arXiv 2402.142192024-02-22eess.SP

认知无线电网络中的大阵列天线频谱感知

Large Array Antenna Spectrum Sensing in Cognitive Radio Networks

Amirhossein Taherpour, Abbas Taherpour, Tamer Khattab

AI总结：

本文针对配备大阵列天线的认知无线电频谱感知问题，基于随机矩阵理论中的线性谱统计提出三种检测器，推导其理论分布并通过仿真验证其在大规模天线场景下的有效性。

摘要：

我们研究了当接收机配备大阵列天线时认知无线电（CR）中的频谱感知问题。基于随机矩阵理论（RMT）领域中的线性谱统计（LSS）概念，我们提出并推导了三种检测器。这些检测器分别对应于传统多天线频谱感知（MASS）中采用的广义似然比（GLR）检验、Frobenius范数检验和Rao检验。随后，利用适用于高维随机矩阵的中心极限定理（CLT），我们计算了所提检测器在纯噪声假设下的高斯分布。我们评估了所提检测器的性能，并分析了天线数量和样本数量对其效能的影响。此外，我们通过将理论结果与仿真结果进行比较，评估了理论结果的准确性。仿真结果表明，所提检测器在配备大阵列天线的无线网络中表现出高效的性能。这些检测器在多种领域具有实际应用，包括大规模MIMO无线通信、雷达系统和天文应用。

英文摘要：

We investigate the problem of spectrum sensing in cognitive radios (CRs) when the receivers are equipped with a large array of antennas. We propose and derive three detectors based on the concept of linear spectral statistics (LSS) in the field of random matrix theory (RMT). These detectors correspond to the generalized likelihood ratio (GLR), Frobenius norm, and Rao tests employed in conventional multiple antenna spectrum sensing (MASS). Subsequently, we compute the Gaussian distribution of the proposed detectors under the noise-only hypothesis, leveraging the central limit theorem (CLT) applied to high-dimensional random matrices. We evaluate the performance of the proposed detectors and analyze the impact of the number of antennas and samples on their efficacy. Furthermore, we assess the accuracy of the theoretical results by comparing them with simulation outcomes. The simulation results provide evidence that the proposed detectors exhibit efficient performance in wireless networks featuring large array antennas. These detectors find practical applications in diverse domains, including massive MIMO wireless communications, radar systems, and astronomical applications.

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