Neuro-fuzzy admission control in mobile communications systems

Transcription

1 University of Wollongong Thesis Collections University of Wollongong Thesis Collection University of Wollongong Year 2005 Neuro-fuzzy admission control in mobile communications systems Raad Raad University of Wollongong Raad, Raad, Neuro-fuzzy admission control in mobile communications systems, PhD thesis, School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, This paper is posted at Research Online.

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3 6. Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 6.1. Outline In the previous two chapters multi-service mobile networks with fixed bandwidth reservation were studied. One conclusion that was made was that as cells get smaller, bandwidth reservation becomes less effective in guaranteeing better performance for handover connections. In this chapter, connection admission control and adaptive bandwidth reservation using fuzzy logic is investigated. In Section 6.2 fuzzy logic is used heuristically to design an adaptive bandwidth reservation scheme that modifies the amount of bandwidth reserved depending on the handover rate. In Section 6.3 fuzzy logic is then applied to the admission control problem where the velocity of the mobile terminal as well as the bandwidth available (in surrounding cells) are taken into account before a connection is admitted. 152

4 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks Adaptive Fuzzy Bandwidth Reservation As discussed in earlier chapters, handover call blocking is more undesirable from a user s point of view than new call blocking. Hence, the handover grade of service (GoS) should be kept under certain constraints. One method that has was investigated in Chapters 4 and 5 was fixed bandwidth reservation. Fixed bandwidth reservation is simple but is not adaptive to the network conditions. To guarantee a certain GoS, a network designer has to dimension each cell for the maximum network load. To maintain the performance of handover blocking, adaptive bandwidth reservation could be used. Adaptive bandwidth reservation attempts to modify the bandwidth being reserved in the cell to meet handover blocking probability constraints. For example, more bandwidth is reserved when the handover rate into a cell increases. There are different proposals for implementing adaptive bandwidth reservation. One is to make certain assumptions about the entire network, derive an approximate model and implement the model in a controller. [NAG1996] for example, has proposed such a method. In most cases simplifying assumptions have to be made in order to make the problem tractable (as was done in Chapters 4 and 5), namely that cell dwell and call holding times are exponentially distributed and that the arrival processes of both new and handover calls are poissonian. While these assumptions hold for larger cells, this may not be true as the cell sizes shrink down. In fact studies in [CHL1995] and [ZAN1996] show that lognormal and gamma distributed call holding and dwell times apply when cells become micro or pico-cellular. Fuzzy logic gives a methodology for numerically designing control systems that seem complex and intractable. In this chapter we propose the use of an adaptive

5 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 154 fuzzy reservation (AFR) scheme to control the amount of reserved bandwidth based on the measured handover rate into a cell. Fuzzy logic allows the design of the controller to be individually adaptable to a particular cell and hence has the potential of allowing better utilisation of bandwidth under particular conditions. In this section, a fuzzy logic controller is designed heuristically and then further refined. Using the design process as in [LIN1996], Figure 6.1 shows the formal design of the Fuzzy Logic Admission Controller based on the data collected from the cellular network simulations. Input Data from External Source Fuzzy Algorithm Design Data Output Data to external world Figure 6.1. Formal design of Fuzzy Logic Admission Controller. The design data in the Figure 6.1 was generated from a simulation of a cellular network. The new call rate was kept constant, while the handover rate was increase and the blocking probability of handover calls was measured. This is also carried out for different values of the number of reserved channels. The design data was then used to generate and modify membership functions. These membership functions in effect stored the design data in a form that can be applied when an input is received that falls within the design range. In this case the input signal is the handover call rate, and the output signal is the number of

