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In the field of wireless communications, energy optimization of mobile devices with limited resources has become a critical challenge, especially when precise user position tracking is required. Traditional solutions, such as Kalman filters or fingerprinting techniques, often present trade-offs between accuracy and battery consumption. A recent innovation in this field combines reconfigurable intelligent surfaces (RIS) with a deep learning approach based on two agents, integrating hybrid neuroevolution and supervised learning. This framework enables real-time control of both the phase of the RIS elements and the user's transmission power, using a single-bit feedback link to minimize overhead. Results show superior tracking accuracy compared to classical methods, even in scenarios with complex mobility.
From a business perspective, this technology has direct applications in fleet management, industrial IoT devices, and indoor localization systems. For companies looking to implement artificial intelligence for businesses solutions, the dual-agent and distributed optimization approach represents a model of how advanced algorithms can be combined with low-cost hardware. The key lies in designing custom applications that integrate sensors, RIS, and prediction models, something that companies like Q2BSTUDIO address from custom software to integration with cloud platforms. Additionally, cybersecurity plays a fundamental role in protecting control links and location data, so having specialized cybersecurity services is essential.
The hybridization of neuroevolution with supervision allows overcoming the non-differentiability of discrete RIS phases, a problem that in real environments is solved using AWS and Azure cloud services to train large-scale models. Likewise, business intelligence services such as Power BI can visualize estimated trajectories in real time and alert about deviations. Q2BSTUDIO precisely offers that layer of AI for businesses and AI agents that automate decision-making based on tracking data. Ultimately, the convergence of RIS, hybrid learning, and decentralized control opens a new path for efficient localization applications, and its successful implementation depends on a development ecosystem that integrates AWS and Azure cloud services, as well as custom applications that adapt the technology to each use case.
