Slicing Resource Allocation for eMBB and URLLC in 5G RAN
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The simulation results show that the proposed resource allocation algorithm can significantly improve the spectral efficiency of the system and URLLC reliability, compared with the adaptive particle swarm optimization (APSO), the equal power allocation (EPA), and the equal subcarrier allocation (ESA) algorithm.Abstract:
This paper investigates the network slicing in the virtualized wireless network. We consider a downlink orthogonal frequency division multiple access system in which physical resources of base stations are virtualized and divided into enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) slices. We take the network slicing technology to solve the problems of network spectral efficiency and URLLC reliability. A mixed-integer programming problem is formulated by maximizing the spectral efficiency of the system in the constraint of users’ requirements for two slices, i.e., the requirement of the eMBB slice and the requirement of the URLLC slice with a high probability for each user. By transforming and relaxing integer variables, the original problem is approximated to a convex optimization problem. Then, we combine the objective function and the constraint conditions through dual variables to form an augmented Lagrangian function, and the optimal solution of this function is the upper bound of the original problem. In addition, we propose a resource allocation algorithm that allocates the network slicing by applying the Powell–Hestenes–Rockafellar method and the branch and bound method, obtaining the optimal solution. The simulation results show that the proposed resource allocation algorithm can significantly improve the spectral efficiency of the system and URLLC reliability, compared with the adaptive particle swarm optimization (APSO), the equal power allocation (EPA), and the equal subcarrier allocation (ESA) algorithm. Furthermore, we analyze the spectral efficiency of the proposed algorithm with the users’ requirements change of two slices and get better spectral efficiency performance.read more
Citations
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Deep reinforcement learning for resource allocation with network slicing in cognitive radio network
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References
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Journal ArticleDOI
5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View
TL;DR: In this paper, the authors study the potential advantages of allowing for non-orthogonal sharing of RAN resources in uplink communications from a set of eMBB, mMTC, and URLLC devices to a common base station.
Journal ArticleDOI
Network Slicing to Enable Scalability and Flexibility in 5G Mobile Networks
Peter Rost,Christian Mannweiler,Diomidis S. Michalopoulos,Cinzia Sartori,Vincenzo Sciancalepore,Nishanth Sastry,Oliver Holland,Shreya Tayade,Bin Han,Dario Bega,Danish Aziz,Hajo Bakker +11 more
TL;DR: In this paper, the authors argue for network slicing as an efficient solution that addresses the diverse requirements of 5G mobile networks, thus providing the necessary flexibility and scalability associated with future network implementations.
Posted Content
5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View
TL;DR: It is revealed that H-NOMA can lead, in some regimes, to significant gains in terms of performance tradeoffs among the three generic services as compared to orthogonal slicing.
Proceedings ArticleDOI
Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks
TL;DR: In this article, a joint eMBB and ultra-low-latency (URLLC) scheduler is proposed to maximize the utility for eMBBs while satisfying instantaneous URLLC demands.