6 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 155 channels that have to be reserved in order to maintain the handover blocking probability. This is clearly shown in Figure 6.1 as the top and bottom states. The Fuzzy Algorithm at the heart of the Fuzzy Logic Admission Controller is shown in the three IF-THEN rules that were initially chosen. The rules dictate how the different membership functions interact to come to the final output signal. These rules were arrived at through trial and error. Three rules were found to be insufficient to represent the data set and eventually more rules were added to better represent the design data, which is to adapt the number of reserved channels to the handover rate. The Fuzzy interference engine or algorithm is shown is more detail in Figure 6.2. More detailed examples of how the calculations are done are shown in Chapter 3. Step 1: Input Data Processing Step 2: Fuzzification Input Data Step 3: Left Hand Computation. (AND operations) Fuzzy Inference Step 4: Right Hand Computation. (OR operations) Output Data Step 5: Output Data Processing Figure 6.2. The Fuzzy Inference Engine.

7 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 156 A fuzzy logic controller was designed with one input and one output. The input and output are the handover rate into the cell and the amount of bandwidth to reserve, respectively. Figures 6.3 and 6.4 show the input and output membership functions of the initial design. Here it is assumed that the new connection load remains constant while handover load into a cell is allowed to vary (hence the single input). Each variable has three membership functions. The handover rate is either: Low, Normal or High and the capacity (to reserve) is either: Low, Moderate or High. Hence the fuzzy logic rules for this case are: IF (handover rate is Low) THEN (capacity reserved is Low). IF (handover rate is Normal) THEN (capacity reserved is Moderate). IF (handover rate is High) THEN (capacity reserved is High). Figure 6.5 shows the amount of bandwidth that needs to be reserved as the handover rate increases (i.e. it is the output of the fuzzy rules stated above when applied to the membership functions in Figures 6.3 and 6.4). The parameters for this simulation were chosen heuristically based on extensive knowledge of the network simulation. The figure shows that there are plateaus around the decision points (where the handover rate goes from Low to Moderate to High). This gives fuzzy logic a more stable performance than other schemes when the input data is being measured from the real world and entered into the system. For instance, there are going to be variations and inaccuracies in the measured data. Hence there has to be a significant shift in the input conditions to bring about a change in the output result. The handover rate has to shift an appropriate value before the decision is made to shift the amount of bandwidth to reserve.

8 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks Low Normal High Degree of membership Handover rate (calls/sec) Figure 6.3. Membership functions of the input handover rate. 1 Low Moderate Degree of membership High Capacity (% of Total) Figure 6.4. Membership functions of the reserved capacity. The designed fuzzy logic controller was used in a simulation and tested for the required GoS requirements. In the simulation, it was assumed that the cell dwell time is 100 seconds, the new call holding time is 100 seconds and both are assumed to be exponentially distributed. The simulated cellular network was assumed to have a ring structure and hence made handover implementation simpler without compromising the results. All the simulations were run independently until the error between separate runs was less than 5%. The measured data (the handover rate into a cell) was exponentially smoothed before being used as an input to reduce the noise due to the short sampling rate, which was 180 seconds. The sampling period was picked based on the handover rate so that a meaningful value could be obtained, while at the same time being short enough to be able to react to fast changing conditions.

9 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks Capacity (Fraction) Handover rate (calls/sec) Figure 6.5. The reserved capacity versus the handover rate. The initial design (stated above) did not meet the required GoS in the simulation. The GoS was set at for handover calls, (this probability is the highest value that is acceptable from a user s point of view and most service providers aim for or lower). The reason for the initial design not being able to meet the GoS constraint is that the amount of bandwidth being reserved in the High state was not sufficient. A more accurate representation of the process was required and hence more membership functions were added. Figures 6.6 and 6.7 show the new membership functions for the handover rate and the capacity to reserve. This number of membership functions came about through trial and error. This is a weakness of fuzzy logic in some respects, but a neuro-fuzzy approach is implemented in Chapter 7 which allows the automatic determination and training of the fuzzy sets. Figure 6.8 shows the resulting reservation output for all handover inputs. The figure shows that the plateaus are still evident but have been reduced in width as more membership functions were added.

