to decorrelate the oversampled noise in time domain and derive the optimal combining weights of the MRC detection. Simulation results show that the proposed detection with receiver oversampling outperforms the MRC detection with Nyquist sampling, and the oversampling MP detection. Finally, we show that adding noise whitening can significantly improve error performance, compared to the MRC detection without noise whitening. This comes at a small additional computational cost, while still remaining much lower than MP detection.

A 5G network’s use-case in the Internet of Things (IoT) is a breakthrough, offering networks the ability to handle billions of connected devices with the proper blend of speed, latency, and cost. The IoT networks implemented in high-speed scenarios like intra and intervehicular communications in autonomous driving vehicles, high-speed vehicles, and trains will experience a Doppler effect. Orthogonal frequency-division multiplexing, the popular transmission technology for existing new-radio IoT (NR-IoT), is limited in providing reliable connections in high-speed vehicular scenarios. The performance of such system degrades with higher-order antenna configuration due to the lack of channel state information in highly mobile environments. The recently proposed orthogonal time-frequency space (OTFS) modulation is a strong contender that can handle high mobility but requires efficient transceiver design to be deployed in vehicular NR-IoT (V-IoT) systems. To conserve the resources and minimize the air time of the devices, we have proposed an embedded pilot design in the Delay–Doppler domain for the V-IoT systems. In the designed frame structure, the pilot’s position is optimized as per the vehicle speed to maximize the spectral efficiency of the system. The increase in spectral efficiency is at the cost of interference in the channel search region of the received Delay–Doppler domain OTFS signal. So a new joint estimator and the low-complex detector are proposed to handle the interference. The proposed efficient transceiver design with the spectral efficient pilot patterns allows us to conserve resources and remove complex encoder–decoders like the low-density parity check in NR-IoT.

Millimeter wave (mm-Wave) frequency band combined with multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) is a key enabler for fifth generation (5G) technology. However, the requirement of high power emission at the mm-Wave transmitter to combat the significant path loss, the enormous bandwidth, and the high frequency design limitations of the integrated circuits involved in mm-Wave systems, result in severe nonlinear distortion in the signals attributed by the RF power amplifier (PA) and other RF circuits. This nonlinear distortion causes non-orthogonality in OFDM subcarriers which in collusion with frequency selective channel may pose great challenges to signal detection. In this paper, we propose an efficient technique for channel estimation and data detection in the presence of nonlinear distortion for mm-Wave MIMO-OFDM systems. The nonlinear impairment causes the posterior distribution of data symbols as non-Gaussian and analytically intractable. To tackle it, an iterative algorithm based on particle filter (PF) for data detection is designed. The detected symbols are then used to estimate and update the channel gains using a sequential maximum likelihood (SQML) estimation. Simulation results validate the proposed algorithm.

Millimeter wave (mm-Wave) is an emerging paradigm towards 5G technology that can support high data rate. The foreseen potential of mm-Wave is limited by huge path loss incurred due to the high frequency operation which can be alleviated by high emission power at the transmitter. This in concurrence with the enormous bandwidth of mm-Wave and high frequency design limitations of the integrated circuits enforce the power amplifier (PA) into non-linear region. Further, the non-linear distortion in collusion with frequency selective channel and carrier frequency offset (CFO) degrade the signal detection performance. To solve this problem, we propose a semi-blind joint estimation of CFO and frequency selective channel gains followed by data detection in the presence of PA non-linearity. The presence of non-linearity results in the posterior probability distribution of complex data symbol to be non-Gaussian and hence, analytically intractable. Therefore, sequential importance resampling based particle filter (PF) is suggested for approximating the intractable posterior distribution of interest by the weighted random probability samples (particles) to detect the data symbols. The detected symbols are then used to jointly update the channel gains and CFO using a novel sequential maximum likelihood (ML) estimation. Extensive simulation results validate the proposed algorithm. This novel scheme enhances the non-linear signal detection performance in presence of CFO and frequency selective channel at the receiver.