most citedBridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications

6 citations · 6 across the 4 of their papers we have counts for

collaborators
Showing eess.SPShow all

7 papers · 1 filter

eess.SP2026

Channel2World: A Wireless Foundation Model for RF Environment Representation

Hyung-Joo Moon, Joonkyu Jang, Kwang Soon Kim +3

Wireless channels are commonly treated as link-specific observations, although their multipath structure is governed by the surrounding radio-frequency (RF) environment. In this pa…

eess.SP2026

Physically Consistent Channel Modeling and Signal Processing for Reconfigurable Wireless Systems

Ahmad Dkhan, Simon Tarboush, Hadi Sarieddeen +3

Reconfigurable antennas are increasingly integrated into multi-antenna communication systems to exploit large apertures while reducing the hardware complexity, energy consumption,…

eess.SP2026

Energy Efficiency Optimization in Distributed MIMO vRAN via Cross-Layer Link Abstraction

Jaebum Park, Chan-Byoung Chae, Robert W. Heath

Virtualized radio access networks (vRAN) run the compute-intensive multiple-input multiple-output (MIMO) baseband as software on shared servers, which makes energy efficiency (EE)…

eess.SP2026

Accelerating vRAN and O-RAN with SIMD: Architectural Perspectives and Performance Evaluation

Jaebum Park, Chan-Byoung Chae, Robert W. Heath

The evolution of radio access networks (RANs) toward virtualization and openness creates new opportunities for flexible, cost-effective, and high-performance deployments. Achieving…

eess.SP2026

Signal Processing Foundations of Reconfigurable Antennas in the Tri-Hybrid MIMO Architecture

Nitish Vikas Deshpande, Joseph Carlson, Siyun Yang +6

To enable larger apertures in multipleinput multipleoutput MIMO systems the trihybrid MIMO architecture offers a promising lowcost and lowpower solution by introducing reconfigurab…

eess.SP2025

AI-Enhanced Wide-Area Data Imaging via Massive Non-Orthogonal Direct Device-to-HAPS Transmission

Hyung-Joo Moon, Chan-Byoung Chae, Kai-Kit Wong +1

Massive Aerial Processing for X MAP-X is an innovative framework for reconstructing spatially correlated ground data, such as environmental or industrial measurements distributed a…