Generalized Low-Rank Optimization for Topological Cooperation in Ultra-Dense Networks

TL;DR

This paper introduces a generalized low-rank optimization method for topological cooperation in ultra-dense networks, effectively managing interference with minimal CSI sharing to enhance system degrees-of-freedom.

Contribution

It develops Riemannian optimization algorithms for non-convex low-rank problems, improving interference management in ultra-dense networks using only connectivity information.

Findings

Achieves higher degrees-of-freedom compared to existing methods.

Demonstrates computational efficiency of the proposed algorithms.

Effectively manages interference with limited CSI sharing.

Abstract

Network densification is a natural way to support dense mobile applications under stringent requirements, such as ultra-low latency, ultra-high data rate, and massive connecting devices. Severe interference in ultra-dense networks poses a key bottleneck. Sharing channel state information (CSI) and messages across transmitters can potentially alleviate interferences and improve system performance. Most existing works on interference coordination require significant CSI signaling overhead and are impractical in ultra-dense networks. This paper investigate topological cooperation to manage interferences in message sharing based only on network connectivity information. In particular, we propose a generalized low-rank optimization approach to maximize achievable degrees-of-freedom (DoFs). To tackle the challenges of poor structure and non-convex rank function, we develop Riemannian optimization algorithms to solve a sequence of complex fixed rank subproblems through a rank growth strategy. By exploiting the non-compact Stiefel manifold formed by the set of complex full column rank matrices, we develop Riemannian optimization algorithms to solve the complex fixed-rank optimization problem by applying the semidefinite lifting technique and Burer-Monteiro factorization approach. Numerical results demonstrate the computational efficiency and higher DoFs achieved by the proposed algorithms.