Time Interleaved ADC mismatch error correction technique in I/Q Digital Beamforming Receivers

The Time-Interleaved ADC（TIADC) is an effective method for implement ultra high-speed data acquisition. However, the errors of channel mismatch are seriously degrade the signal-to-noise ratio of the system, such as Time-skew error, Gain error and Offset error. This paper have done some researches and analysis, and given the modeling of the three channels mismatch. What's more, it also given a detailed analysis of error and the method of measure it, derived the formula of signal to noise and distortion ratio(SINAD) and spurious free dynamic range(SFDR). All of them provide a reference for the tolerance range of TIADC channel mismatch error. Meanwhile, the result of this paper has provided a theoretical basis for eliminating TIADC channel mismatch error.

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A background extraction technique for bandwidth mismatch error in a two-channel time-interleaved ADC

10th IEEE International NEWCAS Conference ◽

10.1109/newcas.2012.6329029 ◽

2012 ◽

Cited By ~ 6

Author(s):

Fatima Ghanem ◽

A Biallais ◽

Philippe Gandy

Keyword(s):

Extraction Technique ◽

Mismatch Error ◽

Background Extraction ◽

Interleaved Adc ◽

Time Interleaved

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Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks

Sensors ◽

10.3390/s21062009 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2009

Author(s):

Fatemeh Najafi ◽

Masoud Kaveh ◽

Diego Martín ◽

Mohammad Reza Mosavi

Keyword(s):

Error Correction ◽

Random Access ◽

Error Correction Codes ◽

Access Memory ◽

Deep Convolutional Neural Networks ◽

Device Identification ◽

Physical Unclonable Functions ◽

Server Side ◽

Correction Technique ◽

Reduction Methods

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.

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