Vol. 29 No. 2 (2026) Cover Image
Vol. 29 No. 2 (2026)

Published: June 20, 2026

Pages: 260-272

Articles

AI-Optimized Wake-Up Radio Systems for 6G IoT

Ibtihal R. Niama ALRubeei 1 Hussain Ali Mutar 2 Abdul Hadi M. Alaidi 3 Haider TH. Salim ALRikabi 4 Iryna Svyd 5
1 Department of Electrical Engineering, Wasit University, Wasit, Iraq
2 Department of Computer Science, Wasit University, Wasit, Iraq.
3 Department of Computer Science, Wasit University, Wasit, Iraq.
4 Department of Electrical Engineering, Wasit University, Wasit, Iraq.
5 Vasyl Stefanyk Precarpathian National University, The department of Computer Engineering and Electronics, 57 Shevchenko Str., Ivano-Frankivsk, 76018, Ukraine
History
  • Received: September 23, 2025
  • Revised: November 11, 2025
  • Accepted: December 16, 2025
  • Available Online: June 20, 2026
DOI

https://doi.org/10.29194/NJES.29020260

Abstract

Battery life is the hard constraint for massive Internet-of-Things (IoT) at 6G scale. Wake-up radios (WuRs)—ultra-low-power auxiliary receivers that “listen” for short wake-up signals while the main transceiver sleeps—offer orders-of-magnitude energy savings, but suffer from false wake-ups, long tail latency under bursty traffic, and sensitivity/coverage limits. This paper proposes an end-to-end AI-optimized WuR stack that combines (i) a TinyML classifier embedded in the WuR path to suppress false triggers and adapt detection thresholds, and (ii) a reinforcement-learning (RL) scheduler at the base station or gateway that co-optimizes wake-up signaling with 3GPP NR DRX/C-DRX timers. The proposed method, tested using a trace-driven simulator calibrated with published WuR power/latency figures, the approach reduces node-average energy by 41–72% versus strong baselines, while meeting 99% latency targets and cutting false wake-ups by >80%.

Keywords

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