1 mobility-based predictive call admission control and bandwidth reservation in wireless cellular...
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Mobility-Based Predictive Call Admission Control and
Bandwidth Reservation in Wireless Cellular Networks
Fei Yu and Victor C.M. Leung
INFOCOM 2001
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OUTLINE
• Introduction
• Model Description
• Mobility Prediction
• CAC and Bandwidth Reservation
• Simulation Results
• Conclusions
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Introduction 1/5
• Future mobile communication system To support broadband multimedia With diverse QoS r
equirements
• Handoff resource not guarantee Performance degradations Magnified in future micro/pico-cellular network
• Call admission control and bandwidth reservation scheme are required.
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Introduction 2/5
• Handoff blocking are more objectionable than new call blocking.
• To keep handoff dropping rate below a target level.
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Introduction 3/5
• Popular CAC Guard channel policy Fractional guard channel policy Distributed call admission control schemes
• Questions of the above assumption Exponentially-distributed channel holding time Perfect knowledge of the rate of handoff
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Introduction 4/5
1. Research efforts to predict user mobility => don’t estimate channel holding time and theref
ore cannot be directly applied for efficient bandwidth reservation.
2. Each mobile will handoff to neighboring cells with equal probability.
=> This assumption may not be accurate in general
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Introduction 5/5
• CAC and bandwidth reservation schemes based on the probabilistic prediction of user mobility.
• The Mobility prediction approach is derived from data compression techniques.
• Novel prediction approach => predict not only where the mobile users will
handoff but also when it will handoff.
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Model Description
• The paper don’t consider Soft handoff in CDMA Delay-insensitive applications
• Subsections Network Topology Channel Holding Time User Mobility Pattern
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Network Topology
Use a generalized graph model to represent the actual cellular network.
Modeled as a connected graph G = (V, E)
V={a,b,c,…..,n}
E={(a,b), (a,c),……(n,l)}
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Channel Holding Time
The paper assumes that the channel holding time follows a general distribution, which allows the i.i.d. exponential channel holding time assumption to be relaxed.
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User Mobility Pattern 1/3 Symmetric random walk model not take int
o account the trajectory and channel holding time of a mobile.
• Mobility of a user during a call can be represented by a sequence of events,
( N, H1, H2, H3, …. Hn,.. E )
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User Mobility Pattern 2/3
sequence of events ( N, H1, H2, H3, …. Hn,. E )• N = (m, i, t) m, represents the mobile i, represents the original cell t, represents the time when the call arrives
• Hn = (Tk, i) Tk, the relative time elapsed since the beginning of the call
i, the cell to which the mobile will handoff
• E = ( Tk )• We quantize the relative time into slots of equal duration T, a design
parameter. So, Tk is the kth time slot since the beginning of the call.
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User Mobility Pattern 3/3
• (N, H1, H2, H3, …. Hn,.. E ) is assumed to b
e generated by a mth order Markov source.
• Most mobile users have favorite routes and habitual movement patterns.
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Mobility Prediction
• Motivated from optimal data compression methods (Ziv-Lempel algorithms )
• Compression Rationale: More probable event => short codewords Less probable event => longer codewords
• A good data compressor should also be a good predictor.
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Optimal Data Compression
• Based on the Ziv-Lempel algorithms for data compression.
1. Parse each block of size n in a greedy manner into distinct substrings
X1, X2, ….., Xn
2. For each j ≧ 1, substring Xj without its last character is equal to some previous substring Xi ,where 0≦ i < j. X
j is encoded by the value i, using ┌ lg (j - 1) ┐ bits
3. Last character of Xj encode as ASCII using ┌ lgα┐ bits. α is the size of the input alphabet set.
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Example 1
• Alphabet = {a, b, c}
• Input string = “aababcbaccababc…”
(a)(ab)(abc)(b)(ac)(c)(aba)(bc)
1 2 3 4 5 6 7 8
The seventh substring “aba”
“ab” match X2 , using ┌ lg (7 - 1) ┐ bits
“a” using ┌ lg 3 ┐ bits
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Input string = “aababcbaccababc…” (a)(ab)(abc)(b)(ac)(c)(aba)(bc)
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Pseudocode of Mobility Prediction
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A Mobility Trie used mobility rediction
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1. Modeling the sequence of events generated by a stationary mth order Markov source
2. Predict next events using the mobility prediction scheme derived from the
Ziv-Lempel algorithm.
=> Predict not only to which cell a mobile will handoff but also when the handoff will occur.
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Implementation Considerations of the Mobility Prediction Scheme
• Maintain the statistics in a trie Create an array of pointers for each node Use a linked list at each node
• Use memory economically , but can be more processing
• A sliding windows may be used.
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Call Admission Control and Bandwidth Reservation
A. Calculation of Pij(Tk)B. The Most Likely Cell-Time (MLCT)C. CAC and Bandwidth Reservation for New c
alls D. Adaptive Control of Admission Threshold
αE. CAC and Bandwidth Reservation for Hand
off Calls
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Calculation of Pij(Tk)
• the probability that a mobile in cell i will visit cell j during time slot Tk
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Example 2:
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The Most Likely Cell-Time (MLCT)
• We select cells and time slots with Pij(Tk) greater than MLCT threshold , a design parameter, to form the MLCT of this mobile.
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CAC and Bandwidth Reservation for New Calls
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Adaptive Control of Admission Threshold
is too small, the handoff dropping probability arises.
is too large, the resource utilization will be decreased.
If Phd(m) < Phd, target (m) , is decreased by
Otherwise , is increased by
is a design parameter
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CAC and Bandwidth Reservation for Handoff Calls
• When mobile node handoff to cell i, the CAC algorithm will admit it if the current free bandwidth of cell i can support the call.
• Bandwidth is reserved for mobile node in its MLCT accordingly.
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Simulation Results
1. Each cell has a fixed link capacity of 40 bandwidth units (BUs)2. Time is quantized into units of T= 30s3. Voice => 1BU, Video => 4BUs4. Call durations are the same for all calls and exponentially distributed wi
th mean value of 120s5. Call requests are generated according to a Poisson process with rate 6. Two cases: low user mobility, 0-40 miles/hour higher user mobility, 40-70 miles/hour7. Target handoff dropping rate Phd is 0.018. MLCT threshold =0.08 , admission threshold =1 adaptive factor =0.02
OfferedLoad = 120 * * (( 1 – Pvoice) * 4 + Pvoice))
Assumptions:
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Pvoice: 0.8 and 1
in the low and high mobility case
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Comparison with static-reservation
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Comparison with cell-reservation
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Conclusions1. Events generated by a stationary mth order Markov sou
rce
2. Predict next events using the mobility prediction scheme derived from the Ziv-Lempel algorithm.
Predict not only to which cell a mobile will handoff but also when the handoff will occur.
• Based on assumptions more realistic than existing proposals.
• better balance of guaranteeing handoff dropping probability while maximizing resource utilization.