Identification via channels (ID) is a goal-oriented (Post-Shannon) communications paradigm that verifies the matching of message (identity) pairs at source and sink. To date, ID research has focused on the upper bound$\lambda $for the probability of a false-positive (FP) identity match, mainly through ID tagging codes that represent the identities through ID codeword sets consisting of position-tag tuples. We broaden the ID research scope by introducing novel ID performance metrics: the expected FP-error probability$\overline {p_{\mathrm {fp}}}$which considers distance properties of ID codeword sets in conjunction with the probability for selecting ID pairs, the threshold probabilities$p_{\epsilon }$that characterize quantiles of FP-probabilities, and the distance tail uplift ratio DiTUR giving the fraction of ID pairs whose distance is increased above the minimum distance (which corresponds to$\lambda $). We define a No-Code (NC) approach that directly conducts the ID operations with the messages (identities) without any additional coding as a baseline for ID. We investigate a concatenated Reed-Solomon ID code and a Reed-Muller ID code, and find that they do not always yield advantages over using no ID code. We analytically characterize the reduction of error-prone ID pairs through sending multiple tags. Overall, our insights point to investigating the distance distribution of ID codes and to incorporating the ID pair distributions of real ID systems in future ID research.

Digital Twin (DT) implementations can reduce latency drastically in future communication systems for steering and control of cyber-physical systems (CPS). When the CPS and its DT periodically exchange information to repair possible desynchronisations between the two systems, this can lead to large data traffic flooding the networks. This work proposes a goal-oriented communication approach based on message identification (ID), which only sends repair data when necessary, but adds control traffic and a probability of not repairing some desynchronisations between the two systems. We compare optimal ID codes and a hash function for detection whether desynchronisation correction is necessary. Our method can reduce the traffic to within 0.3% of its optimal value while repairing 99.97% of all desynchronisations for 4 kbit of data and grows even better for larger data.