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首页> 外文期刊>IEEE Transactions on Circuits and Systems. I, Regular Papers >GSR: A New Genetic Algorithm for Improving Source and Channel Estimates
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GSR: A New Genetic Algorithm for Improving Source and Channel Estimates

机译：GSR：一种用于改进源和信道估计的新遗传算法

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摘要

In this paper, we introduce a new genetic algorithm, which allows us to refine the estimates of information source symbols and channel estimates obtained by any identification algorithm. Instead of searching the entire space, the proposed algorithm searches for the refined estimates in the subspaces near the initial estimate. Creation of initial guesses by using problem specific information and new specially tailored nonblind genetic operators, based on the ideas from schema theory, for realizing the proposed approach are described. The new genetic source symbol refinement (GSR) algorithm is tested to cope with rapidly varying finite-impulse response channels with additive noise model. The method is capable of offering fast convergence with directed search ability and exhibits a unique feature of automatic adjustment in the number of cost function evaluations with the varying signal-to-noise ratio (SNR). Computational results show that the GSR can achieve the bit-error-rate performance near to the simulated annealing bound. As compared with recent sophisticated alternatives for the problem, the GSR performance is superior over a wide range of SNR, with reduced complexity
机译：在本文中，我们介绍了一种新的遗传算法，该算法可以使我们改进信息源符号的估计以及通过任何识别算法获得的信道估计。代替搜索整个空间，所提出的算法在初始估计附近的子空间中搜索精确估计。描述了通过使用特定于问题的信息和新的特别定制的非盲遗传算子来创建初始猜测，基于图式理论的思想，以实现所提出的方法。测试了新的遗传源符号细化（GSR）算法，以应对带有加性噪声模型的快速变化的有限冲激响应通道。该方法能够提供具有定向搜索能力的快速收敛，并具有随信噪比（SNR）变化而自动调整成本函数评估次数的独特功能。计算结果表明，GSR可以在模拟退火边界附近实现误码率性能。与最新的解决方案相比，GSR性能在宽范围的SNR上具有优异的性能，并且降低了复杂性

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