6 papers
Neural Beamforming with Doppler-Aware Sparse Attention for High Mobility Environments
Cemil Vahapoglu, Timothy J. O'Shea, Wan Liu +1
Beamforming has significance for enhancing spectral efficiency and mitigating interference in multi-antenna wireless systems, facilitating spatial multiplexing and diversity in den…
Transformer-Driven Neural Beamforming with Imperfect CSI in Urban Macro Wireless Channels
Cemil Vahapoglu, Timothy J. O'Shea, Wan Liu +2
The literature is abundant with methodologies focusing on using transformer architectures due to their prominence in wireless signal processing and their capability to capture long…
Deep Learning Based Joint Multi-User MISO Power Allocation and Beamforming Design
Cemil Vahapoglu, Timothy J. O'Shea, Tamoghna Roy +1
The evolution of fifth generation (5G) wireless communication networks has led to an increased need for wireless resource management solutions that provide higher data rates, wide…
Deep Learning Based Uplink Multi-User SIMO Beamforming Design
Cemil Vahapoglu, Timothy J. O'Shea, Tamoghna Roy +1
The advancement of fifth generation (5G) wireless communication networks has created a greater demand for wireless resource management solutions that offer high data rates, extensi…
Hierarchical Over-the-Air FedGradNorm
Cemil Vahapoglu, Matin Mortaheb, Sennur Ulukus
Multi-task learning (MTL) is a learning paradigm to learn multiple related tasks simultaneously with a single shared network where each task has a distinct personalized header netw…
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning
Matin Mortaheb, Cemil Vahapoglu, Sennur Ulukus
Multi-task learning (MTL) is a novel framework to learn several tasks simultaneously with a single shared network where each task has its distinct personalized header network for f…