- Title
- Approaching the capacity of AWGN channels using multi-layer Raptor codes and superposition modulation
- Creator
- Shirvanimoghaddam, Mahyar; Johnson, Sarah J.
- Relation
- 2016 IEEE International Symposium on Information Theory (ISIT). Proceedings of the 2016 IEEE International Symposium on Information Theory (Barcelona, Spain 10-15 July, 2016) p. 1914-1918
- Publisher Link
- http://dx.doi.org/10.1109/ISIT.2016.7541632
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2016
- Description
- We propose a capacity approaching coding strategy for additive white Gaussian noise (AWGN) channels by using multi-layer Raptor codes and superposition modulation. Each AWGN channel is divided into several binary-input AWGN (BI-AWGN) channels at a very low signal to noise ratio (SNR), where a capacity approaching Raptor code can be used to encode the message over each layer. A single capacity approaching degree distribution is then used for the Raptor codes over all the layers. A well-known multi-stage decoder is used for successive interference cancellation and decoding the multi-layer Raptor code, where each decoding stage is modeled by a BI-AWGN channel of a fixed SNR. This allows the development of a capacity approaching code for an AWGN channel at every SNR by using a single Raptor code with a fixed degree distribution as the component code.
- Subject
- multi-stage decoder; Raptor codes; superposition modulation; successive interference cancellation
- Identifier
- http://hdl.handle.net/1959.13/1319579
- Identifier
- uon:23906
- Identifier
- ISBN:9781509018062
- Rights
- © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
- Language
- eng
- Full Text
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