Deep Learning-Based Receiver for Low-Complexity 6G Partial LIS Architectures

Abstract

The sixth generation (6G) of wireless networks demands extreme energy efficiency and massive connectivity, positioning large intelligent surfaces (LIS) as a pivotal technology. However, the practical deployment of LIS is constrained by the overwhelming computational complexity and power consumption required to process thousands of antenna elements. To address these challenges, this article proposes a deep learning-based receiver architecture that integrates the spatial efficiency of Partial LIS with advanced non-linear detection. By activating only a subset of antenna panels closest to the user terminal (Partial LIS), the system significantly reduces hardware overhead and Radio Frequency (RF) power consumption. To compensate for the performance loss, the multi-user interference (MUI) generated by the linear combining stage, and the increased MUI inherent in a reduced-aperture environment, a specialized Multilayer Perceptron (MLP) network is implemented. Unlike traditional Zero-Forcing (ZF) or Minimum Mean Squared Error (MMSE) receivers, which require energy-intensive matrix inversions for each frequency component, the proposed neural-network-enabled receiver achieves near-optimal performance using low-complexity combining followed by intelligent learning-based interference suppression. Simulation results demonstrate that the proposed hybrid architecture provides a scalable, “green” solution for 6G uplink scenarios. Notably, the deep learning approach is shown to effectively suppress the performance loss of reduced apertures, achieving a BER comparable to traditional linear benchmarks even with a reduced physical aperture, maintaining good Bit Error Rate (BER) performance while dramatically reducing the computational and hardware footprint.