Duality of Channel Encoding and Decoding - Part I: Rate-1 Binary Convolutional Codes

In this paper, we revisit the forward, backward and bidirectional Bahl-Cocke-Jelinek-Raviv (BCJR) soft-input soft-output (SISO) maximum a posteriori probability (MAP) decoding process of rate-1 binary convolutional codes. From this we establish some interesting explicit relationships between encoding and decoding of rate-1 convolutional codes. We observe that the forward and backward BCJR SISO MAP decoders can be simply represented by their dual SISO channel encoders using shift registers in the complex number field. Similarly, the bidirectional MAP decoding can be implemented by linearly combining the shift register contents of the dual SISO encoders of the respective forward and backward decoders. The dual encoder structures for various recursive and non-recursive rate-1 convolutional codes are derived.Comment: 32 pages, 20 figures, to appear in ET

An algorithm for list decoding number field codes

We present an algorithm for list decoding codewords of algebraic number field codes in polynomial time. This is the first explicit procedure for decoding number field codes whose construction were previously described by Lenstra [12] and Guruswami [8]. We rely on a new algorithm for computing the Hermite normal form of the basis of an OK -module due to Biasse and Fieker [2] where OK is the ring of integers of a number field K

From nominal to true a posteriori probabilities: an exact Bayesian theorem based probabilistic data association approach for iterative MIMO detection and decoding

It was conventionally regarded that the existing probabilistic data association (PDA) algorithms output the estimated symbol-wise a posteriori probabilities (APPs) as soft information. In this paper, however, we demonstrate that these probabilities are not the true APPs in the rigorous mathematicasense, but a type of nominal APPs, which are unsuitable for the classic architecture of iterative detection and decoding (IDD) aided receivers. To circumvent this predicament, we propose an exact Bayesian theorem based logarithmic domain PDA (EB-Log-PDA) method, whose output has similar characteristics to the true APPs, and hence it is readily applicable to the classic IDD architecture of multiple-input multiple-output (MIMO) systems using the general M-ary modulation. Furthermore, we investigate the impact of the PDA algorithms' inner iteration on the design of PDA-aided IDD receivers. We demonstrate that introducing inner iterations into PDAs, which is common practice in PDA-aided uncoded MIMO systems, would actually degrade the IDD receiver's performance, despite significantly increasing the overall computational complexity of the IDD receiver. Finally, we investigate the relationship between the extrinsic log-likelihood ratio (LLRs) of the proposed EB-Log-PDA and of the approximate Bayesian theorem based logarithmic domain PDA (AB-Log-PDA) reported in our previous work. We also show that the IDD scheme employing the EB-Log-PDA without incorporating any inner PDA iterations has an achievable performance close to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a significantly lower computational complexity in the scenarios considered