Semantic Enabled 6G LEO Satellite Communication for Earth Observation: A Resource-Constrained Network Optimization

TL;DR

This paper tackles bandwidth-limited Earth-observation downlinks from LEO satellites by applying semantic communication to transmit only meaning-relevant information. The authors propose a semantic-communication enabled 6G LEO architecture with onboard feature extraction and joint source-channel encoding over OFDMA, and formulate a latency-minimization MINLP that couples data-length $\mathbf{l_X}$ and subcarrier assignment $\boldsymbol{\gamma}$ to meet SC-QoS, expressed as $O(\mathbf{l_X}, \boldsymbol{\gamma}) = \frac{1}{K} \sum_{k=1}^K t(\mathbf{l_X}, \boldsymbol{\gamma}_k)$ with $t(\mathbf{l_X}, \boldsymbol{\gamma}_k) = \frac{\mathbf{l_X}}{R^u_k}$ and $R^u_k(\boldsymbol{\gamma}_k) = \sum_{u=1}^U \gamma_{k,u} B^u_k \log_2(1+\Gamma_k)$, while enforcing $\mathbb{E}[PSNR_k(X)] \ge \Psi_k$. A two-stage solver is proposed: a Discrete Whale Optimization Algorithm (DWOA) handles the integer decisions (e.g., $\mathbf{l_X}$) under PSNR constraints, and a one-to-one matching game assigns subcarriers to satellites. Simulation results on disaster-relief imagery demonstrate reduced latency and preserved reconstruction quality, outperforming baseline schemes across network configurations.

Abstract

Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major challenge for satellite-to-ground communications operating under limited bandwidth capacities. This paper explores semantic communication (SC) as a potential alternative to traditional communication methods. The rationality for adopting SC is its inherent ability to reduce communication costs and make spectrum efficient for 6G non-terrestrial networks (6G-NTNs). We focus on the critical satellite imagery downlink communications latency optimization for Earth observation through SC techniques. We formulate the latency minimization problem with SC quality-of-service (SC-QoS) constraints and address this problem with a meta-heuristic discrete whale optimization algorithm (DWOA) and a one-to-one matching game. The proposed approach for captured image processing and transmission includes the integration of joint semantic and channel encoding to ensure downlink sum-rate optimization and latency minimization. Empirical results from experiments demonstrate the efficiency of the proposed framework for latency optimization while preserving high-quality data transmission when compared to baselines.

Figures (5)

• Figure 1: The architectural framework for facilitating multi-satellite semantic communication in Earth observation for disaster relief systems.
• Figure 2: Communication performance evaluation.
• Figure 3: Compression ratio comparison.
• Figure 4: The original image and the reconstruction images with different compression ratios under the SNR = 3dB.
• Figure 5: PSNR evaluation.

Theorems & Definitions (2)

• Definition 1
• Definition 2