Hybrid Generative Semantic and Bit Communications in Satellite Networks: Trade-offs in Latency, Generation Quality, and Computation

TL;DR

This paper introduces a hybrid semantic and bit communication framework for satellite networks, balancing latency, quality, and computation using a new metric and DRL-based resource optimization.

Contribution

It proposes a multi-layer hybrid communication framework with a novel SEM metric and a DRL algorithm for resource allocation in satellite communications.

Findings

Framework demonstrates adaptability in satellite networks.

SEM metric effectively evaluates trade-offs.

DRL algorithm optimizes resource allocation.

Abstract

As satellite communications play an increasingly important role in future wireless networks, the issue of limited link budget in satellite systems has attracted significant attention in current research. Although semantic communications emerge as a promising solution to address these constraints, it introduces the challenge of increased computational resource consumption in wireless communications. To address these challenges, we propose a multi-layer hybrid bit and generative semantic communication framework which can adapt to the dynamic satellite communication networks. Furthermore, to balance the semantic communication efficiency and performance in satellite-to-ground transmissions, we introduce a novel semantic communication efficiency metric (SEM) that evaluates the trade-offs among latency, computational consumption, and semantic reconstruction quality in the proposed framework. Moreover, we utilize a novel deep reinforcement learning (DRL) algorithm group relative policy optimization (GRPO) to optimize the resource allocation in the proposed network. Simulation results demonstrate the flexibility of our proposed transmission framework and the effectiveness of the proposed metric SEM, illustrate the relationships among various semantic communication metrics.