Mutual Information-Maximizing Quantized Belief Propagation Decoding of Regular LDPC Codes

In mutual information-maximizing lookup table (MIM-LUT) decoding of low-density parity-check (LDPC) codes, table lookup operations are used to replace arithmetic operations. In practice, large tables need to be decomposed into small tables to save the memory consumption, at the cost of degraded error performance. In this paper, we propose a method, called mutual information-maximizing quantized belief propagation (MIM-QBP) decoding, to remove the lookup tables used for MIM-LUT decoding. Our method leads to a very efficient decoder, namely the MIM-QBP decoder, which can be implemented based only on simple mappings and fixed-point additions. Simulation results show that the MIM-QBP decoder can always considerably outperform the state-of-the-art MIM-LUT decoder, mainly because it can avoid the performance loss due to table decomposition. Furthermore, the MIM-QBP decoder with only 3 bits per message can outperform the floating-point belief propagation (BP) decoder at high signal-to-noise ratio (SNR) regions when testing on high-rate codes with a maximum of 10-30 iterations

Periodicities in the occurrence of aurora as indicators of solar variability

A compilation of records of the aurora observed in China from the Time of the Legends (2000 - 3000 B.C.) to the mid-18th century has been used to infer the frequencies and strengths of solar activity prior to modern times. A merging of this analysis with auroral and solar activity patterns during the last 200 years provides basically continuous information about solar activity during the last 2000 years. The results show periodicities in solar activity that contain average components with a long period (approx. 412 years), three middle periods (approx. 38 years, approx. 77 years, and approx. 130 years), and the well known short period (approx. 11 years)

A Novel Location Free Link Prediction in Multiplex Social Networks

In recent decades, the emergence of social networks has enabled internet service providers (e.g., Facebook, Twitter and Uber) to achieve great commercial success. Link prediction is recognized as a common practice to build the topology of social networks and keep them evolving. Conventionally, link prediction methods are dependent of location information of users, which suffers from information leakage from time to time. To deal with this problem, companies of smart devices (e.g., Apple Inc.) keeps tightening their privacy policy, impeding internet service providers from acquiring location information. Therefore, it is of great importance to design location free link prediction methods, while the accuracy still preserves. In this study, a novel location free link prediction method is proposed for complex social networks. Experiments on real datasets show that the precision of our location free link prediction method increases by 10 percent