Semantic-Empowered Utility Loss of Information Transmission Policy in Satellite-Integrated Internet
Jianhao Huang, Jian Jiao, Ye Wang, Rongxing Lu, Qinyu Zhang
Abstract
The recent pervasive network intelligence for 6G has further triggered extensive research into semantic communication. In response to the challenges of its effectiveness aspects, we propose a novel semantic metric, utility loss of information (UoI), that can quantify the impact of duration and severity of mismatch transceivers, and address the shortcomings of existing semantic metrics. Then, this paper contributes meaningful results centered around UoI by utilizing a deep reinforcement learning (DRL) technology to intelligently choose when to sample, how to adopt the appropriate number of coded packets, and whether to retransmit in a network coding hybrid automatic repeat request (NC HARQ) aided satellite-integrated Internet, which is characterized by high bit error rate (BER), delayed feedback, and rapid channel dynamics. The approach aims to strike an optimal tradeoff between UoI and energy consumption (TOUE). More precisely, we cast the joint minimization problem of UoIand energy consumption as a partially observable Markov decision process (POMDP). Subsequently, a prioritized experience replay-aided dueling double deep Q-network (PER-D3QN) is adopted to address this problem. Simulation results validate that our TOUE policy can yield significant gains over several state-of-the-art sampling and transmission polices, and demonstrate its sensitivity to changes in goal requirements.