Search NASA⌕ Search

DOE OSTI · 1632643

Communication device, spread-spectrum receiver, and related method using normalized matched filter for improving signal-to-noise ratio in harsh environments

Abstract

A communication device, a method of operating a communication device, and a spread-spectrum receiver are disclosed. The method includes receiving an incoming RF signal, demodulating the incoming RF signal to generate a baseband signal, filtering the baseband signal with a normalized matched filter having filter characteristics matched to a pulse-shaping filter of the transmitter that generated the incoming RF signal, and extracting a received signal from a normalized output generated by the normalized matched filter. As a result, interferences and noise from harsh environments may be suppressed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Moradi, Hussein, Driggs, Jonathan D., Majid, Arslan J., Farhang, Behrouz, Sibbett, Taylor M.. 2020-03-03. Communication device, spread-spectrum receiver, and related method using normalized matched filter for improving signal-to-noise ratio in harsh environments. https://www.osti.gov/biblio/1632643

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