10 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 159 Mod High High Very High 1 Low Normal Extremely High 0.8 Degree of membership Extremely Very High Handover rate (calls/sec) Figure 6.6. The new input membership functions for handover rate. moderate Modhigh high llow Very high Extremely High Extremely very high Degree of membership Capacity (Fraction of Total) Figure 6.7. The output membership functions of the modified fuzzy logic contoller design Reserved Bandwidth (Fraction 0.04 of Total) Handover rate (calls/sec)

11 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 160 Figure 6.8. The output of fuzzy logic reservation with 1 input obtained from the modified fuzzy logic controller design. The new and handover connection blocking results are shown in Figure 6.9. The figure shows that the modified fuzzy logic controller is able to maintain the handover call blocking rate at by reserving the appropriate amount of bandwidth based on the measured handover rate in the simulation. The results in Figure 6.9 are also compared to the fixed bandwidth reservation case where a fixed amount of bandwidth is reserved to maintain a better handover blocking rate. In this case the fixed amount of reservation is 30kb/sec. The figure shows that for a new call arrival rate of 0.82 calls/sec, the performance of fixed bandwidth reservation has been optimized for this set of conditions by producing a new call blocking rate of 0.02 and a handover call blocking rate of Hence the adaptive fuzzy reservation (AFR) is being compared against the best possible fixed bandwidth reservation scheme (FR3 in Figure 6.9). In the fixed reservation case the handover blocking probability deteriorates to an unacceptable level once the handover rate increases. A further comparison of the AFR scheme and fixed bandwidth reservation is made through a cost factor. In this case the cost factor is calculated through the following equation: C = P + 10P n This cost factor C gives a weighting of 10 for each dropped handover call, where P n is the new call blocking probability and P h is the handover blocking probability. Figure 6.10 shows that as the load increases, the AFR significantly outperforms the most optimal fixed reservation scenario. h

12 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks Blocking Probability AFR new call blocking AFR handover call blocking FR3 new call blocking FR3 handover call blocking New Call load in call/sec Figure 6.9. New and handover blocking probabilities obtained from a network simulation using the modified fuzzy logic controller (AFR) AFR FR Cost New Call load in call/sec Figure Cost Comparisons of Adaptive Reservation and Fixed Bandwidth Reservation.

13 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks Velocity Based Fuzzy Logic Admission Control In Section 6.2, fuzzy logic-based dynamic bandwidth reservation was investigated. The results indicated that the GoS performance of handover connections can be maintained under dynamically changing conditions. In Section 6.2 only the new call rate was used as an input to the fuzzy controller. In this section, the objective is to determine whether by adding more information to the fuzzy logic controller it is possible to improve this performance further. Here, terminal velocity and congestion conditions in surrounding cells are taken into account before a connection is admitted to the network. By combining the velocity and bandwidth variables, the system is inherently taking into account the handover rate. (Recall from earlier chapters that the handover rate is dependent on the mean cell dwell time and mean call holding time. The velocity, in combination with the cell size is used to determine the mean cell dwell time). The main advantage of considering velocity as a separate input is that a group of fast moving or slow moving terminals can be separated out using the fuzzy controller Velocity Based Admission Control A new fuzzy admission controller (FAC) that is based on three inputs: the current available bandwidth in the cell, the bandwidth availability in the next cell (the cell the mobile is most likely to handover to) and the velocity of the mobile terminal is thus proposed. The main objective of the FAC is to reject new connection attempts from mobile terminals that are likely to require many handovers when the current cell or neighboring cells are in a congested state. The idea is that a more accurate decision can be made about admission once this information is available.

