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Multi Parameter Based Vertical Handoff Decision in Next Generations Networks Presentation by: Anita, Lecturer, Computer Science & Engg. Deptt., DCR University.

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Presentation on theme: "Multi Parameter Based Vertical Handoff Decision in Next Generations Networks Presentation by: Anita, Lecturer, Computer Science & Engg. Deptt., DCR University."— Presentation transcript:

1 Multi Parameter Based Vertical Handoff Decision in Next Generations Networks Presentation by: Anita, Lecturer, Computer Science & Engg. Deptt., DCR University of Science & Tech., Murthal, Sonipat, India. Dr. Nupur Prakash, Professor and Dean, School of Information Tech., GGS Indraprastha University, New Delhi, India.

2 Vertical Handoff Vertical Handoff means handoff is between two network access points or Base Stations that uses the different network access technologies. Steps of Vertical Handoff System Discovery: Mobile terminals equipped with multiple interfaces deploy a system discovery agent to determine which networks can be used and the services available in each network. Handoff decision: Based on several parameters like RSS, availability of free channel and service charges, the mobile devices determine which network it should connect to. Handoff execution: The connections are rerouted from the existing network to the new network in a seamless manner.

3 Overlay Structure Cellular coverage MT -- WLAN 1 MT WLAN 2 Reasons for integration of the IEEE 802.11 WLAN and cellular 3G systems WLAN and cellular networks coexist Many cellular devices support dual RF interfaces for WLAN and cellular access WLAN and cellular networks are complementary technologies.

4 Vertical Handoff Decision The classes of metrics in the envisioned 4G system are: Service type: if it is time bound (i.e. real time or non real time application) or reliable. Monetary cost: Different networks employ different billing strategies and operational costs that may affect users choice of Handoff. Network Conditions: network parameters like traffic, available bandwidth and number of users. System Performance: has certain crucial parameters like battery power. If battery level is low the user may switch to a network with lower power requirements. Mobile terminal condition: includes dynamic factors like moving speed, moving pattern of the terminal. User preference: can be added to cater special requests. Based on these above classes, seven input parameters are proposed for vertical Handoff decision.

5 Input Parameters for VHD 1.Available Bandwidth (B AV ): It is the amount of unused bandwidth of the candidate Base Station (BS) or Access point (AP).WLAN have greater bandwidth than cellular Network (UMTS). 2.Speed of mobile terminal (V MT ): It is the velocity with which the mobile terminal (MT) is moving. For high speed MT, UMTS is preferred because of greater coverage area. 3.Number of Users (U N ): The Q oS of WLAN is U N sensitive. As the number of users increase, the collisions increase and results in poor Q oS. 4.Received Signal Strength (RSS): It is the strength of the signal received, as the RSS of the neighboring network rises above the threshold the Vertical Handoff is feasible i.e. the handoff takes place if and only if RSS of the BS or AP is above the threshold.

6 Input Parameters for VHD 5.Battery Level (B L ): The attachment to the closest AP or BS is known to consume the least power for individual mobile devices at a given instant. So if battery level is low the MT must handoff to the closest AP or BS provided RSS is above threshold. The number of users also increases the congestion and in turn even the nearest AP or BS consumes more power. 6.Cost of operation (C): It is the cost of the operation network. If the cost is above a certain threshold value, the user will consider that network to be too expensive to be viable. 7.Traffic Type ( T T ): It could be either real time or non-real time. For real time applications i.e. time bound services cellular networks are preferred and for non-real time applications WLAN is preferred.

7 Why Neuro-Fuzzy approach This blends elements of uncertainty of data by using Fuzzy and adaptive capabilities by using Neural network. It can tap the primary strength of fuzzy networks that in a system can be initialized by the existing semantic knowledge and have structured information extracted from it in an interpretable format. The reasons for using neural network are parallel processing of information Inherent learning capabilities

8 Proposed Neuro-Fuzzy Vertical Handoff Decision Steps The various steps involved in the proposed system are : Fuzzification : Converts real valued data into a fuzzified representation with the help of membership functions. Training: The neural network is trained with the fuzzified information Defuzzification: De-fuzzify the result to produce real values of the desired output. After the system is trained to satisfaction, fuzzy rules can be extracted from the trained neural network.

