在有和没有本地通信的冲突信道上的分布式远程估计
Distributed remote estimation over the collision channel with and without local communication
摘要：
物联网和网络物理系统的出现需要以自主和分布式方式协调对有限通信资源的访问。本文考虑了具有n个传感器的无线传感系统的最优设计问题，其中n个传感器通过有限容量k（k < n）的冲突信道与融合中心通信。特别是，它示出的问题，最小化的均方误差受到基于阈值的策略在发射机是准凸的。因此，可以应用低复杂度的数值优化方法。当传感器之间的协调是不可能的，最优阈值策略的性能接近的集中下限。由于分散化的损失是彻底的特点。传感器之间的本地通信（使用稀疏连接图），使在线学习的统计模型的未知参数。这些学习的参数被用来本地和自主地计算期望的阈值。研究和分析了基于神经网络的参数估计策略。一种策略接近分散式方法的性能，具有快速收敛性，第二种策略接近集中式方法的性能，尽管收敛较慢。提出了一种结合两种方法优点的混合算法，具有收敛速度快、收敛性能好的特点。
英文摘要：
The emergence of the Internet-of-Things and cyber-physical systems necessitates the coordination of access to limited communication resources in an autonomous and distributed fashion. Herein, the optimal design of a wireless sensing system with n sensors communicating with a fusion center via a collision channel of limited capacity k (k < n) is considered. In particular, it is shown that the problem of minimizing the mean-squared error subject to a threshold-based strategy at the transmitters is quasi-convex. As such, low complexity, numerical optimization methods can be applied. When coordination among sensors is not possible, the performance of the optimal threshold strategy is close to that of a centralized lower bound. The loss due to decentralization is thoroughly characterized. Local communication among sensors (using a sparsely connected graph), enables the on-line learning of unknown parameters of the statistical model. These learned parameters are employed to compute the desired thresholds locally and autonomously. Consensus-based strategies are investigated and analyzed for parameter estimation. One strategy approaches the performance of the decentralized approach with fast convergence and a second strategy approaches the performance of the centralized approach, albeit with slower convergence. A hybrid scheme that combines the best of both approaches is proposed offering a fast convergence and excellent convergent performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
