arXiv subjects

Ikbeom Lee

Publications and source records attributed to Ikbeom Lee.

4 recordsLinked to original sources

arXiv2026

Non-Hermitian stealthy hyperuniformity

Symmetry-driven wave physics in open systems, exemplified by parity-time (PT) symmetry, has extended the landscape of crystalline phases in materials science to include gain-loss media. Given the growing interest in engineering disorder for wave manipulation, such non-Hermitian crystals motivate the extension of non-Hermitian frameworks into the realm of correlated disorder. Here, we propose hyperuniformity and stealthiness in non-Hermitian systems as a generalization of PT-symmetric crystals to correlated disorder. We extend the scattering-microstructure correspondence to open systems, formulating non-Hermitian hyperuniformity and stealthiness that encompass their Hermitian counterparts. This approach, incorporating a statistical crystallography framework for non-Hermitian materials, demonstrates that real-imaginary cross-correlations of the material potential are irrelevant for achieving hyperuniformity but are essential for characterizing stealthiness, revealing unidirectional scattering phases that are inaccessible in Hermitian materials and in non-Hermitian crystals. By analysing the microstructural statistics of the resulting materials, our results, building on non-Hermitian wave physics, establish a connection to materials science, encompassing conventional descriptors of correlated disorder.

physics.optics↗

arXiv2025

Online Data Generation for MIMO-OFDM Channel Denoising: Transfer Learning vs. Meta Learning

Channel denoising is a practical and effective technique for mitigating channel estimation errors in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. However, adapting denoising techniques to varying channel conditions typically requires prior knowledge or incurs significant training overhead. To address these challenges, we propose a standard-compatible strategy for generating online training data that enables online adaptive channel denoising. The key idea is to leverage high-quality channel estimates obtained via data-aided channel estimation as practical substitutes for unavailable ground-truth channels. Our data-aided method exploits adjacent detected data symbols within a specific time-frequency neighborhood as virtual reference signals, and we analytically derive the optimal size of this neighborhood to minimize the mean squared error of the resulting estimates. By leveraging the proposed strategy, we devise two channel denoising approaches, one based on transfer learning, which fine-tunes a pre-trained denoising neural network, and the other based on meta learning, which rapidly adapts to new channel environments with minimal updates. Simulation results demonstrate that the proposed methods effectively adapt to dynamic channel conditions and significantly reduce channel estimation errors compared to conventional techniques.

eess.SP↗

arXiv2025

Heterogeneous networks for phase-sensitive engineering of optical disordered materials

Heterogeneous networks provide a universal framework for extracting subsystem-level features of a complex system, which are critical in graph colouring, pattern classification, and motif identification. When abstracting physical systems into networks, distinct groups of nodes and links in heterogeneous networks can be decomposed into different modes of multipartite networks, allowing for a deeper understanding of both intra- and inter-group relationships. Here, we develop heterogeneous network modelling of wave scattering to engineer multiphase random heterogeneous materials. We devise multipartite network decomposition determined by material phases, which is examined using uni- and bi-partite network examples for two-phase multiparticle systems. We show that the directionality of the bipartite network governs the phase-sensitive alteration of microstructures. The proposed modelling enables a network-based design to achieve phase-sensitive microstructural features, while almost preserving the overall scattering response. With examples of designing quasi-isoscattering stealthy hyperuniform materials, our results provide a general recipe for engineering multiphase materials for wave functionalities.

physics.optics↗

arXiv2025

Hypergraph modelling of wave scattering to speed-up material design

Hypergraphs offer a generalized framework for understanding complex systems, covering group interactions of different orders beyond traditional pairwise interactions. This modelling allows for the simplified description of simultaneous interactions among multiple elements in coupled oscillators, graph neural networks, and entangled qubits. Here, we employ this generalized framework to describe wave-matter interactions for material design acceleration. By devising the set operations for multiparticle systems, we develop the hypergraph model, which compactly describes wave interferences among multiparticles in scattering events by hyperedges of different orders. This compactness enables an evolutionary algorithm with O(N1/2) time complexity and approximated accuracy for designing stealthy hyperuniform materials, which is superior to traditional methods of O(N) scaling. By hybridizing our hypergraph evolutions to the conventional collective-coordinate method, we preserve the original accuracy, while achieving substantial speed-up in approaching near the optimum. Our result paves the way toward scalable material design and compact interpretations of large-scale multiparticle systems.

physics.optics↗