Jul 2026· Al-Nahrain Journal for Engineering Sciences· Vol 29, pp. 260-272· 0 citations· 33 references
TL;DR
This paper proposes an end-to-end AI-optimized WuR stack that combines a TinyML classifier embedded in the WuR path to suppress false triggers and adapt detection thresholds, and a reinforcement-learning scheduler at the base station or gateway that co-optimizes wake-up signaling with 3GPP NR DRX/C-DRX timers.
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%.
Battery-free sensors expose short, stochastic communication windows and may lose timing state whenever their main energy domain browns out. MagPie combines a microampere wake-up radio (WuR) with a separately backed low-power real-time clock (LP-RTC): the WuR widens the first-contact window, while the LP-RTC preserves t...
IEEE 802.11ax Target Wake Time (TWT) coordinates station sleep and wake schedules for power savings, while RF energy harvesting promises battery-free operation for IoT sensors. Though studied in isolation, the two are coupled; the same access point (AP) that schedules TWT service periods, controls the RF harvest of bat...
A. Maksud, Marcelo M. Carvalho· Proceedings of the 21st Work...· 0 citations
Internet of Things (IoT) devices operating in remote, rural, or disaster-affected areas often suffer from blocked or weak connections to terrestrial Base Stations (BSs). This paper proposes a High Altitude Platform Stations (HAPSs)-assisted uplink architecture in which a passive Reconfigurable Intelligent Surface (RIS)...
Alissa Nauman, Tahir Khurshaid, S. Rhee· Electronics· 0 citations
As the demand for high-speed, low-latency connectivity escalates, fifth generation (5G) and emerging sixth generation (6G) networks face significant challenges in managing energy consumption while maintaining performance standards. This paper investigates the application of Reinforcement Learning (RL) for dynamic energ...
C. Katsigiannis, Konstantinos Tsachrelias, V. Kokkinos et al.· International Symposium on N...· 0 citations
Vehicular Sensor Networks (VSNs) represent a critical pillar of the Internet of Vehicles (IoV), enabling continuous Vehicle-to-Everything (V2X) communication for Intelligent Transportation Systems (ITS), autonomous driving, and road safety. However, high mobility and dense vehicle deployments in 5G-enabled environments...
These findings validate that embedding hierarchical micro-cooperative intelligence directly inside the communication mesh significantly enhances adaptability without increasing computational load.
A. Shenbagarajan, G. Shenbagalakshmi· International Journal of Com...· 0 citations
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