Set-Membership Adaptive Filtering for High Performance Communication Systems

由于在无线通信系统中存在快速衰落和同信道干扰，自适应均衡和干扰抑制的问题需要常规方案无法达到的解决方案，例如，基于最小二乘和最小均方的方法。 该研究项目应用新的自适应信号处理技术，称为集成员自适应递归技术（SMART），以解决先进通信系统中出现的关键问题。 SMART的新奇源于以下：（1）它们的结果总是满足估计误差幅度的规定瞬时界限，在自适应均衡中，估计误差是期望输出和滤波输出之间的差。 (2)它们通过一组可行的参数来表征，这些参数满足规范而不需要存在真实参数。 (3)它们根据输入数据的创新有选择地更新参数估计。 该特征导致适配器共享范例，其显著提高了自适应信号处理中的成本效益和数据使用效率。 在仿真研究中，SMART在自适应均衡和干扰消除等应用中表现出上级性能和较低的计算复杂度。 更一般地说，这个项目将建立一个良好的理论基础集成员自适应滤波（SMAF），并探讨使用SMART在这些应用程序的性能要求可能超过传统算法可以提供，例如，现代多址通信系统。 我们预期，这项研究将推进国家的最先进的自适应滤波，在理论和实践中，并显着提高现代无线通信系统的性能。

Due to the existence of fast fading and co-channel interference in wireless communications systems, the problems of adaptive equalization and interference suppression call for solutions that are beyond the reach of conventional schemes, e.g., those based on least squares and least-mean-squares. This research project is applying novel adaptive signal processing techniques, referred to as Set- Membership Adaptive Recursive Techniques (SMART), to resolve critical problems that arise in advanced communications systems. The novelty of SMART stems from the following: (1) Their outcomes always satisfy a prescribed instantaneous bound on the magnitude of the estimation error, which in adaptive equalization, is the difference between the desired output and the filtered output. (2) They are characterized through a feasible set of parameters, which meet the specification without requiring the existence of a true parameter. (3) They update parameter estimates selectively depending on the innovation of the input data. This feature leads to an adaptor sharing paradigm that significantly improves the cost-effectiveness and data usage efficiency in adaptive signal processing. In simulation studies, SMART have exhibited superior performance and lower computational complexity in applications such as adaptive equalization and interference cancellation. More generally, this project will establish a sound theoretical foundation for Set-Membership Adaptive Filtering (SMAF), and explore the use of SMART in those applications where performance requirements may exceed what traditional algorithms can offer, e.g., modern multiple access communication systems. We anticipate that this research will advance the state-of-the-art in adaptive filtering, both theoretically and practically, and significantly enhance the performance of modern wireless communication systems.