Leveraging mobility and content caching for proactive load balancing in heterogeneous cellular networks

Manzoor, Sanaullah; Mazhar, Suleman; Asghar, Ahmad Bilal; Mian, Adnan Noor; Imran, Ali; Crowcroft, Jon

doi:10.1002/ett.3739

Cited by 15 publications

(16 citation statements)

References 49 publications

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“…Correspondingly, they update their local content space with similar contents based on the obtained similarity scores (line 8). At the MBS, LSTM model is used to predict top-n contents and prepare contents for proactive caching at the RSU level (line [13][14][15][16][17][18][19][20][21][22][23][24]. Based on these information, RSUs update their local content space (line 25), and corresponding rework to determine top-k similar contents of top-n popular contents to share with MBS in the next round.…”

Section: Proposed Method: Dcol For Proactive Content Cachingmentioning

confidence: 99%

“…However, the underlying complexities of deep learning models are still overlooked. Similarly, in [17], [18], the authors consider user's mobility behaviors and social impacts on content preference that distort the performance of content requests. They propose a long-term strategy of proactive caching to minimize the sum of communication costs to get the requested contents.…”

Section: Related Workmentioning

confidence: 99%

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DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

2021

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Caching popular content at the network edge, such as roadside units (RSUs), is a promising solution that enhances the user's quality-of-experience (QoE) and reduces network traffic. In this regard, the most challenging issue is to correctly predict the future popularity of contents and effectively store them in the cache of edge nodes. Thus, in this paper, we propose a distributed proactive caching scheme at the edge to optimize the content retrieval cost and improve the QoE of the mobile users. This proactive content caching scheme, namely Distributed Collaborative Learning (DCoL), is a non-parametric content popularity prediction mechanism in a distributed setting. Next, we show the advantage of DCoL as two folds: (i) it leverages distributed content popularity information to develop local content caching strategy, and (ii) it exploits the regional database using the long short-term memory (LSTM)-based prediction model to capture the dependency between requested contents. Simulation results using real datasets demonstrate that our scheme yields 8.9% and 18% gains, respectively, in terms of the cache hit efficiency and content retrieval cost, compared with a competitive centralized baseline, and outperforms other traditional caching strategies.INDEX TERMS proactive content caching, mobile edge computing, distributed learning, collaborative filtering, neural network.

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Section: Proposed Method: Dcol For Proactive Content Cachingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

2021

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“…User Mobility Prediction can be one of the key enablers for AI based network automation and next generation proactive SON [28]. This can enable the reservation of network resources in future identified cells for seamless HO experience [4] as well as for traffic forecasting purposes for load balancing [29] and driving the energy saving SON functions [5], [30] as well as optimizing battery life [3].…”

Section: A Case Study Using Syntheticnet: Ai-assisted Mobility Prmentioning

confidence: 99%

SyntheticNET: A 3GPP Compliant Simulator for AI Enabled 5G and Beyond

et al. 2020

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The rapid evolution of cellular system design towards 5G and beyond gives rise to a need for investigation of the new features, design proposals and solutions in realistic settings for various deployments and use case scenarios. While many system level simulators for 4G and 5G exist today, there is particularly a dire need for a 3GPP compliant system level holistic and realistic simulator that can support evaluation of the plethora of AI based network automation solutions being proposed in literature. In this paper we present such a simulator developed at AI4networks Lab, called SyntheticNET. To the best of authors' knowledge, SyntheticNET is the very first python-based simulator that fully conforms to 3GPP 5G standard release 15 and is upgradable to future releases. The key distinguishing features of SyntheticNET compared to existing simulators include: 1) a modular structure to facilitate cross validation and upgrading to future releases; 2) flexible propagation modeling using measurement based, ray tracing based or AI based propagation modeling; 3) ability to import data sheet based on measurement based realistic vendor specific base station features such as antenna and energy consumption pattern; 4) support for 5G standard based adaptive numerology; 5) realistic and user-specific mobility patterns that are yielded from actual geographical maps; 6) detailed handover (HO) process implementation; and 7) incorporation of database aided edge computing. Another key feature of the SyntheticNET is the ease with which it can be used to test AI based network automation solutions. Being the first python based 5G simulator, this ease, in part stems for SyntheticNET's built-in capability to process and analyze large data sets and integrated access to Machine Learning libraries. Thus, SyntheticNET simulator offers a powerful platform for academia and industry alike to investigate not only new solutions for optimally designing, deploying and operating existing and emerging cellular networks but also for enabling AI empowered deep automation in the future. INDEX TERMS 5G, cellular networks, network simulator, flexible frame structure, mobility, edge computing.

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“…Except for using handover for load balancing, network operators can also achieve load balance via content caching management based on cell prediction. A Proactive Load Balancing (PLB) framework is investigated in [97]. Specifically, the authors exploit users' trajectory to predict their future crossing cells and model users' content profile to predict their most expected future data.…”

Section: ) Load Balancingmentioning

confidence: 99%

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

2020

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Mobility prediction is a powerful tool for network operators to optimize network performance. From cell level, if network operators know the cells to which the users will be connected in advance, wireless resources can be pre-allocated to improve network performance and better user experience can be provided in location-based services. Many next-cell prediction models and methods have been suggested and implemented. This paper is devoted to next-cell prediction (cell level mobility prediction) in cellular networks, and provides a thorough survey of the prediction schemes and applications. Particularly, a two-level classification methodology was proposed and applied. We first divided the prediction schemes into three categories based on the mobility data used for prediction, i.e. Current Movement State based Approaches (CMSA), Historical Movement Pattern based Approaches (HMPA), and HybriD Approaches (HDA). Prediction schemes in each category were further classified based on the used prediction methods. The typical application scenarios were introduced as well, including handover management, resource allocation, etc. Finally, current challenges and potential trends in the near future were further discussed.

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Leveraging mobility and content caching for proactive load balancing in heterogeneous cellular networks

Cited by 15 publications

References 49 publications

DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

SyntheticNET: A 3GPP Compliant Simulator for AI Enabled 5G and Beyond

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

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