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arXiv 0905.4023cs.ITmath.IT
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LR辅助线性译码器在一般信道、格型设计和系统模型下的DMT最优性

DMT Optimality of LR-Aided Linear Decoders for a General Class of Channels, Lattice Designs, and System Models

Joakim Jalden, Petros Elia

摘要:

这项工作确定了第一个一般的,明确的,非随机的MIMO编码器-解码器结构,保证最优的分集复用权衡(DMT),而不采用计算昂贵的最大似然(ML)接收机。具体来说,这项工作建立了一类正则格解码器的DMT最优性,更重要的是,它们的格约简(LR)辅助线性对应物的DMT最优性。结果适用于所有通道统计,所有通道尺寸,最有趣的是,无论应用的特定格码。作为特殊情况,已确定MMSE-GDFE格型解码器的基于LL L的LR辅助线性实现有助于以最坏情况复杂度对任何格型码进行DMT最佳解码,该最坏情况复杂度在数据率中最多线性增长。这表示当与复杂度通常为指数速率的ML解码相比时解码复杂度的根本降低。结果的通用性使它们适用于大量的相关通信场景,如准静态MIMO,MIMO-OFDM,ISI,协作中继,MIMO-ARQ信道,在所有这些都保证了LR辅助线性解码器的DMT最优性。所采用的方法产生的洞察力,并激励进一步的研究,到联合收发器的设计与改进的SNR差距ML解码。

英文摘要:

The work identifies the first general, explicit, and non-random MIMO encoder-decoder structures that guarantee optimality with respect to the diversity-multiplexing tradeoff (DMT), without employing a computationally expensive maximum-likelihood (ML) receiver. Specifically, the work establishes the DMT optimality of a class of regularized lattice decoders, and more importantly the DMT optimality of their lattice-reduction (LR)-aided linear counterparts. The results hold for all channel statistics, for all channel dimensions, and most interestingly, irrespective of the particular lattice-code applied. As a special case, it is established that the LLL-based LR-aided linear implementation of the MMSE-GDFE lattice decoder facilitates DMT optimal decoding of any lattice code at a worst-case complexity that grows at most linearly in the data rate. This represents a fundamental reduction in the decoding complexity when compared to ML decoding whose complexity is generally exponential in rate. The results' generality lends them applicable to a plethora of pertinent communication scenarios such as quasi-static MIMO, MIMO-OFDM, ISI, cooperative-relaying, and MIMO-ARQ channels, in all of which the DMT optimality of the LR-aided linear decoder is guaranteed. The adopted approach yields insight, and motivates further study, into joint transceiver designs with an improved SNR gap to ML decoding.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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