Parallel Two-Way Relay Channel Estimation in Cloud-Based 5G Radio Access Networks

• Main Authors: Ali A. El-Moursy, Saeed Abdallah, Mohamed Saad, Khawla Alnajjar
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Distributed processing; parallelization; performance analysis; cloud radio access network; two-way relay network; semi-blind channel estimation
• Online Access: https://ieeexplore.ieee.org/document/9159127/

The cloud radio access network (C-RAN) aims at migrating the traditional base station functionality to a cloud-based centralized base band unit (BBU) pool, thereby providing a promising paradigm for fifth-generation (5G) wireless systems. This results in a novel wireless architecture in which mobile users communicate with the cloud via distributed remote radio heads (RRHs) as relays, through two successive wireless links. The availability of accurate channel state information at the BBU pool is a critical requirement in such systems. This paper addresses the channel estimation problem at the terminals of a C-RAN using a two-way relay network (TWRN) model. To the best of our knowledge, for the first time we introduce a cloud-based channel estimation algorithm implementation leveraging cloud computing capabilities of virtualization and parallelization. By bridging the gap between cloud computing and wireless communication, this work achieves a step towards the open problem of network function virtualization (NFV) in C-RANs. Through a deep serial algorithm analysis, we are able to utilize data decomposition as well as exploratory decomposition in order to achieve significant speedup, which scales well with problem size. We assess the performance gains of our cloud-based algorithm via extensive simulation experiments, and report almost 5 × reduction in computation time as compared to the state-of-the-art.