Tanisha Fairuz
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Research Areas
Biography and Research Information
OverviewAI-generated summary
Tanisha Fairuz's research focuses on advancements in communication systems, specifically investigating channel estimation techniques for Massive Multiple-Input Multiple-Output (MIMO) systems. Her recent publications explore the application of hybrid transformer-based models, enhanced by low-rank adaptation optimization, to improve the accuracy and efficiency of channel estimation in these complex systems. Fairuz has also contributed to the development of synthetic datasets designed to facilitate research in this area, particularly for hybrid transformer-MMSE channel estimation methods. Her work aims to address challenges in wireless communication by leveraging sophisticated machine learning architectures and data generation strategies.
Metrics
- Publications: 4
Selected Publications
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Hybrid Transformer-Based Channel Estimation for Massive Multiple-Input Multiple-Output Systems Using Low-Rank Adaptation Optimization (2026)
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Hybrid Transformer-Based Channel Estimation for Massive Multiple-Input Multiple-Output Systems Using Low-Rank Adaptation Optimization (2026)
Collaboration Network
Top Collaborators
- Hybrid Transformer-Based Channel Estimation for Massive Multiple-Input Multiple-Output Systems Using Low-Rank Adaptation Optimization
- Hybrid Transformer-Based Channel Estimation for Massive Multiple-Input Multiple-Output Systems Using Low-Rank Adaptation Optimization
- Hybrid Transformer-Based Channel Estimation for Massive Multiple-Input Multiple-Output Systems Using Low-Rank Adaptation Optimization
- Hybrid Transformer-Based Channel Estimation for Massive Multiple-Input Multiple-Output Systems Using Low-Rank Adaptation Optimization
- Hybrid Transformer-Based Channel Estimation for Massive Multiple-Input Multiple-Output Systems Using Low-Rank Adaptation Optimization
- Hybrid Transformer-Based Channel Estimation for Massive Multiple-Input Multiple-Output Systems Using Low-Rank Adaptation Optimization
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