aiqbench analysis
This research applies Grover's quantum search algorithm to solve active device detection in symbiotic radio networks, offering a quadratic speedup over classical maximum-likelihood methods. By reducing the number of required iterations for identifying IoT devices, the approach significantly lowers computational complexity while maintaining decoding reliability in energy-harvesting systems. The study demonstrates a concrete intersection where quantum algorithms directly enhance the scalability and efficiency of next-generation wireless communication protocols.
Quantum-Aided Active Device Detection in Energy-Harvesting Symbiotic Radio Networks
arXiv:2609.26565v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2609.26565 [cs.AI]
(or arXiv:2609.26565v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.26565 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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