Authors: Lilly Abau Yosia Odwa; Yueyun Chen
Addresses: School of Computer and Communication Engineering, University of Science and Technology Beijing, Haidian District, Beijing, China ' School of Computer and Communication Engineering, University of Science and Technology Beijing, Haidian District, Beijing, China
Abstract: In cloud radio access networks, it is difficult to efficiently use the baseband unit resources. To improve resource utilisation, this paper proposes the Predictive Borrow and Lend method, which maps between baseband units and remote radio heads based on channel prediction. The channel state is predicted based on regularised particle filters, and multiple remote radio heads are mapped to a single baseband unit depending on baseband unit capacity. The proposed method aims at maximising the number of busy baseband units, by combining remote radio heads to a single baseband unit. When baseband unit utilisation exceeds the upper limit, the remote radio heads are switched to another baseband unit with low resource usage. The delay is also reduced; and the spectral and energy efficiency of the proposed method are improved, compared to borrow and lend method.
Keywords: cloud radio access network; resource allocation; regularised particle filter; baseband unit aggregation; spectral efficiency; energy efficiency.
International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.2, pp.167 - 178
Received: 09 Feb 2021
Accepted: 19 Feb 2022
Published online: 08 Jun 2022 *