Cell phone subscribers mobility prediction using enhanced Markov Chain algorithm

Hadachi, Amnir; Batrashev, Oleg; Lind, Artjom; Singer, Georg; Vainikko, Eero

doi:10.1109/ivs.2014.6856442

Cited by 35 publications

(12 citation statements)

References 15 publications

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“…A set of CR user current path states are taken as s = { s 1 , s 2 … s n }, where s 1 , s 2 represents the geographical co‐ordinates (latitude and longitude). The CR user's path from state s 1 to state s n is represented in transition matrix which are formed using Markov's Chain algorithm as follows

P = ([], \begin{matrix} ρ_{11} ρ_{12} ⋯ ρ_{1 m} \\ ρ_{21} ρ_{22} ⋯ ρ_{2 m} \\ ⋯ ⋯ ⋯ ⋯ ⋯ \\ ρ_{m 1} ρ_{m 2} ⋯ ρ_{m m} \end{matrix})

where m is the possible number transitions.…”

Section: Proposed Methodsmentioning

confidence: 99%

Enhanced spectrum aggregation based frequency‐band selection routing protocol for cognitive radio ad‐hoc networks

Priya¹,

Kannan²

2018

Concurrency and Computation

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Summary Providing mobility along with maximum performance in shared spectrum environment is the main objective of Cognitive Radio Systems. Adopting substantial list of parameters to a spectrum aggregation based dynamic route frequency‐band switching is important in Cognitive Radio Mobile Ad‐Hoc Networks (CR‐MANET). Frequent frequency‐band switching affects the performance of the CR‐MANET. In this work geographical position (GPS) detail and spectrum source blind‐spot details are added to the band selection procedure of the Cognitive Radio system. In addition a dedicated Path Prediction Procedure is added with the band selection algorithm to avoid undesirable frequency‐band switching of a mobile CR device. Optimized frequency‐band switching reduces unwanted handovers, thus switching delay and End‐to‐End delay are reduced. Power used to switching between bands is kept in control by sustaining in stable frequency band for longer durations. The combination of GPS data and Path Prediction Procedure is designed to improve the Quality of Service (QoS) parameters like throughput, bandwidth utilization, and Energy efficiency.

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P = ([], \begin{matrix} ρ_{11} ρ_{12} ⋯ ρ_{1 m} \\ ρ_{21} ρ_{22} ⋯ ρ_{2 m} \\ ⋯ ⋯ ⋯ ⋯ ⋯ \\ ρ_{m 1} ρ_{m 2} ⋯ ρ_{m m} \end{matrix})

where m is the possible number transitions.…”

Section: Proposed Methodsmentioning

confidence: 99%

Enhanced spectrum aggregation based frequency‐band selection routing protocol for cognitive radio ad‐hoc networks

Priya¹,

Kannan²

2018

Concurrency and Computation

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“…In [73], subscriber's mobility is predicted using the enhanced Markov chain algorithm. The core idea is to add the behavior pattern and temporal data of the users from CDR into the Local Prediction Algorithm (LPA) and the Global Prediction Algorithm (GPA).…”

Section: B) Enhanced-markov Chainmentioning

confidence: 99%

Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions

et al. 2020

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The exponential rise in mobile traffic originating from mobile devices highlights the need for making mobility management in future networks even more efficient and seamless than ever before. Ultra-Dense Cellular Network vision consisting of cells of varying sizes with conventional and mmWave bands is being perceived as the panacea for the eminent capacity crunch. However, mobility challenges in an ultradense heterogeneous network with motley of high frequency and mmWave band cells will be unprecedented due to plurality of handover instances, and the resulting signaling overhead and data interruptions for miscellany of devices. Similarly, issues like user tracking and cell discovery for mmWave with narrow beams need to be addressed before the ambitious gains of emerging mobile networks can be realized. Mobility challenges are further highlighted when considering the 5G deliverables of multi-Gbps wireless connectivity, <1ms latency and support for devices moving at maximum speed of 500km/h, to name a few. Despite its significance, few mobility surveys exist with the majority focused on adhoc networks. This paper is the first to provide a comprehensive survey on the panorama of mobility challenges in the emerging ultra-dense mobile networks. We not only present a detailed tutorial on 5G mobility approaches and highlight key mobility risks of legacy networks, but also review key findings from recent studies and highlight the technical challenges and potential opportunities related to mobility from the perspective of emerging ultra-dense cellular networks.

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“…The mobility history of the user is recorded and probability of user transition into next cell is derived. The common methods of deriving probability of transition into next cell involve Markov chain model [5], hidden markov model [6], neural networks and machine learning [7], route clustering [8] etc. 2) Measurement based: These schemes do not rely on the user mobility history rather they derive probability of user transition to next cell based on real time measurements (e.g., RSSI, geometry, user angle, distance etc).…”

Section: Related Workmentioning

confidence: 99%

Framework to Support Mobility Context Awareness in Cellular Networks

Kuruvatti

Schotten

2016

2016 IEEE 83rd Vehicular Technology Conference (VTC Spring)

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Mobile communication is arguably the most ubiquitously used technology in contemporary world, evolving towards its fifth generation (5G). The key challenges being faced by present day mobile communication are growing number of mobile users and subsequent high traffic volume posed by them. Providing uniform service quality and best quality of experience (QoE) in such dense scenarios is a major motive of 5G. Context awareness is a concept of extracting information from the user and his environment, and utilizing it to optimize user performance. Context awareness is recognized as one of the key pillars in enabling uniform quality of experience for mobile users. For instance, predicting the next cell for user transition, predicting the crowd formation in a cell etc., will assist the basestation to reserve or manage resources and prepare the cell well in advance for a future event, targeting to provide uninterrupted and uniform QoE. This paper investigates context aware procedures with a focus on user mobility, finds commonalities among different procedures and proposes a general framework to support mobility context awareness. The new information and interfaces which are required from various entities (e.g., vehicular infrastructure) are discussed. Further, a context aware resource allocation algorithm is designed, exploiting new interfaces and information arising from vehicular infrastructure. Simulation results demonstrate improvements in user throughput, thus validating the requirement of new interfaces.

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Cell phone subscribers mobility prediction using enhanced Markov Chain algorithm

Cited by 35 publications

References 15 publications

Enhanced spectrum aggregation based frequency‐band selection routing protocol for cognitive radio ad‐hoc networks

Enhanced spectrum aggregation based frequency‐band selection routing protocol for cognitive radio ad‐hoc networks

Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions

Framework to Support Mobility Context Awareness in Cellular Networks

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