Fast Geometric Learning of MIMO Signal Detection over Grassmannian Manifolds

TL;DR

This paper introduces a geometric learning method using Grassmannian manifolds and geodesic flow kernels for MIMO signal detection, enabling robust performance with minimal training data and no online retraining.

Contribution

It proposes a novel GFK-based classification approach on Grassmannian manifolds for MIMO detection, reducing training data needs and handling domain shifts effectively.

Findings

Achieves competitive performance with only 1,200 training samples

Outperforms existing methods like OAMPnet and MMNet

Operates without online retraining in dynamic environments

Abstract

Domain or statistical distribution shifts are a key staple of the wireless communication channel, because of the dynamics of the environment. Deep learning (DL) models for detecting multiple-input multiple-output (MIMO) signals in dynamic communication require large training samples (in the order of hundreds of thousands to millions) and online retraining to adapt to domain shift. Some dynamic networks, such as vehicular networks, cannot tolerate the waiting time associated with gathering a large number of training samples or online fine-tuning which incurs significant end-to-end delay. In this paper, a novel classification technique based on the concept of geodesic flow kernel (GFK) is proposed for MIMO signal detection. In particular, received MIMO signals are first represented as points on Grassmannian manifolds by formulating basis of subspaces spanned by the rows vectors of the received signal. Then, the domain shift is modeled using a geodesic flow kernel integrating the subspaces that lie on the geodesic to characterize changes in geometric and statistical properties of the received signals. The kernel derives low-dimensional representations of the received signals over the Grassman manifolds that are invariant to domain shift and is used in a geometric support vector machine (G-SVM) algorithm for MIMO signal detection in an unsupervised manner. Simulation results reveal that the proposed method achieves promising performance against the existing baselines like OAMPnet and MMNet with only 1,200 training samples and without online retraining.