14 citations · 16 across the 6 of their papers we have counts for
7 papers
ISTA-Based Joint Dictionary Learning and Channel Estimation for XL-MIMO Systems
Arttu Arjas, Italo Atzeni
Channel estimation in extra-large multiple-input multiple-output systems is challenging due to near-field propagation, where the array response depends on both the angle and distan…
Cross-Predictive Sparse Bayesian Learning with Application to XL-MIMO Channel Estimation
Arttu Arjas, Italo Atzeni
Accurate channel estimation is a key requirement in extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Sparse Bayesian learning (SBL) is a well-established fra…
Nonlinear Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation with Hardware Impairments
Arttu Arjas, Italo Atzeni
Accurate channel estimation is critical for realizing the performance gains of massive multiple-input multiple-output (MIMO) systems. Traditional approaches to channel estimation t…
Enhanced Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation
Arttu Arjas, Italo Atzeni
We consider the problem of sparse channel estimation in massive multiple-input multiple-output systems. In this context, we propose an enhanced version of the sparse Bayesian learn…
Sequential model correction for nonlinear inverse problems
Arttu Arjas, Mikko J. Sillanpää, Andreas Hauptmann
Inverse problems are in many cases solved with optimization techniques. When the underlying model is linear, first-order gradient methods are usually sufficient. With nonlinear mod…
Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data
Arttu Arjas, Erwin J. Alles, Efthymios Maneas +4
Many interventional surgical procedures rely on medical imaging to visualise and track instruments. Such imaging methods not only need to be real-time capable, but also provide acc…
Blind hierarchical deconvolution
Arttu Arjas, Lassi Roininen, Mikko J. Sillanpää +1
Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement. Nevertheless, the majority of mod…