Spectral-Efficient Massive MIMO in 6G: Integrated Classical-Quantum Deep Learning Architectures for Predictive Beamforming and Detection in Real-Time

The gigantic proliferation of the connected
devices and the insatiable appetite for ultralow latency and
ultrahigh throughput communication are driving the new
technologies required to implement them in the envisioned
6G wireless networks. Massive MIMO: One of the key
enablers for 6G, massive multiple input multiple output
significantly increases spectral efficiency and user capacity. Nevertheless, real-time optimization of beamforming and detection is still difficult, especially in complex and dynamic scenarios with low latency demands. In this paper, we present new hybrid deep learning model which integrates traditional neural networks with quantum-inspired types for predictive beamforming and signal detection in the context of Massive MIMO systems. The model consists of pseudoconvolutional and recurrent layers for spatial-temporal feature extraction concatenated with variational quantum circuits (VQCs) for better representational learning along with improved decision making. We also present a novel
hybrid training loop based on reinforcement learning and
use it to adapt beam patterns and detection filters that are
dictated by channel state feedback. The proposed method
guarantees real-time reactivity and spectral efficiency, as
well as lessens computational load by performing Large
Learning in the Edge (LEARN). Quantum-enhanced
components allow higher generalization in high-dimensional
feature spaces, especially under partial or uncertain channel
state information (CSI). The numerical results are supported
with extensive simulations on a custom 6G Massive MIMO
testbed that is modeled with real-time traffic profiles,
stochastic mobility, and hardware impairments. We
demonstrate the proposed architecture to provide higher
spectral efficiency by 23%, reduced latency by 31% and
reliability in data transmission is improved up to 18% as
compared with conventional deep learning-based systems.
The federated quantum learning method exhibits better
scalability as well as privacy preservability and thus is
readily applicable to distributed 6G infrastructure. This
work demonstrates the capability of hybrid classicalquantum deep learning to simultaneously speed-up and
improve accuracy for massive MIMO. The model further
establishes the groundwork that can pave the way for realtime, cognitive and reconfigurable 6G communication
systems by seamlessly embedding predictive intelligence in beamforming and detection pipelines. Future extensions
may include incorporating IRS, increasing quantum circuit
depth for enhanced modeling capabilities, and better energy
efficiency over decentralized networks.