Design of a 45 nm Complementary Metal Oxide Semiconductor Low Noise Amplifier for a 30 GHz Millimeter-Wave Wireless Transceiver in Radar Sensor Applications
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引用次数: 0
Abstract
The millimeter-wave (mmWave) frequency band is rapidly becoming utilized in wireless technologies due to its large bandwidth and high data throughput. Wireless technology is increasingly becoming the backbone of the Internet of Things (IoT). This has resulted in increased applications of the radio frequency (RF) spectrum and congestion of the microwave band. This can be solved by utilizing more bandwidth at higher frequency bands. One notable application of IoT pertains to radar sensing, which has experienced increased popularity across various domains such as autonomous vehicles, gesture recognition, drones, and health monitoring. Radar sensors have been employed in these applications to perform tasks including proximity sensing, direction detection, speed measurement, target localization, and capturing physiological indicators such as heartbeat and breathing. Several factors have an impact on the performance of radar sensors, encompassing the maximum range for target detection, measurement precision, capability to differentiate between multiple targets, and ability to operate effectively in environments with high levels of noise. This paper presents the design of a 45 nm complementary metal-oxide-semiconductor (CMOS) low noise amplifier (LNA) for a mmWave Ka-band wireless transceiver for radar sensors. The LNA was designed to operate at 0.6V and 700 μA for low power consumption. The LNA consists of an inductive degenerated common source (CS) and a common gate (CG) diode-connected load. The LNA achieves a power gain of 31.19 dB and a noise figure (NF) of 0.133 dB at 30 GHz consuming 0.42 mW of power.
Big DataCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍:
Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.
Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government.
Big Data coverage includes:
Big data industry standards,
New technologies being developed specifically for big data,
Data acquisition, cleaning, distribution, and best practices,
Data protection, privacy, and policy,
Business interests from research to product,
The changing role of business intelligence,
Visualization and design principles of big data infrastructures,
Physical interfaces and robotics,
Social networking advantages for Facebook, Twitter, Amazon, Google, etc,
Opportunities around big data and how companies can harness it to their advantage.