通过源感知分配实现异构无线传感器网络的功率降低
Power Reduction in Heterogeneous Wireless Sensor Networks via Source-Aware Allocation
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中文总结 AI 辅助
针对异构无线传感器网络在资源受限衰落信道传输模拟信号的问题，利用Renyi信息维度，结合率失真理论和香农信道容量，得出闭式SNR下界，引入跨层资源分配框架，实现功率节省并保证重建质量和中断约束。
中文摘要 AI 辅助
在空间和极端环境中的异构无线传感器网络（HWSN）必须在资源受限的衰落信道上可靠地传输各种模拟物理信号，同时受带宽限制、功率预算和重建质量要求的约束。本文解决两个基本问题：（i）无论使用何种解码器，传感链路必须维持的最小信噪比（SNR）是多少才能在规定失真下重建模拟信号；（ii）如何利用信号的内在结构知识在HWSN中联合分配功率和带宽。通过Renyi信息维度（RID）回答了这两个问题，它量化了模拟源分布的内在复杂性。通过将RID与率失真理论和香农信道容量相结合，得出了一个仅由源RID参数化的闭式SNR下界。在此基础上，引入了一个跨层资源分配框架，利用每个节点的RID联合分配发射功率和带宽，相对于高斯假设基线实现了严格的功率节省，同时保证每个节点的规定重建质量和中断约束。
英文摘要
Heterogeneous wireless sensor networks (HWSNs) in space and extreme environments must reliably transmit diverse analog physical signals over resource-constrained fading channels, subject to bandwidth limitations, power budgets, and reconstruction quality requirements. This paper addresses two fundamental questions: (i) what is the minimum signal-to-noise ratio (SNR) a sensing link must sustain to reconstruct an analog signal at a prescribed distortion, regardless of the decoder used, and (ii) how can knowledge of the signal's intrinsic structure be exploited to jointly allocate power and bandwidth across an HWSN? Both questions are answered through the Renyi information dimension (RID), which quantifies the intrinsic complexity of an analog source distribution. By combining the RID with rate-distortion theory and Shannon channel capacity, a closed-form SNR lower bound is derived, parameterized solely by the source RID. Building on these foundations, a cross-layer resource allocation framework is introduced that exploits the per-node RID to jointly assign transmit power and bandwidth, achieving strict power saving relative to a Gaussian-assumption baseline while guaranteeing prescribed reconstruction quality and outage constraints at every node.
