Machine Learning–Guided Cognitive RF System with Dynamic FFT Resolution and Multiplier Reconfiguration for Adaptive Anti-Jamming Communication

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 01 | Page :
    By

    Soham Das,

  • A S Rassel,

  1. Student, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  2. Student, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Abstract

This paper presents a hierarchical adaptive RF communication system that integrates signal quality-based pre- processing with machine learning-driven signal classification to achieve robust and resource-efficient operation in dynamic, interference-prone environments. Unlike prior art that addresses adaptive RF, ML classification, or anti-jamming individually, this work uniquely combines real-time SNR/RSSI-based signal strength estimation with dynamic FFT size selection (64-, 256- , or 512-point) and arithmetic-level multiplier reconfiguration (CORDIC, Distributed Arithmetic, and hybrid Booth-Wallace) prior to ML inference. A quantized 1D-CNN model deployed on an MCU classifies signals into normal, narrowband jammer, wideband jammer, or noise categories, and triggers adaptive notch filtering when jamming is detected. Experimental results on an SX-1278 LoRa-based hardware prototype demonstrate signifi- cant BER reduction under low-SNR conditions, high classification accuracy across all signal classes, and effective jammer frequency attenuation, with real-time prediction latencies as low as 1.2 ms. For low-power embedded deployments, the suggested architecture minimises hardware resource utilisation, lowers computing complexity, and further improves spectral efficiency. Furthermore, the adaptive communication architecture strengthens resistance to spectrum congestion, channel interference, and signal distortion, which makes it ideal for real-time adaptive RF monitoring applications in challenging operating environments, IoT security, and next-generation intelligent wireless communication with increased scalability and dependability.

Keywords: Adaptive RF Communication, Anti-Jamming, FFT Reconfiguration, Cognitive Radio, Machine Learning, Booth- Wallace Multiplier, CORDIC, Distributed Arithmetic, 1D- CNN, Embedded DSP

How to cite this article:
Soham Das, A S Rassel. Machine Learning–Guided Cognitive RF System with Dynamic FFT Resolution and Multiplier Reconfiguration for Adaptive Anti-Jamming Communication. International Journal of Radio Frequency Innovations. 2026; 04(01):-.
How to cite this URL:
Soham Das, A S Rassel. Machine Learning–Guided Cognitive RF System with Dynamic FFT Resolution and Multiplier Reconfiguration for Adaptive Anti-Jamming Communication. International Journal of Radio Frequency Innovations. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijrfi/article=2026/view=245155


References

  1. Shamayleh A, Awad M, Farhat J. IoT based predictive maintenance management of medical equipment. Journal of medical systems. 2020 Apr;44(4):72.
  2. Mpitziopoulos A, Gavalas D, Konstantopoulos C, Pantziou G. A survey on jamming attacks and countermeasures in WSNs. IEEE communications surveys & tutorials. 2009 Dec 1;11(4):42-56.
  3. Davaslioglu K, Soltani S, Erpek T, Sagduyu YE. DeepWiFi: Cognitive WiFi with deep learning. IEEE Transactions on Mobile Computing. 2019 Oct 28;20(2):429-44.
  4. De Lorenzo DS, Lo SC, Enge PK, Rife J. Calibrating adaptive antenna arrays for high-integrity GPS. GPS solutions. 2012 Apr;16(2):221-30.
  5. Thomas Jr DD, inventor; SRC Inc, assignee. Architecture for multi- channel digital signal processing. United States patent US 7,430,258. 2008 Sep 30.
  6. Rappaport TS. Wireless Communications–Principles and Practice, (The Book End). Microwave Journal. 2002 Dec 1;45(12):128-9.
  7. Haykin S. Cognitive radio: brain-empowered wireless communications. IEEE journal on selected areas in communications. 2005 Feb 7;23(2):201-20.
  8. O’shea T, Hoydis J. An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking. 2017 Oct 2;3(4):563-75.
  9. Clarkson PM. Optimal and adaptive signal processing. Routledge; 2017 Nov 1.
  10. Etter DM. An introduction to adaptive signal processing. Computers & electrical engineering. 1992 May 1;18(3-4):189-93.
Ahead of Print Subscription Review Article
Volume 04
01
Received 22/05/2026
Accepted 25/05/2026
Published 27/05/2026
Publication Time 5 Days
1

Login


My IP

PlumX Metrics