OTFS-based Integrated Positioning and Communication Systems with Low-Resolution ADCs

This paper proposes a two-phase orthogonal time frequency space (OTFS)-based integrated positioning and communication (IPAC) framework under realistic low-resolution analog-to-digital converters (ADCs). In the uplink phase, the positioning signal is used to estimate channel parameters, which are subsequently used to determine the user’s position. The spatial smoothing-multiple signal classification algorithm is introduced to estimate the angle-of-arrival, whereas an iterative interference cancellation scheme is conceived for the remaining parameters’ estimation. The corresponding Cramer-Rao lower bounds of channel parameters and user position are also derived. During the downlink communication phase, the estimated parameters are exploited to improve beamforming at the base station. Simulation results evaluate the impact of ADC quantizer resolutions. Specifically, it is shown that enhanced downlink bit error rate performance can be achieved with improved uplink positioning, while the use of low-resolution ADCs induces noticeable performance degradation in the OTFS-IPAC system.

💡 Research Summary

This paper presents a two‑phase integrated positioning and communication (IPAC) framework that leverages orthogonal time‑frequency space (OTFS) modulation while operating with low‑resolution analog‑to‑digital converters (ADCs). The authors consider a time‑division duplex (TDD) system consisting of a base station (BS) equipped with a uniform linear array (ULA) of transmit and receive antennas and a single‑antenna user terminal. In the uplink phase, the user transmits a positioning‑oriented OTFS signal. The received signal is first modeled through the additive quantization noise model (AQNM), which captures the effect of low‑resolution ADCs by a scaling factor α (dependent on the number of quantization bits) and an additive Gaussian quantization noise term whose covariance is a function of α and the signal power.

Channel estimation proceeds in two steps. First, spatial smoothing MUSIC (SS‑MUSIC) is applied to the covariance matrix constructed from overlapping sub‑arrays of the receive antenna array. This mitigates inter‑path correlation and yields high‑resolution angle‑of‑arrival (AoA) estimates that are independent of delay and Doppler. Second, an iterative interference‑cancellation algorithm estimates the remaining parameters (delay, Doppler, and complex gain) for each multipath component. For each path, a correlation metric is maximized over a discrete Doppler search set, followed by a golden‑section refinement of the fractional Doppler. The complex gain is then obtained in closed form using the residual signal after canceling previously detected paths.

The authors derive the Cramér‑Rao lower bounds (CRLBs) for all channel parameters under the AQNM noise model. Starting from the Gaussian log‑likelihood of the quantized received vector, they compute the Fisher information matrix (FIM) by taking second‑order derivatives with respect to the parameter vector {h_p, θ_p, k_p+κ_p}. The required partial derivatives of the effective channel matrix with respect to each parameter are provided, allowing an explicit expression for the FIM entries. By inverting the FIM, the minimum achievable variances for each parameter are obtained. Since only the line‑of‑sight (LoS) path carries reliable positioning information, the user’s 2‑D position is expressed as (x, y) = c τ_0