Junda Liao | Searcharxiv

arXiv2026

Real Interference Alignment for Active IRS-Aided Systems: A Rate-Profile Learning-Based Approach

With additional spatial degrees of freedom provided by the active intelligent reflecting surface (IRS), interference alignment (IA) can be achieved at low cost. In this letter, we propose a real IA scheme for an active IRS-aided system. The proposed scheme only requires the IRS to know the instantaneous channel coefficients under the assumption of blocked direct links. To maximize the achievable sum rate subject to individual minimum rate requirements and transmission power constraints, we propose a rate-profile learning-based algorithm. The algorithm uses offline-trained achievable rate profiles to decouple the original problem into multiple feasibility subproblems, which are then solved by generalized eigenvalue decomposition. Simulation results demonstrate that our proposed algorithm outperforms the conventional weighted minimum mean square error algorithm, while requiring significantly less program execution time.

cs.IT↗

arXiv2026

Architectural Unification for Polarimetric Imaging Across Multiple Degradations

Polarimetric imaging aims to recover polarimetric parameters, including Total Intensity (TI), Degree of Polarization (DoP), and Angle of Polarization (AoP), from captured polarized measurements. In real-world scenarios, these measurements are frequently affected by diverse degradations such as low-light noise, motion blur, and mosaicing artifacts. Due to the nonlinear dependency of DoP and AoP on the measured intensities, accurately retrieving physically consistent polarimetric parameters from degraded observations remains highly challenging. Existing approaches typically adopt task-specific network architectures tailored to individual degradation types, limiting their adaptability across different restoration scenarios. Moreover, many methods rely on multi-stage processing pipelines that suffer from error accumulation, or operate solely in a single domain (either image or Stokes domain), failing to fully exploit the intrinsic physical relationships between them. In this work, we propose a unified architectural framework for polarimetric imaging that is structurally shared across multiple degradation scenarios. Rather than redesigning network structures for each task, our framework maintains a consistent architectural design while being trained separately for different degradations. The model performs single-stage joint image-Stokes processing, avoiding error accumulation and explicitly preserving physical consistency. Extensive experiments show that this unified architectural design, when trained for specific degradation types, consistently achieves state-of-the-art performance across low-light denoising, motion deblurring, and demosaicing tasks, establishing a versatile and physically grounded solution for degraded polarimetric imaging.

eess.IV↗