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20202026
most citedTowards Self-learning Edge Intelligence in 6G

3 citations · 18 across the 20 of their papers we have counts for

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5 papers · 1 filter

eess.SP2026

Large Language Models over Networks: Collaborative Intelligence under Resource Constraints

Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +2

Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a gro…

eess.SP2025

A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication

Wei Xu, Zhaohui Yang, Derrick Wing Kwan Ng +4

The rapid evolution of forthcoming sixth-generation (6G) wireless networks necessitates the seamless integration of artificial intelligence (AI) with wireless communications to sup…

eess.SP2025

LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks

Majumder Haider, Imtiaz Ahmed, Zoheb Hassan +2

Next-generation (NextG) wireless networks are expected to require intelligent, scalable, and context-aware radio resource management (RRM) to support ultra-dense deployments, diver…

eess.SP2024

A Reconfigurable Subarray Architecture and Hybrid Beamforming for Millimeter-Wave Dual-Function-Radar-Communication Systems

Xin Jin, Tiejun Lv, Wei Ni +4

Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks. By integrating hybrid analog-digital (HAD) beamforming into a multi-user…

eess.SP2023

From OTFS to DD-ISAC: Integrating Sensing and Communications in the Delay Doppler Domain

Weijie Yuan, Lin Zhou, Saeid K. Dehkordi +4

Next-generation vehicular networks are expected to provide the capability of robust environmental sensing in addition to reliable communications to meet intelligence requirements.…