Slicing Architecture for Managing User Mobility in Next-Generation Heterogeneous Wireless Networks

2019 ◽  
Author(s):  
Ali Saeed Dayem Alfoudi ◽  
Qusay Omran Mosa ◽  
Wesam Ch. Alisawi ◽  
Rafid Nabil Jaffer
Keyword(s):  
Wireless Networks ◽  
Heterogeneous Wireless Networks ◽  
User Mobility ◽  
Next Generation

scholarly journals PMIPv6 Assistive Cross-Layer Design to reduce handover latency in VANET Mobility for Next Generation Wireless Networks

2015 ◽  
Vol 7 (3) ◽  
pp. 1 ◽  
Author(s):  
Haider Noori AL-Hashimi ◽  
Waleed Noori Hussein
Keyword(s):  
Wireless Networks ◽  
Mobility Management ◽  
Heterogeneous Wireless Networks ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Next Generation ◽  
Handover Delay ◽  
Next Generation Wireless Networks ◽  
Management Protocol ◽  
Management Schemes

VANET Networks are one of the main next generation wireless networks which are envisaged to be an integration of homogeneous and heterogeneous wireless networks. The inter-networking of these wireless networks with the Internet will provide ubiquitous access to roaming network users. However, a seamless handover mechanism with negligible handover delay is required to maintain active connections during roaming across these networks. Several solutions, mainly involving host-based localized mobility management schemes, have been widely proposed to reduce handover delay among homogeneous and heterogeneous wireless networks. However, the handover delay remains high and unacceptable for delay-sensitive services such as real-time and multimedia services. Moreover, these services will be very common in next generation wireless networks. Unfortunately, these widely proposed host-based localized mobility management schemes involve the vehicle in mobility-related signalling hence effectively increasing the handover delay. Furthermore, these schemes do not properly address the advanced handover scenarios envisaged in future wireless networks. This paper, therefore, proposes a VANET mobility management framework utilizing cross-layer design, the IEEE 802.21 future standard, and the recently emerged network-based localized mobility management protocol, Proxy Mobile IPv6, to further reduce handover delay.

scholarly journals Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

2021 ◽  
Author(s):  
Abdulmalik Alwarafy ◽  
Mohamed Abdallah ◽  
Bekir Sait Ciftler ◽  
Ala Al-Fuqaha ◽  
Mounir Hamdi
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Reinforcement Learning ◽  
Large Scale ◽  
Heterogeneous Wireless Networks ◽  
Future Research ◽  
Radio Resource Allocation ◽  
Next Generation ◽  
Radio Resource ◽  
Next Generation Wireless Networks

<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

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