1 citations · 1 across the 17 of their papers we have counts for
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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…
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…
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…
Retrieval Augmented Generation with Multi-Modal LLM Framework for Wireless Environments
Muhammad Ahmed Mohsin, Ahsan Bilal, Sagnik Bhattacharya +1
Future wireless networks aim to deliver high data rates and lower power consumption while ensuring seamless connectivity, necessitating robust optimization. Large language models (…
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…
Computation Offloading Strategies in Integrated Terrestrial and Non-Terrestrial Networks
Muhammad Ahmed Mohsin, Muhammad Umer, Amara Umar +2
The rapid growth of computation-intensive applications like augmented reality, autonomous driving, remote healthcare, and smart cities has exposed the limitations of traditional te…
Task and Perception-aware Distributed Source Coding for Correlated Speech under Bandwidth-constrained Channels
Sagnik Bhattacharya, Muhammad Ahmed Mohsin, Ahsan Bilal +1
Emerging wireless AR/VR applications require real-time transmission of correlated high-fidelity speech from multiple resource-constrained devices over unreliable, bandwidth-limited…
Optimum Power Allocation for Low Rank Wi-Fi Channels: A Comparison with Deep RL Framework
Muhammad Ahmed Mohsin, Sagnik Bhattacharya, Kamyar Rajabalifardi +2
Upcoming Augmented Reality (AR) and Virtual Reality (VR) systems require high data rates ( 500 Mbps) and low power consumption for seamless experience. With an increasing num…
Optimum Power-Subcarrier Allocation and Time-Sharing in Multicarrier NOMA Uplink
Sagnik Bhattacharya, Kamyar Rajabalifardi, Muhammad Ahmed Mohsin +1
Currently used resource allocation methods for uplink multicarrier non-orthogonal multiple access (MC-NOMA) systems have multiple shortcomings. Current approaches either allocate t…
Successive Interference Cancellation-aided Diffusion Models for Joint Channel Estimation and Data Detection in Low Rank Channel Scenarios
Sagnik Bhattacharya, Muhammad Ahmed Mohsin, Kamyar Rajabalifardi +1
This paper proposes a novel joint channel-estimation and source-detection algorithm using successive interference cancellation (SIC)-aided generative score-based diffusion models.…
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…
Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks
Muhammad Ahmed Mohsin, Hassan Rizwan, Muhammad Jazib +5
This work explores the deployment of active reconfigurable intelligent surfaces (A-RIS) in integrated terrestrial and non-terrestrial networks (TN-NTN) while utilizing coordinated…