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 evolution towards Industry 5.0 underscores the critical need for privacy-preserving and human-centric technologies within Industrial Internet of Things (IIoT) ecosystems. Federated Learning (FL), an essential framework enabling decentralized model training while safeguarding data privacy, continues to encounter significant obstacles such as non-IID data distributions, adversarial threats, and limited scalability. To address these issues, we propose <sc><bold>BioMutFed+</bold></sc>, a biologically inspired federated learning framework featuring adaptive mutation-based gradient perturbations, pheromone-driven adaptive client selection, and robust trimmed-mean aggregation. Comprehensive theoretical analysis guarantees convergence at a rate of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/\sqrt{T})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:msqrt><mml:mi>T</mml:mi></mml:msqrt><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="hawbani-ieq1-3707971.gif"/></alternatives></inline-formula>. Empirical evaluations on Digits (visual tasks), NASA FD004 (predictive maintenance), and Wisconsin Breast Cancer datasets demonstrate rapid convergence within 20 rounds, variance reductions of 1.2–2.9× against established FL methods, and enhanced adversarial robustness under 20% malicious client scenarios compared to current approaches. Furthermore, we introduce <sc>FedMutAdam</sc>, a lightweight <sc>BioMutFed+</sc> variant integrating adaptive gradient clipping with server side Adam style updates, optimized for resource-constrained IIoT devices. Our framework thus represents a scalable and robust solution advancing secure and adaptive intelligent systems for IIoT systems.
Raiha Tallat, Xingfu Wang, Ammar Hawbani et al.· IEEE Transactions on Mobile...· 1 citation
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