Importance of Cloud Computing in 5G Radio Access Networks

2020 ◽  
pp. 255-268
Author(s):  
Wael S. Afifi ◽  
Ali A. El-Moursy ◽  
Mohamed Saad ◽  
Salwa M. Nassar ◽  
Hadia M. El-Hennawy
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Network Architecture ◽  
Access Networks ◽  
Multimedia Services ◽  
Fifth Generation ◽  
Radio Access ◽  
Data Rates ◽  
Computing Centers

The fifth generation of wireless networks (5G) will kick off with evolved mobile broadband services as promised by several mobile-related associations, researchers, and operators. Compared to 4G, 5G aims to provide greater data rates with lower latency and higher coverage to numerous users who stream ubiquitous multimedia services. 5G benefits the innovation of internet of things (IoT) as well. To this end, several modifications in the network architecture are required. This chapter is discussing the role of cloud computing centers in 5G networks, and how such integration could be implemented as found in the literature. The benefits of cloud/5G integration will be explained as well. In addition, some challenges related to the integration will be demonstrated.

Importance of Cloud Computing in 5G Radio Access Networks

2021 ◽  
pp. 226-239
Author(s):  
Wael S. Afifi ◽  
Ali A. El-Moursy ◽  
Mohamed Saad ◽  
Salwa M. Nassar ◽  
Hadia M. El-Hennawy
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Network Architecture ◽  
Access Networks ◽  
Multimedia Services ◽  
Fifth Generation ◽  
Radio Access ◽  
Data Rates ◽  
Computing Centers

The fifth generation of wireless networks (5G) will kick off with evolved mobile broadband services as promised by several mobile-related associations, researchers, and operators. Compared to 4G, 5G aims to provide greater data rates with lower latency and higher coverage to numerous users who stream ubiquitous multimedia services. 5G benefits the innovation of internet of things (IoT) as well. To this end, several modifications in the network architecture are required. This chapter is discussing the role of cloud computing centers in 5G networks, and how such integration could be implemented as found in the literature. The benefits of cloud/5G integration will be explained as well. In addition, some challenges related to the integration will be demonstrated.

scholarly journals The role of dedicated data computing centers in the age of cloud computing

2017 ◽  
Vol 898 ◽  
pp. 082009
Author(s):  
Costin Caramarcu ◽  
Christopher Hollowell ◽  
William Strecker-Kellogg ◽  
Antonio Wong ◽  
Alexandr Zaytsev
Keyword(s):  
Cloud Computing ◽  
Computing Centers

scholarly journals Heterogeneous Cloud Radio Access Networks: Enhanced Time Allocation for Interference Mitigation

2018 ◽  
Vol 2018 ◽  
pp. 1-17
Author(s):  
Imad Al-Samman ◽  
Reham Almesaeed ◽  
Angela Doufexi ◽  
Mark Beach
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Interference Mitigation ◽  
Base Station ◽  
Small Cell ◽  
Access Networks ◽  
Cloud Radio Access Networks ◽  
Radio Access Networks ◽  
Radio Access ◽  
Heterogeneous Cloud

Responding to the unprecedented challenges imposed by the 5G technologies, mobile operators have given significant attention to Heterogeneous Cloud Radio Access Networks (H-CRAN) due to their beneficial features of performing optimization, cost effectiveness, and improving spectral and energy efficiency performance. H-CRAN inherits the attractive benefits of Heterogeneous Networks (HetNet) and the cloud computing by facilitating interference mitigation, scalability, and radio resource control. Consequently, H-CRAN is proposed in this article as a cost-effective potential solution to alleviate intertier interference and improve cooperative processing gains in HetNets by employing cloud computing. H-CRAN can provide efficient resource sharing at the spectrum, network, and infrastructure levels. Therefore, this article proposes H-CRAN cooperative interference mitigation method that enhances the time sharing among Radio Remote Heads (RRH) users. The study proposes an enhanced Almost Blank Subframe (ABSF) technique to increase the SINR and throughput of the small-cell (low power base station) and macrocell users. Simulation results show that the proposed Dynamic Programming-Diverse Almost Blank Subframe (ABSF) Pattern (DP-DAP) scheme improved the macro- and small-cell users up to 56% and 35%, respectively, as compared to other state-of-the-art ABSF schemes.

Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks

2019 ◽  
pp. 163-198 ◽  
Author(s):  
Ioan-Sorin Comşa ◽  
Sijing Zhang ◽  
Mehmet Emin Aydin ◽  
Pierre Kuonen ◽  
Ramona Trestian ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Access Networks ◽  
High Definition ◽  
Quality Metric ◽  
Radio Access ◽  
Data Rates ◽  
Packet Scheduler ◽  
Important Quality ◽  
User Rate ◽  
Over Time

The user experience constitutes an important quality metric when delivering high-definition video services in wireless networks. Failing to provide these services within requested data rates, the user perceived quality is strongly degraded. On the radio interface, the packet scheduler is the key entity designed to satisfy the users' data rates requirements. In this chapter, a novel scheduler is proposed to guarantee the bit rate requirements for different types of services. However, the existing scheduling schemes satisfy the user rate requirements only at some extent because of their inflexibility to adapt for a variety of traffic and network conditions. In this sense, the authors propose an innovative framework able to select each time the most appropriate scheduling scheme. This framework makes use of reinforcement learning and neural network approximations to learn over time the scheduler type to be applied on each momentary state. The simulation results show the effectiveness of the proposed techniques for a variety of data rates' requirements and network conditions.

scholarly journals Resource Allocation Scheme for Fog-Enabled Wireless Access Networks under the QoS of Users

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Huilin Jiang ◽  
Lili Chen ◽  
Xiang Song ◽  
Xueming Liu
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Network Architecture ◽  
Access Point ◽  
Access Networks ◽  
System Throughput ◽  
Wireless Access Networks ◽  
Resource Allocation Algorithm ◽  
Radio Access ◽  
Resource Allocation Scheme

With the complexity of the network architecture, the diversity of network slicing, and the introduction of advanced techniques such as device to device (D2D), it is difficult for the next-generation (5G+ or 6G) networks to comprehensively consider the requirements of users from different slices and jointly allocate wireless resources to improve network energy efficiency. This paper studies the energy efficiency optimization problem for D2D-enabled fog radio access networks (FRANs). A resource allocation algorithm is proposed to maximize the network energy efficiency by jointly optimizing the beamforming vector, resource block allocation, and transmission power of the remote radio heads (RRHs), fog access point (FAP), and D2D users. The developed algorithm is based on nonlinear programming, convex optimization, and Lagrangian duality. Simulation results show that, by applying the proposed algorithm, the system throughput is significantly improved, and the network energy consumption is greatly reduced, which can ultimately improve the network energy efficiency obviously.

Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks

2021 ◽  
pp. 151-186
Author(s):  
Ioan-Sorin Comşa ◽  
Sijing Zhang ◽  
Mehmet Emin Aydin ◽  
Pierre Kuonen ◽  
Ramona Trestian ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Access Networks ◽  
High Definition ◽  
Quality Metric ◽  
Radio Access ◽  
Data Rates ◽  
Packet Scheduler ◽  
Important Quality ◽  
User Rate ◽  
Over Time

The user experience constitutes an important quality metric when delivering high-definition video services in wireless networks. Failing to provide these services within requested data rates, the user perceived quality is strongly degraded. On the radio interface, the packet scheduler is the key entity designed to satisfy the users' data rates requirements. In this chapter, a novel scheduler is proposed to guarantee the bit rate requirements for different types of services. However, the existing scheduling schemes satisfy the user rate requirements only at some extent because of their inflexibility to adapt for a variety of traffic and network conditions. In this sense, the authors propose an innovative framework able to select each time the most appropriate scheduling scheme. This framework makes use of reinforcement learning and neural network approximations to learn over time the scheduler type to be applied on each momentary state. The simulation results show the effectiveness of the proposed techniques for a variety of data rates' requirements and network conditions.

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