Estimation of the contribution of the frontend to the total noise figure

In a radio receiver composed of two stages, the total noise factor \(F\) can be computed using Friis’s formula as\[F = F_1 + \frac{F_2 – 1}{G_1},\]where \(F_1\) is the noise factor of the first block, \(G_1\) is the gain of the first stage and \(F_2\) is the noise factor of the second stage. If \(G_1\) is large enough, then the contribution of the second factor is small and the total noise factor of the whole system is essentially the same as the noise factor of the first stage. This is the reason why a low noise amplifier is useful as a frontend, because it has a low noise factor \(F_1\) and high gain \(G_1\).

If \(F_2\) and \(G_1\) are known (perhaps only approximately), then it is easy to check if the contribution of the frontend to the total noise figure is large enough so that the total noise figure is determined by the noise figure such frontend alone. However, it may happen that one or both of \(F_2\) and \(G_1\) are not known. In email communication, Leif Åsbrink mentioned that there is an easy way of checking the contribution of the frontend without knowing these parameters. The method is to switch off the frontend and note the drop in the noise floor. He gave the following estimates: if the noise floor drops by more than 10dB, then the total noise figure is the same as the noise figure of the frontend up to 1dB; if the noise floor drops by more than 17dB, then the total noise figure is the same as the noise figure of the frontend up to 0.1dB. Here I present the maths behind these kind of estimates.

Another look at recursive quadrature oscillators

In a recent post, we looked at which \(2\times 2\) Toeplitz real matrices \(T\) gave useful quadrature oscillators by the recurrence \(x_{n+1}=T x_n\). There, we computed their eigenvalues and solved the recurrence in terms of them. Of course, there are many other ways to approach this problem. Here we look at another approach that gives a good geometric picture of what happens, can be applied to general \(2\times 2\) matrices, and may be used as a starting point for the \(n\times n\) case.

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A look at a new digital quadrature oscillator

Two sinusoidal signals are said to be in quadrature if they have a constant phase difference of 90º. Quadrature signals are widely used in signal processing. A digital quadrature oscillator is just an algorithm that computes the sequence \(x_n = (\cos(\omega n), \sin(\omega n))\), \(n\geq 0\), or a similar sequence of sinusoids in quadrature. Here \(\omega\) is the oscillator frequency in radians per sample. Direct computation of this sequence is very time consuming, because the trigonometric functions have to be evaluated for each sample. Therefore, it is a good idea to use a linear recurrence scheme to compute \(x_n\). Using basic trigonometric identities, we see that\[x_{n+1} = A x_n,\quad x_0=\begin{pmatrix}1\\0\end{pmatrix},\]with\[A = \begin{pmatrix}\alpha_1 & -\alpha_2\\\alpha_2 & \alpha_1\end{pmatrix},\quad \alpha_1 = \cos(\omega),\ \alpha_2=\sin(\omega).\]

However, to actually perform these computations in a digital processor, one has to quantize \(\alpha_1,\alpha_2\), meaning that one has to replace \(\alpha_1,\alpha_2\) by approximations. It is easy to see that if one replaces \(\alpha_1,\alpha_2\) by some perturbation, then the eigenvalues of \(A\) are no longer in the unit circle, so the oscillation can grow or decay exponentially and one would need to apply an AGC scheme to keep this method stable.

Here we will look at a new quadrature oscillator by Martin Vicanek that has appeared recently and solves this problem.

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