arXiv subjects

Hongqiang Cheng

Publications and source records attributed to Hongqiang Cheng.

3 recordsLinked to original sources

arXiv2025

A Tutorial on MIMO-OFDM ISAC: From Far-Field to Near-Field

Integrated sensing and communication (ISAC) is one of the key usage scenarios for future sixth-generation (6G) mobile communication networks, where communication and sensing (C&S) services are simultaneously provided through shared wireless spectrum, signal processing modules, hardware, and network infrastructure. Such an integration is strengthened by the technology trends in 6G, such as denser network nodes, larger antenna arrays, wider bandwidths, higher frequency bands, and more efficient utilization of spectrum and hardware resources, which incentivize and empower enhanced sensing capabilities. As the dominant waveform used in contemporary communication systems, orthogonal frequency division multiplexing (OFDM) is still expected to be a very competitive technology for 6G, rendering it necessary to thoroughly investigate the potential and challenges of OFDM ISAC. Thus, this paper aims to provide a comprehensive tutorial overview of ISAC systems enabled by large-scale multi-input multi-output (MIMO) and OFDM technologies and to discuss their fundamental principles, advantages, and enabling signal processing methods. To this end, a unified MIMO-OFDM ISAC system model is first introduced, followed by four frameworks for estimating parameters across the spatial, delay, and Doppler domains, including parallel one-domain, sequential one-domain, joint two-domain, and joint three-domain parameter estimation. Next, sensing algorithms and performance analyses are presented in detail for far-field scenarios where uniform plane wave (UPW) propagation is valid, followed by their extensions to near-field scenarios where uniform spherical wave (USW) characteristics need to be considered. Finally, this paper points out open challenges and outlines promising avenues for future research on MIMO-OFDM ISAC.

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arXiv2025

Mixed Near-field and Far-field Localization in Extremely Large-scale MIMO Systems

In this paper, we study efficient \emph{mixed near-field and far-field} target localization methods in extremely large-scale multiple-input multiple-output (XL-MIMO) systems Compared with existing works, we address two new challenges in target localization of MIMO communication systems via using decoupled subspace methods, arising from the half-wavelength antenna spacing constraint and \emph{hybrid uniform planar array} (UPA) architectures.To this end, we propose a new three-step mixed-field localization method. First, we reconstruct the equivalent signals received at UPA antennas by judiciously designing analog combining matrices over time with minimum recovery errors.Second, based on recovered signals, we extend the modified multiple signal classification (MUSIC) algorithm to the UPA architectures by constructing a new covariance matrix of a virtual sparse UPA (S-UPA) to decouple the 2D angles and range estimation.Due to the structure of the S-UPA, there exist ambiguous angles when estimating true angles of targets.In the third step, we design an effective classification method to distinguish mixed-field targets, determine true angles of all targets, as well as estimate the ranges of near-field targets.In particular, angular ambiguity is resolved by showing an important fact that the three types of estimated angles (i.e., far-field, near-field, and ambiguous angles) exhibit significantly different patterns in the range-domain MUSIC spectrum.Furthermore, to characterize the estimation error lower-bound, we obtain a matrix closed-form Cram\'er-Rao bounds for mixed-field target localization.Finally, numerical results demonstrate the effectiveness of our proposed mixed-field localization method, which improves target-classification accuracy and achieves a lower root mean square error than various benchmark schemes.

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arXiv2024

Near-Field Localization With Coprime Array

Large-aperture coprime arrays (CAs) are expected to achieve higher sensing resolution than conventional dense arrays (DAs), yet with lower hardware and energy cost. However, existing CA far-field localization methods cannot be directly applied to near-field scenarios due to channel model mismatch. To address this issue, in this paper, we propose an efficient near-field localization method for CAs. Specifically, we first construct an effective covariance matrix, which allows to decouple the target angle-and-range estimation. Then, a customized two-phase multiple signal classification (MUSIC) algorithm for CAs is proposed, which first detects all possible targets' angles by using an angular-domain MUSIC algorithm, followed by the second phase to resolve the true targets' angles and ranges by devising a range-domain MUSIC algorithm. Finally, we show that the proposed method is able to locate more targets than the subarray-based method as well as achieve lower root mean square error (RMSE) than DAs.

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