Pictorial View of Acyclic Convolution

Figure 8.2: Schematic depiction of the acyclic convolution of two signals.

Figure 8.2 shows schematically the result of convolving two zero-padded signals $x$ and $h$. In this case, the signal $x(n)$ starts some time after $n=0$, say at $n=n_x$. Since $h(n)$ begins at time 0, the output starts promptly at time $n_x$, but it takes some time to ``ramp up'' to full amplitude. (This is the transient response of the FIR filter $h$.) If the length of $h$ is $N_h$, then the transient response is finished at time $n=n_x+N_h-1$. Next, when the input signal goes to zero at time $n_x+N_x$, the output reaches zero $N_h-1$ samples later (after the filter ``decay time''), or time $n_x+N_x+N_h-1$. Thus, the total number of nonzero output samples is $N_x+N_h-1$.

If we don't add enough zeros, some of our convolution terms ``wrap around'' and add back upon others (due to modulo indexing). This can be called time domain aliasing. Zero-padding in the time domain results in more samples (closer spacing) in the frequency domain, i.e., a higher `sampling rate' in the frequency domain. If we have a high enough spectral sampling rate, we can avoid time aliasing.

The motivation for implementing acyclic convolution using a zero-padded cyclic convolution is that we can use the Fast Fourier Transform (FFT) to implement cyclic convolution when its length $N$ is a power of 2.