Quantum search boosts device detection in energy harvesting IoT networks
Quantum-Aided Active Device Detection in Energy-Harvesting Symbiotic Radio Networks
Artificial Intelligence
Summary
Many future internet-connected devices need to communicate without using much power or bandwidth. The paper looks at a system where small devices use energy from nearby cellular signals to send messages by reflecting these signals in a special way. Detecting which devices are active at any time is challenging and important. The authors use a quantum search technique to find active devices more efficiently, making detection faster while keeping accuracy high.
What this means in practice
- •For wireless network engineers: Improve detection efficiency of active IoT devices in energy-harvesting backscatter networks using quantum search to reduce computational load without losing accuracy.
- •For quantum algorithm developers: Use quantum search algorithms to speed up device identification tasks in large-scale, low-power communication systems.
Authors
Remon Polus, Deemah Tashman, Soumaya Cherkaoui
Abstract
Massive connectivity in next-generation networks demands energy- and spectrum-efficient solutions for large-scale Internet of Things (IoT) deployments. Symbiotic radio (SR) enables passive IoT devices to communicate by backscattering existing cellular transmissions. A key challenge in uplink SR is active device detection (ADD), which directly affects decoding reliability, interference management, and system throughput. We propose an energy-harvesting code-domain non-orthogonal multiple access (NOMA)-SR system in which IoT devices harvest energy from ambient uplink signals and backscatter information using low-density spreading (LDS) codes. To reduce the complexity of ADD, Grover's quantum search algorithm is employed, providing a quadratic reduction in oracle-query complexity over exhaustive maximum-likelihood (ML) search. Numerical results show that the proposed approach closely approaches ML performance while substantially reducing the number of search iterations, demonstrating its potential for scalable ambient IoT systems.