Machine Learning-based Signal Detection for PMH Signals in Load-modulated MIMO System

TL;DR

This paper introduces machine learning-based detection schemes for load-modulated MIMO systems with PMH signals, achieving high accuracy with reduced complexity by leveraging EM algorithms and KD-tree structures.

Contribution

It proposes novel hypersphere clustering detection methods using EM and KD-tree techniques for load-modulated MIMO systems without requiring prior CSI.

Findings

HEM-ML achieves detection accuracy comparable to optimal ML detection.

HEM-KD significantly reduces computational complexity without performance loss.

Simulation results confirm the effectiveness of the proposed schemes.

Abstract

Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the \textit{load-modulated} multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CPA). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum likelihood (ML) detection scheme incurs high complexity which increases exponentially with the number of antennas and the number of bits carried per antenna in the PMH modulation. To detect the PMH signals without knowing the prior CSI, we first propose a signal detection scheme, termed as the hypersphere clustering scheme based on the expectation maximization (EM) algorithm with maximum likelihood detection (HEM-ML). By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector. To further reduce the computational complexity in the ML detection in HEM-ML, we also propose the second signal detection scheme, termed as the hypersphere clustering scheme based on the EM algorithm with KD-tree detection (HEM-KD). The CSI obtained from the EM algorithm is used to build a spatial KD-tree receiver codebook and the signal detection problem can be transformed into a nearest neighbor search (NNS) problem. The detection complexity of HEM-KD is significantly reduced without any detection performance loss as compared to HEM-ML. Extensive simulation results verify the effectiveness of our proposed detection schemes.