量子辅助的赋能采集共生无线网络中的活跃设备检测

中文摘要 AI 辅助

下一代网络中的大规模连接要求为大规模物联网（IoT）部署提供能量和频谱高效的解决方案。共生无线电（SR）使无源IoT设备能够通过反向散射现有的蜂窝传输进行通信。上行链路SR中的一个关键挑战是活跃设备检测（ADD），它直接影响解码可靠性、干扰管理和系统吞吐量。我们提出了一种能量采集码域非正交多址接入（NOMA）-SR系统，其中IoT设备从环境上行链路信号中采集能量，并使用低密度扩频（LDS）码反向散射信息。为了降低ADD的复杂度，采用了Grover量子搜索算法，与穷举最大似然（ML）搜索相比，在预言机查询复杂度上实现了二次方减少。数值结果表明，所提出的方法在显著减少搜索迭代次数的同时，紧密接近ML性能，展示了其在可扩展环境IoT系统中的潜力。

英文摘要

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.

发表机构

• Polytechnique Montréal（蒙特利尔理工学院）

机构由 AI 辅助整理，请以论文原文为准。