Geometric Analysis of Blind User Identification for Massive MIMO Networks

TL;DR

This paper introduces a blind user identification method for massive MIMO systems using Nearest Convex Hull Classification, which requires no prior knowledge of channel or modulation, and evaluates its performance through advanced statistical methods.

Contribution

The paper proposes a novel blind classification approach for user identification in massive MIMO, utilizing convex hulls and replica analysis for performance evaluation.

Findings

The method achieves accurate user identification without channel knowledge.

Theoretical analysis via replica method supports the effectiveness of the approach.

Monte Carlo simulations verify the existence of the Operator Valued Free Fourier Transform.

Abstract

Applying Nearest Convex Hull Classification (NCHC) to blind user identification in a massive Multiple Input Multiple Output (MIMO) communications system is proposed. The method is blind in the way that the Base Station (BS) only requires a training sequence containing unknown data symbols obtained from the user without further knowledge on the channel, modulation, coding or even noise power. We evaluate the algorithm under the assumption of gaussian transmit signals using the non-rigorous replica method. To facilitate the computations the existence of an Operator Valued Free Fourier Transform is postulated, which is verified by Monte Carlo simulation. The replica computations are conducted in the large but finite system by applying saddle-point integration with inverse temperature $\beta$ as the large parameter. The classifier accuracy is estimated by gaussian approximation through moment-matching.