THz UM-MIMO Channel Estimation via a Noise-Conditioned Fixed-Point Network

ZHANG Huawei; NIU Yaning; JIANG Zhanjun; LIU Yingting

doi:10.11999/JEIT260420

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ZHANG Huawei, NIU Yaning, JIANG Zhanjun, LIU Yingting. THz UM-MIMO Channel Estimation via a Noise-Conditioned Fixed-Point Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260420

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ZHANG Huawei, NIU Yaning, JIANG Zhanjun, LIU Yingting. THz UM-MIMO Channel Estimation via a Noise-Conditioned Fixed-Point Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260420

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THz UM-MIMO Channel Estimation via a Noise-Conditioned Fixed-Point Network

doi: 10.11999/JEIT260420 cstr: 32379.14.JEIT260420

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Funds: The National Natural Science Foundation of China(62561037), Natural Science Foundation of Gansu Province (26JRRA053)

Accepted Date: 2026-07-06
Rev Recd Date: 2026-07-06

Available Online: 2026-07-19

Abstract

Abstract

Objective Terahertz(THz)ultra-massive multiple-input multiple-output(UM-MIMO)systems are expected to support future high-capacity wireless communications. However, accurate channel estimation remains difficult under hybrid near-/far-field propagation and array-of-subarrays(AoSA)architectures, where limited radio-frequency chains, low signal-to-noise ratio(SNR), noise uncertainty, and structural perturbations degrade compressed observations. Existing compressed sensing, Bayesian inference, and deep unfolding methods usually rely on fixed statistical assumptions, limiting their adaptability across different SNR and mismatch conditions. To address these issues, this paper proposes a noise-conditioned fixed-point network, termed FPN-NCAS(FPN Noise-Conditioned AoSA-aligned Shrinkage), for robust THz UM-MIMO channel estimation. The objective is to improve estimation accuracy, cross-SNR adaptability, robustness, and iterative stability by introducing noise awareness into nonlinear recovery. Methods FPN-NCAS is developed based on an OAMP-style fixed-point unfolding framework. A coarse noise-power estimate is obtained from repeated pilot differences and injected into the nonlinear recovery module as an explicit conditioning variable. After each linear update, the vector-domain estimate is reshaped into an AoSA-aligned tensor to exploit subarray-level structural priors. The nonlinear restoration chain contains three components. Token-Gate performs lightweight subarray reliability pre-calibration to suppress unreliable responses under low-SNR or structurally inconsistent conditions. Block Shrink serves as the core noise-conditioned block-sparse proximal operator, where a smooth dual-threshold mechanism balances strong denoising at low SNR and structure preservation at medium-to-high SNR. G-HMTD(Gated Hybrid Multi-scale Transformer Denoiser)further refines residual errors by combining local multi-scale enhancement and global contextual modeling. Bridge relaxation and nonlinear residual scaling are also introduced to improve inter-stage stability. Results and Discussions Simulation results show that FPN-NCAS consistently outperforms LS, OAMP, ISTA-Net+, FPN-OAMP, and FPN-OTFN over the 0–20 dB SNR range (Figure 6). At SNR = 0 dB, FPN-NCAS achieves approximately 3.0 dB and 1.9 dB NMSE gains over FPN-OAMP and FPN-OTFN, respectively; at SNR = 20 dB, the gains increase to about 4.5 dB and 2.6 dB (Figure 6(a)). The convergence curves show that FPN-NCAS reaches a stable plateau after about three layers at SNR = 5 dB and about five layers at SNR = 15 dB, indicating stable fixed-point iteration behavior (Figure 6(b), Figure 6(c)). Repeated-pilot analysis shows that four repeated pilot pairs introduce only 3.13% extra pilot overhead while reducing the relative standard deviation of the coarse noise estimate to 25.00%. Under moderate noise-power mismatch, NMSE degradation is generally within 0.3 dB. The method also remains robust under colored Gaussian noise, impulsive noise, field-region shifts, path-number variation, AoSA subarray shuffle, and RF gain/phase mismatch (Figure 7, Figure 8). Ablation results indicate that Block Shrink is the dominant contributor, while Token-Gate and G-HMTD provide complementary gains through structural calibration and residual refinement (Figure 9). Conclusions This paper proposes FPN-NCAS for noise-conditioned fixed-point channel estimation in THz UM-MIMO systems. By integrating repeated-pilot-based noise conditioning, AoSA-aware feature reshaping, Token-Gate calibration, Block Shrink recovery, and G-HMTD refinement, the proposed method improves NMSE performance, robustness, and iterative stability under different SNR and non-ideal conditions. However, the improved performance is accompanied by higher inference complexity. Future work will focus on lightweight implementation and extensions to wideband, multi-user, and hardware-impaired THz communication scenarios.
- THz communications,
- Ultra-Massive MIMO,
- Channel estimation,
- Fixed-Point Network,
- Noise conditioning

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References(20)

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