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A Low-Complexity Solution to Decode Diversity-Oriented Block Codes in MIMO Systems with Inter-Symbol Interference

机译:一种低复杂度解决方案,用于在具有符号间干扰的MIMO系统中解码面向分集的分组码

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摘要

In this paper we first propose a block-code based general model to combat the Inter-Symbol Interference (ISI) caused by frequency selective channels in a Multi-Input Multi-Output (MIMO) system and/or by asynchronous cooperative transmissions. The general model is not only exemplified by the Time-Reversed Space-Time Block Code (TR-STBC) scheme, but also by the Asynchronous Cooperative Liner Dispersion Codes (ACLDC) scheme. In these schemes a guard interval has to be inserted between adjacent transmission blocks to mitigate the effect of ISI. Consequently, this could degrade the effective symbol rate for a small block size. A larger block size would enhance the effective symbol rate and also substantially increase the decoding complexity. In the general model proposed in this paper, we further present a novel low-complexity breadth-adjustable tree-search algorithm and compare it with Sphere-Decoding (SD) based algorithms. With simulation results we will illustrate that our algorithm is able to achieve the optimal performance in terms of Bit Error Rate (BER) with a complexity much lower than the SD-based algorithms, whether the ACLDC or TR-STBC scheme is employed. Through simulations we further demonstrate that when the block size of the ACLDC is equivalent to 20, the complexity of the proposed algorithm is only a fraction of 10^{-8} that of the Maximum Likelihood (ML) algorithm. This would allow us to practically enhance the effective symbol rate without any performance degradation.
机译:在本文中,我们首先提出了一种基于块码的通用模型,以应对由多输入多输出(MIMO)系统中的选频信道和/或异步协作传输引起的符号间干扰(ISI)。通用模型不仅以时间反向空时分组码(TR-STBC)方案为例,而且以异步协作衬管色散码(ACLDC)方案为例。在这些方案中,必须在相邻的传输块之间插入保护间隔,以减轻ISI的影响。因此,这可能会降低小块大小的有效符号率。较大的块大小将提高有效符号率,并且还将大大增加解码复杂度。在本文提出的通用模型中,我们进一步提出了一种新的低复杂度宽度可调树搜索算法,并将其与基于球形解码(SD)的算法进行了比较。通过仿真结果,我们将说明,无论采用ACLDC还是TR-STBC方案,我们的算法都能够以误码率(BER)达到最佳性能,其复杂度远低于基于SD的算法。通过仿真,我们进一步证明,当ACLDC的块大小等于20时,所提出算法的复杂度仅为最大似然(ML)算法的10 ^ {-8}的一小部分。这将使我们能够在不降低性能的情况下切实提高有效符号率。

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