4 papers
Floating-Point Microformat Quantization and Pruning for Efficient MU-MIMO Neural Receivers
SaiKrishna Saketh Yellapragada, Esa Ollila, Mário Costa +1
Neural receivers outperform conventional 5G NR processing chains, but their compute and memory demands hinder real-time deployment. For a standard-compliant multi-user MIMO neural…
Computationally Efficient Neural Receiver via Axial Self-Attention
SaiKrishna Saketh Yellapragada, Atchutaram K. Kocharlakota, Mário Costa +2
Deep learning-based neural receivers offer promising physical-layer solutions for next-generation wireless systems. We propose an axial self-attention transformer neural receiver t…
Efficient Quantization-Aware Neural Receivers: Beyond Post-Training Quantization
SaiKrishna Saketh Yellapragada, Esa Ollila, Mario Costa
As wireless communication systems advance toward Sixth Generation (6G) Radio Access Networks (RAN), Deep Learning (DL)-based neural receivers are emerging as transformative solutio…
Efficient Deep Neural Receiver with Post-Training Quantization
SaiKrishna Saketh Yellapragada, Esa Ollila, Mario Costa
Deep learning has recently garnered significant interest in wireless communications due to its superior performance compared to traditional model-based algorithms. Deep convolution…