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1 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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2 OUTLINE Introduction Model Description Mobility Prediction CAC and Bandwidth Reservation Simulation Results Conclusions

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3 Introduction 1/5 Future mobile communication system To support broadband multimedia With diverse QoS requirements 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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4 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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5 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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6 Introduction 4/5 1. Research efforts to predict user mobility => dont estimate channel holding time and therefore 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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7 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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8 Model Description The paper dont consider Soft handoff in CDMA Delay-insensitive applications Subsections Network Topology Channel Holding Time User Mobility Pattern

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9 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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10 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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11 User Mobility Pattern 1/3 Symmetric random walk model not take into 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, H 1, H 2, H 3, …. H n,.. E )

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12 User Mobility Pattern 2/3 sequence of events ( N, H 1, H 2, H 3, …. H n,. E ) N = (m, i, t) m, represents the mobile i, represents the original cell t, represents the time when the call arrives Hn = (T k, i) T k, the relative time elapsed since the beginning of the call i, the cell to which the mobile will handoff E = ( T k ) We quantize the relative time into slots of equal duration T, a design parameter. So, T k is the kth time slot since the beginning of the call.

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13 User Mobility Pattern 3/3 (N, H 1, H 2, H 3, …. H n,.. E ) is assumed to be generated by a mth order Markov source. Most mobile users have favorite routes and habitual movement patterns.

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14 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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15 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 X 1, X 2, ….., X n 2.For each j 1, substring X j without its last character is equal to some previous substring X i,where 0 i < j. X j is encoded by the value i, using lg (j - 1) bits 3.Last character of X j encode as ASCII using lg α bits. α is the size of the input alphabet set.

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16 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 X 2, using lg (7 - 1) bits a using lg 3 bits

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17 Input string = aababcbaccababc… (a)(ab)(abc)(b)(ac)(c)(aba)(bc)

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18 Pseudocode of Mobility Prediction

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19 A Mobility Trie used mobility rediction

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20 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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21 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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22 Call Admission Control and Bandwidth Reservation A. Calculation of P ij (T k ) B. The Most Likely Cell-Time (MLCT) C. CAC and Bandwidth Reservation for New calls D. Adaptive Control of Admission Threshold α E. CAC and Bandwidth Reservation for Handoff Calls

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23 Calculation of P ij (T k ) the probability that a mobile in cell i will visit cell j during time slot T k Example 2:

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24 The Most Likely Cell-Time (MLCT ) We select cells and time slots with P ij (T k ) greater than MLCT threshold, a design parameter, to form the MLCT of this mobile.

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25 CAC and Bandwidth Reservation for New Calls

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26 Adaptive Control of Admission Threshold is too small, the handoff dropping probability arises. is too large, the resource utilization will be decreased. If P hd (m) < P hd, target (m), is decreased by Otherwise, is increased by is a design parameter

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27 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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28 Simulation Results 1.Each cell has a fixed link capacity of 40 bandwidth units (BUs) 2.Time is quantized into units of T= 30s 3.Voice => 1BU, Video => 4BUs 4.Call durations are the same for all calls and exponentially distributed with mean value of 120s 5.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/hour 7.Target handoff dropping rate Phd is 0.01 8.MLCT threshold =0.08, admission threshold =1 adaptive factor =0.02 OfferedLoad = 120 * * (( 1 – Pvoice) * 4 + Pvoice)) Assumptions:

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29 P voice : 0.8 and 1 in the low and high mobility case

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30 Comparison with static-reservation

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31 Comparison with cell-reservation

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32 Conclusions 1.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. Based on assumptions more realistic than existing proposals. better balance of guaranteeing handoff dropping probability while maximizing resource utilization.

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