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Phase Modulation on the Hypersphere (PMH) is a power efficient modulationscheme for the \textit{load-modulated} multiple-input multiple-output (MIMO)transmitters with central power amplifiers (CPA). However, it is difficult toobtain the precise channel state information (CSI), and the traditional optimalmaximum likelihood (ML) detection scheme incurs high complexity which increasesexponentially with the number of antennas and the number of bits carried perantenna in the PMH modulation. To detect the PMH signals without knowing theprior CSI, we first propose a signal detection scheme, termed as thehypersphere clustering scheme based on the expectation maximization (EM)algorithm with maximum likelihood detection (HEM-ML). By leveraging machinelearning, the proposed detection scheme can accurately obtain information ofthe channel from a few of the received symbols with little resource cost andachieve comparable detection results as that of the optimal ML detector. Tofurther reduce the computational complexity in the ML detection in HEM-ML, wealso propose the second signal detection scheme, termed as the hypersphereclustering 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-treereceiver codebook and the signal detection problem can be transformed into anearest neighbor search (NNS) problem. The detection complexity of HEM-KD issignificantly reduced without any detection performance loss as compared toHEM-ML. Extensive simulation results verify the effectiveness of our proposeddetection schemes.

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