A predictive handoff mechanism for 5G ultra dense networks

Ghosh, Shankar K.; Ghosh, S.

doi:10.1109/nca.2017.8171395

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Then, the comprehensive weight of network attributes wj can be obtained by formula (9), where wj s and wj o are the subjective and objective weights of network attributes, respectively.…”

Section: Determination Of Network Parameter Attribute Weightsmentioning

confidence: 99%

Research on 5G Heterogeneous Ultra Dense network selection algorithm based on Dueling- DDQN

Xie

Zhu

2023

Preprint

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In the 5G Heterogeneous Ultra Dense Network environment, due to the dense deployment of the network and the diversity of user service types, a more flexible network selection algorithm is required to reduce the network blocking rate and improve the user’s quality of service (QoS). Considering the QoS requirements and preferences of the users, a network selection algorithm based on Dueling-DDQN is proposed by using deep reinforcement learning, which defines the state, action space and reward function of the different services to maximize the cumulative reward value of the network selection algorithm. The simulation results show that compared with other algorithms, the proposed algorithm can effectively reduce the network blocking rate while reducing the switching times.

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“…Then, the comprehensive weight of network attributes wj can be obtained by formula (9), where wj s and wj o are the subjective and objective weights of network attributes, respectively.…”

Section: Determination Of Network Parameter Attribute Weightsmentioning

confidence: 99%

Research on 5G Heterogeneous Ultra Dense network selection algorithm based on Dueling- DDQN

Xie

Zhu

2023

Preprint

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“…Through this mechanism, the achievable data receiving rate increases and the data blocking probability reduces. Other useful studies presented on hand-off include hand-off management in software-defined 5G networks, predictive hand-off mechanism, and fast and secure handover for 5G [11][12][13].…”

Section: Major Milestones Of Next Generation Networkmentioning

confidence: 99%

Big Data Perspective and Challenges in Next Generation Networks

2018

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With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks.

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“…HM is responsible for making network (dis)connection decisions in a timely manner [ 1 , 2 ]. In this sense, HM is pivotal for providing service continuity, enormous reliability and extreme-low latency, and meeting sky-high data rates, in current and upcoming wireless communications [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Semantic and Knowledge-Based Approach for Handover Management

Vivas

Rendón

Nieves

2021

Sensors

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Handover Management (HM) is pivotal for providing service continuity, enormous reliability and extreme-low latency, and meeting sky-high data rates, in wireless communications. Current HM approaches based on a single criterion may lead to unnecessary and frequent handovers due to a partial network view that is constrained to information about link quality. In turn, HM approaches based on multicriteria may present a failure of handovers and wrong network selection, decreasing the throughput and increasing the packet loss in the network. This paper proposes SIM-Know, an approach for improving HM. SIM-Know improves HM by including a Semantic Information Model (SIM) that enables context-aware and multicriteria handover decisions. SIM-Know also introduces a SIM-based distributed Knowledge Base Profile (KBP) that provides local and global intelligence to make contextual and proactive handover decisions. We evaluated SIM-Know in an emulated wireless network. When the end-user device moves at low and moderate speeds, the results show that our approach outperforms the Signal Strong First (SSF, single criterion approach) and behaves similarly to the Analytic Hierarchy Process combined with the Technique for Order Preferences by Similarity to the Ideal Solution (AHP-TOPSIS, multicriteria approach) regarding the number of handovers and the number of throughput drops. SSF outperforms SIM-Know and AHP-TOPSIS regarding the handover latency metric because SSF runs a straightforward process for making handover decisions. At high speeds, SIM-Know outperforms SSF and AHP-TOPSIS regarding the number of handovers and the number of throughput drops and, further, improves the throughput, delay, jitter, and packet loss in the network. Considering the obtained results, we conclude that SIM-Know is a practical and attractive solution for cognitive HM.

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A predictive handoff mechanism for 5G ultra dense networks

Cited by 9 publications

References 16 publications

Research on 5G Heterogeneous Ultra Dense network selection algorithm based on Dueling- DDQN

Research on 5G Heterogeneous Ultra Dense network selection algorithm based on Dueling- DDQN

Big Data Perspective and Challenges in Next Generation Networks

A Semantic and Knowledge-Based Approach for Handover Management

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