works on

From the 1 of 29 linked papers with an AI index.

collaborators

29 papers

eess.SP2026

LiTCom: A Lightweight Transmitter and Inference-Capable Receiver Framework for 6G Uplink

Chunmei Xu, Siqi Zhang, Zhi Ding +2

LiTCom is a framework that uses a very simple transmitter with low‑pass filtering and minimal channel coding, while relying on large generative AI models at the receiver to reconst…

eess.SP2026

Effective Depth in Joint Source-Channel Coding: An Implicit Equilibrium Analysis

Kaiwen Yu, Gang Wu, Xiaodong Xu +2

A fundamental design question in deep joint source-channel coding (Deep JSCC) remains insufficiently explored: given a channel signal-to-noise ratio (SNR), what effective computati…

eess.SP2026

Inference-Driven Uplink for 6G: Architecture, Principles, and Challenges

Chunmei Xu, Yi Ma, Rahim Tafazolli +1

Next-generation wireless networks (6G) face a critical uplink challenge arising from stringent device-side resource constraints and the growing demand for intelligent services. Thi…

eess.SP2026

IPRU: Input-Perturbation-based Radio Frequency Fingerprinting Unlearning for LAWNs

Ce Liu, Rui Meng, Yinqiu Liu +4

Radio Frequency Fingerprinting (RFF) is a key technology for identity authentication in wireless networks. However, due to the rapid dynamics of Autonomous Aerial Vehicles (AAVs) i…

cs.IT2026

Reliable Remote Inference from Unreliable Components: Joint Communication and Computation Limits

Zhenyu Liu, Yi Ma, Rahim Tafazolli

Classical information theory typically assumes reliable receiver-side processing. We study remote inference when communication is noisy and the receiver itself is built from unreli…

eess.SP2026

Low-Complexity Tone Injection via Candidate Ranking for PAPR Reduction in OFDM and AFDM Systems

Yupeng Zheng, Ang Li, Jinfei Wang +2

Tone injection (TI) is a promising distortionless PAPR reduction technique that incurs no spectral efficiency loss. However, state-of-the-art TI schemes based on random candidate g…