scholarly journals An Analytical Model for 5G Network Resource Sharing with Flexible SLA-Oriented Slice Isolation

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1177
Author(s):  
Natalia Yarkina ◽  
Yuliya Gaidamaka ◽  
Luis M. Correia ◽  
Konstantin Samouylov
Keyword(s):  
Resource Sharing ◽  
Performance Metrics ◽  
Access Network ◽  
Radio Spectrum ◽  
Arrival Process ◽  
Convex Programming Problem ◽  
Network Resource ◽  
Communication Services ◽  
Radio Access ◽  
Efficient Resource

Network slicing is a novel key technology in 5G networks which permits to provide a multitude of heterogeneous communication services over a common network infrastructure while satisfying strict Quality of Service (QoS) requirements. Since radio spectrum resources are inherently scarce, the slicing of the radio access network should rely on a flexible resource sharing policy that provides efficient resource usage, fairness and slice isolation. In this article, we propose such a policy for bandwidth-greedy communication services. The policy implies a convex programming problem and is formalized to allow for session-level stochastic modeling. We developed a multi-class service system with service rates obtained as a solution to the optimization problem, a Markovian Arrival Process and state-dependent preemptive priorities. We use matrix-analytic methods to find the steady state distribution of the resulting continuous-time Markov chain and the expressions for important performance metrics, such as data rates. Numerical analysis illustrates the efficiency of the proposed slicing scheme compared to the complete sharing and complete partitioning policies, showing that our approach leads to a data rate about the double of that obtained under complete partitioning for the analyzed scenario.

scholarly journals The benefits of convergence

2016 ◽  
Vol 374 (2062) ◽  
pp. 20140442 ◽  
Author(s):  
Gee-Kung Chang ◽  
Lin Cheng
Keyword(s):  
Communication Networks ◽  
Resource Sharing ◽  
Access Network ◽  
High Capacity ◽  
Radio Spectrum ◽  
System Capacity ◽  
Modulation Formats ◽  
Frequency Bands ◽  
Cell Coverage ◽  
Radio Access

A multi-tier radio access network (RAN) combining the strength of fibre-optic and radio access technologies employing adaptive microwave photonics interfaces and radio-over-fibre (RoF) techniques is envisioned for future heterogeneous wireless communications. All-band radio spectrum from 0.1 to 100 GHz will be used to deliver wireless services with high capacity, high link speed and low latency. The multi-tier RAN will improve the cell-edge performance in an integrated heterogeneous environment enabled by fibre–wireless integration and networking for mobile fronthaul/backhaul, resource sharing and all-layer centralization of multiple standards with different frequency bands and modulation formats. In essence, this is a ‘no-more-cells’ architecture in which carrier aggregation among multiple frequency bands can be easily achieved with seamless handover between cells. In this way, current and future mobile network standards such as 4G and 5G can coexist with optimized and continuous cell coverage using multi-tier RoF regardless of the underlying network topology or protocol. In terms of users’ experience, the future-proof approach achieves the goals of system capacity, link speed, latency and continuous heterogeneous cell coverage while overcoming the bandwidth crunch in next-generation communication networks.

scholarly journals Resource Management in Cloud Radio Access Network: Conventional and New Approaches

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2708 ◽  
Author(s):  
Rehenuma Tasnim Rodoshi ◽  
Taewoon Kim ◽  
Wooyeol Choi
Keyword(s):  
Resource Management ◽  
Performance Metrics ◽  
Research Work ◽  
Access Network ◽  
Wireless Sensor ◽  
Research Issues ◽  
Radio Access Network ◽  
Cloud Radio Access Network ◽  
Radio Access ◽  
Management Techniques

Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction.

scholarly journals Dynamic Q-learning and Fuzzy CNN Based Vertical Handover Decision for Integration of DSRC, mmWave 5G and LTE in Internet of Vehicles (IoV)

2021 ◽  
pp. 155-166
Author(s):  
Shaik Mazhar Hussain ◽  
◽  
Kamaludin Mohamad Yusof
Keyword(s):  
Performance Metrics ◽  
Selection Process ◽  
Success Probability ◽  
Access Network ◽  
Vertical Handover ◽  
Network Selection ◽  
Internet Of Vehicles ◽  
Handover Decision ◽  
Q Learning ◽  
Radio Access

Internet of vehicles commonly known as IOV is a newly emerged area which with the help of internet assisted communication provides the support to the vehicles. Due to the access of more than one radio access network, 5G makes the connectivity ubiquitous. Vehicle mobility demands for handover in such heterogeneous networks. Instead of using better technology for long ranges and other types of traffic, the vehicles are using devoted short range communications at short ranges. Commonly, networks for handovers were used to be selected directly or with the available radio access it used to connect automatically. With the help of this, the hand over occurrence now takes places frequently. This paper is based on the incorporation of DSRC, LTE as well as mm Wave on Internet of vehicles which is integrated with the Handover decision making algorithm, Network Selection and Routing. The decision of the handovers is to ensure that if there is any requirement of the vertical handovers using dynamic Q-learning algorithms in which entropy function is used to predict the threshold according to the characteristics of the environment. The network selection process is done using Fuzzy Convolution Neural Network commonly known as FCNN which makes the fuzzy rules by considering the parameters such as strength of its signal, its distance, the density of the vehicle, the type of its data as well the Line of Sight (LoS). V2V chain routing is presented in such a manner that V2V pairs are also selected with the help of jellyfish optimization algorithm considering three metrics – Vehicle metrics, Channel metrics and Vehicle performance metrics. OMNET++ simulator is the software in which system is developed. The performance evaluation is done according to its Handover Success Probability, Handover Failure, Redundant Handover, Mean Throughput, delay and Packet Loss.

