Abstract
Ultra-dense networks (UDNs) have been employed to solve the pressing problems in relation to the increasing demand for higher coverage and capacity of the fifth generation (5G) wireless networks. The deployment of UDNs in a very large scale has been envisioned to break the fundamental deadlocks of beyond 5G or the sixth generation (6G) networks and deliver many more orders of magnitude gains that today’s technologies achieve. However, the mathematical tool to optimize the system performance under the stringent radio resource constraints is widely recognized to be a formidable challenge. System-level performance optimization of current UDNs are usually conducted by relying on numerical simulations, which are often time-consuming and have become extremely difficult in the context of 6G with extremely high density. As such, there is an urgent need for developing a realistic mathematical model for optimizing the 6G UDNs. In this paper, we introduce challenges as well as issues that have to be thoroughly considered while deploying UDNs in realistic environment. We revisit efficient mathematical techniques including game theory and real-time optimization in the context of optimizing UDNs performance. In addition, emerging technologies which are suitable to apply in UDNs are also discussed. Some of them have already been used in UDNs with high efficiency while the others which are still under investigation are expected to boost the performance of UDNs to achieve the requirements of 6G. Importantly, for the first time, we introduce the joint optimal approach between realtime optimization and game theory (ROG) which is an effective tool to solve the optimization problems of large-scale UDNs with low complexity. Then, we describe two approaches for using ROG in UDNs. Finally, some case study of ROG are given to illustrate how to apply ROG for solving the problems of different applications in UDNs.
Highlights
Since 2020, the fifth generation (5G) networks have begun rolling out in many countries [1]
It is predicted that 5G may have not enough capability to be applied in services with the requirements of data rate to achieve terabits per second, latency to be less than hundreds of microseconds, and connectivity to be more than tens of million connections per km2 in the near future [2], [3]
In consideration of small cells corresponding to self-interested players, we model optimization problems (OPs) (19) as a Non-cooperative game (NCG) denoted by the normal form GAM E =
Summary
Since 2020, the fifth generation (5G) networks have begun rolling out in many countries [1]. Using optimization in realistic applications in UDNs witnesses many challenges such as creating a suitable model, solving method, the complexity of the solution, realtime computation. In UDNs with massive data, the large amount of BSs, UEs, multiple tiers, designing a realtime optimization method for an OP is much more challenging. Game theory (GT) as a promising solution is a distributed optimal framework to apply efficiently in UDNs which has complex interactions between network elements. We discuss some potential technologies and solutions, which are open and efficient research directions for applying UDNs with high-quality network performance. We develop an amalgamated GT and practical optimization method, relying on real models of UDNs in realtime contexts This approach is proposed for a significant reduction of the computational complexity and processing time of large-scale UDN scenarios.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
