AI-Integrated Extreme Massive MIMO Scheduling for Hyper Reliable Low-Latency Communication

Abstract

6G hyper reliable low-latency communication (HRLLC) services with high data rates require higher levels of quality of service (QoS) guarantees along with high spectral efficiency (SE). To meet these demands, extreme massive multi-input multi-output (E-MIMO) scheduling with large spatial dimensions by leveraging channel distribution information (CDI) is critical. However, a practical E-MIMO CDI acquisition may suffer from diverse channel environments, large CDI parameter sizes, and marginal channel estimation, resulting in large CDI feedback overhead, significant QoS mismatch, and SE loss. In this letter, an AI-integrated E-MIMO scheduling method is proposed to resolve these issues by jointly designing compact CDI management, AI-based reliable scheduling performance prediction, and scheduling optimization, which enables efficient CDI reporting and pre-configured resource allocation for providing HRLLC QoS with high SE. Its design and performance advantages are demonstrated in a correlated Rayleigh E-MIMO scenario.