Adaptive Coding and Modulation for Sun Outage Alleviation in Ultradense LEO Satellite Networks: A DRL Approach

Yuhan Xia, Yu Zhang, Xin Zhang, Lei Deng, Wei Han, Bo Bai, Haibo Zhou

Abstract: The ultradense low-earth orbit (LEO) satellite networks (ULSNs) have become an important component of next-generation (6G) wireless networks, offering large-scale coverage and high-capacity service. Unlike traditional terrestrial network backbones deployed in closed, protected environments, satellite networks are exposed to highly dynamic environments, where space environment interference can severely affect channel conditions. This article addresses the impact of sun outages, one of the most significant spatial interference factors, on satellite communication and proposes an adaptive coding and modulation (ACM) scheme that dynamically adjusts the modulation and coding schemes (MCSs) based on real-time channel conditions to enhance the performance and communication quality of ULSNs. First, we model the channel environment of satellite–terrestrial microwave and intersatellite laser links under sun outage interferences in ULSNs, which involves sun outage occurrence prediction and their interference quantification. Subsequently, to implement the ACM scheme, we use the seasonal autoregressive integrated moving average (SARIMA) algorithm combined with the bidirectional long short-term memory (BiLSTM) algorithm for time-series prediction of the channel state. Based on this, we apply the knowledge distillation-assisted proximal policy optimization (KD-PPO) algorithm to select the appropriate MCS. Simulation results show that by calculating the sun outage duration and the resulting interference, as well as predicting the channel state, the proposed KD-PPO algorithm can minimize the bit error rate (BER) and maximize the spectrum utilization (SU) in ULSNs.

External IDs:dblp:journals/iotj/XiaZZDHBZ25