14 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 163 The advantage of using fuzzy logic for such a scheme is quickly evident. By simply stating the IF THEN rules, the fuzzy logic admission controller is implemented. It would have been difficult to relate inputs such as velocity and bandwidth in an analytical sense (but it is possible with the admission boundary changing as a function of velocity and available bandwidth). However, it is also evident from Table 6.1 (which shows that 27 rules are needed to achieve the desired results) that fuzzy logic suffers from scalability problems and the number of rules grows exponentially as the number of inputs increases. The membership functions for the bandwidth availability in the current cell and in the next cell are shown in Figures 6.11 and 6.12, respectively. They were chosen in the following manner. Each variable has 3 membership functions: bandwidth being Available, Congested and Not Available. Both variables have the same membership functions but Figure 6.12 shows a zoomed-in view of the last two membership functions. The speed variable has 5 membership functions: Stationary, Slow, Average, Fast and Very Fast as shown in Figure For the output decision which is to accept or reject the call, there are four membership functions, Reject, Weakly Reject, Weakly Accept and Accept, which are shown in Figure The reason for having 4 membership functions for the output is that it allows a smoother transition between the Reject and Accept decisions than if only two membership functions were present. Table 6.1. The Fuzzy Rule Set for Fuzzy Admission Control. 1. If (Current Bandwidth is No Bandwidth) then (Decision is Reject) (1) 2. If (Current Bandwidth is congested) and (Next Bandwidth is Available) and (Speed is Very Fast) then (Decision is Accept) (1) 3. If (Current Bandwidth is congested) and (Next Bandwidth is Available) and (Speed is Fast) then (Decision is Accept) (1) 4. If (Current Bandwidth is congested) and (Next Bandwidth is Available) and (Speed is Average) then (Decision is Accept) (1) 5. If (Current Bandwidth is congested) and (Next Bandwidth is Available) and (Speed is Slow) then (Decision is Accept) (1) 6. If (Current Bandwidth is congested) and (Next Bandwidth is Available) and (Speed is Stationary) then (Decision is Weakly Accept) (1)

15 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks If (Current Bandwidth is congested) and (Next Bandwidth is congested) and (Speed is Stationary) then (Decision is Weakly Accept) (1) 8. If (Current Bandwidth is congested) and (Next Bandwidth is congested) and (Speed is Slow) then (Decision is Weakly Accept) (1) 9. If (Current Bandwidth is congested) and (Next Bandwidth is congested) and (Speed is Average) then (Decision is Weakly Accept) (1) 10. If (Current Bandwidth is congested) and (Next Bandwidth is congested) and (Speed is Fast) then (Decision is Weakly Accept) (1) 11. If (Current Bandwidth is congested) and (Next Bandwidth is congested) and (Speed is Very Fast) then (Decision is Weakly Accept) (1) 12. If (Current Bandwidth is congested) and (Next Bandwidth is No Bandwidth) and (Speed is Very Fast) then (Decision is Reject) (1) 13. If (Current Bandwidth is congested) and (Next Bandwidth is No Bandwidth) and (Speed is Fast) then (Decision is Weakly Reject) (1) 14. If (Current Bandwidth is congested) and (Next Bandwidth is No Bandwidth) and (Speed is Average) then (Decision is Weakly Accept) (1) 15. If (Current Bandwidth is congested) and (Next Bandwidth is No Bandwidth) and (Speed is Slow) then (Decision is Accept) (1) 16. If (Current Bandwidth is congested) and (Next Bandwidth is No Bandwidth) and (Speed is Stationary) then (Decision is Accept) (1) 17. If (Current Bandwidth is Available) and (Next Bandwidth is Available) then (Decision is Accept) (1) 18. If (Current Bandwidth is Available) and (Next Bandwidth is congested) and (Speed is Stationary) then (Decision is Accept) (1) 19. If (Current Bandwidth is Available) and (Next Bandwidth is congested) and (Speed is Slow) then (Decision is Accept) (1) 20. If (Current Bandwidth is Available) and (Next Bandwidth is congested) and (Speed is Average) then (Decision is Accept) (1) 21. If (Current Bandwidth is Available) and (Next Bandwidth is congested) and (Speed is Fast) then (Decision is Weakly Accept) (1) 22. If (Current Bandwidth is Available) and (Next Bandwidth is congested) and (Speed is Very Fast) then (Decision is Weakly Reject) (1) 23. If (Current Bandwidth is Available) and (Next Bandwidth is No Bandwidth) and (Speed is Very Fast) then (Decision is Reject) (1) 24. If (Current Bandwidth is Available) and (Next Bandwidth is No Bandwidth) and (Speed is Fast) then (Decision is Weakly Reject) (1) 25. If (Current Bandwidth is Available) and (Next Bandwidth is No Bandwidth) and (Speed is Average) then (Decision is Weakly Accept) (1) 26. If (Current Bandwidth is Available) and (Next Bandwidth is No Bandwidth) and (Speed is Slow) then (Decision is Accept) (1) 27. If (Current Bandwidth is Available) and (Next Bandwidth is No Bandwidth) and (Speed is Stationary) then (Decision is Accept) (1) The membership functions for the current and next cell bandwidth are generalized bell curves, while the membership functions for speed and the output decision are bell curves. Generalised bell curves have the same characteristic slopes as the bell curve but could take on a higher variance. This suits the amount of bandwidth better as this covers a wider range of values of available bandwidth than the standard bell curve. The standard bell curve is more

