2 citations · 2 across the 16 of their papers we have counts for
18 papers
Compressed LLM Reprogramming for Vision-Aided Beam Prediction in Vehicular Networks
Kai Dong, Lei Wang, Changyi Li +2
Large Language Model (LLM) reprogramming-based beam prediction demonstrates strong data efficiency by adapting pretrained language models for vehicle-to-infrastructure (V2I) beam p…
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…
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…
Finer Parameter Steps for Low-Rank PEFT: A Controlled Study with CP Tensor Adapters
Xinjue Wang, Xiuheng Wang, Yejun Zhang +3
Low-rank adapters are usually compared by sweeping a small set of ranks, but the rank also fixes the resolution of the parameter budget. For a OPT attention proj…
Tensor Train Decomposition Based Noise Reduction and Enhanced Parameter Estimation for FMCW MIMO Radar Systems
Luoyan Zhu, Sergiy A. Vorobyov, Jie Wang +2
Frequency modulated continuous wave (FMCW) radar is widely used in autonomous driving and industrial inspection due to its high-resolution target location and velocity estimation c…
Anisotropic Tensor Deconvolution of Hyperspectral Images
Xinjue Wang, Xiuheng Wang, Esa Ollila +1
Hyperspectral image (HSI) deconvolution is a challenging ill-posed inverse problem, made difficult by the data's high dimensionality.We propose a parameter-parsimonious framework b…
Estimating Sequences with Memory for Minimizing Convex Non-smooth Composite Functions
Endrit Dosti, Sergiy A. Vorobyov, Themistoklis Charalambous
First-order optimization methods are crucial for solving large-scale data processing problems, particularly those involving convex non-smooth composite objectives. For such problem…
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…
Privacy-Preserving Quantized Federated Learning with Diverse Precision
Dang Qua Nguyen, Morteza Hashemi, Erik Perrins +3
Federated learning (FL) has emerged as a promising paradigm for distributed machine learning, enabling collaborative training of a global model across multiple local devices withou…
Frequency Range 3 for ISAC in 6G: Potentials and Challenges
Gayan Aruma Baduge, Mojtaba Vaezi, Janith K. Dassanayake +3
Spanning 7-24 GHz, frequency range 3 (FR3), is a key enabler for next-generation wireless networks by bridging the coverage of sub-6 GHz and the capacity of millimeter-wave bands.…
Wasserstein Distributionally Robust Adaptive Beamforming
Kiarash Hassas Irani, Sergiy A. Vorobyov, Yongwei Huang
Distributionally robust optimization (DRO)-based robust adaptive beamforming (RAB) enables enhanced robustness against model uncertainties, such as steering vector mismatches and i…
Pilot Contamination-Aware Graph Attention Network for Power Control in CFmMIMO
Tingting Zhang, Sergiy A. Vorobyov, David J. Love +2
Optimization-based power control algorithms are predominantly iterative with high computational complexity, making them impractical for real-time applications in cell-free massive…
SINR Maximizing Distributionally Robust Adaptive Beamforming
Kiarash Hassas Irani, Yongwei Huang, Sergiy A. Vorobyov
This paper addresses the robust adaptive beamforming (RAB) problem via the worst-case signal-to-interference-plus-noise ratio (SINR) maximization over distributional uncertainty se…
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…
A Tutorial on AI-Empowered Integrated Sensing and Communications
Mojtaba Vaezi, Gayan Aruma Baduge, Esa Ollila +1
Integrating sensing and communication (ISAC) can help overcome the challenges of limited spectrum and expensive hardware, leading to improved energy and cost efficiency. While full…
Pilot Contamination Aware Transformer for Downlink Power Control in Cell-Free Massive MIMO Networks
Atchutaram K. Kocharlakota, Sergiy A. Vorobyov, Robert W. Heath
Learning-based downlink power control in cell-free massive multiple-input multiple-output (CFmMIMO) systems offers a promising alternative to conventional iterative optimization al…
AdaBoost-Based Efficient Channel Estimation and Data Detection in One-Bit Massive MIMO
Majdoddin Esfandiari, Sergiy A. Vorobyov, Robert W. Heath
The use of one-bit analog-to-digital converter (ADC) has been considered as a viable alternative to high resolution counterparts in realizing and commercializing massive multiple-i…