Link-level evaluations confirm improved error vector magnitude and maintained bit/block error rates relative to the RT baseline, validating that the proposed physical-data collaborative paradigm not only enhances estimation accuracy but also preserves communication reliability, while offering a promising foundation for future detection-aware and integrated-sensing-and-communication optimizations.
Abstract
Accurate channel estimation in millimeter-wave MIMO-OFDM systems is hindered by limited pilot resources and the high sensitivity of physical priors to propagation conditions. This paper proposes PERL, a physics-enhanced residual learning framework that integrates ray-tracing (RT) priors with deep neural networks to refine coarse RT-based estimates. Unlike direct channel reconstruction, PERL learns a residual correction atop the RT baseline, with the correction magnitude adaptively gated according to noise level and RT reliability, thereby relying more on physical priors under low SNR and exploiting pilot observations for refinement at high SNR. The framework leverages a Transformer-based multimodal attention mechanism to deeply fuse sparse pilot observations, path-level propagation features (delay, angle, power, and phase), and scene-level statistical descriptors, enabling physical constraints and data-driven refinement to interact effectively. Experiments on a 28 GHz urban macro-cellular MIMO-OFDM scenario demonstrate that PERL achieves an overall NMSE of −28.82 dB, outperforming the RT baseline by 5.30 dB and the MMSE estimator by over 20 dB, with notably larger gains under non-line-of-sight conditions where the RT prior is less accurate. Link-level evaluations further confirm improved error vector magnitude and maintained bit/block error rates relative to the RT baseline, validating that the proposed physical-data collaborative paradigm not only enhances estimation accuracy but also preserves communication reliability, while offering a promising foundation for future detection-aware and integrated-sensing-and-communication optimizations.
In multiple-input multiple-output systems, the acquisition of high-fidelity channel state information is hindered by the complex interaction between time-frequency fading and limited pilot signal resources. Current deep learning methods often lack physical consistency and require large computational costs. This letter proposes PiPNet, a lightweight physics-aware Transformer architecture that integrates specialized a priori knowledge into the model, including Pilot-Aware Encoding which establishes a sparsity-preserving manifold projection mechanism for noise filtering; the Hyper-Informed Physical Attention block which utilizes a physical triad comprising spectral, phase, and differential domains, combined with an inverted attention mechanism to reduce computational complexity; and the Phasor Rectification Feed-forward block to refine signal edge details. Simulation results demonstrate that PiPNet consistently achieves superior performance compared with existing methods. Furthermore, PiPNet exhibits an improved balance between estimation accuracy and computational efficiency, indicating its potential for real-time wireless applications.
Ngoc-Ha Truong, Thanh-Dat Tran, S. N. Truong et al.· IEEE Wireless Communications...· 0 citations
Reliable channel estimation (CE) in unmanned aerial vehicles (UAVs)-assisted orthogonal frequency-division multiplexing (OFDM) systems is fundamentally challenged by mobility-induced Doppler dynamics and frequency-dependent beam squint, which jointly distort pilot observations and reduce channel coherence across subcarriers. These impairments limit the effectiveness of conventional model-based estimators and increase retransmissions, thereby degrading spectral and energy efficiency. This paper develops a structured UAV-assisted OFDM framework that explicitly captures Doppler-induced phase evolution and wideband spatial distortion. Least-squares (LS) estimation and a genie-aided minimum mean square error (MMSE) equalizer are employed as analytical references, where the latter serves as an ideal upper performance bound. Building upon this foundation, we introduce a physics-informed contextual deep learning formulation that refines LS estimates by exploiting cross-subcarrier frequency correlation. The proposed hybrid architecture, termed CRDBA-Net, integrates dilated residual convolution, bidirectional sequential modeling, and attention-based subcarrier weighting to capture multi-scale frequency structure and mobility-driven variation. Extensive bit error rate (BER) evaluations demonstrate that the proposed framework consistently outperforms classical and representative learning-based baselines across a wide signal-to-noise ratio range and under severe Doppler and beam-squint conditions, while approaching genie-aided MMSE performance without requiring prior channel statistics or matrix inversion. The results highlight the potential of structured deep learning to enhance reliability and computational efficiency in high-mobility green UAV communication networks.
Muhammad Usman, I. Hameed, Md Habibur Rahman et al.· IEEE Transactions on Green C...· 0 citations
Accurate multipath parameter estimation is critical for modern wireless communication systems, particularly in challenging low-SNR environments. Traditional Maximum Likelihood Estimation algorithms, such as CLEAN, provide high-resolution parameter extraction but suffer from prohibitive computational complexity due to exhaustive grid search. Conversely, purely data-driven deep learning approaches lack physical grounding and struggle to generalize across variable multipath densities and off-grid parameters. To address these limitations, this paper proposes Neural Network-Assisted CLEAN (NN-CLEAN), a hybrid framework that embeds a multi-head residual network directly into the iterative CLEAN extraction loop. By replacing the exhaustive grid search with rapid, parallelizable forward passes while delegating residual subtraction to exact mathematical models, NN-CLEAN isolates physical multipath parameters without accumulating non- physical errors. Extensive Monte Carlo simulations demonstrate that NN-CLEAN achieves estimation accuracy exceeding 96% at 5 dB SNR, matching the traditional Grid-Search CLEAN (GS- CLEAN) baseline, while providing a massive reduction in computational complexity and substantially outperforming subspace methods and standalone one-shot neural networks. Crucially, NN-CLEAN exhibits a near-flat scaling in execution runtime and memory consumption as batch sizes increase. This highly efficient parallelization establishes NN-CLEAN as a robust, real- time solution for channel estimation in MIMO systems.
