Abstract | ||
---|---|---|
A new Decision-Feedback Equalizer (DFE) is proposed to overcome Inter-Symbol Interference (ISI) faster in the communication system without the aid of training sequences. Unlike the conventional DFE, the feedforward filter component and the feedback filter component in the proposed DFE operate with Constant Modulus Algorithm (CMA) and Decision-Directed (DD) algorithm respectively. It makes full use of the advantages of the CMApsilas ldquoeye-openedrdquo ability and DDpsilas faster convergence rate ability. Consequently the proposed DFE has the faster convergence rate than the conventional DFE which can improve the performance for the system. Simulations with underwater acoustic channels are presented and the simulation results are shown to prove the efficiency of the proposed DFE. |
Year | DOI | Venue |
---|---|---|
2009 | 10.1109/SSME.2009.60 | SSME |
Keywords | Field | DocType |
conventional dfe,feedback filter component,underwater acoustic channels,proposed dfe,full use,decision-directed algorithm,underwater acoustic communication,convergence rate,decision-feedback equalizer,feedforward filter component,constant modulus algorithm,communication system,mixed blind equalization algorithm,inter-symbol interference,convergence rate ability,decision feedback equalisers,intersymbol interference,new decision-feedback equalizer,feedforward neural networks,underwater acoustics,blind equalization,convergence,bandwidth | Convergence (routing),Intersymbol interference,Feedforward neural network,Underwater acoustic communication,Control theory,Computer science,Communications system,Algorithm,Rate of convergence,Interference (wave propagation),Blind equalization | Conference |
ISBN | Citations | PageRank |
978-0-7695-3729-0 | 0 | 0.34 |
References | Authors | |
6 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Wei Rao | 1 | 0 | 2.37 |
Fei Xia | 2 | 10 | 1.35 |
Yuan-yuan Liu | 3 | 0 | 0.34 |
Wen-qun Tan | 4 | 0 | 0.34 |
Huijun Xu | 5 | 7 | 2.87 |
Kang-ming Yuan | 6 | 0 | 0.34 |
Jian-bing Liu | 7 | 0 | 0.34 |