ReFLEX: Length-Generalizable CSI Denoising for MIMO-OFDM via Relative-Frequency Bias

Zhibin Zhang; Robert Potekhin; Ziwei Wan; Vladimir Lyashev; Zhen Gao

eess.SPMay 29, 2026

ReFLEX: Length-Generalizable CSI Denoising for MIMO-OFDM via Relative-Frequency Bias

Authors: Zhibin Zhang, Robert Potekhin, Ziwei Wan, Vladimir Lyashev, Zhen Gao

Organizations: Moscow Institute of Physics and Technology (State University), Moscow, Russia · Yangtze Delta Region Academy, Beijing Institute of Technology (Jiaxing), Jiaxing 314019, China · School of Interdisciplinary Science, Beijing Institute of Technology, Beijing 100081, China

Abstract

This letter studies CSI denoising for MIMO--OFDM with variable NR resource block (RB) allocations. ReFLEX is a length-generalizable Transformer whose frequency attention uses a relative-frequency position bias (RFPB) generated from subcarrier offsets. A single checkpoint handles unseen RB lengths and can be applied to sparse DM-RS observations in the tested RB5/RB10 PUSCH setup without retraining. In a 3GPPTR38.901 UMa NLOS channel, ReFLEX achieves about $-9.6$ ~~dB NMSE on unseen RB lengths. In NR PUSCH/UL-SCH simulations, ReFLEX denoising followed by time-frequency interpolation reduces the 10% BLER threshold by about 2--3~~dB.

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Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach

Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim, Christopher G. Brinton+1

Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA · Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA

In general, reliable communication via multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) requires accurate channel estimation at the receiver. The existing literature largely focuses on denoising methods for channel estimation that depend on either (i) channel analysis in the time-domain with prior channel knowledge or (ii) supervised learning techniques which require large pre-labeled datasets for training. To address these limitations, we present a frequency-domain denoising method based on a reinforcement learning framework that does not need a priori channel knowledge and pre-labeled data. Our methodology includes a new successive channel denoising process based on channel curvature computation, for which we obtain a channel curvature magnitude threshold to identify unreliable channel estimates. Based on this process, we formulate the denoising mechanism as a Markov decision process, where we define the actions through a geometry-based channel estimation update, and the reward function based on a policy that reduces mean squared error (MSE). We then resort to Q-learning to update the channel estimates. Numerical results verify that our denoising algorithm can successfully mitigate noise in channel estimates. In particular, our algorithm provides a significant improvement over the practical least squares (LS) estimation method and provides performance that approaches that of the ideal linear minimum mean square error (LMMSE) estimation with perfect knowledge of channel statistics.

Joint Channel Estimation Image Denoising

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Abstract

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