4 papers
Feasibility-Aware Learning-to-Optimize in Wireless Communication Resource Allocation
Hanwen Zhang, Haijian Sun
The emergence of 6G wireless communication enables massive edge device access and supports real-time intelligent services such as the Internet of things (IoT) and vehicle-to-everyt…
HoP: Homeomorphic Polar Learning for Hard Constrained Optimization
Ke Deng, Hanwen Zhang, Jin Lu +1
Constrained optimization demands highly efficient solvers which promotes the development of learn-to-optimize (L2O) approaches. As a data-driven method, L2O leverages neural networ…
Model-based Deep Learning for Wireless Resource Allocation in RSMA Communications Systems
Hanwen Zhang, Mingzhe Chen, Alireza Vahid +2
Rate-splitting multiple access (RSMA) has been proven as an effective communication scheme for 5G and beyond. However, current approaches to RSMA resource management require compli…
Model-based Deep Learning for QoS-Aware Rate-Splitting Multiple Access Wireless Systems
Hanwen Zhang, Mingzhe Chen, Alireza Vahid +2
Next generation communications demand for better spectrum management, lower latency, and guaranteed quality-of-service (QoS). Recently, Artificial intelligence (AI) has been widely…