Dynamic fuzzy Q-learning for handover parameters optimization in 5G multi-tier networks

Wu, Jihong; Liu, Jing; Huang, Zhangpeng; Zheng, Shuqiang

doi:10.1109/wcsp.2015.7341220

Cited by 39 publications

(44 citation statements)

References 7 publications

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“…In [108], authors focus on a two-tier network composed of macro cells and small cells, and propose a dynamic fuzzy Q learning algorithm for mobility management. To apply Q learning, the call drop rate together with the signaling load caused by handover constitutes the system state, while the action space is defined as the set of possible values for the adjustment of handover margin.…”

Section: A Reinforcement Learning Based Approachesmentioning

confidence: 99%

“…Another alternative for mobility management is using fuzzy logic controller (FLC). In [108] and [110], numeric simulation has demonstrated the advantages of fuzzy Q learning over FLC whose performance is limited by available prior knowledge. Specifically, it is reported in [108] that fuzzy Q learning can still achieve competitive performance even without enough prior knowledge, while it is is shown to reach better long-term performance in [110] compared with the FLC based method.…”

Section: K Alternatives For Mobility Managementmentioning

confidence: 99%

“…By involving machine learning, especially reinforcement learning, each BS can self-optimize its resource allocation, handover parameter configuration, and so on. In summary, this motivation applies to [42], [45], [51], [54], [56]- [61], [64], [65], [68], [90], [92], [108]- [110].…”

Section: • Developing Low-complexity Algorithms For Wirelessmentioning

confidence: 99%

See 2 more Smart Citations

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

494

290

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As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.

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Section: A Reinforcement Learning Based Approachesmentioning

confidence: 99%

Section: K Alternatives For Mobility Managementmentioning

confidence: 99%

See 1 more Smart Citation

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

494

290

View full text Add to dashboard Cite

show abstract

“…where f f g i , f ig i , f cg i and f og i are the outputs of forget, input, cell and output gates defined as 8 8 In this paper, we drop the bias for simplicity.…”

Section: Dqn In Ho Controllermentioning

confidence: 99%

“…One way to optimize the HO process is tuning the HO parameters adaptively by implementing threshold comparisons with several specific metrics [6]- [8]. In [6], an adaptive HHM approach was proposed to lower the number of HOs, which uses a predefined RSRQ threshold and path loss factor to adapt the HHM.…”

Section: Introductionmentioning

confidence: 99%

Handover Control in Wireless Systems via Asynchronous Multiuser Deep Reinforcement Learning

Wang

et al. 2018

IEEE Internet Things J.

119

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In this paper, we propose a two-layer framework to learn the optimal handover (HO) controllers in possibly large-scale wireless systems supporting mobile Internet-of-Things (IoT) users or traditional cellular users, where the user mobility patterns could be heterogeneous. In particular, our proposed framework first partitions the user equipments (UEs) with different mobility patterns into clusters, where the mobility patterns are similar in the same cluster. Then, within each cluster, an asynchronous multi-user deep reinforcement learning scheme is developed to control the HO processes across the UEs in each cluster, in the goal of lowering the HO rate while ensuring certain system throughput. In this scheme, we use a deep neural network (DNN) as an HO controller learned by each UE via reinforcement learning in a collaborative fashion. Moreover, we use supervised learning in initializing the DNN controller before the execution of reinforcement learning to exploit what we already know with traditional HO schemes and to mitigate the negative effects of random exploration at the initial stage. Furthermore, we show that the adopted global-parameter-based asynchronous framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. Finally, simulation results demonstrate that the proposed framework can achieve better performance than the state-of-art on-line schemes, in terms of HO rates.

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Fractional squirrel–dolphin echolocation with deep belief network for network‐controlled vertical handoff in disparate and heterogeneous wireless network

Patil

2021

Int J Communication

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The innovative characteristics of different heterogeneous wireless networks provide better service. The disparate wireless access networks consist of wireless metropolitan access network (WMAN) and cellular networks. Various techniques are devised for dynamic network selection, but these techniques concentrated on attaining improved performance. To address this issue, a novel method is devised to improve the energy efficiency of disparate heterogeneous wireless networks. Here, the proposed fractional squirrel-dolphin echolocation (FrSqDE) algorithm is devised by integrating squirrel search optimization (SSA) and fractional dolphin echolocation (fractional DE) algorithm. In addition, the fitness function is considered with certain factors that include delay, bit error rate (BER), jitter, packet loss, energy consumption, probability of hot spot, average bit rate (ABR), and received signal strength (RSS). The proposed FrSqDE optimization algorithm is used for training deep belief networks (DBNs) to select optimal weights. Here, the network selection factors are given as input to the proposed FrSqDE-based DBN, wherein optimal decision is made to handle vertical handoff. The proposed FrSqDE-based DBN offered improved performance with a minimal call drop probability of 0.050, minimal delay of 0.013 s, minimal energy consumption of 0.393 J, and maximal throughput of 117.972 kbps.

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Dynamic fuzzy Q-learning for handover parameters optimization in 5G multi-tier networks

Cited by 39 publications

References 7 publications

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Handover Control in Wireless Systems via Asynchronous Multiuser Deep Reinforcement Learning

Fractional squirrel–dolphin echolocation with deep belief network for network‐controlled vertical handoff in disparate and heterogeneous wireless network

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