Chao-Fan Deng, Lin-Yu Sun, Jaeho Lee et al.· arXiv.org· 0 citations
Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) enable high data rates but rely on accurate channel state information (CSI) for efficient beamforming and data transmission. Approximate message passing (AMP) and learned variants (LAMP/LDAMP) suffer from performance degradation under high sparsity levels and propagation environments. Therefore, we propose a novel deep unfolding framework that integrates model-driven LDAMP with data-driven residual learning to enhance beamspace channel estimation accuracy, called Res-LDAMP. The Res-LDAMP method embeds a residual denoising convolutional neural network (DnCNN) and connected network within the LDAMP iterative structure, enabling effective nonlinear signal recovery. Furthermore, we introduce a dual Onsager correction for leveraging original measurement and its conjugate to improve convergence and reduce estimation error. The simulations are conducted using the Saleh–Valenzuela (S-V) channel model and the DeepMIMO dataset under signal-to-noise ratio (SNR). The results demonstrate that Res-LDAMP outperforms AMP, LAMP, and LDAMP. Improved channel estimation accuracy leads to enhance beam selection performance and reduced sum-rate degradation. The findings show the combination of deep unfolding and residual learning for next-generation mmWave and beyond wireless communication systems.
Farhan M. A. Nashwan, Halah Hassen Aldumaini, Khaled A. Al Soufy et al.· 2026 6th International Confe...· 0 citations
The scalability of modern Massive MIMO systems and prospective 6G networks is fundamentally constrained by the “pilot contamination” effect and the prohibitive overhead of time-frequency resources required for orthogonal pilot transmission. In ultra-dense deployment scenarios, traditional pilot-aided channel estimation methods exhibit a critical degradation in spectral efficiency. The study aims to develop a resource-efficient method for blind channel estimation that is invariant to antenna array topology, with the goal of minimizing signaling overhead and maximizing throughput capacity under conditions of complex spatial correlation. The proposed approach is based on the statistical processing of the sample covariance matrix of received signals utilizing a deep convolutional neural network. In contrast to direct reconstruction techniques, the algorithm employs a residual learning strategy to isolate and mitigate estimation noise arising from finite sample sizes. To resolve the phase ambiguity of the signal subspace, a “virtual pilot” concept (a single reference symbol) is introduced, ensuring that resource overhead approaches zero asymptotically. The study is validated across a wide spectrum of configurations, including linear, rectangular, and circular arrays, as well as distributed antenna systems. Simulation results confirm that the proposed method yields a significant gain in the system’s aggregate spectral efficiency by liberating resources previously allocated to pilot sequences. Despite a marginal degradation in estimation accuracy compared to conventional methods, the algorithm demonstrates high robustness to various spatial correlation profiles and antenna geometries. The proposed method facilitates the realization of massive connectivity scenarios on existing base station hardware architectures, effectively overcoming throughput limitations imposed by the coherence interval length.
Cong Quyen Pham, E. Glushankov· Infokommunikacionnye tehnolo...· 0 citations
Massive multiple-input multiple-output (MIMO) technology underpins the spectral efficiency targets of fifth-generation (5G) New Radio (NR) networks, and its performance depends critically on accurate channel state information at the receiver. Classical pilot-based channel estimators face three fundamental constraints: pilot overhead that scales with the antenna count, matrix inversion complexity that grows cubically with the array size, and severe noise sensitivity at low signal-to-noise ratio (SNR) or under high-mobility conditions. The objective of this study is to develop a channel estimator that accurately reconstructs the full time-frequency channel response from a sparse pilot grid while remaining computationally feasible for real-time operation. A two-stage hybrid deep learning estimator is proposed in which least-squares estimates at pilots placed at every twelfth subcarrier are first expanded by two-dimensional bilinear interpolation and then refined by a time-distributed convolutional neural network (CNN) coupled with a long short-term memory (LSTM) recurrent stage; the model is trained and evaluated on time-varying 3GPP TR 38.901 tapped delay line channels with Jakes Doppler fading at speeds of up to 120 km/h. Evaluated on a 4×4 MIMO-OFDM link with 624 subcarriers over an SNR range of 0-20 dB, the proposed estimator reduces the normalized mean-squared error by up to 88%, approximately halves the bit-error rate at mid-range SNRs, and raises the spectral efficiency from approximately 0.13 to 4.3-5.0 bits/s/Hz relative to a conventional two-dimensional interpolation baseline, while consuming only half the pilot overhead of a dense-pilot configuration; inference latency on a graphics processing unit is below 1 ms per frame. These results indicate that hybrid CNN-LSTM processing offers a practical route to accurate, low-overhead, and latency-compliant channel estimation for 5G massive MIMO deployments.
Chirag Pradhan· Journal of Intelligent Decis...· 0 citations
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