In this paper, we study a machine learning enabled smart beam scheduling approach for wireless virtualization in large‐scale multiple‐input–multiple‐output (MIMO) system. Large‐scale MIMO is regarded as an emerging technology to enhance data rate of future wireless networks and the wireless virtualization is regarded as an efficient paradigm to enhance the radio frequency (RF) spectrum utilization by subleasing RF slices of wireless infrastructure providers to mobile virtual network operators (MVNOs). We leverage machine learning approach for scheduling the beams in large‐scale MIMO where RF slices with the help of subsets of antennas are subleased for MVNOs. Performance of the proposed approach is evaluated using simulation results. The results show that the proposed approach outperforms the state of the art approach.
Wireless virtualization is emerging as a new paradigm to improve wireless spectrum utilization by subleasing radio frequency (RF) channels through slicing. This paper investigates wireless virtualization where wireless resources for virtual wireless networks adapted based on availability of leasable RF slices as well as the demands from the users of virtual wireless networks. The user utilities are subject to quality‐of‐experience requirements such as coverage, rate, mobility, and delay requirements. With the help of software defined network controller, wireless infrastructure providers (WIPs) slice their RF bands to sublease those slices to mobile virtual network operators (MVNOs). In wireless virtualization, MVNOs work as independent service provides, and thus, the end users negotiate directly to MVNOs regardless of WIPs used behind the scene (this concept is analogous to the Uber being a taxi company without owning any vehicles). The performance of the proposed approach is evaluated with the help of numerical results obtained from simulations by using different metrics such as percentage of RF band of WIP, outage probability, and data rate.
more » « less- NSF-PAR ID:
- 10453717
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Transactions on Emerging Telecommunications Technologies
- Volume:
- 30
- Issue:
- 3
- ISSN:
- 2161-3915
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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