This paper discusses the results of simulations relating to the performances of turbo codes, low density parity check (LDPC) codes, and polar codes over an additive white Gaussian noise (AWGN) channel in the presence of inter symbol interference, denoting the disturbances that altered the original signal. To eliminate the negative effects of inter symbol interference (ISI), an equalizer was used at the level of the receiver. Practically, two types of equalizers were used: zero forcing (ZF) and minimum mean square error (MMSE), considering the case of perfect channel estimation and the case of estimation using the least square algorithm. The performance measure used was the modification of the bit error rate compared to a given signal to noise ratio; in this sense, the MMSE equalizer offered a higher performance than the ZF equalizer. The aspect of channel equalization considered here is not novel, but there have been very few works that dealt with equalization in the context of the use of turbo codes, especially LDPC codes and polar codes for channel coding. In this respect, this research can be considered a contribution to the field of digital communications.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965925PMC
http://dx.doi.org/10.3390/s23041942DOI Listing

Publication Analysis

Top Keywords

turbo codes
12
ldpc codes
12
codes polar
12
polar codes
12
inter symbol
12
symbol interference
12
codes
9
codes ldpc
8
awgn channel
8
channel presence
8

Similar Publications

The gender classification from names is crucial for uncovering a myriad of gender-related research questions. Traditionally, this has been automatically computed by gender detection tools (GDTs), which now face new industry players in the form of conversational bots like ChatGPT. This paper statistically tests the stability and performance of ChatGPT 3.

View Article and Find Full Text PDF

Large-scale identification of social and behavioral determinants of health from clinical notes: comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models.

BMC Med Inform Decis Mak

October 2024

Foundational Medical Studies, Population Health Informatics, Oakland University William Beaumont School of Medicine, Oakland University, 586 Pioneer Dr, 460 O'Dowd Hall, Rochester, MI, 48309-4482, USA.

Article Synopsis
  • Social and behavioral determinants of health (SBDH) are often overlooked in electronic health records (EHR), prompting a study to use machine learning methods to identify these factors from unstructured clinical notes.* -
  • The research utilized Latent Semantic Indexing (LSI) on over 2 million clinical notes, achieving an average positive predictive value (PPV) of 83%, with LSI's F1 score outperforming traditional ICD-9 codes and GPT-3.5 but falling short compared to GPT-4.* -
  • The findings suggest that LSI can effectively detect SBDH information, performing comparably to advanced language models while ensuring all cases were processed fully, unlike GPT models which faced size limitations.*
View Article and Find Full Text PDF

Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal (GI) tract. Fecal calprotectin (fCAL) is a noninvasive laboratory test used in the diagnosis and monitoring of IBDs such as Crohn's disease and ulcerative colitis. The fCAL send-out test that our facility has been offering so far uses an ELISA-based method.

View Article and Find Full Text PDF

A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry.

Entropy (Basel)

August 2024

Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602, USA.

This paper presents a history of the development of channel codes in deep-space telemetry and aeronautical mobile telemetry. The history emphasizes "firsts" and other remarkable achievements. Because coding was used first in deep-space telemetry, the history begins with the codes used for and .

View Article and Find Full Text PDF
Article Synopsis
  • The research investigates the use of GPT models for automated psychological text analysis across 15 datasets consisting of nearly 48,000 annotated tweets and news headlines in 12 languages.
  • Results indicate that GPT outperformed traditional English-language dictionary analysis and sometimes matched or exceeded the performance of advanced machine learning models, particularly benefiting lesser-spoken languages.
  • The study suggests that GPT simplifies and democratizes automated text analysis, making it more accessible for researchers with little coding experience, and encourages further research in understudied languages.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!