Robust C-RAN Precoder Design for Wireless Fronthaul with Imperfect Channel State Information

Cloud Radio Access Network (C-RAN) architecture with optical fiber fronthaul has been confirmed as a promising solution to achieve high capacity and low latency signal transmission, which has been a key technology and trend of the evolving fifth generation (5G) cellular networks. However, with the f...

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Veröffentlicht in:IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC S. 1 - 6
Hauptverfasser: Dong Wang, Ying Wang, Ruijin Sun, Xiangyang Zhang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.03.2017
Schlagworte:
Channel state information
Interference
Optimization
Precoding
Robustness
Signal to noise ratio
Wireless communication
ISSN:1558-2612
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Zusammenfassung:Cloud Radio Access Network (C-RAN) architecture with optical fiber fronthaul has been confirmed as a promising solution to achieve high capacity and low latency signal transmission, which has been a key technology and trend of the evolving fifth generation (5G) cellular networks. However, with the fiber fronthaul increasing, the complexity and cost of the CRAN fronthaul networks will grow exponentially. Accordingly, the hybrid fronthaul network of wireless and optical will be the direction of C-RAN architecture design in the future. In this paper, we study the wireless fronthaul C-RAN system in downlink and propose a robust precoder design. The channel state information (CSI) at the baseband unit (BBU) pool and remote radio head (RRH) cluster is assumed to be imperfect, where the additive channel state information error is modeled as Gaussian distributed. Based on this model, we propose a robust C-RAN precoder design that minimizes the total transmit power under a signal-to-interference-plus-noise ratio (SINR) constraint at each user terminal. The original goal is to establish SINR constrained power allocation formulations in the form of convex conic optimization problem. The analysis results reveal that the original problem formulation is non-convex, in general. We develop a novel conservative approximation scheme for handling the non-convex constraint. Furthermore, we solve the optimization problem by transforming it into a semidefinite program with relaxation, which can be efficiently solved. Simulation results show the advantage of using the proposed power-conserving robust precoding algorithm.
ISSN:1558-2612
DOI:10.1109/WCNC.2017.7925528