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Shengheng Liu

4 accepted papers

2025

Fine-Grained Graph Representation Learning for Heterogeneous Mobile Networks with Attentive Fusion and Contrastive Learning

AAAI 2025technical

AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent network self-driving holds the key, it heavily relies on expert experience and…

2024

Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB

AAAI 2024technical

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due…

Cited by 8SourcePDFScholar
2021

Low-Complexity Parameter Learning for OTFS Modulation Based Automotive Radar

ICASSP 2021accepted

Orthogonal time frequency space (OTFS) as an emerging modulation technique in the 5G and beyond era exploits full time-frequency diversity and is robust against doubly-selective channels in high mobility scenarios. In this work, we consider an OTFS modulation based automotive joint radar-communicati…

Cited by 0SourceScholar
2016

Automatic human fall detection in fractional fourier domain for assisted living

ICASSP 2016accepted

Fast and accurate detection of elderly falls can significantly reduce the rate of morbidity and mortality. In the past decade, extensive research has been performed to achieve real-time fall monitoring solutions. In this paper, we consider the radar-based modality and utilize the family of fractiona…

Cited by 0SourceScholar