101 citations · 301 across the 44 of their papers we have counts for
48 papers
Minimum Rate For Partially Observable Linear System with Side Information: LQG Plant and Gaussian-Markov Source
Sijie Li, Hyeji Kim
This paper studies the minimum rate required for a partially observable linear system with side information. The Linear Quadratic Gaussian(LQG) plant and the Gaussian-Markov source…
Attention-Aided MMSE with Ridge Denoising: How to Train under Noisy Channel Samples
TaeJun Ha, Hyeji Kim, Jeonghun Park
Deep neural channel estimators are typically trained with clean channel state information (CSI), which is unavailable in practical orthogonal frequency-division multiplexing (OFDM)…
Lower Bound of Networked Control with Multiple Sensors and One Controller And The Application to Tracking Gaussian-Markov Source
Sijie Li, Takashi Tanaka, Hyeji Kim
This paper investigates the causal rate-distortion function for networked control systems with multiple encoders and a single decoder, a longstanding open problem in information an…
Structured Masked Diffusion for Shared-Codebook Unsourced Random Access Decoding
Taekyun Lee, Jiyoung Yun, Jeffrey G. Andrews +1
In shared-codebook unsourced random access (URA), a receiver must recover an unordered set of messages from simultaneous transmissions. Its outer decoder assembles ambiguous slot-w…
Dynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer
Yulun Wu, Sravan Kumar Ankireddy, Samuel Sharpe +4
Efficiently aggregating spatial or temporal horizons to acquire compact representations has become a unifying principle in modern deep learning models, yet learning data-adaptive r…
TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya +3
Large Reasoning Models (LRMs) achieve impressive performance on complex reasoning tasks via Chain-of-Thought (CoT) reasoning, which enables them to generate intermediate thinking t…
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…
Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee +4
A natural desideratum for generative models is self-correction--detecting and revising low-quality tokens at inference. While Masked Diffusion Models (MDMs) have emerged as a promi…
Deep-OFDM: Neural Modulation for High Mobility
S. Ashwin Hebbar, Sravan Kumar Ankireddy, Harshithanjani Athi +3
Orthogonal Frequency Division Multiplexing (OFDM) is the dominant waveform in modern wireless systems, but suffers performance degradation in high-mobility environments due to Dopp…
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…
Neural Cover Selection for Image Steganography
Karl Chahine, Hyeji Kim
In steganography, selecting an optimal cover image, referred to as cover selection, is pivotal for effective message concealment. Traditional methods have typically employed exhaus…
Generating High Dimensional User-Specific Wireless Channels using Diffusion Models
Taekyun Lee, Juseong Park, Hyeji Kim +1
Deep neural network (DNN)-based algorithms are emerging as an important tool for many physical and MAC layer functions in future wireless communication systems, including for large…
Enhancing K-user Interference Alignment for Discrete Constellations via Learning
Rajesh Mishra, Syed Jafar, Sriram Vishwanath +1
In this paper, we consider a K-user interference channel where interference among the users is neither too strong nor too weak, a scenario that is relatively underexplored in the l…
Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
Alliot Nagle, Adway Girish, Marco Bondaschi +3
We formalize the problem of prompt compression for large language models (LLMs) and present a framework to unify token-level prompt compression methods which create hard prompts fo…
Local to Global: Learning Dynamics and Effect of Initialization for Transformers
Ashok Vardhan Makkuva, Marco Bondaschi, Chanakya Ekbote +4
In recent years, transformer-based models have revolutionized deep learning, particularly in sequence modeling. To better understand this phenomenon, there is a growing interest in…
LightCode: Light Analytical and Neural Codes for Channels with Feedback
Sravan Kumar Ankireddy, Krishna Narayanan, Hyeji Kim
The design of reliable and efficient codes for channels with feedback remains a longstanding challenge in communication theory. While significant improvements have been achieved by…
DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
S Ashwin Hebbar, Sravan Kumar Ankireddy, Hyeji Kim +2
Progress in designing channel codes has been driven by human ingenuity and, fittingly, has been sporadic. Polar codes, developed on the foundation of Arikan's polarization kernel,…
Attention with Markov: A Framework for Principled Analysis of Transformers via Markov Chains
Ashok Vardhan Makkuva, Marco Bondaschi, Adway Girish +4
Attention-based transformers have achieved tremendous success across a variety of disciplines including natural languages. To deepen our understanding of their sequential modeling…
LASER: Linear Compression in Wireless Distributed Optimization
Ashok Vardhan Makkuva, Marco Bondaschi, Thijs Vogels +3
Data-parallel SGD is the de facto algorithm for distributed optimization, especially for large scale machine learning. Despite its merits, communication bottleneck is one of its pe…
Task-aware Distributed Source Coding under Dynamic Bandwidth
Po-han Li, Sravan Kumar Ankireddy, Ruihan Zhao +5
Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. In such networks, each sensor independently compresses the data a…
Compressed Error HARQ: Feedback Communication on Noise-Asymmetric Channels
Sravan Kumar Ankireddy, S. Ashwin Hebbar, Yihan Jiang +2
In modern communication systems with feedback, there are increasingly more scenarios where the transmitter has much less power than the receiver (e.g., medical implant devices), wh…
TinyTurbo: Efficient Turbo Decoders on Edge
S Ashwin Hebbar, Rajesh K Mishra, Sravan Kumar Ankireddy +3
In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much be…