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20242026
most citedRIS-Assisted Aerial Non-Terrestrial Networks: An Intelligent Synergy with Deep Reinforcement Learning

1 citations · 2 across the 18 of their papers we have counts for

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9 papers · 1 filter

eess.SP2026

Canonical Optimization for MIMO MAC Design

Muhammad Umer, Muhammad Ahmed Mohsin, Ahsan Bilal +1

Resource allocation in the multiple-input multiple-output (MIMO) multiple access channel (MAC) is a fundamental problem in multiuser communications, yet it is increasingly treated…

eess.SP2025

Neural Gaussian Radio Fields for Channel Estimation

Muhammad Umer, Muhammad Ahmed Mohsin, Ahsan Bilal +1

Accurate channel state information (CSI) is a critical bottleneck in modern wireless networks, with pilot overhead consuming 11\% to 21\% of transmission bandwidth and feedback del…

eess.SP2025

On Energy-Efficient Passive Beamforming Design of RIS-Assisted CoMP-NOMA Networks

Muhammad Umer, Muhammad Ahmed Mohsin, Aamir Mahmood +4

This paper investigates the synergistic potential of reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) to enhance the energy efficiency and perfor…

eess.SP2025

Continual Learning for Wireless Channel Prediction

Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +2

Modern 5G/6G deployments routinely face cross-configuration handovers--users traversing cells with different antenna layouts, carrier frequencies, and scattering statistics--which…

eess.SP2025

Resource Allocation for RIS-Assisted CoMP-NOMA Networks using Reinforcement Learning

Muhammad Umer, Muhammad Ahmed Mohsin, Huma Ghafoor +1

This thesis delves into the forefront of wireless communication by exploring the synergistic integration of three transformative technologies: STAR-RIS, CoMP, and NOMA. Driven by t…

eess.SP2025

Intelligent Spectrum Sharing in Integrated TN-NTNs: A Hierarchical Deep Reinforcement Learning Approach

Muhammad Umer, Muhammad Ahmed Mohsin, Ali Arshad Nasir +2

Integrating non-terrestrial networks (NTNs) with terrestrial networks (TNs) is key to enhancing coverage, capacity, and reliability in future wireless communications. However, the…

eess.SP2025

Hierarchical Deep Reinforcement Learning for Adaptive Resource Management in Integrated Terrestrial and Non-Terrestrial Networks

Muhammad Ahmed Mohsin, Hassan Rizwan, Muhammad Umer +3

Efficient spectrum allocation has become crucial as the surge in wireless-connected devices demands seamless support for more users and applications, a trend expected to grow with…

eess.SP2024★ 1 cited

RIS-Assisted Aerial Non-Terrestrial Networks: An Intelligent Synergy with Deep Reinforcement Learning

Muhammad Umer, Muhammad Ahmed Mohsin, Aryan Kaushik +3

Reconfigurable intelligent surface (RIS)-assisted aerial non-terrestrial networks (NTNs) offer a promising paradigm for enhancing wireless communications in the era of 6G and beyon…

eess.SP2024

Deep Reinforcement Learning for Trajectory and Phase Shift Optimization of Aerial RIS in CoMP-NOMA Networks

Muhammad Umer, Muhammad Ahmed Mohsin, Aamir Mahmood +4

This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) networks. We co…