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Model Based Design of an Adaptive Transmission Module for Non-Line of Sight OFDM System (WiMAX)

机译:基于模型的非视网膜系统自适应传输模块设计(WiMAX)

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In this article we present a specific application of a relevant trend due to the speed with which requirements and technologies change. The modeling of an adaptive transmission module for a non line of sight OFDM (Orthogonal Frequency Division Multiplexing) utilizing base don the IEEE802.16 Standard we employ module based design. This implementation approach allows for rapid creation of prototypes because model based design offers flexibility in the elaboration of systems. The adaptive transmission module is composed of two main parts: the SNR (Signal to Noise Ratio) estimator and the modulation mode selector. The designed module was created using model based design, which consists of the elaboration of systems by implementing blocks with previously established or constructed tunable parameters. We used Simulink from Matlab for this implementation because it's a software tool with ideal mathematical foundation for the design of digital signal processing solutions for communications systems. In order to estimate the SNR we used correlation to calculate the quantity of noised contained by the received signal. The mode selector module follows the IEEE802.16 standard and the capacity to transmit at 256QAM. The selected mode depends on the SNR and can also specify the (FEC) Forward Error Correction recommended for the next burst profile. The development of this module is tested with random data generated for several theoretic SNR levels. We proceeded to compare the estimated levels with the theoretic levels.
机译:在本文中,我们提出了由于要求和技术改变的速度而具有相关趋势的具体应用。用于非视线的自适应传输模块的建模DDM(正交频分复用)利用基础的IEEE802.16标准我们采用基于模块的设计。这种实现方法允许快速创建原型,因为基于模型的设计在制定系统方面提供了灵活性。自适应传输模块由两个主要部分组成:SNR(信噪比)估计器和调制模式选择器。设计模块是使用基于模型的设计创建的,该设计包括通过实现具有先前建立或构造的可调参数的块来制定系统。我们使用Matlab的Simulink为此实现,因为它是一个具有理想数学基础的软件工具,用于通信系统的数字信号处理解决方案。为了估算SNR,我们使用相关性来计算接收信号所含的声音量。模式选择器模块遵循IEEE802.16标准和在256QAM时传输的容量。所选模式取决于SNR,也可以指定用于下一个突发配置文件的(FEC)前向纠错。该模块的开发是用针对几个理论SNR水平生成的随机数据进行测试。我们继续将估计的水平与理论水平进行比较。

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