无线通信链路仿真与系统仿真接口方法研究

发布时间：2018-10-16 15:10

【摘要】：通信系统中主要通过系统级仿真评估系统的整体性能。它作为一种多点对多点的仿真,需要两点之间的链路性能作为基础,然而在系统仿真中对链路性能进行测量的开销十分巨大,因此引入了一种链路级仿真到系统级仿真(Link to System, L2S)的接口方法,将链路仿真与系统仿真分离。在众多的接口技术中,指数有效信噪比映射(Exponential Effective SNR Mapping, EESM)算法与互信息量有效信噪比映射(Mutual Information Effective SNR Mapping, MIESM)算法的应用最为广泛。它们通过将各子载波上的信噪比映射为一个有效值来预测链路的性能。本文主要对这两种L2S算法进行研究。指数有效信噪比映射算法是一种基于子载波上的误符号率推导出的近似算法,能够通过简单的计算预测链路的性能。然而由于该算法缺乏严格的数学推理,使得高阶调制时的预测性能急剧下降。本文首先给出一种参考曲线的建模方法以减少参考曲线的偶然误差,并将其用于算法的仿真中。然后,针对高阶调制下该算法性能下降的原因,给出了基于Logistic模型的改进算法。通过实际仿真,验证了该改进算法不仅在高阶调制时性能更优,而且实现复杂度更低。互信息量有效信噪比映射算法是一种基于信息理论的接口算法,使用比特互信息量作为衡量信道质量的指标,因此该算法的性能是现有接口算法中最优的,能在整体意义上很好地预测链路的误块率。然而,上层仿真往往更加关注某一关键区域的预测性能,非关键区域的误差会对算法参数的优化造成负面影响,针对此问题,本文给出了一种加权参数优化方法,旨在提高该关键区域中算法的准确度。通过仿真,验证了该方法在某些场景的有效性。现有的链路仿真方法均是通过软件完成的,然而用软件模拟的信道比较理想,不足以反映真实信道的性能特点。针对软件仿真平台的不足,本文给出了一种软硬件结合的仿真平台设计方案,通过软件对数据进行基带处理,信道部分则通过信道硬件仿真仪实现。该方案避免了电路设计的复杂工作,从而控制了成本,而且由于仿真依靠物理信号实现,更加接近真实场景。通过系统的实际测试,证明了该方案的可行性。
[Abstract]:In the communication system, the overall performance of the system is evaluated by system-level simulation. As a kind of multi-point to multi-point simulation, it needs the link performance between two points as the basis. However, it is very expensive to measure the link performance in the system simulation. Therefore, an interface method between link level simulation and system level simulation (Link to System, L2S) is introduced, which separates link simulation from system simulation. Among many interface technologies, exponential effective SNR mapping (Exponential Effective SNR Mapping, EESM) algorithm and mutual information effective signal-to-noise ratio mapping (Mutual Information Effective SNR Mapping, MIESM) algorithm are the most widely used. They predict link performance by mapping SNR on each subcarrier to a RMS. In this paper, the two L 2S algorithms are studied. Exponential effective signal-to-noise ratio mapping algorithm is an approximate algorithm based on symbol error rate on subcarriers. However, due to the lack of strict mathematical reasoning, the prediction performance of high order modulation is reduced sharply. In this paper, a modeling method of reference curve is presented to reduce the accidental error of the reference curve, and it is used in the simulation of the algorithm. Then, an improved algorithm based on Logistic model is proposed to improve the performance of the algorithm under high order modulation. The simulation results show that the improved algorithm not only performs better in higher order modulation, but also has lower implementation complexity. Mutual information effective signal-to-noise ratio mapping algorithm is an interface algorithm based on information theory. Bit mutual information is used as an index to measure channel quality, so the performance of this algorithm is the best among the existing interface algorithms. It can predict the block error rate of the link well in the whole sense. However, the upper layer simulation often pays more attention to the prediction performance of a key region, and the error of the non-critical region will have a negative impact on the optimization of algorithm parameters. In view of this problem, a weighted parameter optimization method is proposed in this paper. The purpose of this paper is to improve the accuracy of the algorithm in the key region. Simulation results show that the method is effective in some scenarios. The existing link simulation methods are all accomplished by software. However, the channel simulated by software is ideal and can not reflect the performance of real channel. Aiming at the deficiency of the software simulation platform, this paper presents a design scheme of the simulation platform combining software and hardware. The data is processed by software and the channel is realized by the channel hardware simulator. This scheme avoids the complex work of circuit design, thus controlling the cost, and because the simulation is realized by physical signal, it is closer to the real scene. The feasibility of the scheme is proved by the practical test of the system.
【学位授予单位】：东南大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN92

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