An Improved Vertical Handoff Decision Algorithm for Heterogeneous Wireless Networks

Ling, Yutao; Yi, Benshun; Zhu, Qiuping

doi:10.1109/wicom.2008.64

Cited by 14 publications

(5 citation statements)

References 8 publications

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“… Enables vertical handoff decisions that depend on various parameters such as minimum bandwidth, delay, and low battery of the mobile terminal in heterogeneous wireless networks [178].…”

Section: Reinforcement Learningmentioning

confidence: 99%

“…This algorithm applies Q-learning to determine the state and action of [172] Q-value in deep-RL with URLLC for immediate reward and to discount the reward from the output of the DNN [205]. However, the efficient handover management, high capacity, and guaranteed high QoS in heterogeneous networks that using The Q-learning algorithm for reinforcement learning achieved [173] related to load balancing [174]- [177], mobility management [178]- [181], user association [182]- [184], and resource allocation [177], [185], [160], [186]- [190], [191].…”

Section: Reinforcement Learningmentioning

confidence: 99%

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A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

et al. 2021

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The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-toend behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.INDEX TERMS Artificial neural networks, artificial intelligence, Internet of Things, sixth-generation wireless communication and network architecture, URLLC.

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“… Enables vertical handoff decisions that depend on various parameters such as minimum bandwidth, delay, and low battery of the mobile terminal in heterogeneous wireless networks [178].…”

Section: Reinforcement Learningmentioning

confidence: 99%

Section: Reinforcement Learningmentioning

confidence: 99%

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

et al. 2021

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“…The handoff algorithm proposed in [20] aims to provide an efficient resource utilization of heterogeneous networks through balancing the traffic load among all the APs/BSs and maximizing the battery lifetime of mobile terminals at same time. Fuzzy logic method can be designed to design vertical handoff algorithm with fuzzified parameters in [16,17]. The resource management framework based on policy is proposed in [18] and all sorts of network resources can be utilized under the framework of the load balancing policy.…”

Section: Previous Workmentioning

confidence: 99%

A noncooperative game-theoretic vertical handoff in 4G heterogeneous wireless networks

Chen

Zhou

Chai

et al. 2010

Proceedings of the 5th International ICST Conference on Communications and Networking in China

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Proactive handoff is expected to be one of the distinct feature of 4G heterogeneous wireless systems, which is obviously different from the passive handoff commonly existed in traditional cellular systems. Users with multi-network interface terminals can actively and easily handoff to access network for better QoS satisfaction at a minimum expense, on the other hand, service providers (SPs) will have to face more intense competition for attracting more users to maximize their profits. In this paper, the relationship between competitive SPs and users is modeled as a noncooperative game, and the Nash equilibrium solutions corresponding to the best response price offered by SP are obtained. Based on the optimal prices, a novel vertical handoff algorithm for maximizing user performance-price-ratio is proposed. Numerical results demonstrate that all SPs can attain maximal profits from the Nash equilibrium results, and both of the network utilization efficiency and user QoS can be guaranteed.

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“…The proposed algorithm reduces unnecessary handover and call dropping probability by using multiple attribute decision-making algorithms. Kassar et al [8] and Mehbodniya et al [9] presented an idea to eliminate the imprecision of data in the work of Ling et al [10] by combining fuzzy logic with multiple attribute decision making. It includes received signal strength, QoS parameters, and mobile velocity attributes with analytic hierarchy process as a weighting scheme.…”

Section: Introductionmentioning

confidence: 99%

Service-Adaptive Fuzzy Multi Criteria Based Intelligent Vertical Handover Decision Algorithm for Heterogeneous Wireless Networks

Narayanan

Rajeswari²,

Sureshkumar

2014

Advances in Intelligent Systems and Computing

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The next-generation wireless networks should be interoperable with other communication technologies to offer the best connectivity to the users.But providing such interworking is a key challenge. Currently, existing decision engines are simple, proprietary, and have no support for profile-based service, and handover is only based on the received signal strength which is not intelligible enough to make handoff decision. The proposed decision algorithm gains intelligence by combining fuzzy logic system, multiple attribute decision making, and context-aware strategies. It synchronizes the user schedule and usage pattern through software agent for making handover at appropriate time. The performance analysis shows that the proposed algorithm efficiently uses the network resources by switching between 3G and Wi-Fi based on the user schedule. It is observed that average handover delay for the experiment is 25-35 ms under good RF conditions, running time of algorithm is around 1 ms, and it reduces the call dropping rate (<0.006), blocking probability (<0.00607), as well as unnecessary handover in heterogeneous networks.

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An Improved Vertical Handoff Decision Algorithm for Heterogeneous Wireless Networks

Cited by 14 publications

References 8 publications

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

A noncooperative game-theoretic vertical handoff in 4G heterogeneous wireless networks

Service-Adaptive Fuzzy Multi Criteria Based Intelligent Vertical Handover Decision Algorithm for Heterogeneous Wireless Networks

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