A intelligence Approach of Analog to Digital Converter using Software Defined Radio technique

Authors

  • S. P. Sairam Nadipalli Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, A.P, India, 522502
  • Sarat K. Kotamraju Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, A.P, India, 522502
  • P. Kanakaraja Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, A.P, India, 522502
  • Aswin Kumer S. V. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, A.P, India, 522502
  • K.Ch Sri Kavya

Keywords:

SDR, ADC, DAC, Signal to Noise Ratio, DSPs, Thermal Noise, Jitter Noise

Abstract

Analog to Digital Converters are universal basic blocks of SDR (Software Defined Ratio) transmission management Architectures. Analog-to-digital converters are explained in this study, which includes converter characteristics and components often used in the field. The opening jitter that caused this paper's vulnerability might be linked to it. Because the speed of the SDR technology is limited by the ambiguity of the comparator, it is also a constraint for ADCs operating at Gs/S rates. Various uncertain ADC designs and circuit advancements have been suggested and implemented in an effort to push back these cut-off points. Lower power dissipation is brought about by a shift toward single-chip ADCs. Sampling rate has additionally been introduced, giving an understanding into their weaknesses. With the beginning of another thousand years, track down ourselves one stage before the development of the third era 3G versatile interchanges frameworks on the planet market. The execution of the 3G and moreover 4G versatile interchanges frameworks is incorporated inside the aims of the purported programming characterized radio (SDR) frameworks. The plan, improvement and the execution of SDR frameworks depend on a mix and development of innovations and methods including for the most part, brilliant receiving wires, radio frequency (RF) down/up converters, simple to computerized converters (ADCs) and advanced to simple converters (DACs), Digital Signal processors (DSPs), demonstrating and framework portrayal dialects. In this paper a quantitative investigation of the essential boundaries of one of the main fragments of a SDR recipient, an ADC is introduced. ADCs of the most recent innovation and their essential details are additionally added.

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References

Terán, M., Aranda, J., Marin, J., Uchamocha, E., &Corzo-Ussa, G. (2021, May). A methodology for signals intelligence using non-conventional techniques and software-defined radio. In 2021 IEEE Colombian Conference on Communications and Computing (COLCOM) (pp. 1-6). IEEE.

Liang, J., Chen, H., & Liew, S. C. (2021). Design and implementation of time-sensitive wireless iot networks on software-defined radio. IEEE Internet of Things Journal, 9(3), 2361-2374.

Liang, J., Chen, H., & Liew, S. C. (2021). Design and implementation of time-sensitive wireless iot networks on software-defined radio. IEEE Internet of Things Journal, 9(3), 2361-2374.

Ren, Q., Sun, G., Zhang, Y., & Zhao, D. (2012, October). Design of Real-Time Data Acquisition Software in Software Defined Radio. In 2012 Fifth International Symposium on Computational Intelligence and Design (Vol. 2, pp. 244-247). IEEE.

Zitouni, R., & George, L. (2016, October). Output power analysis of a software defined radio device. In 2016 IEEE Radio and Antenna Days of the Indian Ocean (RADIO) (pp. 1-2). IEEE.

Nunes, B. A. A., Mendonca, M., Nguyen, X. N., Obraczka, K., &Turletti, T. (2014). A survey of software-defined networking: Past, present, and future of programmable networks. IEEE Communications surveys & tutorials, 16(3), 1617-1634.

Goeller, L., & Tate, D. (2014, October). A technical review of software defined radios: Vision, reality, and current status. In 2014 IEEE military communications conference (pp. 1466-1470). IEEE.

Nesimoglu, T. (2010, August). A review of software defined radio enabling technologies. In 2010 10th Mediterranean Microwave Symposium (pp. 87-90). IEEE.

Chappell, W. J., Naglich, E. J., Maxey, C., &Guyette, A. C. (2014). Putting the radio in “Software-defined radio”: Hardware developments for adaptable RF systems. Proceedings of the IEEE, 102(3), 307-320.

Krishnan, R., Babu, R. G., Kaviya, S., Kumar, N. P., Rahul, C., & Raman, S. S. (2017, September). Software defined radio (SDR) foundations, technology tradeoffs: A survey. In 2017 IEEE international conference on power, control, signals and instrumentation engineering (ICPCSI) (pp. 2677-2682). IEEE.

Pawłowski, P., Dąbrowski, A., Skrzypek, P., Roszak, P., Pałejko, A., Walenciak, T., &Mor, M. (2011, April). Software defined radio-design and implementation of complete platform. In 14th IEEE International Symposium on Design and Diagnostics of Electronic Circuits and Systems (pp. 155-158). IEEE.

