Statistical CSI Acquisition for Multi-frequency Massive MIMO Systems
Jinke Tang, Li You, Xinrui Gong, Chenjie Xie, Xiqi Gao, Xiang-Gen Xia, Xueyuan Shi

TL;DR
This paper introduces novel methods for efficiently obtaining and predicting statistical CSI in multi-frequency massive MIMO systems, improving high-frequency transmission performance with reduced overhead.
Contribution
It proposes an AR-based covariance prediction and a low-complexity APS estimation algorithm, advancing statistical CSI acquisition in multi-frequency MIMO systems.
Findings
AR method accurately predicts covariance across frequencies
ME-based algorithm achieves high-resolution APS estimation
Multi-frequency cooperation enhances high-frequency transmission performance
Abstract
Multi-frequency massive multi-input multi-output (MIMO) communication is a promising strategy for both 5G and future 6G systems, ensuring reliable transmission while enhancing frequency resource utilization. Statistical channel state information (CSI) has been widely adopted in multi-frequency massive MIMO transmissions to reduce overhead and improve transmission performance. In this paper, we propose efficient and accurate methods for obtaining statistical CSI in multi-frequency massive MIMO systems. First, we introduce a multi-frequency massive MIMO channel model and analyze the mapping relationship between two types of statistical CSI, namely the angular power spectrum (APS) and the spatial covariance matrix, along with their correlation across different frequency bands. Next, we propose an autoregressive (AR) method to predict the spatial covariance matrix of any frequency band…
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