Machine Learning-based Signal Detection for PMH Signals in Load-modulated MIMO Systems
Document Type
Article
Publication Date
7-1-2021
Abstract
Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the 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 transmitting 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.
Identifier
85101777794 (Scopus)
Publication Title
IEEE Transactions on Wireless Communications
External Full Text Location
https://doi.org/10.1109/TWC.2021.3058970
e-ISSN
15582248
ISSN
15361276
First Page
4452
Last Page
4464
Issue
7
Volume
20
Grant
JCKY2016204A603
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Zhu, Jinle; Li, Qiang; Hu, Li; Chen, Hongyang; and Ansari, Nirwan, "Machine Learning-based Signal Detection for PMH Signals in Load-modulated MIMO Systems" (2021). Faculty Publications. 3996.
https://digitalcommons.njit.edu/fac_pubs/3996
