Additive white gaussian noise

Definition

Additive white Gaussian noise (AWGN) refers to a type of noise that is characterized by its statistical properties, where the noise is added to a signal and has a constant spectral density. This noise is 'white' because it contains equal power across all frequencies, and 'Gaussian' because its amplitude follows a Gaussian distribution. AWGN is crucial in analyzing communication systems and understanding error rates and reliability functions in the context of transmitting information.

5 Must Know Facts For Your Next Test

1. AWGN is often used as a simplifying assumption in theoretical studies of communication systems, as it provides a clear model for analyzing performance.
2. In AWGN, the power spectral density is constant, meaning that the noise affects all frequency components equally.
3. The probability density function of AWGN amplitudes follows a Gaussian distribution, which means most values are clustered around the mean with fewer extreme values.
4. Error exponents measure how quickly the probability of error decreases as the signal-to-noise ratio increases in the presence of AWGN.
5. AWGN models are critical for calculating reliability functions, helping to understand how well a communication system can perform under noisy conditions.

Review Questions

• How does additive white Gaussian noise impact the performance of a communication system?
  • Additive white Gaussian noise affects communication system performance by introducing errors during transmission. It adds uncertainty to the signal being sent, leading to potential misinterpretation at the receiver's end. As SNR decreases due to increased noise levels, the bit error rate typically increases, resulting in reduced reliability of the communication system.
• Discuss how error exponents relate to additive white Gaussian noise and their significance in evaluating communication system performance.
  • Error exponents provide insights into how quickly the probability of making an error decreases as signal strength improves against additive white Gaussian noise. They highlight the effectiveness of coding schemes and modulation techniques in reducing errors. Understanding these exponents helps in designing robust communication systems capable of operating effectively even under noisy conditions.
• Evaluate the implications of assuming additive white Gaussian noise in practical communication systems and how it shapes reliability functions.
  • Assuming additive white Gaussian noise simplifies analysis but may not fully represent real-world conditions, where other types of noise could be present. This assumption allows for easier calculation of reliability functions and channel capacity, providing benchmarks for system design. However, designers must consider that actual performance might vary significantly due to factors like impulse noise or fading effects, which AWGN does not account for.

Related terms

Signal-to-Noise Ratio (SNR): A measure that compares the level of a desired signal to the level of background noise, indicating the quality of the signal in communication systems.

Bit Error Rate (BER): The percentage of bits received incorrectly compared to the total number of bits transmitted, commonly used to evaluate the performance of digital communication systems.

Channel Capacity:

The maximum rate at which information can be reliably transmitted over a communication channel without error.