End-to-End Resource Slicing for Coexistence of eMBB and URLLC Services in 5G-Advanced/6 G Networks
Taskou, Shiva Kazemi; Rasti, Mehdi; Hossain, Ekram (2023-12-12)
IEEE
12.12.2023
S. K. Taskou, M. Rasti and E. Hossain, "End-to-End Resource Slicing for Coexistence of eMBB and URLLC Services in 5G-Advanced/6G Networks," in IEEE Transactions on Mobile Computing, vol. 23, no. 7, pp. 8015-8032, July 2024, doi: 10.1109/TMC.2023.3341810
https://rightsstatements.org/vocab/InC/1.0/
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403152249
https://urn.fi/URN:NBN:fi:oulu-202403152249
Tiivistelmä
Abstract
We study the problem of end-to-end (E2E) network slicing, i.e., joint slicing of the radio access network (RAN) and core network (CN), for the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC) services in future generation cellular (e.g., 5G-Advanced/6G) networks. The E2E resource slicing problem is defined as a mixed-integer non-linear programming problem to minimize the E2E energy consumption and the cost of utilized resources. To overcome the difficulty of solving this problem, we decompose it into two sub-problems, namely, RAN resource allocation (RRA) and CN resource allocation (CRA) problems. In both RRA and CRA problems, the existence of binary variables makes them intractable. To tackle this difficulty, we relax the binary variables by introducing penalty functions. Then, we make the RRA and CRA problems convex by employing the majorization-minimization approximation method. Via simulation results, we compare our proposed joint RAN and CN resource allocation algorithm (JRCRA) with the disjoint solution where RAN and CN resources are allocated to users separately. The joint allocation of resources in the RAN and CN has the advantage that the E2E tolerable latency of users can be flexibly divided between RAN and CN. In contrast, if resources in RAN and CN are allocated separately, a predefined part of the E2E tolerable latency should be considered as the tolerable latency in RAN and CN. The simulation results illustrate that our proposed JRCRA algorithm obtains a 34% improvement in energy consumption and a 24% improvement in cost compared to the disjoint one. Moreover, via simulation results, we illustrate that in comparison with existing algorithms, our proposed JRCRA obtains a higher performance. Besides, simulation results confirm that JRCRA reaches a close performance to the optimal solution.
We study the problem of end-to-end (E2E) network slicing, i.e., joint slicing of the radio access network (RAN) and core network (CN), for the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC) services in future generation cellular (e.g., 5G-Advanced/6G) networks. The E2E resource slicing problem is defined as a mixed-integer non-linear programming problem to minimize the E2E energy consumption and the cost of utilized resources. To overcome the difficulty of solving this problem, we decompose it into two sub-problems, namely, RAN resource allocation (RRA) and CN resource allocation (CRA) problems. In both RRA and CRA problems, the existence of binary variables makes them intractable. To tackle this difficulty, we relax the binary variables by introducing penalty functions. Then, we make the RRA and CRA problems convex by employing the majorization-minimization approximation method. Via simulation results, we compare our proposed joint RAN and CN resource allocation algorithm (JRCRA) with the disjoint solution where RAN and CN resources are allocated to users separately. The joint allocation of resources in the RAN and CN has the advantage that the E2E tolerable latency of users can be flexibly divided between RAN and CN. In contrast, if resources in RAN and CN are allocated separately, a predefined part of the E2E tolerable latency should be considered as the tolerable latency in RAN and CN. The simulation results illustrate that our proposed JRCRA algorithm obtains a 34% improvement in energy consumption and a 24% improvement in cost compared to the disjoint one. Moreover, via simulation results, we illustrate that in comparison with existing algorithms, our proposed JRCRA obtains a higher performance. Besides, simulation results confirm that JRCRA reaches a close performance to the optimal solution.
Kokoelmat
- Avoin saatavuus [38865]