Publication

Asymptotically Optimal Joint Source-Channel Coding with Minimal Delay

Bixio Rimoldi, Marius Kleiner
2009
Conference Papers

Abstract

We present and analyze a joint source-channel coding strategy for the transmission of a Gaussian source across a Gaussian channel in n channel uses per source symbol. Among all such strategies, the scheme presented here has the following properties: i) the resulting mean-squared error scales optimally with the signal-to-noise ratio, and ii) the scheme is easy to implement and the incurred delay is minimal, in the sense that a single source symbol is encoded at a time.

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https://infoscience.epfl.ch/entities/publication/76e5493f-cbdc-4836-828c-bfc0d1d7c276

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Bixio Rimoldi, Marius Kleiner
2009
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/76e5493f-cbdc-4836-828c-bfc0d1d7c276

About this result

Related concepts (18)

Signal-to-noise ratio

Signal-to-noise ratio (SNR or S/N) is a measure used in science and engineering that compares the level of a desired signal to the level of background noise. SNR is defined as the ratio of signal power to noise power, often expressed in decibels. A ratio higher than 1:1 (greater than 0 dB) indicates more signal than noise. SNR is an important parameter that affects the performance and quality of systems that process or transmit signals, such as communication systems, audio systems, radar systems, imaging systems, and data acquisition systems.

Quantization (signal processing)

Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding. Quantization also forms the core of essentially all lossy compression algorithms.

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Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. The modifiers denote specific characteristics: Additive because it is added to any noise that might be intrinsic to the information system. White refers to the idea that it has uniform power spectral density across the frequency band for the information system. It is an analogy to the color white which may be realized by uniform emissions at all frequencies in the visible spectrum.

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