Massive multiple-input multiple-output (MIMO) is regarded as one enabling technique to improve channel capacity and energy/spectrum efficiency of 5G communications. To attain such potential benefits, accurate channel information is critical to the transmitter, which yet remains a challenging task for frequency division duplexing (FDD) systems, i.e., the channel state information (CSI) feedback tends to be resource-demanding especially for massive MIMO communications. In this work, we propose a novel CSI feedback method with low complexity and high precision, which is inspired by randomized matrix approximation. Our approach exploits the inherent low-rank characteristic of a large channel matrix, and approximates it by small sub-matrices which are then reported to transmitter to recover a CSI matrix. Theoretical bounds of the recovered CSI in both error-free and error cases are derived. Simulation results demonstrate our method could recover CSI accurately via an extremely low complexity and yet achieve a largely reduced compression ratio (or feedback overhead), compared to other schemes. It thus has the great potential in the emerging massive MIMO FDD communications.

中文翻译：

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基于随机低秩逼近的大规模 MIMO CSI 压缩

大规模多输入多输出 (MIMO) 被认为是一种提高 5G 通信信道容量和能量/频谱效率的使能技术。为了获得这些潜在的好处，准确的信道信息对发射机来说至关重要，这对于频分双工 (FDD) 系统来说仍然是一项具有挑战性的任务，即信道状态信息 (CSI) 反馈往往需要资源，尤其是对于大规模 MIMO通讯。在这项工作中，我们提出了一种新的 CSI 反馈方法，其复杂度低，精度高，其灵感来自随机矩阵逼近。我们的方法利用大信道矩阵固有的低秩特性，并通过小子矩阵对其进行近似，然后将小子矩阵报告给发射机以恢复 CSI 矩阵。导出了在无错误和错误情况下恢复的 CSI 的理论界限。仿真结果表明，与其他方案相比，我们的方法可以通过极低的复杂度准确地恢复 CSI，同时大大降低了压缩比（或反馈开销）。因此，它在新兴的大规模 MIMO FDD 通信中具有巨大的潜力。