Isabel de-la-Bandera
Papers
1
Total Citations
6
H-Index
1
About
Isabel de-la-Bandera is a leading researcher in wireless communications, with a focus on resource allocation and intelligent network management for next-generation industrial systems. Her work lies at the intersection of deep reinforcement learning (DRL) and Industry 4.0, where she addresses critical challenges in device-to-device (D2D) communication within complex, infrastructure-limited environments. Her most cited paper, "Distributed Deep Reinforcement Learning Resource Allocation Scheme For Industry 4.0 Device-To-Device Scenarios" (2021, 6 citations), introduces a novel DRL-based methodology that enables autonomous mobile robots (AMRs) to independently manage radio resources in indoor factories without relying on centralized network infrastructure. By leveraging deep neural networks (DNNs) to optimize decision-making policies, de-la-Bandera’s work empowers robots to dynamically allocate spectrum, enhancing both reliability and efficiency in real-time industrial operations. This contribution is particularly impactful for the evolution of smart manufacturing, where seamless, low-latency communication is essential. Her research not only advances the theoretical understanding of distributed intelligence in wireless systems but also provides practical frameworks for scalable, self-organizing networks. With a growing citation record, de-la-Bandera continues to shape the future of autonomous, AI-driven communication in industrial settings.
Research Focus
Key Achievements
Top Papers
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