Two-timescale Beamforming Optimization for Downlink Multi-user Holographic MIMO Surfaces

TL;DR

This paper introduces a two-timescale beamforming optimization method for holographic MIMO surfaces that reduces computational complexity by leveraging statistical CSI for beam pattern design and instantaneous CSI for precoding, enhancing efficiency in future wireless networks.

Contribution

The paper proposes a novel two-timescale optimization scheme for HMIMOS beamforming that balances performance and complexity using CSSCA and CSI-based design.

Findings

Effective power minimization under QoS constraints.

Reduced computational complexity compared to traditional methods.

Simulation results show improved performance over baselines.

Abstract

Benefiting from the rapid development of metamaterials and metasurfaces, the holographic multiple-input and multiple-output surface (HMIMOS) has been regarded as a promising solution for future wireless networks recently. By densely packing numerous radiation elements together, HMIMOS is capable of realizing accurate beamforming with low hardware complexity. However, enormous radiation elements on the HMIMOS lead to high computational complexity and signaling overhead when applying traditional beamforming schemes relying on instantaneous channel state information (CSI). To tackle this problem, we propose a two-timescale optimization scheme to minimize the required transmission power under the constraint of all users' quality-of-service (QoS). Specifically, the beampatterns at the base station (BS) and the user equippment (UE) are optimized over the slowly changing statistical CSI based on the constrained stochastic successive convex approximation (CSSCA) algorithm. Then, the instantaneous CSI is utilized to design the precoding matrix in order to ensure the system performance without significant increase in computational cost, due to the small number of feeds on the HMIMOS. Simulation results demonstrate the effectiveness of our proposed method compared to other baselines.