A comparative study on the modified Max-Log-MAP turbodecoding by extrinsic information scaling

A simple but effective technique to improve the performance of the Max-Log-MAP algorithm is to scale the extrinsic information exchanged between two MAP decoders. A comprehensive analysis of the selection of the scaling factors according to channel conditions and decoding iterations is presented in this paper. Choosing a constant scaling factor for all SNRs and iterations is compared with the best scaling factor selection for changing channel conditions and decoding iterations. It is observed that a constant scaling factor for all channel conditions and decoding iterations is the best solution and provides a 0.2-0.4 dB gain over the standard Max- Log-MAP algorithm. Therefore, a constant scaling factor should be chosen for the best compromise.

The modified Max-Log-MAP turbo decoding algorithm by extrinsic information scaling for wireless applications

The iterative nature of turbo-decoding algorithms increases their complexity compare to conventional FEC decoding algorithms. Two iterative decoding algorithms, Soft-Output-Viterbi Algorithm (SOVA) and Maximum A posteriori Probability (MAP) Algorithm require complex decoding operations over several iteration cycles. So, for real-time implementation of turbo codes, reducing the decoder complexity while preserving bit-error-rate (BER) performance is an important design consideration. In this chapter, a modification to the Max-Log-MAP algorithm is presented. This modification is to scale the extrinsic information exchange between the constituent decoders. The remainder of this chapter is organized as follows: An overview of the turbo encoding and decoding processes, the MAP algorithm and its simplified versions the Log-MAP and Max-Log-MAP algorithms are presented in section 1. The extrinsic information scaling is introduced, simulation results are presented, and the performance of different methods to choose the best scaling factor is discussed in Section 2. Section 3 discusses trends and applications of turbo coding from the perspective of wireless applications.

Associations between Belief in Conspiracy Theories and the Maladaptive Personality Traits of the Personality Inventory for DSM-5

Conspiracy theories can be treated as both rational narratives of the world as well as outcomes of underlying maladaptive traits. Here, we examined associations between belief in conspiracy theories and individual differences in personality disorders. An Internet-based sample (N=259) completed measures of belief in conspiracy theories and the 25 facets of the Personality Inventory for DSM-5 (PID-5). Preliminary analyses showed no significant differences in belief in conspiracy theories across participant sex, ethnicity, and education. Regression analyses showed that the PID-5 facets of Unusual Beliefs and Experiences and, to a lesser extent, Suspiciousness, significantly predicted belief in conspiracy theories. These findings highlight a role for maladaptive personality traits in understanding belief in conspiracy theories, but require further investigation.