基于消息传递的协作MIMO-OFDM ISAC系统参数估计
Message Passing based Parameter Estimation in Cooperative MIMO-OFDM ISAC Systems
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中文总结 AI 辅助
本文提出一种基于消息传递的参数估计方法，用于协作MIMO-OFDM ISAC系统，通过多视角观测和联合信号处理提高感知性能。
中文摘要 AI 辅助
在集成感知与通信（ISAC）网络中，多个基站（BSs）协同感知共同目标，利用多个观测视角的多样性以及联合信号处理来提高感知性能。本文介绍了一种新的基于消息传递（MP）的参数估计框架，用于协作MIMO-OFDM ISAC系统，共同估计目标的位置和速度。首先，基于几何关系建立信号传播模型，并构建因子图来表示未知参数。然后，将求和-乘积算法（SPA）应用于该因子图以联合估计多维参数向量。为了减少通信开销和计算复杂度，我们采用分层消息传递方案并采用高斯近似。通过采用参数化消息分布和分层处理，所提方法显著降低了计算复杂度和基站间的通信开销。仿真结果展示了所提基于消息传递的参数估计算法的有效性，并突显了多视角观测和联合信号处理在MIMO-OFDM ISAC系统协作感知中的优势。
英文摘要
In integrated sensing and communication (ISAC) networks, multiple base stations (BSs) collaboratively sense a common target, leveraging diversity from multiple observation perspectives and joint signal processing to enhance sensing performance. This paper introduces a novel message-passing (MP)-based parameter estimation framework for collaborative MIMO-OFDM ISAC systems, which jointly estimates the target's position and velocity. First, a signal propagation model is established based on geometric relationships, and a factor graph is constructed to represent the unknown parameters. The sum-product algorithm (SPA) is then applied to this factor graph to jointly estimate the multi-dimensional parameter vector. To reduce communication overhead and computational complexity, we employ a hierarchical message-passing scheme with Gaussian approximation. By adopting parameterized message distributions and layered processing, the proposed method significantly reduces both computational complexity and inter-BS communication overhead. Simulation results demonstrate the effectiveness of the proposed MP-based parameter estimation algorithm and highlight the benefits of multi-perspective observations and joint signal processing for cooperative sensing in MIMO-OFDM ISAC systems.
