A personalized QoE-aware handover decision based on distributed reinforcement learning

Ghahfarokhi, Behrouz Shahgholi; Movahhedinia, Naser

doi:10.1007/s11276-013-0572-2

Cited by 13 publications

(2 citation statements)

References 45 publications

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“…TITLE-ABS-KEY( ("reinforcement learning" OR "contextual bandit") AND ("personalization" OR "personalized" OR "personal" OR "personalisation" OR "personalised" OR "customization" OR "customized" OR "customised" OR "customised" OR "individualized" OR "individualised" OR "tailored")) Table 7 Table containing all included publications. The first column refers to the data items in Table 2 # Value Publications 1 n [1,4,10,11,13,16,[18][19][20][24][25][26][27][28][29]31,32,35,36,38,[40][41][42][43][44][45]48,49,52,56,63,66,68,70,74,82,85,86,88,91,93,94,99,101,104,[106]…”

Section: Den Hengst Et Al / Reinforcement Learning For Personalizatimentioning

confidence: 99%

Reinforcement learning for personalization: A systematic literature review

Hengst

Grua

Hassouni

et al. 2020

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The major application areas of reinforcement learning (RL) have traditionally been game playing and continuous control. In recent years, however, RL has been increasingly applied in systems that interact with humans. RL can personalize digital systems to make them more relevant to individual users. Challenges in personalization settings may be different from challenges found in traditional application areas of RL. An overview of work that uses RL for personalization, however, is lacking. In this work, we introduce a framework of personalization settings and use it in a systematic literature review. Besides setting, we review solutions and evaluation strategies. Results show that RL has been increasingly applied to personalization problems and realistic evaluations have become more prevalent. RL has become sufficiently robust to apply in contexts that involve humans and the field as a whole is growing. However, it seems not to be maturing: the ratios of studies that include a comparison or a realistic evaluation are not showing upward trends and the vast majority of algorithms are used only once. This review can be used to find related work across domains, provides insights into the state of the field and identifies opportunities for future work.

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Section: Den Hengst Et Al / Reinforcement Learning For Personalizatimentioning

confidence: 99%

Reinforcement learning for personalization: A systematic literature review

Hengst

Grua

Hassouni

et al. 2020

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“…QoE of ongoing users is used as an indicator to select the best target network. Authors in [14] introduced a personalized user centric handoff decision method to decide about the time and target of handover based on User Perceived Quality (UPQ) feedbacks. This method is slow converging and lucks intelligence in handover decision.…”

Section: Related Workmentioning

confidence: 99%

QoE-Fuzzy VHO Approach for Heterogeneous Wireless Networks (HWNs)

Zineb¹,

Ayadi²,

Tabbane³

2016

2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)

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Different wireless technologies are assumed to cooperate in a single heterogeneous environment in future network deployment schemes. Such environment is characterized by a multitude of new services based on multimedia services like gaming and real time video streaming. When dealing with video services, traditional QoS solutions are not sufficient for ensuring video awareness and good user's perception. Quality of Experience (QoE) management approach have been introduced to estimate the quality level of multimedia services. In an heterogeneous environment every user can switch between many networks during his communication in a vertical handover (VHO) process and QoS may decrease as well as the video quality. That is why, maintaining the requested QoS and keeping a satisfying QoE during a VHO process is the main goal for HWNs.In this paper, we proposes a QoE-fuzzy vertical handover algorithm with QoS management and QoE decision. Simulation results of the proposed system show performances improvement for throughput and delays.

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