Distributed Joint Multi-cell Optimization of IRS Parameters with Linear Precoders

TL;DR

This paper introduces distributed methods for joint optimization of IRS phase-shifts and beamformers in cellular networks, relying on intra-cell training signals and SINR estimation without explicit channel estimation, effectively mitigating interference.

Contribution

It proposes novel distributed algorithms for IRS and beamformer optimization that do not require explicit channel estimation, improving interference management in multi-cell networks.

Findings

Distributed methods achieve near-centralized performance.

Significant improvement over interference-ignoring schemes.

Effective interference mitigation with limited training sequences.

Abstract

We present distributed methods for jointly optimizing Intelligent Reflecting Surface (IRS) phase-shifts and beamformers in a cellular network. The proposed schemes require knowledge of only the intra-cell training sequences and corresponding received signals without explicit channel estimation. Instead, an SINR objective is estimated via sample means and maximized directly. This automatically includes and mitigates both intra- and inter-cell interference provided that the uplink training is synchronized across cells. Different schemes are considered that limit the set of known training sequences from interferers. With MIMO links an iterative synchronous bi-directional training scheme jointly optimizes the IRS parameters with the beamformers and combiners. Simulation results show that the proposed distributed methods show a modest performance degradation compared to centralized channel estimation schemes, which estimate and exchange all cross-channels between cells, and perform significantly better than channel estimation schemes which ignore the inter-cell interference.