Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language

Abstract

Goal-oriented semantic communication will be a pillar of next-generation wireless networks. Despite significant recent efforts in this area, most prior works are focused on specific data types (e.g., image or audio), and they ignore the goal and effectiveness aspects of semantic transmissions. In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment. A common language based on a hierarchical belief set is proposed to enable semantic communications between speaker and listener. The speaker, acting as an observer of the environment, utilizes the beliefs to transmit an initial description of its observation (called event) to the listener. The listener is then able to infer on the transmitted description and complete it by adding related beliefs to the transmitted beliefs of the speaker. As such, the listener reconstructs the observed event based on the completed description, and it then takes appropriate action in the environment based on the reconstructed event. An optimization problem is defined to determine the perfect and abstract description of the events while minimizing the transmission and inference costs with constraints on the task execution time and belief efficiency. Then, a novel bottom-up curriculum learning (CL) framework based on reinforcement learning is proposed to solve the optimization problem and enable the speaker and listener to gradually identify the structure of the belief set and the perfect and abstract description of the events. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution cost and time, reliability, and belief efficiency.

Adaptive resource optimization for edge inference with goal-oriented communications

“…More recently, a transformer-based approach has also been investigated in [29] to support both image and text transmission. Alternative methods were also proposed in [30] and [31], to define an optimized common-language between a listener and a speaker, employing reinforcement learning (RL) and curriculum learning (CL). Other interesting examples can be found in [32] and [33], concerning, respectively, image classification in an unmanned aerial vehicle (UAV) scenario and visual question answering (VQA) tasks.…”

“…The resource allocation problem at the UE aims at optimizing the transmission rate R(t), the compression factor ρ(t) , and the UE CPU frequency cycles f d (t) in (30) at every time t. Omitting the time index t, the subproblem at the UE can be cast as: Problem ( 31) is a mixed-integer optimization program that, by the same arguments and bounds used for the MEDA problem, can be proved to be strictly convex with respect to the transmission rate R, for any fixed compression factor ρ and computational clock frequency f d , with optimal closed form solution for Q tx (t) > 0 , and R * = 0 otherwise. Thus, the overall optimal solution R * (t) can be found by an exhaustive search in the product space F d × S of the UE clock frequencies and compression factors, by comparing the obtained objective values in (31) for the |F d ||S| potential solutions R * (ρ, f d ) . The procedure follows the same steps already described in Algorithm 1.…”