On the capacity of finite state channels and the analysis of convolutional accumulate-m codes
On the capacity of finite state channels and the analysis of convolutional accumulate-m codes
Minimum distance of error correcting codes versus encoding complexity, symmetry, and pseudorandomness
Decoding turbo-like codes via linear programming
Journal of Computer and System Sciences - Special issue on FOCS 2002
The minimum distance of turbo-like codes
IEEE Transactions on Information Theory
Unveiling turbo codes: some results on parallel concatenated coding schemes
IEEE Transactions on Information Theory
Serial concatenation of interleaved codes: performance analysis, design, and iterative decoding
IEEE Transactions on Information Theory
On interleaved, differentially encoded convolutional codes
IEEE Transactions on Information Theory
Coding theorems for turbo code ensembles
IEEE Transactions on Information Theory
Random codes: minimum distances and error exponents
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
The serial concatenation of rate-1 codes through uniform random interleavers
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Coding for Parallel Channels: Gallager Bounds and Applications to Turbo-Like Codes
IEEE Transactions on Information Theory
Analysis, design, and iterative decoding of double serially concatenated codes with interleavers
IEEE Journal on Selected Areas in Communications
Good concatenated code ensembles for the binary erasure channel
IEEE Journal on Selected Areas in Communications - Special issue on capaciyy approaching codes
| Hi-index | 754.84 |
In this paper, the ensembles of repeat multiple-accumulate codes (RAm), which are obtained by interconnecting a repeater with a cascade of m accumulate codes through uniform random interleavers, are analyzed. It is proved that the average spectral shapes of these code ensembles are equal to 0 below a threshold distance Ɛm and, moreover, they form a nonincreasing sequence in m converging uniformly to the maximum between the average spectral shape of the linear random ensemble and 0. Consequently the sequence Ɛm converges to the Gilbert-Varshamov (GV) distance. A further analysis allows to conclude that if m ≥ 2 the RAm are asymptotically good and that Ɛm is the typical normalized minimum distance when the interleaver length goes to infinity. Combining the two results it is possible to conclude that the typical distance of the ensembles RAm converges to the Gilbert-Varshamov bound.