用低频信道信息推算高频信道，大幅降低毫米波通信的开销。
Deep Learning-Based Channel Extrapolation for Dual-Band Massive MIMO Systems
- 融合多域特征的专家模型，学习低频到高频信道映射关系。
- 训练所需导频数减少，且在不同天线规模和信噪比下表现更优。
- 适合需要低开销高速率通信的5G/6G系统设计者。
未来无线通信系统将越来越多地结合毫米波（mmWave）与低于6GHz频段，以满足高速传输与广覆盖的异构需求。为充分发挥毫米波大规模多输入多输出（Massive MIMO）系统的潜力，需获取高精度信道状态信息（CSI）。然而，由于毫米波信道维度大、信噪比低（受严重路径损耗与遮挡衰减影响），直接估计其信道需大量导频开销。本文提出一种高效的多域融合信道外推器（MDFCE），通过将低于6GHz频段的信道信息外推至毫米波频段，从而降低双频段大规模MIMO系统中毫米波信道估计的导频开销。与基于数学建模的传统方法不同，MDFCE结合混合专家框架与多头自注意力机制，融合低于6GHz CSI的多域特征，有效表征从低频到高频信道的映射关系。仿真结果表明，相比现有方法，MDFCE在不同天线阵列规模与信噪比水平下均具备更优性能，且计算效率显著更高。
原文摘要 · Abstract (English)
Future wireless communication systems will increasingly rely on the integration of millimeter wave (mmWave) and sub-6 GHz bands to meet heterogeneous demands on high-speed data transmission and extensive coverage. To fully exploit the benefits of mmWave bands in massive multiple-input multiple-output (MIMO) systems, highly accurate channel state information (CSI) is required. However, directly estimating the mmWave channel demands substantial pilot overhead due to the large CSI dimension and low signal-to-noise ratio (SNR) led by severe path loss and blockage attenuation. In this paper, we propose an efficient \textbf{M}ulti-\textbf{D}omain \textbf{F}usion \textbf{C}hannel \textbf{E}xtrapolator (MDFCE) to extrapolate sub-6 GHz band CSI to mmWave band CSI, so as to reduce the pilot overhead for mmWave CSI acquisition in dual band massive MIMO systems. Unlike traditional channel extrapolation methods based on mathematical modeling, the proposed MDFCE combines the mixture-of-experts framework and the multi-head self-attention mechanism to fuse multi-domain features of sub-6 GHz CSI, aiming to characterize the mapping from sub-6 GHz CSI to mmWave CSI effectively and efficiently. The simulation results demonstrate that MDFCE can achieve superior performance with less training pilots compared with existing methods across various antenna array scales and signal-to-noise ratio levels while showing a much higher computational efficiency.
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