Channel Estimation for Massive MIMO-OFDM Systems Using Heterogeneous Edge-Enhanced Graph Hamiltonian Quantum Generative Adversarial Networks with Imperfect Channel State Information

Massive MIMO-OFDM systems are integral to next-generation wireless communication networks due to their ability to achieve high spectral efficiency and data rates. However, accurate channel estimation in these systems is challenging, particularly when channel state information (CSI) is imperfect. Traditional methods often fail to generalize across varying environments or struggle with high computational complexity, leading to suboptimal performance. This paper introduces a novel framework Heterogeneous Edge-Enhanced Graph Hamiltonian Quantum Generative Adversarial Networks (Hedge-GAN) for channel estimation in Massive MIMO-OFDM systems. The Hedge-GAN component effectively models the probabilistic nature of imperfect CSI, leveraging quantum computation for enhanced expressivity and efficiency. The Hedge-GAN component captures the spatial and temporal dependencies in the system, ensuring robust feature extraction even under heterogeneous network conditions. Existing solutions suffer from limited scalability, inadequate utilization of spatial information, and subpar optimization of network parameters. To address these, the proposed framework integrates the Musical Chairs Optimization Algorithm (MCOA), a bio-inspired metaheuristic, to optimize Hedge-GAN’s hyperparameters. This integration enhances convergence speed and improves estimation accuracy. Comparative analysis against state-of-the-art methods demonstrates that the proposed approach significantly improves performance in terms of mean squared error (0.40), spectral efficiency (8.6bps/Hz), and robustness to imperfect CSI, paving the way for more reliable and efficient wireless communication systems.