In Orlicz-Lorentz sequence space Aψ,w with the Orlicz norm, uniform monotonic- ity, points of upper local uniform monotonicity and lower local uniform monotonicity are characterized. Moreover, the monotonicity coeffi...In Orlicz-Lorentz sequence space Aψ,w with the Orlicz norm, uniform monotonic- ity, points of upper local uniform monotonicity and lower local uniform monotonicity are characterized. Moreover, the monotonicity coefficient in Aψ,w are discussed.展开更多
Blind equalization based on adaptive forgetting factor, recursive least squares （RLS） with constant modulus algorithm （CMA）, is investigated. The cost function of CMA is simplified to meet the second norm form to ...Blind equalization based on adaptive forgetting factor, recursive least squares （RLS） with constant modulus algorithm （CMA）, is investigated. The cost function of CMA is simplified to meet the second norm form to ensure the stability of RLS-CMA, and thus an improved RLS-CMA （RLS-SCMA） is established. To further improve its performance, a new adaptive forgetting factor RLS-SCMA （ARLS-SCMA） is proposed. In ARLS-SCMA, the forgetting factor varies with the output error of the blind equalizer during the iterative process, which leads to a faster convergence rate and a smaller steady-state error. The simulation results prove the effectiveness under the condition of the underwater acoustic channel.展开更多
基金supported by the National Science Foundation of China(11271248 and 11302002)the National Science Research Project of Anhui Educational Department(KJ2012Z127)the PhD research startup foundation of Anhui Normal University
文摘In Orlicz-Lorentz sequence space Aψ,w with the Orlicz norm, uniform monotonic- ity, points of upper local uniform monotonicity and lower local uniform monotonicity are characterized. Moreover, the monotonicity coefficient in Aψ,w are discussed.
基金financially supported in part by the National Natural Science Foundation of China(Grant No.61201418)Fundamental Research Funds for the Central Universities(Grant No.DC12010218)Scientific and Technological Research Project for Education Department of Liaoning Province(Grant No.2010046)
文摘Blind equalization based on adaptive forgetting factor, recursive least squares （RLS） with constant modulus algorithm （CMA）, is investigated. The cost function of CMA is simplified to meet the second norm form to ensure the stability of RLS-CMA, and thus an improved RLS-CMA （RLS-SCMA） is established. To further improve its performance, a new adaptive forgetting factor RLS-SCMA （ARLS-SCMA） is proposed. In ARLS-SCMA, the forgetting factor varies with the output error of the blind equalizer during the iterative process, which leads to a faster convergence rate and a smaller steady-state error. The simulation results prove the effectiveness under the condition of the underwater acoustic channel.
