Dependency task offloading in semantic communications

Abstract

In the realm of advanced wireless communications, the significance of semantic communication has become increasingly pronounced, particularly in its focus on the meaning and efficiency of information transmission. Given the voluminous nature of task data and the intricacy of task interconnections, executing semantic segmentation tasks directly on user-end devices often falls short due to limitations in processing power. Leveraging mobile edge computing, however, presents a strategic enhancement in performing semantic segmentation tasks by delegating computational workloads to proximal devices, thus diminishing latency and reducing energy expenditure. This document delineates a task offloading framework aimed at refining the management of interdependent tasks, thereby boosting the efficiency of processing complex tasks within environments of semantic communication. The framework abstracts tasks in the form of directed acyclic graphs (DAG) and approaches the optimization challenge through a Markov Decision Process (MDP), targeting an optimal balance between task completion, delay reduction, and energy savings. To adapt to fluctuating network conditions, a novel approach employing the Deep Deterministic Policy Gradient (DTO-DDPG) algorithm is introduced for optimization decision-making. Our empirical tests underscore the framework’s capacity to significantly elevate task completion rates while simultaneously decreasing both energy consumption and latency in scenarios of semantic communication.