Location Management Based on the Mobility Patterns of Mobile Users

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Location Management Based on the Mobility
Patterns of Mobile Users

• Authors
• Ignacio Martinez-Arrue
• Pablo Garcia-Escalle
• Vicente Casares-Giner
• GIRBA-ITACA, Universidad Politecnica de Valencia
• Presented by
• Ignacio Martinez-Arrue

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Contents

• Introduction
• Overview on location management
• Proposed mobility model
• Location update procedure
• Terminal paging procedure
• Numerical results
• Conclusions

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1. Introduction

• Mobility models
• Location management depends on mobility patterns
  of Mobile Terminals (MTs)
• Random walk mobility model commonly used
• We propose
• A new mobility model that generalizes the random
  walk model
• A versatile model that considers mobility
  patterns through a directional movement parameter
• A valid model for
• Macrocellular scenarios (low mobility and random
  movement)
• Microcellular scenarios (high mobility and
  directional movement)

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2. Overview on location management (I)

• Location management set of procedures that allow
  an MT being locatable at any time

Location Update (LU)
Location Management

• There is a trade off between LU and PG procedures

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2. Overview on location management (II)

• LU procedures
• Static schemes Location Areas (LAs)
• Dynamic schemes
• Time-based
• Movement-based
• Distance-based
• General framework of the movement-based LU
  scheme each time the MT revisits the cell it had
  contact with the fixed network
• Increases the movement-counter with probability p
• Freezes (stops) the movement-counter with
  probability q
• Resets the movement-counter with probability r
• p q r 1
• PG procedure
• One-step PG
• Selective PG

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3. Proposed mobility model (I)

• Scenario with hexagonal cells
• Cell sojourn time featured by a generalized gamma
  distribution
• Probability density function (pdf) fc(t)
• Mean value 1/?m (?m is the mobility rate)
• Laplace Transform of the pdf fc(s)
• Call arrivals Poisson process with rate ?c
• a fc(?c) probability that the MT leaves its
  current cell before a new incoming call is
  received
• Call-to-Mobility Ratio (CMR) ? ?c /?m

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3. Proposed mobility model (II)

• Directional movement parameter (a) values within
  0,8
• 0 a lt 1 High probability of moving towards an
  inner ring or being roaming within the same ring
• a 1 Random walk mobility model
• 1 lt a lt 8 High probability of moving towards an
  outer ring

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3. Proposed mobility model (III)

• 2D Markov chain and 1D Markov chain
• Each label of the cell layout (x,i) represents a
  state of the 2D Markov chain

Cells are grouped by rings to obtain a 1D Markov
chain that simplifies the model
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4. Location update procedure (I)

• Considered movement-based LU mechanism
• When the MT revisits the cell where it had
  contact with the fixed network by last time the
  movement counter is
• Increased with probability p
• Stopped (frozen) with probability q
• Reset with probability r
• (p,q,r) (1,0,0) Conventional movement-based
  scheme (LU in A)
• (p,q,r) (0,1,0) Frozen scheme (LU in B)
• (p,q,r) (0,0,1) Reset scheme (LU in C)

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4. Location update procedure (II)

• a(z) probability that there are z boundary
  crossings between two call arrivals
• Ms(z) expected number of LUs triggered by the MT
  in z movements given that the MT is initially in
  ring 0 and its movement-counter value is s
• It depends on the movement threshold (D), p, q, r
  and a
• M0(a) Z-Transform of M0(z) evaluated at a
• LU cost (CLU)

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5. Terminal paging procedure

• Shortest-distance-first (SDF) PG
• The Registration Area (RA) is divided into l PG
  Areas (PAs)
• ps,i (z) probability that the MT is roaming
  within ring i after z movements given that the MT
  is initially in ring 0 and its movement-counter
  value is s
• p0,i probability that the MT is roaming within
  ring i when a call arrival occurs
• ?k probability that the MT is in the PA Ak when
  a call arrives
• Computed from p0,i by adding the terms where the
  ring i belongs to the PA Ak
• ?k number of cells polled until the MT is found
  in the PA Ak
• PG cost (CPG)

V Cost of polling a cell
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6. Numerical results (I)

• Total location management cost CT CLU CPG
• Influence of p, q and r on CT with random walk
  model (a 1)
• Best performance for the reset strategy (p,q,r)
  (0,0,1)
• Worst performance for the movement strategy
  (p,q,r) (1,0,0)

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6. Numerical results (II)

• Influence of p, q and r on CT with high and low
  values of a
• Best performance for the reset strategy (p,q,r)
  (0,0,1)
• Worst performance for the movement strategy
  (p,q,r) (1,0,0)
• The cost is less sensitive to the values of p, q
  and r as a increases

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6. Numerical results (III)

• Convex functions optimum threshold (D) and
  optimum cost (CT)
• CLU dominates for D lt D and CPG dominates for D
  gt D
• Selective PG CPG decreases and D increases
  because CPG dominates for greater values of D
• Distance-based scheme cost increases quickly as a
  is greater
• All strategies perform equally if a tends to
  infinity

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6. Numerical results (IV)

• CT (distance) lt CT (reset) lt CT (frozen) lt CT
  (movement)
• The distance-based scheme is more sensitive to a
  than other schemes
• As movement is more directional (increasing a),
  all costs are approaching among them
• For a lt 10, differences between CTs become
  significative

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7. Conclusions

• Proposed mobility model
• Valid for microcellular (directional movement)
  and macrocellular (random movement) environments
• Directional movement modeled through the a
  parameter
• a 1 Random walk mobility model
• As a increases from 1 to infinity, a more
  directional movement is modeled
• Studied location management schemes
• The distance-based scheme yields the best
  performance
• In the movement-based general framework, the
  lowest cost is achieved by the reset scheme
• The cost of all policies is equal as a tends to
  infinity
• The distance-based mechanism is more complex to
  implement
• When movement is directional, a reset scheme may
  be more suitable for its simplicity

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THE END

• Thank you very much for your attention
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