面向能量受限分布式估计的多跳扩散 LMS
Multi-hop Diffusion LMS for Energy-constrained Distributed Estimation
AI总结：
本文提出多跳扩散 LMS 策略，通过多跳中继组合估计、权重选择与信息邻域优化，在局部及全网能量约束下实现与单跳 ATC 扩散相同的稳态 MSD 且能耗更低。
摘要：
我们提出了一种用于传感器网络的多跳扩散策略，以在局部和全网能量约束下执行分布式最小均方（LMS）估计。在该策略的每次迭代中，每个节点都可以通过多跳中继路径，组合来自其物理邻居以外节点的中间参数估计。我们提出了一条为多跳邻居选择组合权重的规则，该规则能够在瞬态与稳态网络均方偏差（MSD）之间取得平衡。我们研究了两类网络：从一个节点到另一个节点具有唯一传输路径的简单网络，以及利用至多两跳扩散协商的任意网络。我们提出了一种方法，在每次扩散迭代受到局部能量预算和全网能量预算约束的情况下，优化每个节点的信息邻域。该优化需要网络拓扑以及每个节点的噪声和数据方差分布，并在扩散过程开始前离线执行。此外，我们开发了一种完全分布式且自适应的算法；在扩散协商至多经过预定义跳数执行的情况下，该算法仅利用局部能量预算约束，近似优化每个节点的信息邻域。数值结果表明，我们提出的多跳扩散策略能够达到与现有单跳先适应后组合（adapt-then-combine）扩散算法相同的稳态 MSD，同时所需能量预算更低。
英文摘要：
We propose a multi-hop diffusion strategy for a sensor network to perform distributed least mean-squares (LMS) estimation under local and network-wide energy constraints. At each iteration of the strategy, each node can combine intermediate parameter estimates from nodes other than its physical neighbors via a multi-hop relay path. We propose a rule to select combination weights for the multi-hop neighbors, which can balance between the transient and the steady-state network mean-square deviations (MSDs). We study two classes of networks: simple networks with a unique transmission path from one node to another, and arbitrary networks utilizing diffusion consultations over at most two hops. We propose a method to optimize each node's information neighborhood subject to local energy budgets and a network-wide energy budget for each diffusion iteration. This optimization requires the network topology, and the noise and data variance profiles of each node, and is performed offline before the diffusion process. In addition, we develop a fully distributed and adaptive algorithm that approximately optimizes the information neighborhood of each node with only local energy budget constraints in the case where diffusion consultations are performed over at most a predefined number of hops. Numerical results suggest that our proposed multi-hop diffusion strategy achieves the same steady-state MSD as the existing one-hop adapt-then-combine diffusion algorithm but with a lower energy budget.
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