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Digital Fountain CodesCommon methods for communicating over such channels employ a feedback channel from receiver to sender that is used to control the retransmission of erased packets. For example, the receiver might send back messages that identify the missing packets, which are then retransmitted. Alternatively, the receiver might send back messages that acknowledge each received packet; the sender keeps track of which packets have been acknowledged and retransmits the others until all packets have been acknowledged. These simple retransmission protocols have the advantage that they will work regardless of the erasure probability f, but purists who have learned their Shannon theory will feel that these retransmission protocols are wasteful. If the erasure probability f is large, the number of feedback messages sent by the first protocol will be large. Under the second protocol, it's likely that the receiver will end up receiving multiple redundant copies of some packets, and heavy use is made of the feedback channel. According to Shannon, there is no need for the feedback channel: the capacity of the forward channel is (1 – f)l bits, whether or not we have feedback.
Efficient coding. Shannon coding in the channel without interference. Enhancements to soft K-means Entropy. Basic properties of entropy. Exact inference for continuous hypothesis spaces Explain the difference in the levels of communication problems. Finding the lowest-cost path Functional diagram of the transmission of information, the purpose of its components. Further applications of arithmetic coding Gaussian distribution Generalized parity-check matrices Give the definition of a stationary random process in the narrow and broad sense. Hash codes How much can we compress? How to measure the information content of a random variable? How to measure the information content of a random variable? Inferring the input to a real channel Inferring the mean and variance of a Gaussian distribution Information content defined in terms of lossy compression Information content of independent random variables Information conveyed by a channel Information types Introduction to convolutional codes Joint entropy Jointly-typical sequences K-means clustering Lempel–Ziv coding Low-Density Parity-Check Codes Low-Density Parity-Check Codes Maximum Likelihood and Clustering Maximum likelihood for a mixture of Gaussians Maximum likelihood for one Gaussian Message classification Message Passing More on trellises More than two variables Noise and distortion in the channels of information transmission. Noisy channels Optimal source coding with symbol codes: Huffman coding Other roles for hash codes Parity-check matrices of convolutional codes and turbo codes Periods of information circulation Pictorial demonstration of Gallager codes Planning for collisions Probabilities and Inference Relative entropy and mutual information Repeat–Accumulate Codes Review of probability and information Simple language models Soft K-means clustering Solving the decoding problems on a trellis Source Models of discrete messages. Symbol codes The binary entropy function The burglar alarm The capacity of a continuous channel of information transfer. The decoder The differential entropy and its properties. The entropy of a discrete source. Full and partial entropy. The Gaussian channel The general problem The information-retrieval problem The junction tree algorithm The junction tree algorithm The main types of signals used in the transmission of information. The min–sum algorithm The noisy-channel coding theorem The Noisy-Channel Coding Theorem The set, which objects of an information transmission system? The sum–product algorithm Turbo codes Typicality Units of information content What are the capabilities of practical error-correcting codes? What are the capabilities of practical error-correcting codes? What are the different forms of representation models of signals. What are the main problems of the theory of information? What are the main stages of treatment information? What Information system is? What is meant by the message and the signal? What is said to be centered random process? What is the difference between a line and a channel of communication? What is the difficulty of exact mathematical description of a random process? What is the essence of fundamental differences in the interpretation of the concept of information? What limit is imposed by unique decode ability? What's the most compression that we can hope for?
Date: 2015-01-29; view: 811
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