 and that between decibel-milliwatts and decibel-microwatts in Table 2.3, it becomes possible to plot the relation- ship of all three metrics. This relationship is illustrated in Figure 2.9. Signal-to-Noise Ratio Although someone with a strong optics background may be a bit puzzled by this book describing the signal-to-noise ratio more commonly used to categorize copper- and atmosphere-based transmission systems, there is a reason for this discussion. By understanding the constraints of copper- based transmission systems, we can better understand the similarities and differences concerning the transmission capability over an optical fiber. TABLE 2.3 Relationship between Decibel- Milliwatts (dBm) and Decibel- Microwatts (dBu). Chapter Two Figure 2.9 The relationship between power in watts, decibel-milli- watts (dBm), and decibel-microwatts (dBu). Power Power Power in Watts (log scale) One of the more important metrics in communications is the signal- to-noise ratio. In all copper-based communication systems there is a cer- tain degree of noise, caused by the movement of electrons, power-line induction, and cross-modulation from adjacent wire pairs or in wireless communications from frequencies in adjacent channels. The signal-to-noise ratio is used primarily to categorize transmission on a copper cable; along with bandwidth, it also affects the transmission capacity of a channel. Thus, information about the signal-to-noise ratio is relevant to all types of copper-based transmission media. Two basic categories of noise are relevant for transmission on a copper cable: ther- mal and impulse noise. Thermal noise, such as the movement of electrons or basic radiation from the sun, is characterized by a near-uniform dis- tribution of energy over the frequency spectrum. Figure 2.10 illustrates thermal noise, which is also referred to as white or gaussian noise. Because thermal noise is characterized by a near-uniform energy distribution over the frequency spectrum, it represents the lower level of sensitivity of a receive