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认知无线电(cognitive radio,CR)和多输入多输出(multiple input multiple output,MIMO)技术能够有效地提高无线频谱资源的利用效率。而线性预编码技术则是实现这一目的的重要手段。但是目前的预编码算法主要针对服从Gauss分布的输入信号,这一前提假设严重地限制了预编码技术在实际情况中的应用。针对这个问题,该文在分析信息论与检测理论基本关系的基础上,结合特征值分解(singular value decomposition,SVD)与水银注水法(mercury water filling,MWF)的优点,提出了一种适用于输入信号服从任意分布的线性预编码算法,有效提高了线性预编码算法的实用价值。仿真表明该算法优于现有算法。
Cognitive radio (CR) and multiple input multiple output (MIMO) can effectively improve the utilization efficiency of wireless spectrum resources. The linear precoding technology is an important means to achieve this goal. However, the current pre-coding algorithm is mainly aimed at the input signal subject to Gauss distribution. This premise hypothesis severely limits the application of precoding technology in the actual situation. In this paper, based on the analysis of the basic relationship between information theory and detection theory, combining the merits of singular value decomposition (SVD) and mercury water filling (MWF) The signal obeys any distributed linear precoding algorithm, which effectively improves the practical value of the linear precoding algorithm. Simulation shows that the algorithm is better than the existing one.