paper-with-me

홈 › Papers

OFDM-Based Positioning with Unknown Data Payloads: Bounds and Applications to LEO PNT

2024-07-26 · Andrew M. Graff, Todd E. Humphreys

This paper presents bounds, estimators, and signal design strategies for exploiting both known pilot resources and unknown data payload resources in time-of-arrival (TOA)-based positioning systems with orthogonal frequency-division multiplexing (OFDM) signals. It is the first to derive the Ziv-Zakai bound (ZZB) on TOA estimation for OFDM signals containing both known pilot and unknown data resources. In comparison to the Cramer-Rao bounds (CRBs) derived in prior work, this ZZB captures the low-signal-to-noise ratio (SNR) thresholding effects in TOA estimation and accounts for an unknown carrier phase. The derived ZZB is evaluated against CRBs and empirical TOA error variances. It is then evaluated on signals with resource allocations optimized for pilot-only TOA estimation, quantifying the performance gain over the best-case pilot-only signal designs. Finally, the positioning accuracy of maximum-likelihood and decision-directed estimators is evaluated on simulated low-Earth-orbit non-terrestrial-network channels and compared against their respective ZZBs.

📄 PDF Abstract BibTeX arXiv:2407.19106

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Joint Delay-Doppler Estimation using OFDMA Payloads for Integrated Sensing and Communications

2025-03-06 · Marc Miranda, Sebastian Semper, Christian Schneider, Reiner Thomä 외

The use of future communication systems for sensing offers the potential for a number of new applications. In this paper, we show that leveraging user data payloads in multi-node Orthogonal Frequency Division Multiple Ac…

Velocity-Form Data-Enabled Predictive Control of Soft Robots under Unknown External Payloads

2025-10-06 · Huanqing Wang, Kaixiang Zhang, Kyungjoon Lee, Yu Mei 외 arxiv

Data-driven control methods such as data-enabled predictive control (DeePC) have shown strong potential in efficient control of soft robots without explicit parametric models. However, in object manipulation tasks, unkno…

Deep Learning Based Joint Channel Estimation and Positioning for Sparse XL-MIMO OFDM Systems

2025-07-26 · Zhongnian Li, Chao Zheng, Jian Xiao, Ji Wang 외 arxiv

This paper investigates joint channel estimation and positioning in near-field sparse extra-large multiple-input multiple-output (XL-MIMO) orthogonal frequency division multiplexing (OFDM) systems. To achieve cooperative…

Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning

2023-11-08 · Fan Zhang, Tianqi Mao, Ruiqi Liu, Zhu Han 외

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guaran…

Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems

2019-10-27 · Chi Wu, Xinping Yi, Wenjin Wang, Li You 외

In this paper, we consider the user positioning problem in the massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system with a uniform planner antenna (UPA) array. Taking adv…