A fundamental challenge in cognitive radio is the detection of primary users in a licensed spectrum. Cooperative sensing, which utilizes multiple receivers distributed across different locations, offers the advantage of utilizing multiple antennas and achieving spatial diversity gain. However, successful implementation of cooperative sensing relies on the ideal exchange of information among cooperating receivers, which may not always be feasible in real-world scenarios. In this paper, we consider the cooperative sensing problem in a non-ideal communication scenario, where the raw data broadcasted from a receiving node can be received by only a subset of the nearby nodes. Existing multiantenna detectors can not deal with such a scenario. To tackle this issue, we propose a novel cooperative sensing scheme, where each node sends only its local correlation coefficients to the fusion center. A detection mechanism based on factorizing the partially received sample covariance matrix is developed. To achieve fast convergence and avoid exhaustive step size tuning, a Bregman proximal method, based on an alternating minimization algorithm (with convergence guarantees), is also developed. The advantages of our proposed cooperative scheme is demonstrated through numerical simulations.

中文翻译：

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非理想通信环境下协作感知的矩阵分解误差比方法


认知无线电的一个基本挑战是检测许可频谱中的主要用户。协作传感利用分布在不同位置的多个接收器，具有利用多个天线并实现空间分集增益的优势。然而，协作感知的成功实施依赖于协作接收器之间理想的信息交换，这在现实场景中可能并不总是可行。在本文中，我们考虑非理想通信场景中的协作感知问题，其中从接收节点广播的原始数据只能被附近节点的子集接收。现有的多天线探测器无法应对这种场景。为了解决这个问题，我们提出了一种新颖的协作感知方案，其中每个节点仅将其本地相关系数发送到融合中心。开发了一种基于分解部分接收的样本协方差矩阵的检测机制。为了实现快速收敛并避免详尽的步长调整，还开发了基于交替最小化算法（具有收敛保证）的 Bregman 近端方法。我们提出的合作方案的优点通过数值模拟得到了证明。