13 papers
ItDPDM: Information-Theoretic Discrete Poisson Diffusion Model
Sagnik Bhattacharya, Abhiram Gorle, Ahsan Bilal +3
Generative modeling of non-negative, discrete data, such as symbolic music, remains challenging due to two persistent limitations in existing methods. Firstly, many approaches rely…
On the Fundamental Limits of LLMs at Scale
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +13
Large Language Models (LLMs) have benefited enormously from scaling, yet these gains are bounded by five fundamental limitations: (1) hallucination, (2) context compression, (3) re…
Transformer-Based Sparse CSI Estimation for Non-Stationary Channels
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +4
Accurate and efficient estimation of Channel State Information (CSI) is critical for next-generation wireless systems operating under non-stationary conditions, where user mobility…
minPIC: Towards Optimal Power Allocation in Multi-User Interference Channels
Sagnik Bhattacharya, Abhiram Rao Gorle, John M. Cioffi
6G envisions massive cell-free networks with spatially nested multiple access (MAC) and broadcast (BC) channels without centralized coordination. This makes optimal resource alloca…
AI Enabled 6G for Semantic Metaverse: Prospects, Challenges and Solutions for Future Wireless VR
Muhammad Ahmed Mohsin, Sagnik Bhattacharya, Abhiram Gorle +2
Wireless support of virtual reality (VR) has challenges when a network has multiple users, particularly for 3D VR gaming, digital AI avatars, and remote team collaboration. This wo…
LZMidi: Compression-Based Symbolic Music Generation
Connor Ding, Abhiram Gorle, Sagnik Bhattacharya +3
Recent advances in symbolic music generation primarily rely on deep learning models such as Transformers, GANs, and diffusion models. While these approaches achieve high-quality re…