7 papers
RL+AHP: A Novel Reinforcement Learning driven AHP for Slice Aware mode selection in D2D enabled Heterogeneous Networks
Souvik Deb, Sumita Majhi, Shankar K. Ghosh +4
The mode selection problem in device-to-device communication (D2D) enabled Fifth generation (5G) heterogeneous networks (HetNet) aims prioritizing four key performance indicators (…
Reinforcement Learning-Enabled Dynamic Code Assignment for Ultra-Dense IoT Networks: A NOMA-Based Approach to Massive Device Connectivity
Sumita Majhi, Kishan Thakkar, Pinaki Mitra
Ultra-dense IoT networks require an effective non-orthogonal multiple access (NOMA) scheme, yet they experience intense interference because of fixed code assignment. We suggest a…
Enhancing NOMA Handover Performance Using Hybrid AI-Driven Modulated Deterministic Sequences
Sumita Majhi, G Vasantha Reddy, Pinaki Mitra
Non-Orthogonal Multiple Access (NOMA) is an information-theoretical approach used in 5G networks to improve spectral efficiency, but it is prone to interference during handovers. I…
A Deep-SIC Channel Estimator Scheme in NOMA Network
Sumita Majhi, Kaushal Shelke, Pinaki Mitra
In 5G and next-generation mobile ad-hoc networks, reliable handover is a key requirement, which guarantees continuity in connectivity, especially for mobile users and in high-densi…
Holographic MIMO Empowered NOMA-ISAC for 6G: Rate-Splitting Enhanced Near-Field Modeling, Multi-Objective Optimization, and Statistical Performance Validation
Sumita Majhi
Holographic multiple-input multiple-output (MIMO) systems with extremely large apertures enable transformational capabilities for sixth-generation (6G) integrated sensing and commu…
Improving Channel Estimation Through Gold Sequences
Sumita Majhi, Kaushal Shelke, Pinaki Mitra +1
This study evaluates Non-Orthogonal Multiple Access (NOMA) systems using Gold coding and Conventional-V-BLAST (C-V-BLAST). Superimposed signals on shared subcarriers make NOMA user…