Saikumar, K. (2020). RajeshV. Coronary blockage of artery for Heart diagnosis with DT Artificial Intelligence Algorithm. Int J Res Pharma Sci, 11(1), 471-479.

Saikumar, K., Rajesh, V. (2020). A novel implementation heart diagnosis system based on random forest machine learning technique International Journal of Pharmaceutical Research 12, pp. 3904-3916.

Raju, K. B., Lakineni, P. K., Indrani, K. S., Latha, G. M. S., & Saikumar, K. (2021, October). Optimized building of machine learning technique for thyroid monitoring and analysis. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1-6). IEEE.

Kailasam, S., Achanta, S. D. M., Rao, P. R. K., Vatambeti, R., &Kayam, S. (2021). An IoT-based agriculture maintenance using pervasive computing with machine learning technique. International Journal of Intelligent Computing and Cybernetics.

Koppula, N., Sarada, K., Patel, I., Aamani, R., & Saikumar, K. (2021). Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm: Deep Learning. In Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies (pp. 278-289). IGI Global.

Rao, K. S., Reddy, B. V., Sarada, K., & Saikumar, K. (2021). A Sequential Data Mining Technique for Identification of Fault Zone Using FACTS-Based Transmission. In Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies (pp. 408-419). IGI Global.

Raju, K., Pilli, S. K., Kumar, G. S. S., Saikumar, K., &Jagan, B. O. L. (2019). Implementation of natural random forest machine learning methods on multi spectral image compression. Journal of Critical Reviews, 6(5), 265-273.

Garigipati, R. K., Raghu, K., & Saikumar, K. (2022). Detection and Identification of Employee Attrition Using a Machine Learning Algorithm. In Handbook of Research on Technologies and Systems for E-Collaboration During Global Crises (pp. 120-131). IGI Global.

Mythreya, S., Murthy, A. S. D., Saikumar, K., & Rajesh, V. (2022). Prediction and Prevention of Malicious URL Using ML and LR Techniques for Network Security: Machine Learning. In Handbook of Research on Technologies and Systems for E-Collaboration During Global Crises (pp. 302-315). IGI Global.

Saikumar, K., Rajesh, V., Babu, B.S. (2022). Heart disease detection based on feature fusion technique with augmented classification using deep learning technology. Traitement du Signal, Vol. 39, No. 1, pp. 31-42. https://doi.org/10.18280/ts.390104

Kailasam, S., Achanta, S.D.M., Rama Koteswara Rao, P., Vatambeti, R., Kayam, S. (2022). An IoT-based agriculture maintenance using pervasive computing with machine learning technique. International Journal of Intelligent Computing and Cybernetics, 15(2), pp. 184–197.

Sankara Babu B., Nalajala S., Sarada K., Muniraju Naidu V., Yamsani N., Saikumar K. (2022) Machine Learning Based Online Handwritten Telugu Letters Recognition for Different Domains. In: Kumar P., Obaid A.J., Cengiz K., Khanna A., Balas V.E. (eds) A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems. Intelligent Systems Reference Library, vol 210. Springer, Cham. https://doi.org/10.1007/978-3-030-76653-5_12

Kiran Kumar M., Kranthi Kumar S., Kalpana E., Srikanth D., Saikumar K. (2022) A Novel Implementation of Linux Based Android Platform for Client and Server. In: Kumar P., Obaid A.J., Cengiz K., Khanna A., Balas V.E. (eds) A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems. Intelligent Systems Reference Library, vol 210. Springer, Cham. https://doi.org/10.1007/978-3-030-76653-5_8

Shravani, C., Krishna, G. R., Bollam, H. L., Vatambeti, R., & Saikumar, K. (2022, January). A Novel Approach for Implementing Conventional LBIST by High Execution Microprocessors. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 804-809). IEEE.

Radio Spectrum in India

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Published

19.12.2022

How to Cite

S. P. Sairam Nadipalli, Sarat K. Kotamraju, P. Kanakaraja, Aswin Kumer S. V., & K.Ch Sri Kavya. (2022). A intelligence Approach of Analog to Digital Converter using Software Defined Radio technique. International Journal of Intelligent Systems and Applications in Engineering, 10(2s), 08–13. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2354

Issue

Vol. 10 No. 2s (2022)

Section

Research Article

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Copyright (c) 2022 S. P. Sairam Nadipalli, Sarat K. Kotamraju, P. Kanakaraja, Aswin Kumer S. V., K.Ch Sri Kavya

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