文件名称:10.1.1.11.5905
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- 人工智能/神经网络/遗传算法
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- [PDF]
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- 2012-11-26
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- 825kb
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- almou******
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This paper compares performance of nite impulse
response (FIR) adaptive linear equalizers based on the recursive least-squares (RLS) and least mean square(LMS) algorithms in nonstationary uncorrelated scattering wireless channels. Simulation results, in terms of steady-state
mean-square estimation error (MSE) and average bit-error rate (BER) metrics, are found for the frequency selective Rayleigh fading wireless channel experienced in a mobile ad hoc network where nodes are lognormally shadowed from each other. For the nonstationary channel models considered, RLS is always found to outperform LMS.
response (FIR) adaptive linear equalizers based on the recursive least-squares (RLS) and least mean square(LMS) algorithms in nonstationary uncorrelated scattering wireless channels. Simulation results, in terms of steady-state
mean-square estimation error (MSE) and average bit-error rate (BER) metrics, are found for the frequency selective Rayleigh fading wireless channel experienced in a mobile ad hoc network where nodes are lognormally shadowed from each other. For the nonstationary channel models considered, RLS is always found to outperform LMS.
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10.1.1.11.5905.pdf