6 papers
Unsupervised Learning in Next-Generation Networks: Real-Time Performance Self-Diagnosis
Faris B. Mismar, Jakob Hoydis
This letter demonstrates the use of unsupervised machine learning to enable performance self-diagnosis of next-generation cellular networks. We propose two simplified applications…
Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operation
Abdelrahman Taha, Yu Zhang, Faris B. Mismar +1
The promising coverage and spectral efficiency gains of intelligent reflecting surfaces (IRSs) are attracting increasing interest. In order to realize these surfaces in practice, h…
Deep Learning Predictive Band Switching in Wireless Networks
Faris B. Mismar, Ahmad AlAmmouri, Ahmed Alkhateeb +2
In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the…
Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination
Faris B. Mismar, Brian L. Evans, Ahmed Alkhateeb
The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing…
Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks
Faris B. Mismar, Brian L. Evans
We propose a method to improve the performance of the downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The standards-compliant m…
A Framework for Automated Cellular Network Tuning with Reinforcement Learning
Faris B. Mismar, Jinseok Choi, Brian L. Evans
Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network pe…