User Preference Aware Resource Management for Wireless Communication Networks

Xiao, Ailing; Huang, Xiaofu; Wu, Sheng; Jiang, Chunxiao; Ma, Li; Han, Zhu

doi:10.1109/mnet.011.2000148

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Wireless mobile caching is regarded as an attractive alternative telecommunication technology 7 . Wireless mobile caching can alleviate peak-hour traffic demands by locally storing frequently requested content on users’ devices during off-peak periods and facilitating content sharing between proximate users, thereby significantly reducing duplicate content requests 17 , 18 . The extent of traffic reduction depends on the storage allocated by users for this purpose 19 .…”

Section: Introductionmentioning

confidence: 99%

“…Particularly in densely populated areas, D2D links offer significant advantages for peer-to-peer content sharing 21 , 23 . The frequent utilization of D2D sharing ultimately alleviates the burden on the radio access link, enhancing the accuracy of predicting users’ content consumption patterns 18 .…”

Section: Introductionmentioning

confidence: 99%

“…Previous research on mobile caching highlights the critical importance of achieving a high content-sharing hit-ratio, which is influenced by both user mobility and content preferences 16,18,21,22 . However, pre-storing content to accommodate diverse user preferences encounters challenges due to the complexity of predicting individual behaviors and mobility patterns [25][26][27] .…”

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confidence: 99%

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Examining spatiotemporal crowdsensing and caching for population-dynamic OTT content delivery

Kim,

Jang,

Choi

et al. 2024

Sci Rep

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This study proposes a novel spatiotemporal crowdsensing and caching (SCAC) framework to address the surging demands of urban wireless network traffic. In the context of rampant urbanization and ubiquitous digitization in cities, effective data traffic management is crucial for maintaining a dynamic urban ecosystem. Leveraging user mobility patterns and content preferences, this study formulates an offloading policy to alleviate congestion across urban areas. Our approach uses an AI-based method at the cell level, providing a practical and scalable solution that can be readily adapted to bustling metropolitan areas. The implementation of our model demonstrated its effectiveness in reflecting real-world urban dynamics, resulting in significant reductions in peak-hour traffic and robust performance across diverse urban settings. The deployment strategy initiates from densely populated transportation hubs, gradually expanding to broader urban areas. This systematic expansion adheres to a policy framework that emphasizes data privacy and sustainable urban development, ensuring alignment with societal needs and regulatory frameworks. By addressing technological efficacy and societal impact, this study enhances the understanding of urban wireless traffic management. It offers mobile network operators, policymakers, and urban planners a comprehensive strategy to harness the potential of spatiotemporal technology, thereby ensuring that cities remain dynamic, efficient, and well-prepared for the future of digital connectivity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Examining spatiotemporal crowdsensing and caching for population-dynamic OTT content delivery

Kim,

Jang,

Choi

et al. 2024

Sci Rep

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“…Social media motivated caching strategy has attracted attentions from both academia and industry [8][9][10][11]. The authors in [8] analyse the Twitter data of 2016 U.S. presidential election utilizing LSTM networks to reduce the service latency.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [8] analyse the Twitter data of 2016 U.S. presidential election utilizing LSTM networks to reduce the service latency. A preference-aware optimization [9] considers user side adaptive streaming, coordinated bandwidth allocation, and network side caching content selection. In [10], caching cost of the base stations (BS) and social factors among mobile users (MU) are considered in ultra-dense small cell networks (UDN) to obtain effective caching incentives and the optimal social group utility.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Latent Events Forecasting in Content Caching Networks

Yang

Liu

Chen

et al. 2022

IEEE Trans. Wireless Commun.

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A novel Twitter context aided content caching (TAC) framework is proposed for enhancing the caching efficiency by taking advantage of the legibility and massive volume of Twitter data. For the purpose of promoting the caching efficiency, three machine learning models are proposed to predict latent events and events popularity, utilizing collected Twitter data with geotags and geographic information of the adjacent base stations (BSs). Firstly, we propose a latent Dirichlet allocation (LDA) model for latent events forecasting because of the superiority of LDA model in natural language processing (NLP). Then, we conceive long short-term memory (LSTM) with skip-gram embedding approach and LSTM with continuous skip-gram-Geoaware embedding approach for the events popularity forecasting. Furthermore, we associate the predict latent events and the popularity of the events with the caching strategy. Lastly, we propose a non-orthogonal multiple access (NOMA) based content transmission scheme. Extensive practical experiments demonstrate that: (1) The proposed TAC framework outperforms conventional caching framework and is capable of being employed in practical applications thanks to the associating ability with public interests. (2) The proposed LDA approach conserves superiority for natural language processing (NLP) in Twitter data. (3) The perplexity of the proposed skip-gram based LSTM is lower compared with conventional LDA approach. (4) Evaluation of the model demonstrates that the hit rates of tweets of the model vary from 50% to 65% and the hit rate of the caching contents is up to approximately 75% with smaller caching space compared to conventional algorithms. Simulation results also shows that the proposed NOMA-enabled caching scheme outperforms conventional least frequently used (LFU) scheme by 25%.

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Incorporating Energy and Load Balance into Virtual Network Embedding Process

Jiang

Zhang

2021

QoS-Aware Virtual Network Embedding

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User Preference Aware Resource Management for Wireless Communication Networks

Cited by 5 publications

References 15 publications

Examining spatiotemporal crowdsensing and caching for population-dynamic OTT content delivery

Examining spatiotemporal crowdsensing and caching for population-dynamic OTT content delivery

Deep Learning for Latent Events Forecasting in Content Caching Networks

Incorporating Energy and Load Balance into Virtual Network Embedding Process

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