An ultra-low-power low-noise amplifier using cross-coupled positive feedback for 5G IoT applications

Gladson, S. Chrisben; Narayana, Adith Hari; Bhaskar, M.

doi:10.1007/s42452-019-1470-8

Cited by 4 publications

(2 citation statements)

References 64 publications

(182 reference statements)

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“…The objective of this study was to build upon existing work on cyber risk standardisation [10], and AI in CPS [11], but with a greater focus on exploring the potential and practical challenges in the use of AI, in the service of improving personal and organisational resilience. The methodology applied in the study follows recommendations in existing studies on adaptive risk models [12]; feedback in IoT systems [13]; in layered IoT architecture [14]; and for optimising decision-making [15]. We identified approaches to model the risk within complex interconnected and coupled systems in cyber-physical environments.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

et al. 2020

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We explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improving organisational resilience and understanding cyber risk. The research is focused on identifying the role of AI in connected devices such as Internet of Things (IoT) devices. Through literature review, we identify wide ranging and creative methodologies for cyber analytics and explore the risks of deliberately influencing or disrupting behaviours to socio-technical systems. This resulted in the modelling of the connections and interdependencies between a system's edge components to both external and internal services and systems. We focus on proposals for models, infrastructures and frameworks of IoT systems found in both business reports and technical papers. We analyse this juxtaposition of related systems and technologies, in academic and industry papers published in the past 10 years. Then, we report the results of a qualitative empirical study that correlates the academic literature with key technological advances in connected devices. The work is based on grouping future and present techniques and presenting the results through a new conceptual framework. With the application of social science's grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

et al. 2020

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“…Thus, the proposed LNA can afford to operate at reduced voltages with reduced power consumption. Compared to the recently proposed LNA in [44], the proposed circuit operates at 55.5% reduced power consumption. Also, the proposed LNA has good performance at reduced supply voltage of 0.6 V making it viable for battery-powered, wireless applications that demand large data processing such as 5G.…”

Section: Resultsmentioning

confidence: 98%

A 219-µW ultra-low power low-noise amplifier for IEEE 802.15.4 based battery powered, portable, wearable IoT applications

Gladson¹,

Narayana

Thenmozhi

et al. 2021

SN Appl. Sci.

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Due to the increased processing data rates, which is required in applications such as fifth-generation (5G) wireless networks, the battery power will discharge rapidly. Hence, there is a need for the design of novel circuit topologies to cater the demand of ultra-low voltage and low power operation. In this paper, a low-noise amplifier (LNA) operating at ultra-low voltage is proposed to address the demands of battery-powered communication devices. The LNA dual shunt peaking and has two modes of operation. In low-power mode (Mode-I), the LNA achieves a high gain ($$S21$$ S 21 ) of 18.87 dB, minimum noise figure ($${NF}_{min.}$$ NF m i n . ) of 2.5 dB in the − 3 dB frequency range of 2.3–2.9 GHz, and third-order intercept point (IIP3) of − 7.9dBm when operating at 0.6 V supply. In high-power mode (Mode-II), the achieved gain, NF, and IIP3 are 21.36 dB, 2.3 dB, and 13.78dBm respectively when operating at 1 V supply. The proposed LNA is implemented in UMC 180 nm CMOS process technology with a core area of $$0.40{\mathrm{ mm}}^{2}$$ 0.40 mm 2 and the post-layout validation is performed using Cadence SpectreRF circuit simulator.

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Future developments in standardisation of cyber risk in the Internet of Things (IoT)

et al. 2020

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In this research article, we explore the use of a design process for adapting existing cyber risk assessment standards to allow the calculation of economic impact from IoT cyber risk. The paper presents a new model that includes a design process with new risk assessment vectors, specific for IoT cyber risk. To design new risk assessment vectors for IoT, the study applied a range of methodologies, including literature review, empirical study and comparative study, followed by theoretical analysis and grounded theory. An epistemological framework emerges from applying the constructivist grounded theory methodology to draw on knowledge from existing cyber risk frameworks, models and methodologies. This framework presents the current gaps in cyber risk standards and policies, and defines the design principles of future cyber risk impact assessment. The core contribution of the article therefore, being the presentation of a new model for impact assessment of IoT cyber risk. Keywords: Cyber risk; Internet of Things cyber risk; Internet of Things risk vectors;Standardisation of cyber risk assessment; Economic impact assessment. University of OxfordUniversity of Oxford 3 analysis to uncover the best method to define a unified cyber risk assessment. In section 7 we propose a new epistemological framework for cyber risk assessment standardisation and we discuss the new impact assessment principles. In Section 8 we present the conclusions and limitations of the research. METHODOLOGYThe methods applied in this study consist of literature review, comparative study, empirical analysis, theoretical and epistemological analysis and case study workshops. The selection of methodologies is based on their flexibility to be applied simultaneously to analyse the same research topic from different perspectives. We use practical studies of major projects in the I4.0 to showcase recent developments of IoT systems in the context of I4.0 high-tech strategies. We need practical studies to bridge the gaps, to assess the impact and overcome some of the cyber risk limitations and to construct the relationship between IoT and high-tech strategies. The proposed design principles support the process of building a holistic IoT cyber risk impact assessment model. Theoretical analysisThe methodology applies theoretical analysis through logical discourse of knowledge, also known as epistemological analysis. An epistemological analysis enables an investigation on how existing knowledge is justified and what makes justified beliefs justified (Steup, 2005), what does it mean to say that we understand something (Wenning, 2009) and how do we understand that we understand.The methodology reported here has two objectives. The first objective is to enable an up-todate overview of existing and emerging cyber risk vectors from IoT advancements, which includes cyber-physical systems, the industrial Internet of things, cloud computing and cognitive computing (MEICA, 2015;Weyer et al., 2015;Liao et al., 2017). If we were performing a vector specific analysis of r...

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An ultra-low-power low-noise amplifier using cross-coupled positive feedback for 5G IoT applications

Cited by 4 publications

References 64 publications

Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

A 219-µW ultra-low power low-noise amplifier for IEEE 802.15.4 based battery powered, portable, wearable IoT applications

Future developments in standardisation of cyber risk in the Internet of Things (IoT)

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