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Relay Selection in Distributed Transmission Based on the Golden Code Using ML and Sphere Decoding in Wireless Networks

机译:无线网络中基于ML和球面解码的基于黄金代码的分布式传输中的中继选择

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摘要

The implementation of cooperative diversity with relays has advantages over point-to-point multiple-input multiple-output (MIMO) systems, inparticular.overcomingcorrelatedpaths due to small inter-element spacing. A simple transmitter with one antenna may exploit cooperative diversity or space time coding gain through distributed relays. In this paper, similar distributed transmission is considered with the golden code, and the authors propose a new strategy for relay selection, called the maximum-mean selection policy, for distributed transmission with the full maximum-likelihood (ML) decoding and sphere decoding (SD) based on a wireless relay network. This strategy performs a channel strength tradeoff at every relay node to select the best two relays for transmission. It improves on the established one-sided selection strategy of maximum-minimum policy. Simulation results comparing the bit error rate (BER) based on different detectors and a scheme without relay selection, with the maximum-minimum and maximum-mean selection schemes confirm the performance advantage of relay selection. The proposed strategy yields the best performance of the three methods.
机译:与中继器一起实现协作分集具有优于点对点多输入多输出(MIMO)系统的优势,尤其是由于元素间的间距较小而克服了相关路径。具有一个天线的简单发射机可以通过分布式中继利用协作分集或时空编码增益。在本文中,考虑了用黄金代码进行类似的分布式传输,并且作者提出了一种新的中继选择策略,称为最大均值选择策略,用于具有最大最大似然(ML)解码和球形解码的分布式传输( SD)基于无线中继网络。该策略在每个中继节点上执行信道强度折衷,以选择最佳的两个中继进行传输。它改进了已建立的最大-最小策略的单边选择策略。仿真结果比较了基于不同检测器的误码率(BER)和没有继电器选择的方案以及最大-最小和最大平均值选择方案,从而确认了继电器选择的性能优势。所提出的策略产生了三种方法的最佳性能。

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