Blind Demixing for Low-Latency Communication

TL;DR

This paper introduces a blind demixing method using low-rank matrix recovery and Riemannian optimization to enable low-latency wireless communication without channel estimation.

Contribution

It develops a novel geometric reformulation and a scalable Riemannian trust-region algorithm for blind demixing in wireless networks.

Findings

Outperforms existing methods in speed and accuracy.

Reduces channel signaling overhead significantly.

Achieves low-latency communication with high reliability.

Abstract

In the next generation wireless networks, lowlatency communication is critical to support emerging diversified applications, e.g., Tactile Internet and Virtual Reality. In this paper, a novel blind demixing approach is developed to reduce the channel signaling overhead, thereby supporting low-latency communication. Specifically, we develop a low-rank approach to recover the original information only based on a single observed vector without any channel estimation. Unfortunately, this problem turns out to be a highly intractable non-convex optimization problem due to the multiple non-convex rankone constraints. To address the unique challenges, the quotient manifold geometry of product of complex asymmetric rankone matrices is exploited by equivalently reformulating original complex asymmetric matrices to the Hermitian positive semidefinite matrices. We further generalize the geometric concepts of the complex product manifolds via element-wise extension of the geometric concepts of the individual manifolds. A scalable Riemannian trust-region algorithm is then developed to solve the blind demixing problem efficiently with fast convergence rates and low iteration cost. Numerical results will demonstrate the algorithmic advantages and admirable performance of the proposed algorithm compared with the state-of-art methods.