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Home > Science, Technology & Agriculture > Technology: general issues > Separation Optimality and Generalized Source-Channel Coding for Time-Varying Channels
Separation Optimality and Generalized Source-Channel Coding for Time-Varying Channels

Separation Optimality and Generalized Source-Channel Coding for Time-Varying Channels


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About the Book

The time-varying nature of the underlying channel is one of the most significant challenges in designing wireless communication systems. In particular, real-time media traffic typically has a stringent delay constraint which precludes retransmissions when data is lost. Moreover, in deep fading states the entire frame may be lost. Furthermore, the receiver may have limited resources to feed back the estimated channel state information to the transmitter, which precludes adaptive transmission. There are generally two approaches to communication over time-varying channels: the pessimistic approach, where communication is based on the worst-case channel state; and the outage approach, which allows certain data loss in some channel states in exchange for higher rates in other states. The worst-case approach is the basis of Shannon capacity, and hence an information-theoretic study of other approaches requires generalizing the definition of channel capacity to include some data loss. We propose more general definitions of channel capacity that includes no errors, outage, or expected capacity, where the transmitter uses a single encoder and the receiver can choose from a collection of decoders based on channel states. We will illustrate these general definitions with some examples. We then extend these ideas beyond data transmission over the channel to end-to-end transmission, i.e. data compression (source coding) as well as channel coding. In time-varying systems, in addition to channel variations, the source statistics may also vary over time. In particular, if the source or channel model is generalized to include non-ergodic statistics, it is natural to develop generalized end-to-end distortion metrics in addition to Shannon distortion, such as the distortion versus outage and the expected distortion. Shannon's renowned source-channel separation theorem enables separate design of source and channel codes with optimal performance. We show that the choice of end-to-end distortion metrics in a given system dictates whether source-channel separation optimality holds, and also which capacity metric coincides with the optimal transmission strategy. When separation is not optimal, the source and channel may communicate through an interface which allows multiple parameters to be agreed upon as opposed to a single number. Through the example of transmission of a binary symmetric source over a composite binary symmetric channel, we illustrate the performance enhancement under the more sophisticated interface.


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Product Details
  • ISBN-13: 9781243561497
  • Publisher: Proquest, Umi Dissertation Publishing
  • Publisher Imprint: Proquest, Umi Dissertation Publishing
  • Height: 246 mm
  • Weight: 259 gr
  • ISBN-10: 1243561491
  • Publisher Date: 01 Sep 2011
  • Binding: Paperback
  • Spine Width: 8 mm
  • Width: 189 mm


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Separation Optimality and Generalized Source-Channel Coding for Time-Varying Channels
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Separation Optimality and Generalized Source-Channel Coding for Time-Varying Channels
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