ICTACT Journal on Communication Technology

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AI-driven Transformer Networks in Shared Spectrum for Enhanced Signal Processing for Nonlinear Receivers


Affiliations
1 Department of Computer Applications, EMEA College of Arts and Science, India
2 Department of Electronics and Communication Engineering, Dhanalakshmi Srinivasan University, India
3 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India
4 Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, India

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Communication systems face challenges from high-power adjacent channel signals, or blockers, inducing nonlinear behavior in RF front ends. Ensuring robust performance in the presence of blockers is crucial for IoT and other spectrum-consuming devices coexisting with advanced transceivers. This paper proposes a flexible, data-driven solution using a Deep Belief Network (DBN) to mitigate third-order intermodulation distortion (IMD) during demodulation. Numerical evaluations of AI-enhanced receivers employing DBN as an IMD canceler and demodulator show significant improvements in bit error rate (BER) performance. The effectiveness of DBN varies with RF front end characteristics, notably the third-order intercept point (IP3).

Keywords

Deep Belief Network, IMD Cancellation, Nonlinear Receivers, RF Front End, Bit Error Rate
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Abstract Views: 59




  • AI-driven Transformer Networks in Shared Spectrum for Enhanced Signal Processing for Nonlinear Receivers

Abstract Views: 59  | 

Authors

E. Shamsudeen
Department of Computer Applications, EMEA College of Arts and Science, India
B. Suganthi
Department of Electronics and Communication Engineering, Dhanalakshmi Srinivasan University, India
P. Ramesh
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India
C. Saravanakumar
Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, India

Abstract


Communication systems face challenges from high-power adjacent channel signals, or blockers, inducing nonlinear behavior in RF front ends. Ensuring robust performance in the presence of blockers is crucial for IoT and other spectrum-consuming devices coexisting with advanced transceivers. This paper proposes a flexible, data-driven solution using a Deep Belief Network (DBN) to mitigate third-order intermodulation distortion (IMD) during demodulation. Numerical evaluations of AI-enhanced receivers employing DBN as an IMD canceler and demodulator show significant improvements in bit error rate (BER) performance. The effectiveness of DBN varies with RF front end characteristics, notably the third-order intercept point (IP3).

Keywords


Deep Belief Network, IMD Cancellation, Nonlinear Receivers, RF Front End, Bit Error Rate