Riemannian optimization on the manifold of unitary and symmetric matrices with application to BD-RIS-assisted systems

TL;DR

This paper introduces a novel Riemannian optimization algorithm on the manifold of unitary and symmetric matrices, enabling efficient MIMO system optimization with BD-RIS without needing parameter tuning.

Contribution

It characterizes the manifold of unitary and symmetric matrices and develops a parameter-free Riemannian optimization algorithm for system performance enhancement.

Findings

Reduces computational cost compared to Takagi decomposition methods

Achieves global convergence to a stationary point

Improves achievable rate in MIMO systems with BD-RIS

Abstract

In this paper, we rigorously characterize for the first time the manifold of unitary and symmetric matrices, deriving its tangent space and its geodesics. The resulting parameterization of the geodesics (through a real and symmetric matrix) allows us to derive a new Riemannian manifold optimization (MO) algorithm whose most remarkable feature is that it does not need to set any adaptation parameter. We apply the proposed MO algorithm to maximize the achievable rate in a multiple-input multiple-output (MIMO) system assisted by a beyond-diagonal reconfigurable intelligent surface (BD-RIS), illustrating the method's performance through simulations. The MO algorithm achieves a significant reduction in computational cost compared to previous alternatives based on Takagi decomposition, while retaining global convergence to a stationary point of the cost function.