We address the problem of real-time remote tracking of a partially observable Markov source in an energy harvesting system with an unreliable communication channel. We consider both sampling and transmission costs. Different from most prior studies that assume the source is fully observable, the sampling cost renders the source partially observable. The goal is to jointly optimize sampling and transmission policies for two semantic-aware metrics: i) a general distortion measure and ii) the age of incorrect information (AoII). We formulate a stochastic control problem. To solve the problem for each metric, we cast a partially observable Markov decision process (POMDP), which is transformed into a belief MDP. Then, for both AoII under the perfect channel setup and distortion, we express the belief as a function of the age of information (AoI). This expression enables us to effectively truncate the corresponding belief space and formulate a finite-state MDP problem, which is solved using the relative value iteration algorithm. For the AoII metric in the general setup, a deep reinforcement learning policy is proposed to solve the belief MDP problem. Simulation results show the effectiveness of the derived policies and, in particular, reveal a non-monotonic switching-type structure of the real-time optimal policy with respect to AoI.

This paper provides query-age-optimal joint sampling and transmission scheduling policies for a heterogeneous status update system, consisting of a stochastic arrival and a generate-at-will source, with an unreliable channel. Our main goal is to minimize the average query age of information (QAoI) subject to average sampling, average transmission, and per-slot transmission constraints. To this end, an optimization problem is formulated and solved by casting it into a linear program. We also provide a low-complexity near-optimal policy using the notion of weakly coupled constrained Markov decision processes.

The paper considers a multi-source relaying status update system with stochastic arrivals and develops three scheduling policies to minimize the sum average age of information subject to transmission capacity and long-run average resource constraints. Namely, we provide these policies under two different scenarios regarding the knowledge of system statistics: known environment and unknown environment. For the known environment, a constrained Markov decision process approach and a drift-plus-penalty method are proposed, and for the unknown environment, a deep learning policy is developed. The structure of an optimal policy is analyzed, and simulation results are provided.