Space-time codes built with multiplexed Alamouti components provide multiplexing as well as diversity gain. Their orthogonal structure also leads to simpler decoding algorithms. We consider a $4times2$ system with multiplexed Alamouti codes. An exhaustive search maximum likelihood (ML) decoding for a $4 times 2$ system is of order $M^4$, for a modulation scheme with constellation size $M$. For multiplexed orthogonal designs, an exact fast ML decoding algorithm has recently been reported whose complexity order is $M^2$ for a $4 times 2$ system with QAM constellations. Nevertheless, the quadratic complexity of this fast ML algorithm may still be infeasible in practice for large constellations (e.g. $M geq$ 64 QAM). In this paper, we present a method for designing low complexity sub-optimal decoders based on a combination of search based ML decoding and linear decoding. Our formulation facilitates a direct investigation of the trade-off between performance and complexity. The complexity of our hybrid decoder is flexible and it can be fixed based on the desired performance for a hardware implementation. Although extendable to more general multiplexed Alamouti systems, we focus here on a $4times2$ space-time-polarization system comprising of two dual-polarized transmit antennas and one dual-polarized receive antenna.
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机译：使用多路复用Alamouti组件构建的时空代码可提供多路复用以及分集增益。它们的正交结构还导致更简单的解码算法。我们考虑一个带有复用的Alamouti码的$ 4times2 $系统。对于星座图大小为$ M $的调制方案，$ 4×2 $系统的穷举搜索最大似然（ML）解码的阶数为$ M ^ 4 $。对于多路复用正交设计，最近已经报道了一种精确的快速ML解码算法，对于具有QAM星座的4 x 2美元系统，其复杂度顺序为$ M ^ 2 $。但是，对于大型星座图（例如，$ M geq $ 64 QAM），这种快速ML算法的二次复杂度在实践中仍然不可行。在本文中，我们提出了一种基于搜索的机器学习解码和线性解码相结合的低复杂度次优解码器设计方法。我们的表述有助于直接研究性能与复杂性之间的权衡。我们的混合解码器的复杂性很灵活，可以根据硬件实现所需的性能来固定。尽管可以扩展到更通用的多路复用Alamouti系统，但在这里我们集中于一个4×2美元的时空极化系统，该系统由两个双极化发射天线和一个双极化接收天线组成。
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