Semantic-aware Sampling and Transmission in Energy Harvesting Systems: A POMDP Approach
arxiv(2023)
摘要
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.
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