9 Membership Functions (mf) The mf associated with a given fuzzy set maps a crisp input value to its appropriate membership value. Various mf are : Piecewise linear functions: These are simple straight line mf namely trimf and trapmf. Gaussian Distribution functions: These achieve smoothness but are unable to specify asymmetric mf. These are gaussmf, guass2mf and gbellmf. Sigmoid Curve: Asymmetric and closed mf are sigmf, dsigmf and psigmf. Quadratic and cubic polynomial curves: Three related mf are Z, S and Pi curves are named so because of their shape. Since trimf is simple we use this mf for fuzzification as selective expansive mf function will further increase the complexity of system.

10 Membership Function for B AV

11 Membership Function for V MT

12 Membership Values Based on membership functions, the parameters are assigned the fuzzy membership values between [ 0 1]. B AV = {Low, Medium, High, Very High} = {LO, ME, HI, VH} V MT = {Very slow, slow, Medium, High Speed} = { VS, SL,MS, HS } U N = { Few, less, medium, more} = { FE, LE, ME, MO } RSS = {Below-thresh, Above- thresh} = {BT, AT} B L = {Very Low, Low, Medium, High} = { VL, LO, ME, HI } C = {Low, Medium, High, Very High} = { LO, ME, HI, VH } T T = { Non-real type, real type } = { NT, RT } Handoff C = { Fit, Medium Fit, Low Fit, Not Fit } = { FI, MF, LF, NF } The V MT and B L are the two input parameters related to mobile terminal and all other parameters are related to network.

13 Rules of Fuzzy Inference Engine S No. B AV V MT UNUN BLBL CT U to W W to U Remarks 1LO/ME/HIVS/SLFE/LEME/HILC/MCNTFIMFThis is ideal condition with B AV requirement not too high & only difference is T T =NT is supported by WLAN & T T = RT by UMTS. 2LO/ME/HIVS/SLFE/LEME/HILC/MCRTMFFI 3LO/ME/HIVS/SLFE/LEME/HIHCNTNFLFHigh cost can only be supported by UMTS, because WLAN supports low cost. 4LO/ME/HIVS/SLFE/LEME/HIHCRTNFMF 5LO/ME/HIVS/SLFE/LEME/HIVH (VC) NT/RTNF Not viable because of very high cost 6LO/ME/HIVS/SLFE/LEVL/LOLC/MCNTLF For B L =VL/LO wide coverage area is preferred, so that handoff is not required frequently. 7LO/ME/HIVS/SLFE/LEVL/LOLC/MCRTNFMF Vertical Handoff between WLAN and UMTS is not reversible i.e. the motive to handoff from WLAN to UMTS is quite different from UMTS to WLAN.

14 Rules of Fuzzy Inference Engine ( cont..2) S No. B AV V MT UNUN BLBL CT U to W W to U Remarks 8LO/ME/HIVS/SLFE/LEVL/LOHCNT NFLF Though negative aspect, High cost is supported to some extend by UMTS & not by WLAN. 9LO/ME/HIVS/SLFE/LEVL/LOHCRT NFMF 10LO/ME/HIVS/SLFE/LEVL/LOVHNT/RT NF Not viable due to very high cost. 11LO/ME/HIVS/SLMEME/HILC/MCNT LFMFAs U N increases the QoS decreases as no. of users in WLAN share same channel ALOHA. 12LO/ME/HIVS/SLMEME/HILC/MCRT NFF 13LO/ME/HIVS/SLMEME/HIHCNT NFMF HC is not supported by WLAN 14LO/ME/HIVS/SLMEME/HIHCRT NFF

15 Neural Network based VHD A perceptron is created and it involves three main steps for calculation of weights by using supervised learning. Initialization Iterative Process Termination

16 Flow chart for VHD NN NN including connections (weights W & bias b) between neurons Rule baseFuzzy Inference Engine Target Vector T Input (P) Trained weights W a= hardlim (W*P + b) Calculate ΔW & Δb If e = 1, then ΔW = P Tr If e = -1, then ΔW= -P Tr Δb = (T-a) = e Compare error e where e =T-a Adjust weights W new = W old + ΔW b new = b old + Δb, If e = 0, ΔW = 0, Δb= 0

17 ILLUSTRATION Input (P) = Neural network Initial wts IW = [0 0 0 0 0 0 0] & Initial bias Ib=[0]; n = W*P + bias n= [0 0 0 0 ] hardlim(n) = {0 if n<0} {1 otherwise} Rule base Fuzzy Inference Engine (Rule No. 1) Trained weights W=W & b=b a=hardlim(W*P+bias ) a = [1 1 1 1 ] e = 1, then ΔW =eP Tr = P Tr e = -1,then ΔW = eP Tr =-P Tr ΔW=[0 -1 -1 -1 -1 0 -1] Δb= (t-a) = [-3] Compare error e where e =T-a e=[ 0 -1 -1 -1] Adjust weights W new = W old + ΔW W new =[ 0 -1 -1 -1 -1 0 -1] b new = b old + Δb = [-3] e = 0, e = 0, ΔW = 0, Δb = 0 Target Vector T= [1 0 0 0 ] 1000 0100 0100 0010 0001 1000 0100

