Novel blind encoder identification of Reed-Solomon codes with low computational complexity
Adaptive modulation and coding (AMC) is commonly used in wireless systems to dynamically change the modulation and coding schemes (MCSs) in subsequent frames such that the spectral efficiency can be adapted to various channel conditions. The spectrum and energy efficiency would decrease if the adopt...
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| Published in | Globecom (2003) pp. 3294 - 3299 |
|---|---|
| Main Authors | , , |
| Format | Conference Proceeding |
| Language | English |
| Published |
IEEE
01.12.2013
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1930-529X |
| DOI | 10.1109/GLOCOM.2013.6831580 |
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| Abstract | Adaptive modulation and coding (AMC) is commonly used in wireless systems to dynamically change the modulation and coding schemes (MCSs) in subsequent frames such that the spectral efficiency can be adapted to various channel conditions. The spectrum and energy efficiency would decrease if the adopted MCS option at the transmitter needs to be dynamically transmitted to the receiver through a secure control channel. To combat this problem, in this paper, we would like to propose a novel blind channel-encoder identification scheme with low computational complexity for Reed-Solomon (RS) codes over Galois field GF(q), which could also be applied to other similar non-binary channel codes as well. Our proposed new scheme involves the estimation of the channel parameters using the expectation-maximization (EM) algorithm, the calculation of the log-likelihood ratio vectors (LLRVs) of the syndrome a posteriori probabilities over GF(q), and the identification of the non-binary RS encoder in use subject to the maximum average log-likelihood ratio (LLR) over the pre-selected candidate encoder set. Simulation results justify the effectiveness of this new mechanism. |
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| AbstractList | Adaptive modulation and coding (AMC) is commonly used in wireless systems to dynamically change the modulation and coding schemes (MCSs) in subsequent frames such that the spectral efficiency can be adapted to various channel conditions. The spectrum and energy efficiency would decrease if the adopted MCS option at the transmitter needs to be dynamically transmitted to the receiver through a secure control channel. To combat this problem, in this paper, we would like to propose a novel blind channel-encoder identification scheme with low computational complexity for Reed-Solomon (RS) codes over Galois field GF(q), which could also be applied to other similar non-binary channel codes as well. Our proposed new scheme involves the estimation of the channel parameters using the expectation-maximization (EM) algorithm, the calculation of the log-likelihood ratio vectors (LLRVs) of the syndrome a posteriori probabilities over GF(q), and the identification of the non-binary RS encoder in use subject to the maximum average log-likelihood ratio (LLR) over the pre-selected candidate encoder set. Simulation results justify the effectiveness of this new mechanism. |
| Author | Hsiao-Chun Wu Hong Jiang Hongting Zhang |
| Author_xml | – sequence: 1 surname: Hongting Zhang fullname: Hongting Zhang email: hzhan26@lsu.edu organization: Sch. of Electr. Eng. & Comput. Sci., Louisiana State Univ., Baton Rouge, LA, USA – sequence: 2 surname: Hsiao-Chun Wu fullname: Hsiao-Chun Wu email: hwu1@lsu.edu organization: Sch. of Electr. Eng. & Comput. Sci., Louisiana State Univ., Baton Rouge, LA, USA – sequence: 3 surname: Hong Jiang fullname: Hong Jiang email: hong.jiang@alcatel-lucent.com organization: Bell Labs., Alcatel-Lucent Inc., Murray Hill, NJ, USA |
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| Snippet | Adaptive modulation and coding (AMC) is commonly used in wireless systems to dynamically change the modulation and coding schemes (MCSs) in subsequent frames... |
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| SubjectTerms | Blind encoder identification Computational complexity Encoding expectation maximization log-likelihood ratio Modulation Probability Reed Solomon codes Signal to noise ratio Transmitters Vectors |
| Title | Novel blind encoder identification of Reed-Solomon codes with low computational complexity |
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