activity
20222025
most citedRISnet: A Domain-Knowledge Driven Neural Network Architecture for RIS Optimization with Mutual Coupling and Partial CSI

17 citations · 17 across the 6 of their papers we have counts for

collaborators

6 papers

eess.SP2025

RIS-Assisted NOMA with Partial CSI and Mutual Coupling: A Machine Learning Approach

Bile Peng, Karl-Ludwig Besser, Shanpu Shen +5

Non-orthogonal multiple access (NOMA) is a promising multiple access technique. Its performance depends strongly on the wireless channel property, which can be enhanced by reconfig…

eess.SP2024

Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks

Bile Peng, Bihan Guo, Karl-Ludwig Besser +6

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challengi…

cs.IT2024★ 17 cited

RISnet: A Domain-Knowledge Driven Neural Network Architecture for RIS Optimization with Mutual Coupling and Partial CSI

Bile Peng, Karl-Ludwig Besser, Shanpu Shen +5

Space-division multiple access (SDMA) plays an important role in modern wireless communications. Its performance depends on the channel properties, which can be improved by reconfi…

eess.SP2024

Energy-Efficient Power Allocation in Cell-Free Massive MIMO via Graph Neural Networks

Ramprasad Raghunath, Bile Peng, Eduard A. Jorswieck

CF-mMIMO systems are a promising solution to enhance the performance in 6G wireless networks. Its distributed nature of the architecture makes it highly reliable, provides sufficie…

eess.SP2023

RISnet: A Scalable Approach for Reconfigurable Intelligent Surface Optimization with Partial CSI

Bile Peng, Karl-Ludwig Besser, Ramprasad Raghunath +2

The reconfigurable intelligent surface (RIS) is a promising technology that enables wireless communication systems to achieve improved performance by intelligently manipulating wir…

eess.SP2022

Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning

Bile Peng, Karl-Ludwig Besser, Ramprasad Raghunath +1

This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. This optimization problem is non-convex and its global optim…