Distributed Remote Estimation Over the Collision Channel With and Without Local Communication
Distributed Remote Estimation Over the Collision Channel With and Without Local Communication
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DOI：
10.1109/tcns.2021.3100405
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发表时间：
2020-05
影响因子：
4.2
通讯作者：
Xu Zhang;M. Vasconcelos;Wei Cui;U. Mitra
中科院分区：
文献类型：
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作者：
Xu Zhang;M. Vasconcelos;Wei Cui;U. Mitra
物联网网络是由大量简单设备组成的大规模分布式系统，通常通过共享的无线介质进行通信。这一新的模式需要新的方法来协调对有限通信资源的访问，而不会引入不合理的延迟。在这里，考虑了具有$n$传感器通过有限容量的碰撞信道与融合中心通信的远程估计系统的最优设计。特别地，对于具有对称概率密度函数的独立同分布观测值，我们证明了最小化关于阈值策略的均方误差的问题是拟凸的。当传感器之间通过本地通信网络进行协调时，可以在线学习概率模型中可能未知的参数，使每个传感器能够自主优化自己的阈值。我们提出了两种本地通信的远程估计策略：1)快速达到最优分散门限策略的性能；2)以较慢的收敛速度接近最优集中式策略的性能。提出了一种结合了这两种方法优点的混合算法，具有收敛速度快、性能好的特点。
Internet of Things networks are the large-scale distributed systems consisting of a massive number of simple devices communicating, typically, over a shared wireless medium. This new paradigm requires novel ways of coordinating access to limited communication resources without introducing unreasonable delays. Herein, the optimal design of a remote estimation system with $n$ sensors communicating with a fusion center via a collision channel of limited capacity $k\leq n$ is considered. In particular, for independent and identically distributed observations with a symmetric probability density function, we show that the problem of minimizing the mean-squared error with respect to a threshold strategy is quasi-convex. When coordination among sensors via a local communication network is available, the online learning of possibly unknown parameters of the probabilistic model is possible, enabling each sensor to optimize its own threshold autonomously. We propose two strategies for remote estimation with local communication: 1) one strategy swiftly reaches the performance of the optimal decentralized threshold policy and 2) the second strategy approaches the performance of the optimal centralized scheme with a slower convergence rate. A hybrid scheme that combines the best of both approaches is proposed, offering fast convergence and excellent performance.