Facilitating AI-Based CSI Feedback Deployment in Massive MIMO Systems With Learngene

Recent advances in artificial intelligence offer groundbreaking alternatives to conventional codebook-based channel state information (CSI) feedback techniques. Confronted with the influx of CSI data from simulations and real-world environments, leveraging neural networks to mine valuable insights poses significant training costs and technical challenges for base station (BS) manufacturers. To address this, we propose a third-party platform serving as a CSI knowledge repository and feedback model hub, reducing training expenses and addressing technical issues for various BS manufacturers. However, tailoring training for each manufacturer's model may lead to proprietary information leaks and inefficient resource utilization. In response, we present 'CSI Meta-knowledge Support', a cutting-edge CSI feedback network deployment strategy using Learngene, enabling seamless transfer of CSI meta-knowledge across heterogeneous networks. This method captures a Learngene unit enriched with vital CSI meta-knowledge during comprehensive training sessions, serving as a plug-and-play prior to facilitate swift convergence and efficient local fine-tuning for manufacturers. The approach introduces adaptable and scalable CSI feedback network configurations, emphasizing reusability, cost-effectiveness, and resource management while safeguarding intellectual property. Our tests demonstrate enhanced performance, reduced training sample demands, and faster convergence relative to conventional techniques.