Slicing Resource Allocation for eMBB and URLLC in 5G RAN

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.

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Virtual Network Resource Allocation Algorithm Based on Reliability and Distribution Strategy Under Network Slicing

10.1007/978-981-16-6554-7_158 ◽

2021 ◽

pp. 1435-1442

Author(s):

Ying Zeng ◽

Yuhang Chen ◽

Zanhong Wu

Keyword(s):

Resource Allocation ◽

Virtual Network ◽

Network Resource ◽

Network Slicing ◽

Resource Allocation Algorithm ◽

Allocation Algorithm ◽

Distribution Strategy ◽

Network Resource Allocation

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Resource Allocation Algorithm of Power Communication Network Based on Reliability and Historical Data Under 5G Network Slicing

Advances in Intelligent Systems and Computing - The 10th International Conference on Computer Engineering and Networks ◽

10.1007/978-981-15-8462-6_183 ◽

2020 ◽

pp. 1599-1607

Author(s):

Yang Yang ◽

Guoyi Zhang ◽

Junhong Weng ◽

Xi Wang

Keyword(s):

Communication Network ◽

Resource Allocation ◽

Historical Data ◽

Network Slicing ◽

Resource Allocation Algorithm ◽

Allocation Algorithm ◽

5G Network

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Offline Joint Network and Computational Resource Allocation for Energy-Efficient 5G and beyond Networks

Applied Sciences ◽

10.3390/app112210547 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10547

Author(s):

Marios Gatzianas ◽

Agapi Mesodiakaki ◽

George Kalfas ◽

Nikos Pleros ◽

Francesca Moscatelli ◽

...

Keyword(s):

Resource Allocation ◽

Energy Efficient ◽

Optimal Solution ◽

Mixed Integer ◽

Network Function Virtualization ◽

User Association ◽

Computational Resource ◽

Mixed Integer Linear Program ◽

Resource Allocation Algorithm ◽

Network Function

In order to cope with the ever-increasing traffic demands and stringent latency constraints, next generation, i.e., sixth generation (6G) networks, are expected to leverage Network Function Virtualization (NFV) as an enabler for enhanced network flexibility. In such a setup, in addition to the traditional problems of user association and traffic routing, Virtual Network Function (VNF) placement needs to be jointly considered. To that end, in this paper, we focus on the joint network and computational resource allocation, targeting low network power consumption while satisfying the Service Function Chain (SFC), throughput, and delay requirements. Unlike the State-of-the-Art (SoA), we also take into account the Access Network (AN), while formulating the problem as a general Mixed Integer Linear Program (MILP). Due to the high complexity of the proposed optimal solution, we also propose a low-complexity energy-efficient resource allocation algorithm, which was shown to significantly outperform the SoA, by achieving up to 78% of the optimal energy efficiency with up to 742 times lower complexity. Finally, we describe an Orchestration Framework for the automated orchestration of vertical-driven services in Network Slices and describe how it encompasses the proposed algorithm towards optimized provisioning of heterogeneous computation and network resources across multiple network segments.

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Greedy Based Radio Resource Allocation Algorithm with Water Filling Power Control Scheme in D2D Underlaying Communication

Journal of Measurements Electronics Communications and Systems ◽

10.25124/jmecs.v8i1.3474 ◽

2021 ◽

Author(s):

Rezha Aulia Riyanda ◽

Nachwan Mufti Adriansyah ◽

Vinsensius Sigit Widhi Prabowo

Keyword(s):

Resource Allocation ◽

Energy Efficiency ◽

Power Control ◽

Greedy Algorithm ◽

Spectral Efficiency ◽

Channel Condition ◽

Resource Allocation Algorithm ◽

Allocation Algorithm ◽

Control Scheme ◽

Water Filling

Device to Device (D2D) is communication between two devices directly without the intervention of eNodeB.This communication can improve sum-rate, spectral efficiency, and decrease the workload of eNodeBbecause using the same spectrum frequency with Cellular User Equipment (CUE). But this communicationshould use the same resource simultaneously with CUE which is called D2D underlaying. This sharingresources also causes interference and should be managed using the resource allocation algorithm. In thiswork, the resource allocation is allocated in a single cell and uplink communication using joint greedyalgorithm with water filling power control scheme. This algorithm is compared with greedy, joint greedy,and greedy algorithm with water filling power control scheme. Joint greedy algorithm works based on thecapacity of eNodeB and D2D pair. While in water filling power control, the power of the user is managedbased on the channel condition and impact to energy efficiency. After all the resource is allocated, theparameter performance of the system is calculated, such as spectral efficiency, energy efficiency, and D2Dfairness. From the simulation result, joint greedy algorithm with water filling power control scheme result29,34 bps/Hz in spectral efficiency, 0.939 x 107 bps/watt in energy efficiency, and 0,996 in D2D fairness.

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