Effective Resource Sharing in Mobile-Cell Environments

Jaffry, Shan; Hasan, Syed Faraz; Gui, Xiang

doi:10.48550/arxiv.1808.01700

Cited by 5 publications

(7 citation statements)

References 32 publications

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“…However, the recent trend in public transport vehicles becoming new cellular hotspots, warrants new communication technologies. This is because of the recent advancements in connected and autonomous car technology and the increased in-vehicle user activities [18], [41]- [43]. Therefore, moving networks will become an essential part of 5G and beyond network architecture.…”

Section: B 5g Cellular Architecturementioning

confidence: 99%

“…The wireless backhaul has low cost of deployment (i.e., CAPEX) and is more suitable for outdoor urban environments. Hence, majority of researchers have considered wireless backhaul for moving networks [41], [45], [72]- [78] etc. These researches discuss two competing technologies (IEEE 802.11p and cellular backhaul) to connect mobile cells to the backbone network.…”

Section: B Wireless Backhaul Linksmentioning

confidence: 99%

“…Authors in [23] reported that shared spectrum between mobile cell's in-vehicle users and fixed layers' (i.e., macro, small cell) users may cause severe QoS degradation due to high interference, subject to unplanned mobility. However, the authors in [24], [41], [45], [77] reported that high link success probabilities and ergodic rates can be achieved using same frequency spectrum for invehicle and out-of-vehicle communication, provided that VPL and SIC are utilized. However, it is highly anticipated that network operators may opt for dedicated spectrum for invehicle and out-of-vehicle communication due to availability of large contiguous bandwidth and low penetration properties of mmWave spectrum to avoid interference in spatially close AL-antenna and OV-antenna.…”

Section: Lessons Learnedmentioning

confidence: 99%

“…[24], [41], [45], [77] Resource sharing for mobile cell access link with out-of-vehicle links (macro-cell user, backhaul, sidehaul).…”

Section: B Securitymentioning

confidence: 99%

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A Comprehensive Survey on Moving Networks

Jaffry

Hussain

Gui

et al. 2020

Preprint

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The unprecedented increase in the demand for mobile data, fuelled by new emerging applications and usecases such as high-definition video streaming and heightened online activities has caused massive strain on the existing cellular networks. As a solution, the fifth generation (5G) of cellular technology has been introduced to improve network performance through various innovative features such as millimeter-wave spectrum and Heterogeneous Networks (HetNet). In essence, HetNets include several small cells underlaid within macro-cell to serve densely populated regions like stadiums, shopping malls, and so on. Recently, a mobile layer of HetNet has been under consideration by the researchers and is often referred to as moving networks. Moving networks comprise of mobile cells that are primarily introduced to improve Quality of Service (QoS) for commuting users inside public transport because the QoS is deteriorated due to vehicular penetration losses and high Doppler shift. Furthermore, the users inside fast moving public transport also exert excessive load on the core network due to large group handovers. To this end, mobile cells will play a crucial role in reducing overall handover count and will help in alleviating these problems by decoupling in-vehicle users from the core network. This decoupling is achieved by introducing separate in-vehicle access link, and out-of-vehicle backhaul links with the core network. Additionally sidehaul links will connect mobile cells with their neighbors.To date, remarkable research results have been achieved by the research community in addressing challenges linked to moving networks. However, to the best of our knowledge, a discussion on moving networks and mobile cells in a holistic way is missing in the current literature. To fill the gap, in this paper, we comprehensively survey moving networks and mobile cells. We cover the technological aspects of moving cells and their applications in the futuristic applications. We also discuss the use-cases and value additions that moving networks may bring to future cellular architecture and identify the challenges associated with them. Based on the identified challenges we discuss the future research directions.

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Section: B 5g Cellular Architecturementioning

confidence: 99%

Section: B Wireless Backhaul Linksmentioning

confidence: 99%

Section: Lessons Learnedmentioning

confidence: 99%

“…[24], [41], [45], [77] Resource sharing for mobile cell access link with out-of-vehicle links (macro-cell user, backhaul, sidehaul).…”

Section: B Securitymentioning

confidence: 99%

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A Comprehensive Survey on Moving Networks

Jaffry

Hussain

Gui

et al. 2020

Preprint

Self Cite

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“…Cellular traffic prediction will enable network operators to promptly distribute resources as per the requirement of competing users. With the informed network state, operators may also allow resource sharing between devices [2], [3]. This will also enable high spectral efficiency and will prevent outages caused due to cell overload.…”

Section: Introductionmentioning

confidence: 99%

Cellular Traffic Prediction with Recurrent Neural Network

Jaffry¹

2020

Preprint

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Autonomous prediction of traffic demand will be a key function in future cellular networks. In the past, researchers have used statistical methods such as Autoregressive integrated moving average (ARIMA) to provide traffic predictions. However, ARIMA based predictions fail to give an exact and accurate forecast for dynamic input quantities such as cellular traffic. More recently, researchers have started to explore deep learning techniques, such as, recurrent neural networks (RNN) and longshort-term-memory (LSTM) to autonomously predict future cellular traffic. In this research, we have designed a LSTM based cellular traffic prediction model. We have compared the LSTM based prediction with the base line ARIMA model and vanilla feed-forward neural network (FFNN). The results show that LSTM and FFNN accurately predicted the future cellular traffic. However, it was found that LSTM train the prediction model in much shorter time as compared to FFNN. Hence, we conclude that LSTM models can be effectively even used with small amount of training data which will allow to timely predict the future cellular traffic.

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Shared Spectrum for Mobile-Cell's Backhaul and Access Link

Jaffry

Hasan

Gui

2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Offloading cellular hotspot regions to small-cells has been the main theme for the fifth generation of cellular network. One such hotspot is the public transport which carries a large number of cellular users who frequently receive low quality of service (QoS) due to vehicular penetration effect (VPE). Hence installation of mobile-cell (MC) within public transport is seen as a potential enabler to enhance QoS for commuting users. However, unlike fixed cells, MC requires wireless backhaul (BH) connectivity along with in-vehicle Access-Link (AL) communication. These additional wireless links for MC communication will pose an excessive burden on an already scarce frequency spectrum. Hence, in this research, we exploit VPE and line-ofsight (LOS) communication to allow the downlink backhaul (DL-BH) sub-channels to be shared by in-vehicle downlink accesslink (DL-AL) transmission. Our analysis and simulations show that using the above-mentioned technique, both links maintain high success probability, especially in regions with low signal to interference ratios.

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Effective Resource Sharing in Mobile-Cell Environments

Cited by 5 publications

References 32 publications

A Comprehensive Survey on Moving Networks

A Comprehensive Survey on Moving Networks

Cellular Traffic Prediction with Recurrent Neural Network

Shared Spectrum for Mobile-Cell's Backhaul and Access Link

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