Over-the-Air Statistical Estimation
This addresses the problem of efficient data aggregation in wireless sensor networks, offering a novel approach that is not incremental but builds on combining estimation and communication.
The paper tackles distributed statistical estimation over a wireless channel, showing that analog schemes leveraging the physical layer reduce estimation error drastically compared to digital methods, achieving risk within a logarithmic factor of lower bounds.
We study schemes and lower bounds for distributed minimax statistical estimation over a Gaussian multiple-access channel (MAC) under squared error loss, in a framework combining statistical estimation and wireless communication. First, we develop "analog" joint estimation-communication schemes that exploit the superposition property of the Gaussian MAC and we characterize their risk in terms of the number of nodes and dimension of the parameter space. Then, we derive information-theoretic lower bounds on the minimax risk of any estimation scheme restricted to communicate the samples over a given number of uses of the channel and show that the risk achieved by our proposed schemes is within a logarithmic factor of these lower bounds. We compare both achievability and lower bound results to previous "digital" lower bounds, where nodes transmit errorless bits at the Shannon capacity of the MAC, showing that estimation schemes that leverage the physical layer offer a drastic reduction in estimation error over digital schemes relying on a physical-layer abstraction.