THz Ultra-Massive 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 Ultra-Massive 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 Ultra-Massive 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 Ultra-Massive 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), The Natural Science Foundation of Gansu Province (26JRRA053)

Received Date: 2026-04-09
Accepted Date: 2026-07-06
Rev Recd Date: 2026-07-04

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 challenging under hybrid near-/far-field propagation and Array-of-SubArrays (AoSA) architectures, where limited Radio-Frequency (RF) chains, low Signal-to-Noise Ratio (SNR), noise uncertainty, and structural perturbations degrade compressed observations. Existing compressed sensing, Bayesian inference, and deep unfolding methods generally rely on fixed statistical assumptions, which limit their cross-SNR generalization under varying noise conditions and statistical mismatches. To address these limitations, this paper proposes a Noise-Conditioned Fixed-Point Network (FPN-NCAS) for robust THz UM-MIMO channel estimation. The proposed method aims to improve estimation accuracy, cross-SNR generalization, robustness, and iterative stability by incorporating noise-aware nonlinear recovery. Methods FPN-NCAS is developed within an Orthogonal Approximate Message Passing (OAMP)-based 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 recovery chain consists of three components. Token-Gate performs lightweight subarray-level reliability pre-calibration to suppress unreliable responses under low-SNR and structurally inconsistent conditions. Block Shrink serves as the core noise-conditioned block-sparse proximal operator, in which a smooth dual-threshold mechanism balances strong denoising at low SNR with structural 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 Context Modeling. Bridge relaxation and nonlinear residual scaling are also incorporated to improve inter-stage stability. Results and Discussions Simulation results demonstrate that FPN-NCAS consistently outperforms LS, OAMP, ISTA-Net+, FPN-OAMP, and FPN-OTFN over the 0～20 dB SNR range (Fig. 6). At SNR = 0 dB, FPN-NCAS achieves Normalized Mean Square Error (NMSE) gains of approximately 3.0 dB and 1.9 dB over FPN-OAMP and FPN-OTFN, respectively. At SNR = 20 dB, the gains increase to approximately 4.5 dB and 2.6 dB (Fig. 6(a)). The convergence curves show that FPN-NCAS reaches a stable plateau after approximately three layers at SNR = 5 dB and five layers at SNR = 15 dB, demonstrating stable fixed-point iterative behavior (Fig. 6(b) and Fig. 6(c)). Analysis of repeated pilots shows that four repeated pilot pairs introduce only 3.13% additional pilot overhead while reducing the relative standard deviation of the coarse noise power estimate to 25.00%. Under moderate noise power mismatch, NMSE degradation remains within 0.3 dB. The proposed method also maintains strong robustness under colored Gaussian noise, impulsive noise, near-/far-field distribution shifts, variation in the number of propagation paths, AoSA subarray shuffling, and RF amplitude/phase mismatch (Fig. 7 and Fig. 8). Ablation studies indicate that Block Shrink provides the largest performance gain, whereas Token-Gate and G-HMTD further improve performance through structural calibration and residual refinement (Fig. 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 conditions and non-ideal scenarios. The improved performance is achieved at the cost of higher inference complexity. Future work will focus on lightweight implementations and extensions to wideband, multi-user, and hardware-impaired THz communication systems.
- THz communications,
- Ultra-Massive MIMO,
- Channel estimation,
- Fixed-Point Network(FPN),
- Noise conditioning

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