Noise in Analog Communication Systems

Analog communication systems are susceptible to various types of noise that can degrade the quality of the received signal. The most common types of noise in analog communication systems are thermal noise, shot noise, and flicker noise.

Noise in communication system

1. Thermal noise: Also known as Johnson noise or white noise, thermal noise is generated by the random motion of electrons in a conductor at a finite temperature. It is proportional to the square root of the bandwidth and the resistance of the conductor.

2. Shot noise: Also called Poisson noise, shot noise is caused by the random arrival of electrons at a detector in a communication system. It is proportional to the square root of the average current and bandwidth.

3. Flicker noise: Also known as 1/f noise, flicker noise is caused by the irregularities in the characteristics of electronic components, such as transistors, and increases as the frequency decreases.

In addition to these, other sources of noise in analog communication systems include external sources such as electromagnetic interference (EMI) and internal sources such as crosstalk and intermodulation distortion (IMD). To minimize the effect of noise in analog communication systems, various techniques such as filtering, equalization, and modulation schemes can be used.

1. Noise is unwanted signal that affects wanted signal
2. Noise is random signal that exists in communication systems

Effect of noise

1. Degrades system performance (Analog and digital)
2. Receiver cannot distinguish signal from noise
3. Efficiency of communication system reduces

Types of noise

1. Thermal noise/white noise/Johnson noise or fluctuation noise
2. Shot noise
3. Noise temperature
4. Quantization noise 

Noise temperature

Equivalent noise temperature is not the physical temperature of amplifier, but a theoretical construct, that is an equivalent temperature that produces that amount of noise power 𝑇𝑒 = (𝐹 − 1)

White noise

One of the very important random processes is the white noise process. Noises in many practical situations are approximated by the white noise process. Most importantly, the white noise plays an important role in modelling of WSS signals.
A white noise process w(t) is a random process that has constant power spectral density at all frequencies. Thus

𝑆𝑊(𝜔) = 𝑁0/2-∞< 𝜔<∞

where 𝑁0 is a real constant and called the intensity of the white noise. The corresponding autocorrelation function is given by
𝑅𝑊(𝜏) = 𝑁/2δ(𝜏) where δ(𝜏) is the Dirac delta

The average power of white noise

The autocorrelation function and the PSD of a white noise process is shown in Figure 1 below.

NARROWBAND NOISE (NBN)

In most communication systems, we are often dealing with band-pass filtering of signals. Wideband noise will be shaped into band limited noise. If the bandwidth of the band limited noise is relatively small compared to the carrier frequency, we refer to this as narrowband noise.
the narrowband noise is expressed as as
n(t)=x(t)cos(2𝜋𝑓𝑐t)-y(t)sin (2𝜋𝑓𝑐t)
where fc is the carrier frequency within the band occupied by the noise. x(t) and y(t) are known as the quadrature components of the noise n(t). The Hibert transform of n(t) is
Proof.
The Fourier transform of n(t) is

Let N ^ (f) be the Fourier transform of n^ ( t). In the frequency domain, N ^ (f) =N(f)[-j sgn(f)]. We simply multiply all positive frequency components of N(f) by -j and all negative frequency components of N(f) by j. Thus

Generation of quadrature components of n(t)
Generation of quadrature components of n(t)
  • Filters at the receiver have enough bandwidth to pass the
  • desired signal but not too big to pass excess noise.
  • Narrowband (NB) fc center frequency is much bigger that the bandwidth.
  • Noise at the output of such filters is called narrowband noise (NBN).
  • NBN has spectral concentrated about some mid-band frequency fc
  • The sample function of such NBN n(t) appears as a sine wave of frequency fc which modulates slowly in amplitude and phase

Noise figure

The Noise figure is the amount of noise power added by the electronic circuitry in the receiver to the thermal noise power from the input of the receiver. The thermal noise at the input to the receiver passes through to the demodulator. This noise is present in the receive channel and cannot be removed. The noise figure of circuits in the receiver such as amplifiers and mixers, adds additional noise to the receive channel. This raises the noise floor at the demodulator

Noise figure = 𝑠𝑖𝑔𝑛𝑎𝑙 𝑡𝑜 𝑛𝑜𝑖𝑠𝑒 𝑟𝑎𝑡𝑖𝑜 𝑎𝑡 𝑖𝑛𝑝𝑢𝑡/𝑠𝑖𝑔𝑛𝑎𝑙 𝑡𝑜 𝑛𝑜𝑖𝑠𝑒 𝑟𝑎𝑡𝑖𝑜 𝑎𝑡 𝑜𝑢𝑡𝑝𝑢𝑡

Noise Bandwidth

A filter’s equivalent noise bandwidth (ENBW) is defined as the bandwidth of a perfect rectangular filter that passes the same amount of power as the cumulative bandwidth of the channel selective filters in the receiver. At this point we would like to know the noise floor in our receiver, i.e. the noise power in the receiver intermediate frequency (IF) filter bandwidth that comes from kTB. Since the units of kTB are Watts/ Hz, calculate the noise floor in the channel bandwidth by multiplying the noise power in a 1 Hz bandwidth by the overall equivalent noise bandwidth in Hz.