Description: This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox Platform: |
Size: 4047872 |
Author:wsy |
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Description: In this paper, a novel demodulation structure in
high-frequency (HF) channel is introduced and studied.Frequency-offset estimation, ractionally spaced adaptive equalization and carrier recovery techniques are involved.Frequency-offset correction is carried out based on channel impulse response estimation. ractionally spaced adaptive equalization and carrier recovery are combined with nonlinear decision feedback loop structure and the initial value
of tap coefficients of equalization are obtained from the previous channel estimation. Simulation is conducted under channel parameters with respect to MIL-STD-188-110B using a Monte Carlo technique. The results show this novel demodulation structure is feasible and efficient in HF channel, the symbol error ratio can reach to 10− 3 in 12dB SNR.-In this paper, a novel demodulation structure in
high-frequency (HF) channel is introduced and studied.Frequency-offset estimation, ractionally spaced adaptive equalization and carrier recovery techniques are involved.Frequency-offset correction is carried out based on channel impulse response estimation. ractionally spaced adaptive equalization and carrier recovery are combined with nonlinear decision feedback loop structure and the initial value
of tap coefficients of equalization are obtained from the previous channel estimation. Simulation is conducted under channel parameters with respect to MIL-STD-188-110B using a Monte Carlo technique. The results show this novel demodulation structure is feasible and efficient in HF channel, the symbol error ratio can reach to 10− 3 in 12dB SNR. Platform: |
Size: 186368 |
Author:Lin |
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Search - mil-std-188-110b