arXiv subjects

Namhyun Kim

Publications and source records attributed to Namhyun Kim.

5 recordsLinked to original sources

arXiv2026

Digital Twin-Aided Prescreening for User Scheduling in MU-MIMO Downlink Systems

In dense deployments, massive multi-user multiple-input multiple-output (MU-MIMO) base stations can acquire instantaneous channel state information (CSI) for only a limited subset of users per scheduling interval, restricting multiuser diversity. We therefore propose Digital Twin User pre-Screening (DiTUS), a digital-twin (DT)-aided framework that identifies promising users before instantaneous CSI acquisition. DiTUS forms spatial covariances from DT-inferred departure angles and path powers. Optional Gaussian-process (GP) calibration mitigates path-power bias, while the dominant rank-r eigenspace of the aggregate covariance yields common reference beams. It prescreens the pool using DiTUS-P, a low-complexity projection-energy rule, or DiTUS-L, a greedy log-determinant rule that promotes spatial compatibility. A two-level protocol collects scalar beam reports from shortlisted users and requests r-dimensional effective-channel vectors only from the scheduled set. The framework also supports proportional-fair scheduling. At 15 dB under DT imperfections, simulations with 128 candidates, a 64-user effective-CSI acquisition budget, and a 64-user shortlist show that DiTUS-L achieves 35.06 +/- 0.49 bps/Hz versus 30.28 +/- 0.60 bps/Hz for semi-orthogonal user selection (SUS) with full-dimensional CSI from 64 users, demonstrating that DT-based prescreening preserves substantial multiuser-diversity gains by identifying strong, spatially compatible users before acquiring effective-channel vectors.

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arXiv2026

LWM-Spectro: A Foundation Model for Wireless Baseband Signal Spectrograms

The received in-phase and quadrature (I/Q) baseband signals inherently encode physical-layer and channel characteristics of wireless links. Learning robust and transferable representations directly from such raw signals, however, remains challenging due to heterogeneous communication systems, diverse propagation environments, and limited labeled data. To address this, we present LWM-Spectro, a transformer-based foundation model pretrained on large-scale I/Q data represented as time-frequency spectrograms. The model leverages self-supervised masked modeling, contrastive learning, and a mixture-of-experts (MoE) architecture to learn general-purpose wireless representations. These representations transfer effectively to downstream tasks such as modulation classification and joint SNR/mobility recognition, even with minimal supervision. Across tasks, LWM-Spectro consistently outperforms state-of-the-art deep learning baselines in both few-shot and data-rich regimes, providing a unified foundation for wireless learning.

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arXiv2025

Reducing Latency by Eliminating CSIT Feedback: FDD Downlink MIMO Transmission for Internet-of-Things Communications

This paper presents a novel framework for low-latency frequency division duplex (FDD) multi-input multi-output (MIMO) transmission with Internet of Things (IoT) communications. Our key idea is eliminating feedback associated with downlink channel state information at the transmitter (CSIT) acquisition. Instead, we propose to reconstruct downlink CSIT from uplink reference signals by exploiting the frequency invariance property of channel parameters. Nonetheless, the frequency disparity between the uplink and downlink makes it impossible to get perfect downlink CSIT, resulting in substantial interference. To address this, we formulate a max-min fairness problem and propose a rate-splitting multiple access (RSMA)-aided efficient precoding method. In particular, to fully harness the potential benefits of RSMA, we propose a method that approximates the error covariance matrix and incorporates it into the precoder optimization process. This approach effectively accounts for the impact of imperfect CSIT, enabling the design of a robust precoder that efficiently handles CSIT inaccuracies. Simulation results demonstrate that our framework outperforms other baseline methods in terms of the minimum spectral efficiency when no direct CSI feedback is used. Moreover, we show that our framework significantly reduces communication latency compared to conventional CSI feedback-based methods, underscoring its effectiveness in enhancing latency performance for IoT communications.

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arXiv2025

Integrated Sensing and Communications in Downlink FDD MIMO without CSI Feedback

In this paper, we propose a precoding framework for frequency division duplex (FDD) integrated sensing and communication (ISAC) systems with multiple-input multiple-output (MIMO). Specifically, we aim to maximize ergodic sum spectral efficiency (SE) while satisfying a sensing beam pattern constraint defined by the mean squared error (MSE). Our method reconstructs downlink (DL) channel state information (CSI) from uplink (UL) training signals using partial reciprocity, eliminating the need for CSI feedback. To obtain the error covariance matrix of the reconstructed DL CSI, we devise an observed Fisher information-based estimation technique. Leveraging this, to mitigate interference caused by imperfect DL CSI reconstruction and sensing operations, we propose a rate-splitting multiple access (RSMA) aided precoder optimization method. This method jointly updates the precoding vector and Lagrange multipliers by solving the nonlinear eigenvalue problem with eigenvector dependency to maximize SE. The numerical results show that the proposed design achieves precise beam pattern control, maximizes SE, and significantly improves the sensing-communication trade-off compared to the state-of-the-art methods in FDD ISAC scenarios.

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arXiv2024

Splitting Messages in the Dark- Rate-Splitting Multiple Access for FDD Massive MIMO Without CSI Feedback

A critical hindrance in realizing frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems is the overhead associated with the downlink (DL) channel state information at the transmitter (CSIT) acquisition. To address this, we propose a novel framework that eliminates the need for CSI feedback, while achieving robust sum spectral efficiency (SE). Specifically, by leveraging partial frequency invariance of channel parameters, we reconstruct the DL CSIT using uplink (UL) pilots with the 2D-Newtonized orthogonal matching pursuit (2D-NOMP) algorithm. Due to discrepancies between the two disjoint bands, however, perfect DL CSIT acquisition is infeasible; resulting in multi-user interference (MUI). To account for this, we reformulate the sum SE maximization problem using the reconstructed channel and its error covariance matrix (ECM). Then, we propose an ECM estimation method based on the observed Fisher information matrix and introduce a precoder optimization technique with rate-splitting multiple access (RSMA). Our simulation results verify the validity of the proposed framework in the practical FDD massive MIMO scenarios, highlighting the essential role of ECM estimation in mitigating MUI to attain RSMA gains.

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