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 deployment of Ultra-Dense Networks (UDNs) in 5G systems is to meet the growing demand for high data rates and massive connectivity. However, the dense deployment of small cells increases handover frequency, leading to challenges such as handover failures (HOF), unnecessary handovers, and the ping-pong effect, leading to degradeuser Quality of Service (QoS). This paper proposes a velocity-aware adaptive handover control approach for efficient mobility management in 5G ultra-dense networks. The proposed approach dynamically adjusts Handover Control Parameters (HCPs) called Time-to-Trigger (TTT) and Handover Margin (HOM) on the real-time velocity of User Equipment (UE) and signal conditions. The system is modeled as a two-tier heterogeneous network consisting of a macrocell overlaid with multiple small cells, and performance is evaluated using the Cost 231-Hata propagation model. The findings demonstrate that the proposed algorithm significantly reduces the total number of handovers, mitigates the ping-pong effect, and lowers handover failure rates compared to conventional static schemes. The results confirm that velocity-aware adaptive control enhances network reliability, reduces signaling overhead, and improves overall mobility performance in 5G ultra-dense environments.
Halah Hassen Aldumaini, Hanadi Esmeail Yahya, Oloof Ameen Mohmmed et al.· 2026 6th International Confe...· 0 citations
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