16 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 165 suited to the speeds where it is expected that different velocity classes of mobile vehicles have velocities that follow a bell curve. No Bandwidth 1 Available Congested 0.8 Degree of membership Current Bandwidth Figure Membership Functions of Current Bandwidth ( in number of channels) No Bandwidth 1 Available Congested 0.8 Degree of membership Next Cell Bandwidth Figure Membership Functions of Bandwidth Availability in the Next Cell (in Number of Channels).

17 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 166 Stationary Slow Average Fast Very Fast Degree of membership Speed Figure Membership Functions of Speed (km/h). Reject 1 Weakly Reject Weakly Accept Accept 0.8 Degree Of membership Decision Figure Membership Functions of the Output Decision.

18 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks The Fuzzy Rule Set The complete fuzzy rule set for the FAC was shown earlier in Table 6.1. While there are many rules (27 in all), only a few of then are critical. These are related to the Reject and Weakly Reject membership functions of the output decision. The critical set of rules is shown in Table 6.2. These include i) when either of the current cells have congestion conditions (i.e. they are using the last 5% of the available bandwidth) and the speed is fast or very fast then weakly reject. ii) when there is no bandwidth available in the next cell, and the speed is very fast then reject the connection. These rules were arrived at heuristically. Table 6.2. The critical set of rules. 12. If (Current Bandwidth is congested) and (Next Bandwidth is No Bandwidth) and (Speed is Very Fast) then (Decision is Reject) (1). 13. If (Current Bandwidth is congested) and (Next Bandwidth is No Bandwidth) and (Speed is Fast) then (Decision is Weakly Reject) (1). 22. If (Current Bandwidth is Available) and (Next Bandwidth is congested) and (Speed is Very Fast) then (Decision is Weakly Reject) (1). 23. If (Current Bandwidth is Available) and (Next Bandwidth is No Bandwidth) and (Speed is Very Fast) then (Decision is Reject) (1). 24. If (Current Bandwidth is Available) and (Next Bandwidth is No Bandwidth) and (Speed is Fast) then (Decision is Weakly Reject) (1) The Admission Control Surface The next four figures show the admission surface of the fuzzy admission controller. The admission surface indicates the output of the fuzzy controller. A higher value indicates that the connection is more likely to be admitted. Figure 6.15 shows the admission surface when bandwidth is available in the next cell (it was set to 50% of the total bandwidth in this example). Here the 50% value is chosen as a demonstration only to show what the admission surface would look

