  There is a broad consensus that artificial intelligence (AI) will be a
defining component of the sixth-generation (6G) networks. As a specific
instance, AI-empowered sensing will gather and process environmental perception
data at the network edge, giving rise to integrated sensing and edge AI (ISEA).
Many applications, such as autonomous driving and industrial manufacturing, are
latency-sensitive and require end-to-end (E2E) performance guarantees under
stringent deadlines. However, the 5G-style ultra-reliable and low-latency
communication (URLLC) techniques designed with communication reliability and
agnostic to the data may fall short in achieving the optimal E2E performance of
perceptive wireless systems. In this work, we introduce an ultra-low-latency
(ultra-LoLa) inference framework for perceptive networks that facilitates the
analysis of the E2E sensing accuracy in distributed sensing by jointly
considering communication reliability and inference accuracy. By characterizing
the tradeoff between packet length and the number of sensing observations, we
derive an efficient optimization procedure that closely approximates the
optimal tradeoff. We validate the accuracy of the proposed method through
experimental results, and show that the proposed ultra-Lola inference framework
outperforms conventional reliability-oriented protocols with respect to sensing
performance under a latency constraint.
