Over-the-Air Collaborative Learning in Joint Decision Making
Over-the-Air Collaborative Learning in Joint Decision Making
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DOI：
10.1109/globecom48099.2022.10001286
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Carlos Feres;B. Levy;Z. Ding
中科院分区：
文献类型：
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作者：
Carlos Feres;B. Levy;Z. Ding
提出了一种无线传感器网络协同决策的空中学习框架。低复杂性框架利用决策服务器的低延迟传感器传输，通过多路访问通道上的传感器数据的空中聚合来协调用于假设检验的测量传感器。我们在不同的实用协议下构造了几个协作空中假设检验问题，用于协作学习和决策。我们为这些网络协议和部署场景(包括信道衰落)开发假设测试。给出了基本似然比检验和广义似然比检验在不同部署条件下的性能基准。我们的结果清楚地证明了增加协作传感器的数量所带来的收益。
We propose an over-the-air learning framework for collaborative decision making in wireless sensor networks. The low complexity framework leverages low-latency sensor transmission for a decision server to coordinate measurement sensors for hypothesis testing through over-the-air aggregation of sensor data over a multiple-access channel. We formulate several collaborative over-the-air hypothesis testing problems under different practical protocols for collaborative learning and decision making. We develop hypothesis tests for these network protocols and deployment scenarios including channel fading. We provide performance benchmark for both basic likelihood ratio test and generalized likelihood ratio test under different deployment conditions. Our results clearly demonstrate gain provided by increasing number of collaborative sensors.