# Some questions about Noise and Power spectral Density

• baby_1
In summary, the conversation discusses the use of noise as a generator and the possibility of using noise voltage to bias a transistor. It is explained that in theory, it is possible to extract power from the noise, but only if there is a temperature difference between the components.
baby_1
Hello
I've got confused about some features of noise.
1)Can use noise a generator?

Well , if we have a noisy resistor and Ideal resistor why can't we use noise voltage according to noise voltage $$Vn=\sqrt(4KTBR)$$

2)why we divide power spectral density to RL( load) when we find power at RL?(it is a normalized voltage^2)?
3)for shot noise, it is better to decrease or increase DC current to reduce noise effect?because according to shot noise current $I_{s}=\sqrt{2I_{dc}qBw}$ I understand we should reduce DC current to decrease noise current but in book text it has mentioned that we should reduce DC current to decrease noise voltage, which is correct and why?

In your diagram there is heat flow from R1 into R2. (I'm also confused by noise)

I have been confused so far

Part of the issue is I don't understand your question as worded. A noisy resistor is an ideal resistor with a series ideal voltage generator (i.e. the noise). The noisy resistor is (I assume) ##R1## since it is at ##T=1000K##. The noise voltage will appear divided across ##R1## and ##R2## where ##v1 = \frac{R1}{R1+R2}V_n## and ##v2=\frac{R2}{R1+R2}V_n##. Does this help?

Thanks Paul Colby for your explanation
Sorry if I explained my problem badly , As a matter of fact as you see we have a noise voltage across the R2 resistor that comes from R1 noise , So I want to know why we can't use this voltage to bias R2? for example if we change R2 with a transistor why It can't bias the transistor? (if we select a high resistor value or high bandwidth ?)

Well, in principle one could rectify the broadband noise at ##R2## supplied by ##R1## and extract energy. This is only possible because ##R2## is at a lower temperature than ##R1##. As I said, in this case there is heat flow. In this case power could be extracted. BTW this would be no different than running a photocell from the light of a fire. Only the transmission is through space rather than through a wire.

On the flip side. If the temperatures are the same, then the power flow is the same in both directions and no net power may be extracted.

## 1. What is noise and how does it affect signals?

Noise is any unwanted or random disturbance that affects a signal. It can be caused by various factors such as electrical interference, thermal noise, or external sources. Noise can degrade the quality and accuracy of a signal, making it difficult to distinguish the desired information.

## 2. What is Power Spectral Density (PSD) and why is it important in signal analysis?

Power Spectral Density (PSD) is a measure of the distribution of signal power over different frequencies. It provides a way to analyze the frequency content of a signal and can help identify the presence of noise. PSD is important in signal analysis as it allows for the characterization of noise and can aid in signal processing and filtering techniques.

## 3. How is PSD calculated and expressed?

PSD is typically calculated by taking the Fourier transform of a signal and then squaring the magnitude of the resulting complex values. It is expressed in units of power per frequency, such as watts per hertz (W/Hz) or decibels relative to a reference level (dB/Hz).

## 4. What is the relationship between noise and PSD?

Noise can be represented in the frequency domain through its PSD. The shape and magnitude of a signal's PSD can reveal information about the type and level of noise present. For example, white noise has a flat PSD, while pink noise has a 1/f shape. Understanding the relationship between noise and PSD can aid in identifying and mitigating noise in a signal.

## 5. How can PSD be used in practical applications?

PSD can be used in a variety of practical applications, such as noise analysis in electronic circuits, signal processing, and spectrum analysis. It is also commonly used in fields such as telecommunications, audio engineering, and physics to analyze and improve signal quality and to filter out unwanted noise.

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