19 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 168 like when half the bandwidth is available. The figure shows that all connections are accepted except for the case when bandwidth is not available in the current cell. Figure 6.16 shows the admission surface when 95% of the bandwidth in the next cell is in use. Since the boundary for accepting or rejecting is chosen to be 5% of the total bandwidth, in this case the same admission surface is obtained as in Figure However, the results in Figure 6.16 show that mobile terminals traveling at high speed have a lower admission value than those traveling at lower speeds. Figure 6.17 shows the admission surface when the bandwidth usage in the next cell is in the congested state (97% of bandwidth is unavailable). The figure shows that as speeds exceed the 100 km/h, most connections are being rejected. It also shows that there is an error when the current bandwidth is 99 channels- it is still allowing connections to be made. This error was later corrected as the membership functions of the admission controller were tuned. The error is shown here to highlight another potential problem with heuristically designing a fuzzy logic controller. The outputs have to be carefully inspected to make sure that they display the correct behavior. In Chapter 7, a neuro-fuzzy approach is used which relies on collected data to build the fuzzy controller and hence significantly reduces these types of errors. Figure 6.18 shows that when no bandwidth is available in the next cell, blocking of connections occurs past the 60 km/h mark. In Figures , the decision output is a value between 0 and 10. The values simply indicate, how correct a decision is, but in this case, any value that is greater than 5 is considered as admit and reject is used otherwise. A hard limit was obtained here to get a better indication of the blocking probabilities, but the range of values from the admission surface (0 to 10) can be utilized to make a fuzzy admission decision. For example, this value could be used to admit the connection but with reduced bandwidth based on the admission value.

20 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 169 Current Bandwidth (Channels) Speed (km/h) Figure Admission Surface Obtained when Bandwidth in the Next Cell is Available. Current Bandwidth (Channels) Speed (km/h) Figure Admission Surface Obtained when Bandwidth in the next Cell is Nearly Congested.

21 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 170 Speed (km/h) Current Bandwidth (Channels) Figure Admission Surface Obtained when Bandwidth in the Next Cell is Congested. Figure Admission Surface Obtained when Bandwidth in the Next Cell is Not Available.

22 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks Simulation Results for Velocity Based Admission Control The ring network simulation similar to the one used in Section was used. However this time instead of having a cell dwell time generated by an exponential distribution, mobile terminals belong to a certain speed category. Each speed category has a mean value and follows a Gaussian distribution. Once a mobile terminal commences a connection at a particular speed that speed is kept constant for the duration of the connection. It is assumed that this velocity is accurately measured and passed on to the fuzzy admission controller. Velocity can be measure through the assistance of Global Positioning System (GPS) or through signal strength measurements over time. Using the fuzzy connection admission controller (FCAC) in the simulation, results for the new and handover call blocking probabilities were obtained. Figure 6.19 shows these results compared to the case when fixed bandwidth reservation is used (in this case 1 channel is reserved). The results show that better new call blocking is obtained when using FCAC at the expense of obtaining worse handover call blocking. FCAC is able to perform a similar function as fixed bandwidth reservation (by allowing handover calls to have a lower handover blocking probability). An advantage of FCAC is the ability to allow more control over the admission process (in this case the system is discriminating against mobile terminals that are going fast when congestion conditions are present). This obviously leads to issues about fairness when congestion conditions arise. However it may be argued that to recover from congestion conditions more quickly one needs to reduce the load into the congested cell and in effect that is what the FCAC is doing.

