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Author

Ammar Hawbani

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Conference Aug 2026

Res-LDAMP: Residual Deep Unfolding for Robust Beamspace Channel Estimation

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...

Farhan M. A. Nashwan, Halah Hassen Aldumaini, Khaled A. Al Soufy et al. · 0 citations
#edge computing Oct 2026

Optimizing Task Offloading in NOMA-Enhanced MEC Systems via Large Model-Assisted Reinforcement Learning

Mobile edge computing effectively reduces delay and improves the system efficiency by offloading computing tasks. However, the standard communication channel, Orthogonal Frequency Division Multiple Access (OFDMA), is difficult to support large-scale connections in dense networks. Meanwhile, existing task offloading met...

Yang Xia, Min-Cong Chen, Qiang He et al. · 0 citations
#federated learning Oct 2026

BioMutFed+: Mutation-Driven Federated Learning for IIoT

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 sig...

Raiha Tallat, Xing-Fu Wang, Ammar Hawbani et al. · 1 citation
Open access Aug 2026

Image captioning using a transformer with topic–word semantic modeling and multimodal feature fusion

A semantic modeling framework consisting of Topic Embedding and Estimation Predictor and Topic-Related Representative Words that jointly fuses semantic representations generated by TEEP and TRRW with appearance and geometric features that produces more descriptive and semantically consistent captions.

Ali Abdullah Yahya, Majjed Al-Qatf, Ammar Hawbani et al. · 0 citations
#edge computing Sep 2026

D3NN: Adaptive Partitioning and Cross-Tier Resource Orchestration for Cloud–Edge Collaborative Inference

Deep Neural Networks (DNNs) have become foundational to intelligent systems, yet deploying them efficiently under strict latency, resource, and privacy constraints remains challenging. While cloud-only inference suffers from transmission latency and privacy risks, and edge-only execution is limited by hardware capacity...

Yong Zhao, Zhenjia Mo, Qiang He et al. · 0 citations

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