Maximum Sum Rate of Repeat-Accumulate Interleave-Division System by Fixed-Point Analysis

Song, Guanghui; Cheng, Jun; Watanabe, Yoichiro

doi:10.1109/tcomm.2012.071812.110611

Cited by 24 publications

(38 citation statements)

References 31 publications

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“…Using the fact that priori mutual information from each user is the same, the EXIT function can be formulated as [18] (4) ovari able node [!] check node.…”

Section: Code Optimizationmentioning

confidence: 99%

“…The bivariate fixed point theory and bivariate fixed point analysis, proposed in [18], is shown to be an efficient method for code optimization. In this section, we generalize the bivariate fixed point analysis in [18] to the code optimization of K-user PCC.…”

Section: Q+m B Bivariate Fixed Point Analysismentioning

confidence: 99%

“…In this section, we generalize the bivariate fixed point analysis in [18] to the code optimization of K-user PCC.…”

Section: Q+m B Bivariate Fixed Point Analysismentioning

confidence: 99%

“…2 and that in [18] is that there are two kinds of priori inputs to the sum nodes in Fig. 2, i.e., priori mutual information If from the repetition decoding and I~from the convolutional decoding at the l-th iteration (see Fig.…”

Section: Q+m B Bivariate Fixed Point Analysismentioning

confidence: 99%

“…Our previous work [18] focused on repeat-accumulate (RA)-coded IDMA system and solved the coding-spreading trade-off problem explicitly by a fixed-point analysis.…”

mentioning

confidence: 99%

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K-user parallel concatenated code for Gaussian multiple-access channel

Song

Cheng

Watanabe

2013

2013 IEEE International Conference on Communications (ICC)

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A K-user parallel concatenated code (PCC) is proposed for a Gaussian multiple-access channel with symbol synchronization and equal power users. In this code, each user employs a PCC with M + 1 component codes, where the first component code is a rate 1/q repetition code and the other M component codes are the same rate-l convolutional code 1~D. The K-user PCC achieves a larger maximum sum rate, at the high rate region, than the conventional scheme of an error correction code serially concatenated with a spreading. I. 1NrR0DUCTIONThe most common coding scheme that used for a Gaussian multiple-access channel (MAC) with large number of users is that each user employs an error correction code (ECC) serially concatenated with a spreading, known as coded code-division multiple-access (CDMA) [1]- [4]. The original thought of this scheme is to overcome the multi-user interference and Gaussian noise separately. Specifically, each bit, after the error correction encoding, is spread into a long signature sequence, if the signature sequence of each user is well designed or even random-like, the transmitted vectors of different users would have very low correlation and can be roughly separated by a despreading at the receiver. Then the ECC of each user mainly overcomes Gaussian noise and ensures reliable communication.After the discovery of Turbo code [5] and sparse-graph code [6], iterative decoding was realized as a low complexity decoding scheme that can approach the optimal decoding. Works [7]-[10] developed the iterative multi-user decodings for coded CDMA system. The interleave-division multipleaccess (IDMA) scheme, where each user employs the spreading followed by a long interleaving for user separation, was proposed and shows the error performance advantage than the conventional CDMA scheme under the iterative detection in [12]- [14]. Our previous work [15] revealed that due to the user interleaving, the IDMA in fact becomes a sparse-graph code which is appropriate for iterative detection.In both the coded CDMA and coded IDMA systems, joint ECC and spreading design is to solve a coding-spreading trade-off problem, i.e., design the optimal code rate of the ECC and the spreading length. Works [11], [16], and [18] focused on the coding-spreading trade-off problem under iterative multi-user decoding. Work [11] and [16] solved this problem for the low-density parity-check (LDPC)-coded CDMA system and convolutional-coded IDMA system, respectively, by observing extrinsic information transfer (EXIT) charts. Our previous work [18] focused on repeat-accumulate (RA)-coded IDMA system and solved the coding-spreading trade-off problem explicitly by a fixed-point analysis.Since the spreading is in fact a repetition encoding, both the coded CDMA and coded IDMA can be regarded as a multiuser serial concatenated code (SCC), where each user is a SCC with an outer ECC and an inner repetition code. Note that the inner repetition code has been shown as a powerful code for user separation [19] [20]. Since the code rate of each user is the...

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“…Using the fact that priori mutual information from each user is the same, the EXIT function can be formulated as [18] (4) ovari able node [!] check node.…”

Section: Code Optimizationmentioning

confidence: 99%

Section: Q+m B Bivariate Fixed Point Analysismentioning

confidence: 99%

“…In this section, we generalize the bivariate fixed point analysis in [18] to the code optimization of K-user PCC.…”

Section: Q+m B Bivariate Fixed Point Analysismentioning

confidence: 99%

Section: Q+m B Bivariate Fixed Point Analysismentioning

confidence: 99%

“…Our previous work [18] focused on repeat-accumulate (RA)-coded IDMA system and solved the coding-spreading trade-off problem explicitly by a fixed-point analysis.…”

mentioning

confidence: 99%

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