Log-likelihood ratio

Definition

The log-likelihood ratio is a statistical measure used to quantify the support for one hypothesis over another based on observed data. It compares the likelihood of the data under two competing hypotheses, often expressed as the logarithm of the ratio of their probabilities. This concept plays a crucial role in decision-making processes, especially in decoding schemes that leverage probabilities to enhance accuracy, particularly in methods that handle uncertain or noisy information.

5 Must Know Facts For Your Next Test

1. The log-likelihood ratio is calculated by taking the logarithm of the ratio of the probability of observing the data under one hypothesis to that under another hypothesis.
2. In soft-decision decoding, log-likelihood ratios help quantify how much more likely a particular bit is to be a 0 or 1, enabling more informed decoding choices.
3. The BCJR algorithm employs log-likelihood ratios in its calculations to provide optimal soft-output metrics for decoding convolutional codes.
4. Log-likelihood ratios can help improve error correction performance by leveraging detailed information about received signals rather than just making hard decisions.
5. When using sequential decoding algorithms, log-likelihood ratios are essential for maintaining efficiency and accuracy while processing sequences in real-time.

Review Questions

• How does the log-likelihood ratio enhance soft-decision decoding in error correction schemes?
  • The log-likelihood ratio enhances soft-decision decoding by providing a nuanced view of the likelihood of each bit being correct based on received signals. Instead of merely deciding whether a bit is a 0 or 1, it evaluates how much more probable one option is over the other. This richer information allows decoders to make better-informed decisions, leading to improved error correction performance and resilience against noise.
• Discuss how sequential decoding algorithms utilize log-likelihood ratios to optimize their performance.
  • Sequential decoding algorithms utilize log-likelihood ratios to make real-time decisions about transmitted sequences by continually updating their beliefs about the most likely transmitted messages. By evaluating the probabilities of different hypotheses at each step, these algorithms can efficiently navigate through potential paths in a trellis diagram. This dynamic updating process improves performance and reduces the computational burden, allowing for faster and more accurate decoding.
• Evaluate the impact of log-likelihood ratios on the BCJR algorithm's effectiveness in decoding convolutional codes.
  • The BCJR algorithm’s effectiveness is significantly enhanced by its use of log-likelihood ratios as it incorporates both forward and backward information about state transitions in convolutional codes. By utilizing these ratios, BCJR provides optimal metrics for decision-making at each stage of the decoding process. This enables it to outperform simpler decoding techniques, particularly in environments with high noise levels, ensuring more reliable communication by accurately estimating the probability of transmitted bits.

Related terms

Likelihood Function:

A function that measures how well a particular set of parameters explains the observed data, forming the basis for statistical inference.

Maximum Likelihood Estimation: A method for estimating the parameters of a statistical model that maximizes the likelihood function, providing the most plausible parameters given the observed data.

Soft Decision: A type of decoding approach that utilizes probabilities to inform decisions about transmitted symbols rather than making hard, binary choices.