Closed-Form Optimal Event-Driven CSI Feedback Design for Time-Correlated Massive MIMO Systems

Abstract

Massive multiple-input multiple-output (MIMO) systems are an essential enabling factor for 5G and beyond networks because of their high spectral efficiency and connectivity advantages. However, the performance depends on the availability of accurate channel state information at the transmitter (CSIT), and the state-of-the-art periodic feedback schemes in 3GPP 5G New Radio (NR) are not suitable for dense antenna user deployments due to the significant and redundant control overhead, increased user equipment (UE) energy consumption, and scalability issues. In this paper, we propose an analytically motivated event-driven CSI feedback framework for time-correlated massive MIMO systems to reduce the feedback overhead while guaranteeing the communication reliability. We model the temporal evolution of wireless channels by Clarke’s isotropic scattering model and a statistically consistent Autoregressive (AR(1)) representation of first-order, based on which the distribution of a normalized Frobenius norm-based CSI deviation metric is derived. We derive a closed-form expression for the probability of channel variation exceeding a given threshold. Moreover, a perturbation-based analysis quantifies the effect of channel aging on signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER), indicating a quadratic relationship between performance degradation and channel mismatch energy. Based on these analytical insights, a unimodal optimization framework is formulated to jointly minimize feedback overhead and reliability degradation, yielding a closed-form optimal triggering threshold that adapts to Doppler variations and remains scalable with antenna dimensions. Monte Carlo simulations show that the CSI feedback overhead can be reduced by as much as 98% relative to conventional periodic reporting with negligible BER degradation over a wide range of Doppler, antenna and SNR conditions. The proposed framework is compatible with practical 5G NR CSI reporting mechanisms and provides a scalable solution for efficient massive MIMO feedback management.