直接序列/码分多址系统中盲均衡的两阶段模因算法
Two-stage memetic algorithm for blind equalization in direct-sequence/code-division multiple-access Systems
AI总结：
本文提出一种两阶段模因算法用于DS/CDMA系统盲均衡，通过估计信道响应和传输数据，在保持较低复杂度（用户翻倍仅增5倍）的同时，节省约80%计算时间，并具有高近远抗性和准确估计，优于传统检测器。
摘要：
本文针对采用直接序列/码分多址（DS/CDMA）共享方案的数字多用户信道，提出了一种新颖的模因算法（MA）用于盲均衡。均衡涉及两个不同的任务：估计（1）信道响应和（2）传输数据。首先分析了相应的信道模型，然后针对该特定通信系统开发了MA。分析了收敛性、种群多样性和近远抗性。数值实验包括与传统多用户检测器以及其他自然启发方法的比较结果。所提出的接收机被证明能够在现有信道上支持更高的传输速率，同时承受更强的干扰以及衰落和时变效应。在大多数情况下，所需的计算需求保持适中。与标准遗传算法相比，所提出的MA节省了约80%的计算时间，与类似的两阶段模因算法相比节省了约15%，同时保持了统计上显著更高的性能。此外，当活跃用户数翻倍时，复杂度仅增加5倍，而最优最大似然算法的复杂度增加32倍。所提出的方法还表现出高近远抗性，并能获得准确的信道响应估计，成为迄今为止所提方法的一个有趣且可行的替代方案。
英文摘要：
This paper proposes a novel memetic algorithm (MA) for the blind equalization of digital multiuser channels with Direct-Sequence / Code-Division Multiple-Access (DS/CDMA) sharing scheme. Equalization involves two different tasks, the estimation of: (1) channel response and (2) transmitted data. The corresponding channel model is first analyzed and then the MA is developed for this specific communication system. Convergence, population diversity and near-far resistance have been analyzed. Numerical experiments include comparative results with traditional multiuser detectors as well as with other nature-inspired approaches. Proposed receiver is proved to allow higher transmission rates over existing channels, while supporting stronger interferences as well as fading and time-variant effects. Required computation requisites are kept moderate in most cases. Proposed MA saves approximately 80% of computation time with respect to a standard genetic algorithm and about 15% with respect to a similar two-stage memetic algorithm, while keeping a statistically significant higher performance. Besides, complexity increases only by a factor of 5, when the number of active users doubles, instead of 32x found for the optimum maximum likelihood algorithm. The proposed method also exhibits high near-far resistance and achieves accurate channel response estimates, becoming an interesting and viable alternative to so far proposed methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
