Abstract
The estimation of channel parameters, such as the fading amplitude and the power spectral density of the interference and noise, is essential to the effective use of soft-decision decoding. Channel estimation may be implemented by the transmission of pilot signals that are processed by the receiver, but pilot signals entail overhead costs, such as the loss of data throughput. Deriving maximum-likelihood channel estimates directly from the received data symbols is often prohibitively difficult. There is an effective alternative when turbo or low-density parity-check codes are used. The expectation-maximization algorithm, which is derived and explained, provides an iterative approximate solution to the maximum-likelihood equations and is inherently compatible with iterative demodulation and decoding. Two examples of advanced spread-spectrum systems that apply iterative channel estimation, demodulation, and decoding are described and analyzed in this chapter. These systems provide good illustrations of the calculations required in the design of advanced systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Torrieri, D. (2015). Chapter 9 Iterative Channel Estimation, Demodulation, and Decoding. In: Principles of Spread-Spectrum Communication Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-14096-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-14096-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14095-7
Online ISBN: 978-3-319-14096-4
eBook Packages: EngineeringEngineering (R0)