Remote monitoring of two-state Markov sources in random access channels: Joint model and state estimation

Abstract

This paper addresses the problem of remote monitoring of two-state Markov sources via a slotted ALOHA random access channel, where the source statistics are not known a priori to the receiver. We develop a joint model and state estimation method using the Baum-Welch algorithm for two different transmission strategies. In the first strategy, the nodes transmissions are independent of the underlying state evolution process (random policy). In the second strategy, the nodes transmit an update only upon observing a state transition (reactive policy). We show that the reactive approach is beneficial not only in terms of reducing the state estimation error probability (a result that was recently established under perfect knowledge of the source statistics), but that it allows a faster learning of the source statistics.

H. Asgari acknowledges the financial support of the Munich Aerospace scholarship within the group “Multi-access and Security Coding for Massive IoT Satellite Systems”. A. Munari and G. Liva acknowledge the financial support by the Federal Ministry of Education and Research of Germany in the programme of “Souver an. Digital. Vernetzt.” Joint project 6G-RIC, project identification number: 16KISK022. G. Cocco acknowledges the financial support by the Ramon y Cajal fellowship program, grant RYC2021-033908-I, funded by the Spanish Ministry of Science and Innovation through MCIN/AEI/10.13039/501100011033 and by the European Union through the “NextGenerationEU” Recovery Plan for Europe.

Peer Reviewed

Postprint (author's final draft)