Concept
Electronic noise represents the unwanted disturbances within an electrical signal, which can degrade the performance of electronic devices by introducing errors and reducing overall signal clarity. It arises from various sources such as thermal agitation, shot noise, and flicker noise, each impacting systems differently based on their origin and characteristics.
Relevant Degrees
Concept
Low-Density Parity-Check (LDPC) codes are a class of linear error-correcting codes that use a sparse bipartite graph to represent parity-check constraints, enabling efficient error correction with iterative decoding algorithms like belief propagation. They are widely used in modern communication systems due to their near-capacity performance and scalability for various block lengths and code rates.
Concept
Low-Density Parity-Check (LDPC) codes are a class of error-correcting codes that enable reliable data transmission over noisy communication channels by using sparse bipartite graphs to represent parity-check matrices. They are known for their capacity-approaching performance and are widely used in modern communication systems, such as 5G and Wi-Fi, due to their efficient iterative decoding algorithms like belief propagation.
Concept
Low-density parity-check (LDPC) codes are a class of linear error-correcting codes that achieve near-optimal performance close to the Shannon limit, making them highly efficient for data transmission over noisy channels. They utilize sparse bipartite graphs for encoding and decoding, allowing for iterative algorithms that significantly reduce computational complexity.
Concept
Gallager codes, also known as Low-Density Parity-Check (LDPC) codes, are a class of linear error-correcting codes that are defined by sparse bipartite graphs. They are known for their capacity-approaching performance on noisy channels and efficient decoding algorithms, making them highly effective in modern communication systems.
Concept
Graphical models are a powerful framework for representing complex dependencies among random variables and building large-scale multivariate statistical models. They are widely used in machine learning and statistics to simplify the representation and computation of joint probability distributions through graph structures.
Concept
Bayesian Networks are graphical models that represent probabilistic relationships among a set of variables using directed acyclic graphs, enabling reasoning under uncertainty. They are widely used for tasks such as prediction, diagnosis, and decision-making by leveraging conditional dependencies and Bayes' theorem.
Concept
Sparse graph codes are a class of error-correcting codes that use graphs with a sparse structure to efficiently encode and decode information, enabling reliable communication over noisy channels. They leverage the sparsity of the graph to achieve low complexity in both encoding and decoding, making them highly suitable for modern communication systems.
Concept
Iterative decoding algorithms are techniques used in error correction for digital communication systems, where the decoder iteratively refines its estimates of the transmitted message by exchanging information between component decoders. These algorithms, such as the belief propagation and turbo decoding, significantly enhance the performance of error-correcting codes by leveraging the structure of the code to improve reliability and efficiency.
Concept
Markov Random Fields (MRFs) are a type of probabilistic graphical model that represent the joint distribution of a set of random variables having a Markov property described by an undirected graph. They are particularly useful for modeling spatial dependencies and are widely used in image processing, computer vision, and statistical physics.
Concept
Soft-Input Soft-Output (SISO) decoding is a technique used in communication systems to improve error correction by taking into account the probability of received symbols and providing probabilistic information about the decoded output. This approach is essential in iterative decoding algorithms, such as Turbo codes and LDPC codes, where it refines the likelihood of bit values through multiple iterations to achieve near-optimal performance.
Concept
LDPC (Low-Density Parity-Check) codes are a type of error-correcting code that allows for efficient data transmission over noisy communication channels by using a sparse bipartite graph to represent the parity-check matrix. They offer near Shannon-limit performance and are widely used in modern communication systems, such as 5G, Wi-Fi, and satellite communications, due to their excellent error correction capabilities and iterative decoding algorithms like belief propagation.
3