低空无线网络中基于NOMA的ISAC用户-目标配对

中文摘要 AI 辅助

本文提出了一种面向低空无线网络（LAWN）的基于非正交多址接入（NOMA）的集成感知与通信（ISAC）框架，其中空中接入点同时服务地面用户并感知空中目标。为了提高通信效率和感知可靠性，提出了一种基于匹配的用户-目标配对（MUTP）方案，该方案首先将强信道和弱信道的NOMA用户配对成NOMA组，然后根据感知感知配对准则将其分配给感知目标。此外，开发了一种自适应功率分配（APA）策略，以平衡通信服务质量要求和目标检测性能。在固定功率分配和APA两种情况下，将所提出的MUTP-APA方案与固定贪婪配对、穷举搜索配对以及传统ISAC基准方案进行了比较。仿真结果表明，与NOMA-ISAC LAWN中的传统ISAC和固定配对基线相比，所提出的方案实现了更高的总速率、更好的最差目标检测概率，以及更低的通信和感知中断概率。此外，所提出的方法在避免高计算复杂度的同时，紧密接近穷举搜索基准的性能。

英文摘要

This paper presents a non-orthogonal multiple access (NOMA)-enabled integrated sensing and communication (ISAC) framework for low-altitude wireless networks (LAWN), where an aerial access point simultaneously serves ground users and senses aerial targets. To improve both communication efficiency and sensing reliability, a matching-based user-target pairing (MUTP) scheme is proposed, in which strong and weak channel NOMA users are first paired into NOMA groups and then assigned to sensing targets according to a sensing-aware pairing criterion. Furthermore, an adaptive power allocation (APA) strategy is developed to balance communication quality-of-service requirements and target detection performance. The proposed MUTP-APA scheme is compared with fixed greedy pairing, exhaustive search pairing, and conventional ISAC benchmark schemes under both fixed power allocation and APA. Simulation results show that our proposed scheme achieves higher sum rate, improved worst-target detection probability, and lower communication and sensing outage probabilities compared with conventional ISAC and fixed pairing baselines in NOMA-ISAC LAWN. Moreover, the proposed method closely approaches the exhaustive search benchmark while avoiding the high computational complexity.

发表机构

• Polytechnique Montreal（蒙特利尔高等理工学院）

机构由 AI 辅助整理，请以论文原文为准。