On the design of turbo codes with convolutional interleavers

Random interleavers are amongst the most effective interleavers for turbo codes. However, due to their random permutations, a compact representation of the code specification is a major obstacle. Thus, to date, much research has been conducted on the design of deterministic interleavers having performances close to random interleavers. These interleavers are mainly constructed as block interleavers, which allows the code to be analyzed as a block code. In contrast to block interleavers, there are non-block interleavers. These utilize a reduced number of memories in their structures and have self-synchronization with their deinterleavers; this simplifies their design. Because of their non-block structures, turbo codes constructed by these interleavers must usually be analyzed in terms of the continuous performance. Previous research confirms that the codes� continuous performance is similar to their block performance, but at the expense of increased complexity of the code analysis and decoding. In order to analyze a turbo code constructed with non-block interleavers as a block code, it is necessary to consider the applied interleavers as block interleavers. This is accomplished by the insertion of stuff bits at the end of each input data block, returning the interleaver memories to zero state. This thesis is related to the application of convolutional interleavers which are the most popular non-block interleavers for turbo codes. It introduces convolutional interleavers as good deterministic interleavers that can perform similar or even better than previous deterministic and random interleavers. The thesis presents two different structures of block-wise convolutional interleavers, created on the basis of distribution of stuff bits in the interleaved data. On the basis of convolutional interleaver properties, a simple algorithm is introduced to analyze code performance at different signal to noise ratios. The code analysis is confirmed with simulation results, which allow selection of the most suitable interleaver. Different models of the selected convolutional interleavers are verified. These models are constructed based on changing the period and space values, which are introduced as the constituent parameters of convolutional interleavers. The performance of interleavers with different periods and a space value 1 are investigated. For a similar number of stuff bits, these interleavers are compared with interleavers constructed with shorter periods and highest fixed space values than 1. Convolutional interleavers with variable space values operating as generalized convolutional interleavers are also presented and their performance is compared with interleavers using the fixed space value. Turbo codes constituted with the mentioned interleavers are analyzed using different input bitstreams. Based on the analysis, suitable modifications are proposed for each model of interleaver so as to improve the turbo code performance through a reduced number of stuff bits. The performance of the modified convolutional interleavers is compared with good deterministic and random block interleavers. The results demonstrate that with an acceptable number of stuff bits contributed to each interleaved data, convolutional interleavers provide similar or improved performance when compared to block interleavers. Finally, the application of designed convolutional interleavers in Unequal Error Protection (UEP) turbo codes is presented. Based on the code specifications and interleaver properties, three different techniques for UEP are suggested to improve protection of priority data, while reducing the overall number of stuff bits inserted into the interleaver memories

Weight distribution of turbo codes with convolutional interleavers

A simple algorithm for the weight calculation of turbo codes with convolutional interleavers is presented. For codes with short interleaver lengths, the weight distributions are computed using conventionally proposed methods and then utilised together with the interleaver properties to determine the weight specifications for the code with a longer desired length. Based on the calculated weights, a new upper bound for the code is computed. It agrees with simulation results of the code performance in the error-floor region

Serially concatenated turbo codes

The paper presents a new scheme of concatenated codes, referred to as Serially Concatenated Turbo (SCT) codes. The code is constructed as the serial combinations of two turbo codes, i.e. turbo Recursive Systematic Convolutional (RSC) codes and turbo Bose Ray Chaudhuri Hocquenghem (BCH) codes, linked by a pseudo-random interleaver. In comparison with the conventional turbo RSC codes, SCT codes have higher minimum distance values. Based on conducted simulations, it is found that SCT codes outperform turbo RSC codes at the waterfall and error floor regions, while they require reasonable number of iterations at their iterative decoding structure to achieve good performance. ©2009 IEEE

Application of convolutional interleavers in turbo codes with unequal error protection

This paper deals with an application of convolutional interleavers in unequal error protection (UEP) turbo codes. The constructed convolutional interleavers act as block interleavers by inserting a number of stuff bits into the interleaver memories at the end of each data block. Based on the properties of this interleaver, three different models of UEP turbo codes are suggested. Simulation results confirm that utilizing UEP can provide better protection for important parts of each data block, while significantly decreasing the number of stuff bits

Energy Intensity and Greenhouse Gas Emissions from Tight Oil Production in the Bakken Formation

The Bakken formation has contributed to the recent rapid increase in U.S. oil production, reaching a peak production of >1.2 × 10⁶ barrels per day in early 2015. In this study, we estimate the energy intensity and greenhouse gas (GHG) emissions from 7271 Bakken wells drilled from 2006 to 2013. We model energy use and emissions using the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) model, supplemented with an open-source drilling and fracturing model, GHGfrack. Overall well-to-refinery-gate (WTR) consumption of natural gas, diesel, and electricity represent 1.3%, 0.2%, and 0.005% of produced crude energy content, respectively. Fugitive emissions are modeled for a “typical” Bakken well using previously published results of atmospheric measurements. Flaring is a key driver of emissions: wells that flared in 2013 had a mean flaring rate that was ≈500 standard cubic feet per barrel or ≈14% of the energy content of the produced crude oil. Resulting production-weighted mean GHG emissions in 2013 were 10.2 g of CO₂ equivalent GHGs per megajoule (henceforth, gCO₂eq/MJ) of crude. Between-well variability gives a 5–95% range of 2–28 gCO₂eq/MJ. If flaring is completely controlled, Bakken crude compares favorably to conventional U.S. crude oil, with 2013 emissions of 3.5 gCO₂eq/MJ for nonflaring wells, compared to the U.S. mean of ≈8 gCO₂eq/MJ