A Unified AI-Driven Signal Processing and Machine Learning Optimization Framework for Energy-Efficient Smart Wearable Devices in 5G/6G Communication Systems

Abstract

As smart wearable devices advance at breakneck speed in 5G and upcoming 6G communication settings, there exists a need for highly reliable, low-latency, and power-efficient signal processing schemes. In this paper, we present an artificial intelligence (AI)-based solution for optimizing wearable signals using signal processing and machine learning (ML). Experimental results suggest that ML-driven optimization may be capable of increasing signal-to-noise ratio by 18–25%, decreasing inference latency to less than 5 ms, and improving energy consumption up to 30–45% relative to traditional signal processing solutions. Additionally, throughput in wearables increases from 9 Gbps in traditional 5G communication systems to 20–45 Gbps in AI-assisted 6G systems with a packet delivery rate greater than 97–98% even when densely mobile. The proposed scheme also shows better performance for adapting to issues related to millimeter wave and terahertz bands. This implies that AI-assisted signal processing is very critical in wearable systems in terms of performance and scalability, which are crucial characteristics of next-generation 5G/6G intelligent healthcare and Internet of Things (IoT) technologies.



Received: 14 April 2026 | Revised: 1 June 2026 | Accepted: 17 June 2026



Conflicts of Interest

The author declares that he has no conflicts of interest to this work.



Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.



Author Contribution Statement

Olarewaju Peter Ayeoribe: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.