Ashok S Kumar | Searcharxiv

arXiv2026

Integrating OTFS in Airplane-Aided Next-Generation Networking

Next-generation networks explore the opportunistic assistance of airliner/high-altitude platforms (HAPs) in delivering high data rates for terrestrial networks to ensure consistent and reliable communication. When an airliner/HAP moves at very high speeds, its mobility has a substantial impact on ensuring seamless connectivity, stable signal strength, and reliable data transmission. Orthogonal time frequency space (OTFS) modulation has been shown to provide notable improvement in performance when handling Doppler effects during high-mobility situations. This paper presents an OTFS-based airplane-aided next-generation networking system. In the proposed system, the airliner/HAPs are equipped with a planar antenna array that applies null steering beamforming (NSB) at the transmitter for communication with terrestrial users. A comprehensive performance comparison between OTFS and orthogonal frequency division multiplexing (OFDM) is performed under varying airliner altitude, velocity, array dimension, and Rician factor conditions. The simulation results show that OTFS consistently outperforms OFDM, achieving a lower bit error rate (BER) and more stable performance across different airliner altitudes, velocities, array dimensions, and propagation environments.

cs.IT↗

arXiv2023

Practical Radar Sensing Using Two Stage Neural Network for Denoising OTFS Signals

Our objective is to derive the range and velocity of multiple targets from the delay-Doppler domain for radar sensing using orthogonal time frequency space (OTFS) signaling. Noise contamination affects the performance of OTFS signals in real-world environments, making radar sensing challenging. This work introduces a two-stage approach to tackle this issue. In the first stage, we use a generative adversarial network to denoise the corrupted OTFS samples, significantly improving the data quality. Following this, the denoised signals are passed to a convolutional neural network model to predict the values of the velocities and ranges of multiple targets. The proposed two-stage approach can predict the range and velocity of multiple targets, even in very low signal-to-noise ratio scenarios, with high accuracy and outperforms existing methods.

cs.IT↗