most citedMatching-Driven Deep Reinforcement Learning for Energy-Efficient Transmission Parameter Allocation in Multi-Gateway LoRa Networks

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI2025

RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction

Xiucheng Wang, Zhongsheng Fang, Nan Cheng +4

Radio maps (RMs) are essential for environment-aware communication and sensing, providing location-specific wireless channel information. Existing RM construction methods often rel…

eess.SP2025

Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks

Jingzhi Hu, Xin Li, Zhou Su +1

In wireless networks with integrated sensing and communications (ISAC), edge intelligence (EI) is expected to be developed at edge devices (ED) for sensing user activities based on…

cs.MA20242 cited

Matching-Driven Deep Reinforcement Learning for Energy-Efficient Transmission Parameter Allocation in Multi-Gateway LoRa Networks

Ziqi Lin, Xu Zhang, Shimin Gong +3

Long-range (LoRa) communication technology, distinguished by its low power consumption and long communication range, is widely used in the Internet of Things. Nevertheless, the LoR…

cs.LG2024

Multi-Time Scale Service Caching and Pricing in MEC Systems with Dynamic Program Popularity

Yiming Chen, Xingyuan Hu, Bo Gu +2

In mobile edge computing systems, base stations (BSs) equipped with edge servers can provide computing services to users to reduce their task execution time. However, there is alwa…

cs.LG2024

Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth?

Jiajie Li, Bo Gu, Shimin Gong +2

Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a compl…