y. We get the maximum signal-to-quantizing noise ratio (SQR) of linear quantizing when the maximum signal power is equal to one (power is a square of the signal value that was defined to be between -1 and +1): SQR=SIN <3q2 (3.6) where S = signal power, N = O2 = power of quantization noise, and q = number of quantum levels. The only noise we consider here is generated by quantizing and then SQR = S/N. We can easily show further that in the case of linear quantizing and binary words, the absolute maximum S/N in decibels in the case of linear quantizing is (3.7) Signals Carried over the Network where n = the number of bits/word. The maximum S/N is achieved with the maximum signal power that is 1. The logarithmic measure decibel is described at the end of this chapter. The preceding formula gives the abso- lute maximum S/N of a system that uses uniform quantizing and codes sam- ple values into n-bit binary words. If we add one bit to the data word representing a linear sample value, we double the number of quantizing levels, which cuts the maximum quantizing error in half. On the other hand, from (3.7) we see that each bit increases the S/N by 6 dB. This means that the quantizing noise power is reduced by a fac- tor of 4 corresponding to error voltage reduction by a factor of 2. However, we assumed that the average power of the analog signal equals the maximum power, that is, all sample words have the maximum value. In practice, this cannot be the case and the average S/N is some tens of decibels lower than the maximum value given by (3.7). How much lower an average S/N we have in a practical system depends on the dynamic range that we reserve for the highest signal levels (the distance between the average sig- nal power and the maximum signal power) to avoid the clipping of the signal and consequent severe distortion. As an example, if average signal power is 20 dB below maximum, the average S/N (or SQR) is 20 dB below its maxi- mum value given by (3.7). We have seen that in the quantizing process accurate informati