6 papers
Foundation-Preserving Optimization in Generalized Eigenspace
Dongjun Kim, Adrian de Wynter, Huancheng Chen +2
While finetuning effectively adapts foundation models to specialized downstream tasks, it can degrade nontarget capabilities acquired during pretraining. Existing forgetting aware…
Grid-Supervised Flow Refinement for Wireless Localization
Taekyun Lee, Tommaso Balercia, Heasung Kim +2
This paper introduces FlowLoc, a framework for high-accuracy user equipment positioning from channel state information observed at multiple base stations. The input is a magnitude…
Residual Diffusion Models for Variable-Rate Joint Source Channel Coding of MIMO CSI
Sravan Kumar Ankireddy, Heasung Kim, Joonyoung Cho +1
Despite significant advancements in deep learning based CSI compression, some key limitations remain unaddressed. Current approaches predominantly treat CSI compression as a source…
Generative Diffusion Model-based Compression of MIMO CSI
Heasung Kim, Taekyun Lee, Hyeji Kim +6
While neural lossy compression techniques have markedly advanced the efficiency of Channel State Information (CSI) compression and reconstruction for feedback in MIMO communication…
Efficient Weighted Sampling via Score-based Generative Models
Heasung Kim, Taekyun Lee, Hyeji Kim +1
Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is a fundamental technique with wide-rang…
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach
Heasung Kim, Sravan Kumar Ankireddy
In this work, we consider the problem of network parameter optimization for rate maximization. We frame this as a joint optimization problem of power control, beam forming, and int…