Layered Downlink Precoding for C-RAN Systems with Full Dimensional MIMO

Abstract

The implementation of a Cloud Radio Access Network (C-RAN) with Full Dimensional (FD)-MIMO is faced with the challenge of controlling the fronthaul overhead for the transmission of baseband signals as the number of horizontal and vertical antennas grows larger. This work proposes to leverage the special low-rank structure of FD-MIMO channel, which is characterized by a time-invariant elevation component and a time-varying azimuth component, by means of a layered precoding approach, so as to reduce the fronthaul overhead. According to this scheme, separate precoding matrices are applied for the azimuth and elevation channel components, with different rates of adaptation to the channel variations and correspondingly different impacts on the fronthaul capacity. Moreover, we consider two different Central Unit (CU) - Radio Unit (RU) functional splits at the physical layer, namely the conventional C-RAN implementation and an alternative one in which coding and precoding are performed at the RUs. Via numerical results, it is shown that the layered schemes significantly outperform conventional non-layered schemes, especially in the regime of low fronthaul capacity and large number of vertical antennas.

29 pages, 12 figures, Submitted to IEEE Transactions on Vehicular Technology

References in corpus (3)

• Kronecker Product Correlation Model and Limited Feedback Codebook Design in a 3D Channel Model
• 3D Channel Model in 3GPP
• Fronthaul Compression and Precoding Design for C-RANs over Ergodic Fading Channel

Cited by in corpus (4)

• Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink
• SWIPT in 3-D Bipolar Ad Hoc Networks with Sectorized Antennas
• Hybrid Precoding for Massive MIMO Systems in Cloud RAN Architecture with Capacity-Limited Fronthauls
• Optimization of Massive Full-Dimensional MIMO for Positioning and Communication