43 research outputs found
Efficient LLR Calculation for Non-Binary Modulations over Fading Channels
Log-likelihood ratio (LLR) computation for non-binary modulations over fading
channels is complicated. A measure of LLR accuracy on asymmetric binary
channels is introduced to facilitate good LLR approximations for non-binary
modulations. Considering piecewise linear LLR approximations, we prove
convexity of optimizing the coefficients according to this measure. For the
optimized approximate LLRs, we report negligible performance losses compared to
true LLRs.Comment: Submitted to IEEE Transactions on Communication
Optimum Linear LLR Calculation for Iterative Decoding on Fading Channels
On a fading channel with no channel state information at the receiver,
calculating true log-likelihood ratios (LLR) is complicated. Existing work
assume that the power of the additive noise is known and use the expected value
of the fading gain in a linear function of the channel output to find
approximate LLRs. In this work, we first assume that the power of the additive
noise is known and we find the optimum linear approximation of LLRs in the
sense of maximum achievable transmission rate on the channel. The maximum
achievable rate under this linear LLR calculation is almost equal to the
maximum achievable rate under true LLR calculation. We also observe that this
method appears to be the optimum in the sense of bit error rate performance
too. These results are then extended to the case that the noise power is
unknown at the receiver and a performance almost identical to the case that the
noise power is perfectly known is obtained.Comment: This paper will be presented in IEEE International Symposium on
Information Theory (ISIT) 2007 in Nice, Franc
Landslide Risk Assessment by Using a New Combination Model Based on a Fuzzy Inference System Method
Landslides are one of the most dangerous phenomena that pose widespread damage to property and human lives. Over the recent decades, a large number of models have been developed for landslide risk assessment to prevent the natural hazards. These models provide a systematic approach to assess the risk value of a typical landslide. However, often models only utilize the numerical data to formulate a problem of landslide risk assessment and neglect the valuable information provided by experts’ opinion. This leads to an inherent uncertainty in the process of modelling. On the other hand, fuzzy inference systems are among the most powerful techniques in handling the inherent uncertainty. This paper develops a powerful model based on fuzzy inference system that uses both numerical data and subjective information to formulate the landslide risk more reliable and accurate. The results show that the proposed model is capable of assessing the landslide risk index. Likewise, the performance of the proposed model is better in comparison with that of the conventional techniques