Abstract
In this paper, the authors propose three low-complexity detection schemes for spatial modulation (SM) systems based on the modified beam search (MBS) detection. The MBS detector, which splits the search tree into some subtrees, can reduce the computational complexity by decreasing the nodes retained in each layer. However, the MBS detector does not take into account the effect of subtree search order on computational complexity, and it does not consider the effect of layers search order on the bit-error-rate (BER) performance. The ost-MBS detector starts the search from the subtree where the optimal solution is most likely to be located, which can reduce total searches of nodes in the subsequent subtrees. Thus, it can decrease the computational complexity. When the number of the retained nodes is fixed, which nodes are retained is very important. That is, the different search orders of layers have a direct influence on BER. Based on this, we propose the oy-MBS detector. The ost-oy-MBS detector combines the detection order of ost-MBS and oy-MBS together. The algorithm analysis and experimental results show that the proposed detectors outstrip MBS with respect to the BER performance and the computational complexity.
Highlights
To meet the demand of wireless communication systems for higher data transmission rate, multiple-input multipleoutput (MIMO) technology has been adopted in mobile terminals
In [7, 8], two low-complexity hard-limiter-based maximum likelihood (ML) (HL-ML) detectors which have the same BER performance as the ML detector were proposed for M-PSK and square- or Journal of Electrical and Computer Engineering rectangular-QAM modulation. e computational complexity has nothing to do with the constellation size
To estimate the computational complexity of an algorithm, we define the computational complexity as the total number of the real-valued multiplications/divisions required in the detection process
Summary
To meet the demand of wireless communication systems for higher data transmission rate, multiple-input multipleoutput (MIMO) technology has been adopted in mobile terminals. Tang et al [15] presented a distanced-based ordered detection (DBD) algorithm to reduce the receiver complexity and achieve a near-maximum likelihood performance. In the MBS detector, the detection sequence of different subtrees is confined to the ascending order of the subtree indices, whereas it ignores the influence of different search orders on the computational complexity. At is to say, the influence of different search orders on the BER performance and the computational complexity is not considered in the MBS detector. We proposed three MBS-based detectors with novel ordering strategies: (1) the ost-MBS detector rearranges the search order of subtrees; (2) the oyMBS detector performs SM signal detection in a descending order of the received signal amplitude; (3) the detection orders of the abovementioned two detectors were jointly considered in the ost-oy-MBS detector. Boldface upper/lower case symbols denote matrices and column vectors; ‖ · ‖F is the Frobenius norm of a vector or a matrix; | · | is the amplitude of a complex quantity or the cardinality of a set; R(·) and I(·) are the real and imaginary parts of a complex-valued quantity; (·)H is the conjugate transpose of a vector or a matrix; CN(μ, σ2) denotes a complex Gaussian random variable with mean μ and variance σ2
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