Mobility prediction based on an ant system

Daoui, Mehammed; M'zoughi, Abdelaziz; Lalam, Mustapha; Belkadi, Malika; Aoudjit, Rachida

doi:10.1016/j.comcom.2008.04.009

Cited by 27 publications

(21 citation statements)

References 7 publications

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“…Big Data as identified in [4] also contains handover reports which contain Cell IDs and corresponding timestamps whenever user is handed over to new cell. Several techniques such as mobility pattern matching using mobility database, periodicity and multi-class classification and bio-inspired approaches as presented in [94]- [96] can be used to predict user mobility behaviour. Markov and hidden markov models have been commonly used for temporal-spatial prediction purposes as in [97], [98].…”

Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

162

109

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Abstract-Mobile data traffic grew by 74% in 2015 and it's expected to grow 8-fold by 2020. Future wireless networks will need to deploy massive number of small cells to cope with this increasing demand. Dense deployment of small cells will require advanced interference mitigation techniques to improve spectral efficiency and enhance much needed capacity. Coordinated multi-point (CoMP) is a key feature for mitigating intercell interference, improve throughput and cell edge performance. However, cooperation will need to be limited to few cells only due to additional overhead required by CoMP due to channel state information (CSI) exchange, scheduling complexity and additional backhaul limitation. Hence small CoMP clusters will need to be formed in the network. This article surveys the stateof-the-art on one of the key challenges of CoMP implementation: CoMP clustering. As a starting point, we present the need for CoMP, the clustering challenge for 5G wireless networks and provide a brief essential background about CoMP and the enabling network architectures. We then provide the key framework for CoMP clustering and introduce self organisation as an important concept for effective CoMP clustering to maximise CoMP gains. Next, we present two novel taxonomies on existing CoMP clustering solutions, based on self organisation and aimed objective function. Strengths and weaknesses of the available clustering solutions in the literature are critically discussed. We then discuss future research areas and potential approaches for CoMP clustering. We present a future outlook on the utilisation of Big Data in cellular context to support proactive CoMP clustering based on prediction modelling. Finally we conclude this paper with a summary of lessons learnt in this field. This article aims to be a key guide for anyone who wants to research on CoMP clustering for future wireless networks.

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Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

162

109

View full text Add to dashboard Cite

show abstract

“…The use of information theory and decision trees allows choosing the most relevant clues for prediction. The disadvantage of this method is the need to store these clues in the MS itself, which consumes memory, and then energy and bandwidth when communicating this information to the BS [19].…”

Section: Classification Of Predictive Mobile-oriented Channel Reservamentioning

confidence: 99%

Predictive mobile-oriented channel reservation schemes in wireless cellular networks

Abdulova

Aybay²

2010

Wireless Netw

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Provision of seamless service to multimedia applications in cellular wireless networks largely depends on the way calls are handled during handoff. Hence, sufficient resources must be provided for handoff (HO) connections when a mobile station (MS) moves from one cell to another. Effective allocation of resources can be achieved when the exact future trajectory of MSs is known in advance. However, such a scenario is unrealistic. The next best possibility is to employ user mobility prediction to determine the cell(s) a MS will likely visit in the near future. In this paper, we present an extensive survey and classification of channel (bandwidth) reservation schemes which employ user mobility prediction in the resource reservation process. We also present a survey and classification of call admission control (CAC) schemes, including discussion of prioritized and non-prioritized handoff schemes, which can be useful for researches both in academia and industry.

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“…TBLM strategy depends on saving MT's trajectories in its local cache in the form of consecutive visited cell IDs. Mobile cache refers to a memory storage area that stores copies of information that is likely to be needed in the near future, so it can be accessed faster [9]. Cell ID refers to the unique number of each cell used in the location database.…”

Section: Introductionmentioning

confidence: 99%

Historical based location management strategies for PCS networks

2016

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Due to the current popularity of wireless communication, personal communication service networks have attracted a lot of attention. As mobile terminals (MTs) in such networks have the ability to communicate on the move, location management (LM) is a critical issue that should be handled carefully. Managing MTs locations is to simultaneously perform two main operations, which are; location update and paging. A good LM strategy maintains both operations with the minimum time and signaling penalties. Although several LM strategies have been introduced, they still suffer from several drawbacks. This paper introduces three novel LM strategies, which are; historical based location management (HBLM), direction based location management (DBLM) and trajectory based location management (TBLM). TBLM strategy is based on geographical position of MTs, while HBLM and DBLM are based on geographical position of MTs as well as time aspects. Visited locations are saved in MT's internal cache in the form of cell identifier as well as the corresponding time interval. Location tracking is fully supervised by the MT's current visitor location register (VLR). On the other hand, the collected location information in MT's cache is sent to VLR when MT passed through a previously defined number of cells or when a previously defined number of time periods has elapsed. In HBLM, paging is done according to the historical movements saved in VLR, while DBLM takes also MT's movement direction into consideration while in TBLM, paging is done according to the historical trajectories that MTs are used to follow. Experimental results have shown that the proposed LM strategies significantly improve MTs' tracking process as they introduce the minimum signaling penalty (for paging and registration) with an acceptable paging time delay compared with recent LM strategies.

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Mobility prediction based on an ant system

Cited by 27 publications

References 7 publications

Coordinated Multi-Point Clustering Schemes: A Survey

Coordinated Multi-Point Clustering Schemes: A Survey

Predictive mobile-oriented channel reservation schemes in wireless cellular networks

Historical based location management strategies for PCS networks

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