3 citations · 5 across the 11 of their papers we have counts for
9 papers · 1 filter
A Majorization-Minimization Framework for Activity Detection in Mixed Near-and-Far-Field Random Access
Xinjue Wang, Zhi-Yong Wang, Sergiy A. Vorobyov +3
Grant-free random access in massive machine-type communications requires detecting a small active set from length-L uplink pilots received at an M-antenna base station. Classical c…
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…
Covariance-Aware MM-PGD for Mixed Near-/Far-Field Activity Detection
Xinjue Wang, Zhi-Yong Wang, Sergiy A. Vorobyov +3
Grant-free activity detection with mixed near-field (NF) and far-field (FF) devices is an important problem that can be addressed via covariance-based detectors. The difficulty is…
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…
Generalized Nonnegative Structured Kruskal Tensor Regression
Xinjue Wang, Esa Ollila, Sergiy A. Vorobyov +1
This paper introduces Generalized Nonnegative Structured Kruskal Tensor Regression (NS-KTR), a novel tensor regression framework that enhances interpretability and performance thro…
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…
Robust Activity Detection for Massive Random Access
Xinjue Wang, Esa Ollila, Sergiy A. Vorobyov
Massive machine-type communications (mMTC) are fundamental to the Internet of Things (IoT) framework in future wireless networks, involving the connection of a vast number of devic…
Activity Detection for Massive Random Access using Covariance-based Matching Pursuit
Leatile Marata, Esa Ollila, Hirley Alves
The Internet of Things paradigm heavily relies on a network of a massive number of machine-type devices (MTDs) that monitor various phenomena. Consequently, MTDs are randomly activ…