Budget-Aware Federated Dual-Side Channel Estimation for Hybrid mmWave Massive MIMO

This work studies communication-constrained federated dual-side channel state information (CSI) estimation in hybrid millimeter-wave (mmWave) massive multiple input multiple output (MIMO) systems. Accurate CSI recovery is challenging because hybrid beamforming yields compressed and noisy observations, while repeated model exchange in federated learning (FL) makes communication efficiency strongly dependent on estimator size. Rather than developing a new federated optimization algorithm, we focus on estimator design under standard federated averaging (FedAvg) and study how to use a limited parameter budget effectively under repeated model exchange. Based on this perspective, we propose a budget-aware recalibrated refinement network (BARRNet), which combines a compact residual backbone with lightweight channel-wise recalibration for dual-side CSI refinement. Simulation results show that BARRNet achieves a better normalized mean squared error (NMSE)--communication tradeoff than the backbone-only control and heavier convolutional neural network (CNN) baselines. At 5 dB SNR, for the -13 dB DL NMSE target, it reduces the cumulative communication required by 28.9\% relative to the architecture-matched backbone-only control. These results indicate that communication-efficient federated CSI estimation depends not only on model compactness, but also on how limited model capacity is used under repeated model exchange.

Grover's Search-Inspired Quantum Reinforcement Learning for Massive MIMO User Scheduling

The efficient user scheduling policy in the massive Multiple Input Multiple Output (mMIMO) system remains a significant challenge in the field of 5G and Beyond 5G (B5G) due to its high computational complexity, scalability, and Channel State Information (CSI) overhead. This paper proposes a novel Grover's search-inspired Quantum Reinforcement Learning (QRL) framework for mMIMO user scheduling. The QRL agent can explore the exponentially large scheduling space effectively by applying Grover's search to the reinforcement learning process. The model is implemented using our designed quantum-gate-based circuit, which imitates the layered architecture of reinforcement learning, where quantum operations act as policy updates and decision-making units. Moreover, the simulation results demonstrate that the proposed method achieves proper convergence and significantly outperforms classical Convolutional Neural Networks (CNN) and Quantum Deep Learning (QDL) benchmarks.