Cross-Layer RRM in Wireless Data Networks

2007 ◽  
pp. 165-171
Author(s):  
A. Gyasi-Agyei
Keyword(s):  
Resource Sharing ◽  
Performance Metrics ◽  
Mac Protocol ◽  
Packet Delay ◽  
System Throughput ◽  
Wireless Data Networks ◽  
Wireless Data ◽  
Medium Access ◽  
Network Resource ◽  
General Aspects

Scheduling is the dynamic process of allocating a shared resource to multiple parallel users in order to optimize some desirable performance metrics. Metrics of interest include: maximization of system throughput, minimization of packet delay and jitter, and the provision of fairness. Scheduling is a key mechanism in RRM and operates in the medium access control (MAC) layer. Only three things can happen to the transmission medium of a multiuser network: resource hogging, resource clogging, or equitable resource sharing. Without a MAC protocol, the desirable third option can hardly occur. In the following we discuss some general aspects of OS, propose a generalized OS design framework, discuss future trends of OS, and list some open issues in OS design.

scholarly journals Deep Reinforcement Learning for 5G Radio Access Network Slicing with Spectrum Coexistence

2021 ◽  
Author(s):  
Yi Shi ◽  
Parisa Rahimzadeh ◽  
Maice Costa ◽  
Tugba Erpek ◽  
Yalin E. Sagduyu
Keyword(s):  
Reinforcement Learning ◽  
Access Network ◽  
Arrival Process ◽  
Practical Implementation ◽  
Network Slicing ◽  
Radio Access Network ◽  
Q Learning ◽  
State Action ◽  
Radio Access ◽  
Network Utility

The paper presents a reinforcement learning solution to dynamic admission control and resource allocation for 5G radio access network (RAN) slicing requests, when the spectrum is potentially shared between 5G and an incumbent user such as in the Citizens Broadband Radio Service scenarios. Available communication resources (frequency-time resource blocks and transmit powers) and computational resources (processor power) not used by the incumbent user can be allocated to stochastic arrivals of network slicing requests. Each request arrives with priority (weight), throughput, computational resource, and latency (deadline) requirements. As online algorithms, the greedy and myopic solutions that do not consider heterogeneity of future requests and their arrival process become ineffective for network slicing. Therefore, reinforcement learning solutions (Q-learning and Deep Q-learning) are presented to maximize the network utility in terms of the total weight of granted network slicing requests over a time horizon, subject to communication and computational constraints. Results show that reinforcement learning provides improvements in the 5G network utility relative to myopic, greedy, random, and first come first served solutions. In particular, deep Q-learning reduces the complexity and allows practical implementation as the state-action space grows, and effectively admits/rejects requests when 5G needs to share the spectrum with incumbent users that may dynamically occupy some of the frequency-time blocks. Furthermore, the robustness of deep reinforcement learning is demonstrated in the presence of the misdetection/false alarm errors in detecting the incumbent user's activity.

Network resource abstraction for 5G radio access network

2019 ◽  
Author(s):  
Kenichi Nakura ◽  
Akiko Nagasawa ◽  
Yukio Hirano ◽  
Kazuyuki Ishida ◽  
Junichi Nakagawa ◽  
...  
Keyword(s):  
Access Network ◽  
Network Resource ◽  
Radio Access Network ◽  
Radio Access

scholarly journals Emergency Communications Based on Throughput-Aware D2D Multicasting in 5G Public Safety Networks

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1901 ◽  
Author(s):  
Mengjun Yin ◽  
Wenjing Li ◽  
Lei Feng ◽  
Peng Yu ◽  
Xuesong Qiu
Keyword(s):  
Resource Sharing ◽  
Public Safety ◽  
High Speed ◽  
Cluster Head ◽  
Access Network ◽  
Base Station ◽  
Emergency Communications ◽  
Radio Access ◽  
Heterogeneous Cloud ◽  
Public Safety Networks

Emergency communications need to meet the developing demand of equipment and the complex scenarios of network in public safety networks (PSNs). Heterogeneous Cloud Radio Access Network (H-CRAN), an important technology of the 5th generation wireless systems (5G), plays an important role in PSN. H-CRAN has the features of resource sharing and centralized allocation which can make up for resource shortage in emergency communications. Therefore, an emergency communications strategy based on Device-to-device (D2D) multicast is proposed to make PSN more flexible and rapid. Nearby users can communicate directly without a base station through D2D. This strategy may guarantee high speed data transmission and stable continuous real-time communications. It is divided into three steps. Firstly, according to the distance between users, the alternative cluster head is divided. Secondly, two kinds of cluster head user selection schemes are developed. One is based on terminal power and the other is based on the number of extended users. Last but not least, the Hungarian Algorithm based on throughput-aware is used to channel multiplexing. The numerical results show that the proposed scheme can effectively extend the coverage of PSN and optimize the utilization of resources.

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