在NOMA辅助的ISAC系统中,联合束形设计和用户集群算法
Qingqing Yang1,2, Runpeng Tang1,2, Yi Peng1,2
1School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
本研究介绍了一种改进的高斯混合模型 (GMM) 用户集群算法,用于非直角多重访问 (NOMA) 辅助的集成传感和通信 (ISAC) 系统. 改进的算法提高了光谱效率和光束强度,超过了传统的ISAC系统.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 信息理论是信息理论.
背景情况:
- 非对角多重接入 (NOMA) 提高了无线系统的频谱效率.
- 综合传感和通信系统 (ISAC) 结合了传感和通信功能.
- 分布式多用户场景在优化NOMA-ISAC性能方面存在挑战.
研究的目的:
- 为NOMA-ISAC系统开发一个改进的基于高斯混合模型 (GMM) 的用户集群算法.
- 为了增强带宽的再利用,并减少分散的NOMA-ISAC场景中的干扰.
- 为了共同优化传感和通信信号性能.
主要方法:
- 一个改进的GMM用户集群算法,包含二次错误总和 (SSE) 以提高集群准确性.
- 引入方向权重因子和基于用户位置和传感功率的惩罚函数.
- 对预期-最大化 (EM) 算法的修改,以改进后置概率和共变矩阵计算.
- 分数编程 (FP) 的应用,以解决非凸的联合束形设计.
主要成果:
- 改进的GMM算法提高了集群精度,并降低了对初始集群中心的敏感性.
- 用户集群更好地与NOMA-ISAC系统特征保持一致,改善干扰抵抗和解码.
- FP方法优化了功率和通道收益,以共同优化传感和通信.
- 模拟显示,用户光谱效率提高了4.3%,基站光束强度增加了5.4%.
结论:
- 拟议的基于GMM的用户集群和FP优化显著提高了NOMA-ISAC系统的性能.
- 该算法有效地应对分布式多用户环境中的挑战.
- 这项工作为推进NOMA-ISAC技术提供了一个强大的框架.
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