18 Epochs used in training

19 Defuzzification methods The aggregate of a fuzzy set encompasses a range of output values so these values must be defuzzified in order to resolve a single output value from the set. Various defuzzification methods used by inference systems are: Centroid Bisector Middle of maximum/largest of maximum/smallest of maximum

20 Conclusion 1.The multi parameter based vertical handoff decision helps determine which network it should handoff to (as incorrect handoff decision will result in poor QoS and at times may even break off current communication). 2.The multi parameter based Vertical Handoff decision would becomes efficient and has reduced complexity if NN is used. 3.The use of metrics increases the complexity of handoff process, making the handoff decision more and more slow. 4.The use of multi parameter based Vertical Handoff Decision implemented using NN, provides blue print for hardware implementation and is thus computationally efficient. The future work involves the performance analysis of this multi parameter based Vertical Handoff decision with the conventional VHDA on parameters like :- Complexity Dropped Packets, Retransmitted packets comparison, WLAN delay comparison.


22 Single perceptron Back

23 Rules of Fuzzy Inference Engine (contd..3) S No. B AV V MT UNUN BLBL CT U- >W W- >U Remarks 15LO/ME/HIVS/SLMEME/HIVHNT/RT NF Not viable due to high cost. Medium no. of users & VL/LO B L results in disruption of connections. As U N = MO, more no. of users in N/W even if B L = MO/HI is not supported in (W+U), Because QoS falls below acceptable levels. 16LO/ME/HIVS/SLMEVL/LOVHNT/RT NF 17LO/ME/HIVS/SLMOVL/LOVHNT/RT NF 18LO/ME/HIMSFE/LE (MS) ME/HILC/MCNT NFMF If V MT = MS then U - W is NF, because MT very frequently moves out of small coverage area of WLAN, therefore frequency Handoff. 19LO/ME/HIMSFE/LEME/HILC/MCRT NFHF 20LO/ME/HIMSFE/LEME/HIHCNT NFMF 21LO/ME/HIMSFE/LEME/HIHCRT NFHF

24 Rules of Fuzzy Inference Engine (contd..4) S No. B AV V MT UNUN BLBL CT U- >W W- >U Remarks 22LO/ME/HIMSFE/LEME/HIVCNT/RT NF Not viable due to very High cost. 23LO/ME/HIMSFE/LEVL/LOLC/MCNT NF VL/LO = B L with MS is still difficult then V MT VS/SL as thus would initiate more Handoff, with VL/LO battery life. 24LO/ME/HIMSFE/LEVL/LOLC/MCRT NFLF 25LO/ME/HIMSFE/LEVL/LOHCNT NF As high cost of operation of N/W is hindrance for W ->U Handoff. 26LO/ME/HIMSFE/LEVL/LOHCRT NFLF 27LO/ME/HIMSFE/LEVL/LOVCNT/RT NF Not feasible / viable. 28LO/ME/HIMSMEME/HILC/MCNT NFMF If V MT= MS & U N is high the QoS is reduced and high B Lis required. 29LO/ME/HIMSMEME/HILC/MCRT NFHF

25 Rules of Fuzzy Inference Engine (contd..5) S No. B AV V MT UNUN BLBL CT U- > W W- >U Remarks 30LO/ME/HIMSMEME/HIHCNT NFLF 31LO/ME/HIMSMEME/HIHCRT NFMF 32LO/ME/HIMSMEME/HIVCNT/RT NF Not feasible. 33LO/ME/HIMSMEVL/LOVCNT/RT NF Because high speed MT & more no. of users causes congestion which cant be controlled & if B L = VL/LO, Handoff is low. 34LO/ME/HIMSMOVL/LOVCNT/RT NF Because high speed MT & too many users causes congestion and neither WLAN nor UMTS can handle these many users as QoS reduces sharply. 35LO/ME/HIHSMOVL/LOVCNT/RT NF Not able to Handoff because of high speed. 36VH Same as B AV ie LO/ME/HI NF Same as B AV LO/ME/HI & NF for W -> U as high BW is not supported by UMTS.

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