Multiple-inputs multiple-outputs (MIMO) technology, including massive MIMO, plays an important role in modern wireless communication systems. Massive MIMO systems with high-order modulation can promote spectrum efficiency. The algorithm complexity and detection performance are the main challenges for the massive MIMO system. Thus, the low complexity and high-performance MIMO detector is important for the practical massive MIMO application. The Expectation Propagation (EP) detector outperforms many conventional detectors in high-order massive MIMO scenarios. However, its complexity increases exponentially with the number of sending antennas and the modulation order. In this paper, we propose a novel information updating scheme for EP MIMO detection algorithm to achieve high performance with low complexity. The high-efficiency EP detector is based on the expectation propagation algorithm with the jointed scheme of successive updating, sorting updating and sphere search aided algorithm. Numerical results show the high-efficiency EP detector reduces over 85% complexity of the original EP detector for the scenario N t = N r = 20 with 64-QAM modulation, and the gain on complexity becomes more evident with the increase of antenna scale and the modulation order. The high-efficiency EP detector can outperform the original EP detector in different high-order massive scenarios. Compared with MMSE algorithm, the proposed scheme can get huge performance gain with 1.5 times complexity for high-order massive MIMO systems.INDEX TERMS Expectation propagation, massive MIMO detector, low complexity, high performance, high-order modulation.
Previous research on the characteristic basis function method (CBFM) has shown that the use of the ultra-wide band CBFM can efficiently solve the problem of computing the broadband monostatic radar cross-section (RCS) of a target by reducing the size of the associated impedance matrix and avoiding the repeated generation of the macro base functions. However, it is still necessary to repeatedly construct the reduced matrix at each frequency in the analysed frequency band, and an interpolation method is proposed to address this problem. It is shown that it is only needed to generate and store the reduced matrices at four frequency samples to cover the entire frequency band, and efficiently derive the reduced matrices at other frequencies via simple interpolation. Numerical results are given to demonstrate the accuracy and efficiency of the proposed method.
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