on about analog signals is lost and we cannot reproduce a perfect original signal anymore. Quantizing errors are heard as noise and to maintain the quality (S/N) of the signal adequately we need to use a large enough number of quantizing levels. The more levels we use, the better the S/N, the longer the binary words used to describe samples, and the higher the data rate required for information transmission. 3.6.4 Nonuniform Quantizing The goal in the coder design is to get as good an average S/N as possible when the sampling rate and the number of bits for each sample are given. Linear quantizing is not the optimum solution because at low signal levels the quantizing noise is high and the S/N is very low. At high signal levels the quantizing noise is the same even though we would tolerate a high noise level. We should define quantizing levels in such a way that performance is acceptable over a wide dynamic range of the voice. This requires that quan- tum levels are not uniformly spaced and we call this nonuniform quantizing. In nonuniform quantizing we use more code words and we have a shorter distance between quantum levels for low-level samples and allow higher quantizing distortion at high-level samples. This is reasonable, Introduction to Telecommunications Network Engineering because higher noise is not SO disturbing when the signal level is higher as well. To do this, we may compress the voice signal in an encoder and expand it in a decoder. This expanding/compressing process is known as companding and is shown in Figure 3.11. One way to understand the companding process is to think of com- pressing the dynamic range of the analog signal first by compressor circuitry, which amplifies low levels more than higher levels (Figure 3.11). After this we may use linear quantization, and the signal values after compression and linear quantizing will actually be nonuniformly quantized. In the decoder of the receiver, we use linear quantizing to reproduce the compressed sample values. Then we lowpass filter the sampl