23 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks FCAC Blocking Probability 10-2 Fixed Reservation 10-3 FCAC new calls FR1 new calls FCAC handover calls FR1 handover calls Call Arrival Rate (calls/sec) Figure Blocking Probabilities for Fuzzy Connection Admission Control (FCAC) and Fixed Reservation with 1 Channel Reserved (FR1). Recall that the results obtained in Chapter 5 indicated that reserving more bandwidth when the handover rate is high compared to new call rate did not yield significantly better performance for handover traffic. To determine whether the FCAC designed in this section can outperform fixed bandwidth reservation the fuzzy logic controller was tested for a range of cell sizes. The results are shown in Figure The arrival rate is kept constant at 0.85 calls/sec and the mobile terminals velocity mix is kept the same for both the FCAC and FR1. The following velocity mix was used: 10% stationary, 20% slow, 50% average, 10% fast and 10% very fast. The results in Figure 6.20 show that the FCAC has very good new call blocking performance across the entire cell size range shown. FR1 has better performance in terms of handover blocking for larger cells but as the

24 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 173 cell size gets smaller and especially below the cell size of 100m, performance of the FCAC is the same as that of the fixed reservation case. Hence as the cell size gets smaller, the FCAC has the same handover performance but has a better new call blocking performance. This can be interpreted as the FCAC having better utilization performance than fixed bandwidth reservation for small cell sizes FCAC new calls FR1 new calls FCAC handover calls FR1 handover calls Blocking Probability FCAC Cell size (m) Figure Blocking Probability for Different Cell Sizes. While this result might look interesting, it is not significant as not reserving any bandwidth at all will result in similar handover blocking performance (as the cell size is reduced), while at the same time having lower new call blocking performance (equal to handover blocking probability). Hence, while the FCAC might outperform fixed bandwidth reservation for small cell sizes, in fact, at the same load, not having any reserved bandwidth is better for this case (when the cell size is small). As a result, a conclusion can be made that while FCAC can provide better control over admission (differentiation at different velocities) it does not significantly outperform fixed bandwidth reservation. The next section

25 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 174 looks at the dynamic behavior of the FCAC and fixed bandwidth reservation, while the subsequent sections deals with the utilization issues of the FCAC Dynamic Performance of FCAC and Fixed Reservation To further check if any advantage is obtained when the FCAC is used instead of fixed bandwidth reservation (FR), dynamic behavior of used bandwidth in one of the cells in the network was obtained for both cases. Figure 6.21(a) and 6.21(b) show the used bandwidth over time for FR1 (Fixed Reservation with 1 channel reserved) and the FCAC, respectively. A general observation between the two graphs does not show any major difference in behavior. Results show that there are sharp rises towards blocking states over short periods of time. The only difference is that when the FCAC is used, the almost cyclic peaks and troughs are more frequent than when using FR1. Used bandwidth in FR1 tends to stay close to blocking states for longer periods than when the FCAC scheme is used Utilization Study of FCAC and Fixed Reservation In this section the utilization performance of the FCAC and FR is examined. The aim is to determine which scheme can accept a higher load while maintaining the GoS constraints. For this case a new call blocking probability of 0.01 and a handover call blocking probability of were used as GoS constraints. Through simulation the highest loads were determined that satisfied the GoS constraints for different cell radii. Figure 6.22 shows the performance of FCAC11 to FCAC14 against the results obtained for FR0 to FR3. FCAC11 to FCAC14 are the original FCAC with value adjustments made to the membership functions. FR0 to FR3 are fixed bandwidth reservations of 0 channels to 3 channels respectively.

26 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks Used Capacity (kb/sec) C lo c k ( s e c o n d s ) (a) Used Capacity (kb/sec) C lo c k ( s e c o n d s ) (b) Figure Dynamic Behavior of FR1(a) and FCAC (b). Figure 6.22 shows that the best load results are obtained by using fixed bandwidth reservation optimized to the particular cell conditions. The best result obtained by the FCAC matched FR2 when the cell size is small (less than 200m). The reason for the FCAC achieving better results when the cell size is small is that the predictions made about the cell dwell time through the velocity input are more accurate as the cell gets smaller and suffer as the cell gets larger. Utilization is held down by the handover blocking constraint and hence the new call blocking probabilities in most of the cases were better than the desired GoS constraints. The corresponding new call blocking probabilities are shown in Figure The figure shows that better results for new call blocking are

27 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 176 obtained by the FCAC against FR2. Again, the conclusion can be made that there is no significant advantage in using the FCAC in this case. Highest load of new calls(calls/sec) FR0 FCAC11 FR1 FR2 FCAC12 FCAC14 FCAC13 FR Cell Radius (m) Figure Optimized Loads for the Constraints of Handover Blocking of and New Call Blocking of 0.01.

