Tootfinder

RIS Assisted Wireless Networks: Collaborative Regulation, Deployment Mode and Field Testing
Yajun Zhao
https://arxiv.org/abs/2404.08644 https://

RIS Assisted Wireless Networks: Collaborative Regulation, Deployment Mode and Field Testing
In recent years, RIS has made significant progress in engineering application research and industrialization and academic research. However, the engineering application research field of RIS still faces several challenges. This article analyzes and discusses the two deployment modes of RIs-assisted wireless networks: Network Controlled Mode and Standalone mode. It also presents three typical collaboration scenarios of RIS networks, including multi-RIS collaboration, multi-user access, and multi…

Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
Dongbo Xi, Zhen Chen, Yuexian Wang, He Cui, Chong Peng, Fuzhen Zhuang, Peng Yan
https://arxiv.org/abs/2404.08361

Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
Feed recommendation is currently the mainstream mode for many real-world applications (e.g., TikTok, Dianping), it is usually necessary to model and predict user interests in multiple scenarios (domains) within and even outside the application. Multi-domain learning is a typical solution in this regard. While considerable efforts have been made in this regard, there are still two long-standing challenges: (1) Accurately depicting the differences among domains using domain features is crucial fo…

Joint Optimization on Uplink OFDMA and MU-MIMO for IEEE 802.11ax: Deep Hierarchical Reinforcement Learning Approach
Hyeonho Noh, Harim Lee, Hyun Jong Yang
https://arxiv.org/abs/2404.02486

Joint Optimization on Uplink OFDMA and MU-MIMO for IEEE 802.11ax: Deep Hierarchical Reinforcement Learning Approach
This letter tackles a joint user scheduling, frequency resource allocation (USRA), multi-input-multi-output mode selection (MIMO MS) between single-user MIMO and multi-user (MU) MIMO, and MU-MIMO user selection problem, integrating uplink orthogonal frequency division multiple access (OFDMA) in IEEE 802.11ax. Specifically, we focus on \textit{unsaturated traffic conditions} where users' data demands fluctuate. In unsaturated traffic conditions, considering packet volumes per user introduces a c…

This https://arxiv.org/abs/2404.08361 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
Feed recommendation is currently the mainstream mode for many real-world applications (e.g., TikTok, Dianping), it is usually necessary to model and predict user interests in multiple scenarios (domains) within and even outside the application. Multi-domain learning is a typical solution in this regard. While considerable efforts have been made in this regard, there are still two long-standing challenges: (1) Accurately depicting the differences among domains using domain features is crucial fo…