Maximum likelihood decoding of long block codes is not feasable due to large complexity. Some classes of codes are shown to be decomposable into multilevel concatenated codes (MLCC). For these codes, multistage decoding provides good trade-off between performance and complexity. In this paper, we derive an upper bound on the probability of block error for MLCC. We use this bound to evaluate difference in performance for different decompositions of some codes. Examples given show that a significant reduction in complexity can be achieved when increasing number of stages of decoding. Resulting performance degradation varies for different decompositions. A guideline is given for finding good m-level decompositions.
The theoretical performance of binary codes for the additive white Gaussian noise (AWGN) channel is discussed. In particular, Gallager’s coding theorem for binary codes is treated extensively. By assuming a binomial weight distribution for linear codes, it is shown that the decoder error probability performance of some of the best, known linear, binary codes is the same as the average performance of the ensemble of all randomly chosen, binary nonlinear codes having the same length and dimension. Assuming a binomial weight distribution, an upper bound is determined for the erasure performance of any code, and it is shown that this can be translated into an upper bound for code performance in the AWGN channel. Finally, theoretical bounds on the construction of error-correction codes are discussed. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Lattice codes are elegant and powerful structures that not only achieve the capacity of the additive white Gaussian noise (AWGN) channel but are also a key ingredient to many multiterminal schemes that exploit linearity properties. However, constructing lattice codes that can realize these benefits with low complexity is still a challenging problem. In this paper, efficient encoding and decoding algorithms are proposed for multilevel binary low-density parity-check (LDPC) lattices constructed via Construction D' whose complexity is linear in the total number of coded bits. Moreover, a generalization of Construction D' is proposed, which relaxes some of the nesting constraints on the parity-check matrices of the component codes, leading to a simpler and improved design. Based on this construction, low-complexity multilevel LDPC lattices are designed whose performance under multistage decoding is comparable to that of polar lattices and close to that of low-density lattice codes on the power-unconstrained AWGN channel.
Guessing Random Additive Noise Decoding (GRAND) has been proposed as an efficient and universal decoding technique for linear block codes [1]. A necessary element of the GRAND decoder is a set of noise sequences of length
nequal to the code length. The decoded codeword is taken as the first valid codeword that results from the binary addition of the noise sequences, in order of likelihood, with the received word. For a binary code of length
nthere are
2nsuch noise sequences. When
nis large, it will typically be intractable to add every noise sequence during decoding. Moreover, these sequences exhibit a number of error bursts
(m)and a total number of bit flips
(l). In [1] the authors provide expressions to determine the probability of each error burst sequence for a two-state Markov channel model. The resulting decoder is called GRAND-Markov Order (GRAND-MO). The authors of [1] describe a GRAND-MO error pattern generator for the two-state Markov channel based upon sequential transition between
(m,l)pairs, incrementing
mfirst, and then treating each value of l. In this paper we address a method of constructing the n-length noise sequences based upon integer compositions on the required
lbit flips as well as the
n−lnon-errors in the sequence. This method allows one to construct noise sequences in order of probability without the need to construct less likely noise sequences. A GRAND decoder may employ a limited number of noise sequences and abandon decoding upon finding a valid code word, or upon reaching an abandonment threshold without decoding success. In this way the most likely noise sequences can be prioritized in decoding.
A universal coding scheme for information from i.i.d., arbitrarily varying sources, or memoryless correlated sources is constructed using LDPC matrices and shown to have an exponential upper bound of decoding error probability. As a corollary, we construct a universal code for the noisy channel model, which is not necessarily BSC. Simulation results show universality of the code with sum-product decoding, and presence of a gap between the error exponent obtained by simulation and that obtained theoretically.
The design and implementation of error correcting codes has long been informed by two fundamental results: Shannon’s 1948 capacity theorem, which established that long codes use noisy channels most efficiently; and Berlekamp, McEliece, and Van Tilborg’s 1978 theorem on the NP-completeness of decoding linear codes. These results shifted focus away from creating code-independent decoders, but recent low-latency communication applications necessitate relatively short codes, providing motivation to reconsider the development of universal decoders. We introduce a scheme for employing binarized symbol soft information within Guessing Random Additive Noise Decoding, a universal hard detection decoder. We incorporate codebook-independent quantization of soft information to indicate demodulated symbols to be reliable or unreliable. We introduce two decoding algorithms: one identifies a conditional Maximum Likelihood (ML) decoding; the other either reports a conditional ML decoding or an error. For random codebooks, we present error exponents and asymptotic complexity, and show benefits over hard detection. As empirical illustrations, we compare performance with majority logic decoding of Reed-Muller codes, with Berlekamp-Massey decoding of Bose-Chaudhuri-Hocquenghem codes, with CA-SCL decoding of CA-Polar codes, and establish the performance of Random Linear Codes, which require a universal decoder and offer a broader palette of code sizes and rates than traditional codes.