293 citations · 466 across the 14 of their papers we have counts for
13 papers · 1 filter
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators
Faris B. Mismar, Abdol Saleh, Ivan Maxmillian Putra Pasaribu +1
This paper presents an explainable AI-driven framework for predicting net churn among prepaid mobile subscribers. The framework formulates churn prediction as a machine learning ta…
Adaptive Compression of Massive MIMO Channel State Information with Deep Learning
Faris B. Mismar, Aliye Özge Kaya
This paper proposes the use of deep autoencoders to compress the channel information in a \review{massive} multiple input and multiple output (MIMO) system. Although autoencoders p…
A Quick Primer on Machine Learning in Wireless Communications
Faris B. Mismar
This is our final issue of the quick primer on the use of Python to build a wireless communications prototype. This prototype simulates multiple-input and multiple-output (MIMO) sy…
Uncoordinated Interference Avoidance Between Terrestrial and Non-Terrestrial Communications
Faris B. Mismar, Aliye Ozge Kaya
This paper proposes an algorithm that uses geospatial analytics and the muting of physical resources in next-generation base stations (BSs) to avoid interference between cellular (…
Deep Learning for Multi-User Proactive Beam Handoff: A 6G Application
Faris B. Mismar, Alperen Gundogan, Aliye Ozge Kaya +1
This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enabl…
Intermodulation Interference Detection in 6G Networks: A Machine Learning Approach
Faris B. Mismar
This paper demonstrates the use of machine learning to detect the presence of intermodulation interference across several wireless carriers. We show a salient characteristic of int…
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 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…
Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells
Faris B. Mismar, Brian L. Evans
We propose a reinforcement learning (RL) based closed loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by s…
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement
Faris B. Mismar, Brian L. Evans
We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the…