| 摘要: |
| 为进一步提升频谱效率及缓解用户间干扰，研究了基于非正交多址接入（NOMA）技术的无人机（UAV）通信与感知一体化（ISAC）系统，构建了在通信和感知任务下的无人机飞行场景.在该场景中，无人机作为空中双功能接入平台，配备垂直安装的均匀线性阵列（ULA），通过NOMA技术发送叠加信号，以实现多用户通信与地面目标感知的双重功能.通过联合优化，设计无人机飞行轨迹和发射波束成形，在满足飞行起始点、终点、飞行最大速率和发射功率的约束下，实现通信用户吞吐量与目标有效感知功率的加权和最大化.引入基于对数函数异步学习因子的改进粒子群（IPSO）算法进行最优化求解.仿真结果表明，相较于采用正交多址接入（OMA）技术，使用NOMA技术的系统能够获得更大的目标函数值，所采用的IPSO算法较传统粒子群（PSO）算法也更具性能优势. |
| 关键词: 通信与感知一体化（ISAC） 非正交多址接入（NOMA）技术 无人机（UAV） 轨迹优化 波束成形设计 |
| DOI：10.20192/j.cnki.JSHNU（NS）.2025.02.007 |
| 分类号:TN929.5 |
| 基金项目: |
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| Joint trajectory and beamforming design based on integrated sensing and communication for NOMA-enabled UAV |
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Lü Yunxin, SU Ying, ZHANG Jing
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College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China
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| Abstract: |
| To further enhance spectral efficiency and alleviate interference among users， an unmanned aerial vehicle （UVA）-based communication and sensing integrated system utilizing non-orthogonal multiple access （NOMA） technology was investigated. A flight scenario was established for UAVs performing both communication and sensing tasks. In this scenario， the UAV was designed as an aerial dual-function access point， equipped with a vertically mounted uniform linear array （ULA）， and NOMA technology was utilized to transmit superposed signals， in order to support multi-user communication and ground target sensing simultaneously. By jointly optimizing the UAV’s flight trajectory and beamforming design， the weighted sum of communication user throughput and target sensing power were maximized， under the constraints of flight starting and ending points， maximum flight speed and transmission power. The optimization was carried out using an improved particle swarm optimization （IPSO） algorithm， which incorporated asynchronous learning factors based on a logarithmic function. Simulation results indicated that higher objective function value was achieved by use of NOMA technology compared to orthogonal multiple access （OMA）， and the IPSO algorithm demonstrated superior performance over the conventional particle swarm optimization（PSO） algorithm. |
| Key words: integrated sensing and communication （ISAC） nonorthogonal multiple access （NOMA） technology unmanned aerial vehicle （UAV） trajectory optimization beamforming design |
