  Wireless sensing has been recognized as a key enabling technology for
numerous emerging applications. For decades, the sensing performance was mostly
evaluated from a reliability perspective, with the efficiency aspect widely
unexplored. Motivated from both backgrounds of rate-distortion theory and
optimal sensing waveform design, a novel efficiency metric, namely, the sensing
estimation rate (SER), is defined to unify the information- and estimation-
theoretic perspectives of wireless sensing. Specifically, the active sensing
process is characterized as a virtual lossy data transmission through
non-cooperative joint source-channel coding. The bounds of SER are analyzed
based on the data processing inequality, followed by a detailed derivation of
achievable bounds under the special cases of the Gaussian linear model (GLM)
and semi-controllable GLM. As for the intractable non-linear model, a
computable upper bound is also given in terms of the Bayesian Cram\'er-Rao
bound (BCRB). Finally, we show the rationality and effectiveness of the SER
defined by comparing to the related works.

Signal Processing