28 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 177 New call blocking probability 10-2 FR0 FR1 FR2 FR3 FCAC11 FCAC12 FCAC13 FCAC Cell radius (m) Figure New call blocking probabilities for various FCAC and FR cases Utilization Study of FCAC and FR with Variable Load In the previous section the mobile network was homogenous with all cells having the same conditions. In this section the performance of the FCAC in the cellular network as a whole is studied. The FCAC was tested when congestion conditions exist in the cellular network but only in particular cells. As a special case, every 20 minutes, each cell has a probability of either being in a congested state or being in a normal state. For this simulation, the probability of being in a congested state is 0.1 and the probability of being in a normal state is 0.9. Hence, 10% of cells in the network would be experiencing congestion conditions, while the other 90% are operating at normal loads. The aim is to compare the two admission control schemes (FCAC and fixed bandwidth reservation) under congestion conditions.

29 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks 178 Figure 6.24 shows that the fuzzy logic controller appears to outperform fixed bandwidth reservation when the cell radius is small. The reason for this is that predictions about handover using the fuzzy logic controller are more accurate when the cell size is small. The figure shows the maximum new connection rate before the blocking constraints are exceeded. Figure 6.25 shows that the new call blocking probability appears to be always better for the FR2 and Figure 6.26 shows that the handover blocking probability appears to be consistently better for the FCAC than FR2. While the results for the FCAC show some improvement over fixed bandwidth reservation, the values are small and insignificant to be considered as an advantage over fixed reservation. Maximum New Call Arrival rate (calls/sec) FR2 FCAC Cell Radius (km) Figure Maximum new call load while maintaining constraints of new call blocking of 0.01 and handover call blocking of with a congestion variation of 20%.

30 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks New Call Blocking Probability FR2 FCAC Cell Radius (km) Figure The new call blocking probability (corresponding to the results in Figure 6.24). Handover Call Blocking Probability FR2 FCAC Cell Radius (km) Figure handover call blocking probability (corresponding to the results in Figure 6.24).

31 Fuzzy Logic Admission Control in Micro-Cellular Mobile Networks Conclusion In this chapter, fuzzy logic was studied in the context of the mobile network. A fuzzy logic controller was designed and simulated to perform adaptive bandwidth reservation. A simple fuzzy logic controller was designed which took into account a dynamically changing handover rate, while assuming the arrival rate remained constant and its performance was investigated and compared to Fixed bandwidth reservation (Section 6.2). The results produced were encouraging but a problem was identified in correctly tuning the fuzzy logic controller to the desired constraints. This problem is pursued in the next chapter where a Neuro- Fuzzy controller is developed. A fuzzy logic controller which considered both terminal velocity and bandwidth being used in surrounding cells was designed, investigated and compared to fixed reservation (Section 6.3). The results showed that the fuzzy controller is able to match the results that fixed bandwidth reservation achieved under static conditions (i.e. the new and handover connection loads are constant) while being able to dynamically guarantee a minimum handover blocking probability (which fixed bandwidth reservation can not achieve) and at the same time giving the network designer more control over the admission policy into the network. Whilst the Fuzzy controller did not out-perform the fixed bandwidth reservation schemes (in terms of utilization), it is clear that it is adaptive to velocity and bandwidth availability in surrounding cells, whilst the fixed reservation scheme is not. In the next chapter the fuzzy controller is replaced by a Neuro-Fuzzy controller that facilitates better performance as more information about the network is made available.