3 citations · 5 across the 11 of their papers we have counts for
16 papers
The Indian Financial Automated Advice Bias (IFAAB) Dataset : Auditing Socioeconomic and Demographic Disparities in LLM-Generated Financial Advice in India
Kriti Mahajan
Large language models are increasingly used for financial guidance, yet little is known about whether their advice changes across social identities in Global South contexts. We int…
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…
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…
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…
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…
Nuisance parameters and elliptically symmetric distributions: a geometric approach to parametric and semiparametric efficiency
Stefano Fortunati, Jean-Pierre Delmas, Esa Ollila
Elliptically symmetric distributions are a classic example of a semiparametric model where the location vector and the scatter matrix (or a parameterization of them) are the two fi…
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.…
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…
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…
Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model
Xinjue Wang, Esa Ollila, Sergiy A. Vorobyov
Graph Neural Networks (GNNs), particularly Graph Convolutional Neural Networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing…