8 citations · 31 across the 10 of their papers we have counts for
10 papers
Secure and Multi-Step Computation Offloading and Resource Allocation in Ultra-Dense Multi-Task NOMA-Enabled IoT Networks
Tianqing Zhou, Yanyan Fu, Dong Qin +3
Ultra-dense networks are widely regarded as a promising solution to explosively growing applications of Internet-of-Things (IoT) mobile devices (IMDs). However, complicated and sev…
Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems
Meiyi Zhu, Chunyan Feng, Caili Guo +2
Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference. In TBMA, codewords are reused across transmitting sensors, with each codeword…
A Self-Configurable Grouping Method for Integrated Wi-SUN FAN and TSCH-based Networks
Xinyu Ni, Michael Baddeley, Nan Jiang +1
Recent applications in large-scale wireless mesh networks (WSN), e.g., Advanced Metering Infrastructure (AMI) scenarios, expect to support an extended number of nodes with higher t…
Analysis of Random Access in NB-IoT Networks with Three Coverage Enhancement Groups: A Stochastic Geometry Approach
Yan Liu, Yansha Deng, Nan Jiang +2
NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connec…
A Decoupled Learning Strategy for Massive Access Optimization in Cellular IoT Networks
Nan Jiang, Yansha Deng, Arumugam Nallanathan +1
Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems. However, their Random Access CHannel (RACH) procedure suffers from unrelia…
Traffic Prediction and Random Access Control Optimization: Learning and Non-learning based Approaches
Nan Jiang, Yansha Deng, Arumugam Nallanathan
Random access schemes in modern wireless communications are generally based on the framed-ALOHA (f-ALOHA), which can be optimized by flexibly organizing devices' transmission and r…
Online Supervised Learning for Traffic Load Prediction in Framed-ALOHA Networks
Nan Jiang, Yansha Deng, Osvaldo Simeone +1
Predicting the current backlog, or traffic load, in framed-ALOHA networks enables the optimization of resource allocation, e.g., of the frame size. However, this prediction is made…
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
Nan Jiang, Yansha Deng, Arumugam Nallanathan +1
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices w…
Cooperative Deep Reinforcement Learning for Multiple-Group NB-IoT Networks Optimization
Nan Jiang, Yansha Deng, Osvaldo Simeone +1
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices w…
Random Access Analysis for Massive IoT Networks under A New Spatio-Temporal Model: A Stochastic Geometry Approach
Nan Jiang, Yansha Deng, Xin Kang +1
Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that faces a plethora of new challenges, where cellular networ…