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I modeled the the acoustic channel noise in matlab: % On the Relationship Between Capacity and Distance in an % Underwater Acoustic Communication Channel Turbulence=17-(30*log10(f)); Noise_Turbulence= power(10,(Turbulence*0.1)); Shipping=40+(20*(s-0.5))+(26*log10(f))-(60*log10((f)+0.03)); Noise_Shipping= power(10,(Shipping*0.1)); ...


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You are missing a couple of zeros. First of all, you must also include the reciprocal of the one located at $z=-2$, that is one at $z=-0.5$ You should also have one more zero coming from the fact that types 3 and 4 are anti-symmetric. This zero must be located at $z=1$, and is responsible for the minus sign in the anti-mirror image polynomial equation: $H(...


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(Adapted from this answer on dsp.SE) The reason that the impulse response (also called the unit pulse response for discrete-time systems) determines the output for arbitrary input $x$ to an LTI system is that The output of a linear time-invariant system in response to input $x$ is the sum of scaled and time-delayed versions of the impulse response. ...


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I think about this in terms of an impulse containing content at all frequencies, so if you know how the system responds to an impulse, you have a complete description of its response. If you're comfortable with Laplace transforms, we can also say that the transfer function for a system can be computed by taking the Laplace transform of the impulse response -...


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This is typically done using a segmented overlap add method or sometimes also refered to as a block convolver. Let's assume your block size if 512 (makes the numbers a little easier). Chop up your impulse response into 32 blocks of 512 samples each. Zero pad each block to 1024 samples and FFT. You know have 32 filters $H_0(z) ... H_{31}(z)$ On each new ...


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