Superimposed Pilot-Based OTFS -- Will it Work?

Orthogonal time frequency space (OTFS) modulation is a promising solution to handle doubly-selective fading, but its channel estimation is a nontrivial task in terms of maximizing spectral efficiency. Conventional pilot assignment approaches face challenges: the standard embedded pilot-based scheme suffers from low transmission rates, and the single superimposed pilot (SP)-based scheme experiences inevitable data-pilot interference, leading to coarse channel estimation. To cope with this issue, focusing on the SP-based OTFS system in channel coded scenarios, we propose a novel pilot assignment scheme and an iterative algorithm. The proposed scheme allocates multiple SPs per frame to estimate channel coefficients accurately. Furthermore, the proposed algorithm performs refined interference cancellation, utilizing a replica of data symbols generated from soft-decision outputs provided by a decoder. Assuming fair and unified conditions, we evaluate each pilot assignment scheme in terms of reliability, channel estimation accuracy, effective throughput, and computational complexity. Our numerical simulations demonstrate that the multiple SP-based scheme, which balances the transmission rate and the interference cancellation performance, has the best throughput at the expense of slightly increased complexity. In addition, we confirm that the multiple SP-based scheme achieves further improved throughput due to the proposed interference cancellation algorithm.

Covert Communications Without Pre-Sharing of Side Information and Channel Estimation Over Quasi-Static Fading Channels

We propose a new covert communication scheme that operates without pre-sharing side information and channel estimation, utilizing a Gaussian-distributed Grassmann constellation for noncoherent detection. By designing constant-amplitude symbols on the Grassmann manifold and multiplying them by random variables, we generate signals that follow an arbitrary probability distribution, such as Gaussian or skew-normal distributions. The mathematical property of the manifold enables the transmitter's random variables to remain unshared with the receiver, and the elimination of pilot symbols that could compromise covertness. The proposed scheme achieved higher covertness and achievable rates compared to conventional coherent Gaussian signaling schemes, without any penalty in terms of complexity.

Boosting Spectral Efficiency with Data-Carrying Reference Signals on the Grassmann Manifold

In wireless networks, frequent reference signal transmission for accurate channel reconstruction may reduce spectral efficiency. To address this issue, we consider to use a data-carrying reference signal (DC-RS) that can simultaneously estimate channel coefficients and transmit data symbols. Here, symbols on the Grassmann manifold are exploited to carry additional data and to assist in channel estimation. Unlike conventional studies, we analyze the channel estimation errors induced by DC-RS and propose an optimization method that improves the channel estimation accuracy without performance penalty. Then, we derive the achievable rate of noncoherent Grassmann constellation assuming discrete inputs in multi-antenna scenarios, as well as that of coherent signaling assuming channel estimation errors modeled by the Gauss-Markov uncertainty. These derivations enable performance evaluation when introducing DC-RS, and suggest excellent potential for boosting spectral efficiency, where interesting crossings with the non-data carrying RS occurred at intermediate signal-to-noise ratios.