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<p>This research paper provides details about an integrated Hybrid Graph Reinforcement and Quantum-Tuned Swarm Optimization (HGQTSO). The ultimate objective of using this framework is for optimal beamform and to improve overall spectrum efficiency while minimizing power output and interference levels through developing the HGQTSO approach as a multi-objective constrained optimization (MOCO) model. This model has been defined as a MOCO problem due to the complexity in control of the beam pattern which is defined by non-convex functions. In order to solve issues with non-convex nature of the control of the beam patterns, the framework implements Graph Neural Reinforcement Learning (GNRL) techniques to permit algorithmic redesign of the beam patterns with respect to changes in topology and a Transformer-Based State Encoder (TSE) to capture and learn from the spatial relationship among users and interference patterns within a network. The optimization process combines Quantum Enhanced Tunicate Swarm Optimization (QE-TSO) and social processes of tunicate swarms to create a highly capable of both global search and converging at accelerated rates. Additionally, a Bio-Adaptive Federated Update Mechanism (BAFUM) is used to synchronize the distributed learning experiences between all BSS. Simulation studies are provided which demonstrate that the HGQTSO outperformed the existing deep Q-networks (DQN), proximal policy optimizations (PPO) and Genetic Algorithms (GA) with higher overall spectrum efficiency, less energy consumed and faster convergence rates when using dense multi-user scenarios. Thus, the empirical evidence demonstrates the high level of performance of the HGQTSO and illustrates its potential as a sustainable optimization solution for the telecommunication industry in the forthcoming generation of intelligent 6G systems.</p>

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Hybrid graph reinforcement and QE-TSO framework for intelligent beamforming in 6G networks

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • N. Ramshankar

摘要

This research paper provides details about an integrated Hybrid Graph Reinforcement and Quantum-Tuned Swarm Optimization (HGQTSO). The ultimate objective of using this framework is for optimal beamform and to improve overall spectrum efficiency while minimizing power output and interference levels through developing the HGQTSO approach as a multi-objective constrained optimization (MOCO) model. This model has been defined as a MOCO problem due to the complexity in control of the beam pattern which is defined by non-convex functions. In order to solve issues with non-convex nature of the control of the beam patterns, the framework implements Graph Neural Reinforcement Learning (GNRL) techniques to permit algorithmic redesign of the beam patterns with respect to changes in topology and a Transformer-Based State Encoder (TSE) to capture and learn from the spatial relationship among users and interference patterns within a network. The optimization process combines Quantum Enhanced Tunicate Swarm Optimization (QE-TSO) and social processes of tunicate swarms to create a highly capable of both global search and converging at accelerated rates. Additionally, a Bio-Adaptive Federated Update Mechanism (BAFUM) is used to synchronize the distributed learning experiences between all BSS. Simulation studies are provided which demonstrate that the HGQTSO outperformed the existing deep Q-networks (DQN), proximal policy optimizations (PPO) and Genetic Algorithms (GA) with higher overall spectrum efficiency, less energy consumed and faster convergence rates when using dense multi-user scenarios. Thus, the empirical evidence demonstrates the high level of performance of the HGQTSO and illustrates its potential as a sustainable optimization solution for the telecommunication industry in the forthcoming generation of intelligent 6G systems.