most citedLow-Overhead CSI Prediction via Gaussian Process Regression

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

eess.SP2026

Improved GPR-Based CSI Acquisition via Spatial-Correlation Kernel

Syed Luqman Shah, Nurul Huda Mahmood, Italo Atzeni

Accurate channel estimation with low pilot overhead and computational complexity is key to efficiently utilizing multi-antenna wireless systems. Motivated by the evolution from pur…

eess.SP2026

A Novel Geometry-Aware GPR-Based Energy-Efficient and Low-Overhead Channel Estimation Scheme

Syed Luqman Shah, Nurul Huda Mahmood

Accurate channel state information (CSI) acquisition under tight pilot and training-energy constraints is essential for next-generation wireless networks. In this work, we model th…

eess.SP20251 cited

Low-Overhead CSI Prediction via Gaussian Process Regression

Syed Luqman Shah, Nurul Huda Mahmood, Italo Atzeni

Accurate channel state information (CSI) is critical for current and next-generation multi-antenna systems. Yet conventional pilot-based estimators incur prohibitive overhead as an…

cs.NI2025

Energy-Efficient and Reliable Data Collection in Receiver-Initiated Wake-up Radio Enabled IoT Networks

Syed Luqman Shah, Ziaul Haq Abbas, Ghulam Abbas +1

In unmanned aerial vehicle (UAV)-assisted wake-up radio (WuR)-enabled internet of things (IoT) networks, UAVs can instantly activate the main radios (MRs) of the sensor nodes (SNs)…

eess.SP2025

Interference Prediction Using Gaussian Process Regression and Management Framework for Critical Services in Local 6G Networks

Syed Luqman Shah, Nurul Huda Mahmood, Matti Latva-aho

Interference prediction and resource allocation are critical challenges in mission-critical applications where stringent latency and reliability constraints must be met. This paper…