Distributed MAC Scheduling Scheme for C-RAN with Non-Ideal Fronthaul in 5G Networks

A typical cloud-radio access network (C-RAN) architecture in fifth-generation (5G) network is composed of one radio cloud center (RCC), moderate amount of radio aggregation units (RAU) and massive remote radio heads (RRH). This paper proposes a distributed medium access control (MAC) scheduling scheme for C-RAN, where RCC and RAU are connected with non-ideal fronthaul. In this scheme, a full scheduling operation and a limited scheduling operation are executed at RCC and RAU, respectively. The full scheduling operation at RCC is based on the overall network information and hence could generate a global-optimized scheduling result with ideal fronthaul. The limited scheduling operation at RAU is based on the latest hybrid automatic repeat request (HARQ) feedback information and channel state information (CSI), and could provide real-time supplement when the fronthaul transfer between RCC and RAU is delayed or lost. The integration of these two scheduling results are also provided to further exert their individual advantages. Simulation results demonstrate that the proposed distributed MAC scheduling scheme could effectively mitigate the performance loss due to non-ideal fronthaul in terms of cell sum throughput and cell edge throughput, and therefore benefits in promoting the feasibility of C-RAN in 5G network architecture evolution.

[1]  Zhengang Pan,et al.  Toward green and soft: a 5G perspective , 2014, IEEE Communications Magazine.

[2]  Christian Bonnet,et al.  Impact of packetization and functional split on C-RAN fronthaul performance , 2016, 2016 IEEE International Conference on Communications (ICC).

[3]  I Chih-Lin,et al.  Rethink fronthaul for soft RAN , 2015, IEEE Communications Magazine.

[4]  Ping Zhang,et al.  An effective approach to 5G: Wireless network virtualization , 2015, IEEE Communications Magazine.

[5]  Lena Wosinska,et al.  Energy performance of C-RAN with 5G-NX radio networks and optical transport , 2016, 2016 IEEE International Conference on Communications (ICC).

[6]  Lei Li,et al.  Recent Progress on C-RAN Centralization and Cloudification , 2014, IEEE Access.

[7]  Ning Ge,et al.  Virtual MIMO in Multi-Cell Distributed Antenna Systems: Coordinated Transmissions with Large-Scale CSIT , 2013, IEEE Journal on Selected Areas in Communications.

[8]  Zhisheng Niu,et al.  Software-defined hyper-cellular architecture for green and elastic wireless access , 2015, IEEE Communications Magazine.

[9]  Shlomo Shamai,et al.  Multivariate Fronthaul Quantization for Downlink C-RAN , 2015, IEEE Transactions on Signal Processing.

[10]  Hwan Seok Chung,et al.  Experimental demonstration of CPRI data compression based on partial bit sampling for mobile front-haul link in C-RAN , 2016, 2016 Optical Fiber Communications Conference and Exhibition (OFC).

[11]  IMT Vision – Framework and overall objectives of the future development of IMT for 2020 and beyond M Series Mobile , radiodetermination , amateur and related satellite services , 2015 .

[12]  Zhongding Lei,et al.  Fast algorithm for utility maximization in C-RAN with joint QoS and fronthaul rate constraints , 2016, 2016 IEEE International Conference on Communications (ICC).

[13]  Shlomo Shamai,et al.  Multivariate fronthaul quantization for C-RAN downlink: Channel-adaptive joint quantization in the cloud , 2016, 2016 IEEE International Conference on Communications (ICC).

[14]  Kai-Kit Wong,et al.  Secrecy and Energy Efficiency in Massive MIMO Aided Heterogeneous C-RAN: A New Look at Interference , 2016, IEEE Journal of Selected Topics in Signal Processing.