INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 2, No 1, 2011

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1 FPGA implementation of a fuzzy logic based handoff controller for microcellular mobile networks Dayal C. Sati 1, Pardeep Kumar 2, Yogesh Misra 3 1,2 Department of Electronics & Communication Engineering, B.R.C.M. College of Engineering & Technology, Bahal, Haryana , India 3 Department of Electronics & Communication Engineering, Mody Institute of Technology and Science (Deemed University), Laxmangarh, Rajasthan , India dayalsati@gmail.com ABSTRACT In mobile cellular systems the handoff is a very important process, which refers to a mechanism that transfers an ongoing call from one Base Station (BS) to another. The performance of the handover mechanism is very important to maintain the desired Quality of Service (QoS). The conventional handoff decisions are normally signal strength based, which are not suitable for modern small sized microcellular networks. In order to maintain reliable communication in a microcellular system new and better handoff algorithms must be needed to keep QoS as high as possible. Handoff algorithms, based on soft computing techniques such as Fuzzy Logic, Neural Networks etc can be used for the same purpose. The purpose of this work is to design and implement a Fuzzy Logic based Handoff controller on an FPGA. The tools used are: MATLAB for designing FIS and Xilinx 9.2i for FPGA implementation. Keywords: Handoff, FPGA, Soft Computing, QoS, Fuzzy Logic. 1. Introduction During the last few years wireless networks have been a very active research area (Leonard Barolli, 2008). In cellular networks it is required to perform handoff successfully and as fast as possible to provide reasonable Quality of service (QoS) levels to the end users. Handoff in the older generation systems was not difficult to achieve efficiently as the cell size in those systems taken large enough, but in modern cellular systems the cell size is kept small to accommodate maximum users by implementing frequency reuse concept. In the case of the smaller cell size with increased probability of the mobile system (MS) crossing a cell boundary, the handoff decision becomes more challenging. This problem becomes further complicated by the fact that there is an overlap of the signals from different base stations in the vicinity of the cell boundary. Therefore Soft Computing approaches based on Genetic Algorithm (GA), Fuzzy Logic (FL), Artificial Neural Networks (ANN) can prove to be efficient for next generation wireless networks. Fuzzy techniques are becoming an attractive approach to handle uncertain, imprecise, or unmodeled data in solving control and intelligent decision making problems (Mamun Bin Ibne Reaz, 2002). The application of fuzzy technologies into real time control problems demands the development of efficient hardware implementations of fuzzy inference mechanisms. Fuzzy controllers are traditionally implemented in a microprocessor but dedicated VLSI chips have also been developed (Michael Mc Kenna and B.M. Wilamowski, 52

2 2001). The purpose of this work is to implement a fuzzy control system in a FPGA and to have resulted control surface as smooth as possible 2. Conventional Handoff Algorithms Normally signal strength based measurements are considered due to its simplicity. The conventional handoff decision compares the Received signal strength (RSS) from the serving base station with that from one of the target base station, using a constant handoff threshold (also called handoff margin) (Nasıf Ekiz, et.al., 2005). However the fluctuations of signal strength causes ping pong effect. Some of the main signal strength metrices used to support handoff decisions are: Relative signal strength, Relative signal strength with threshold, Relative signal strength with hysteresis, Relative signal strength with threshold and hysteresis. Figure 1: Conventional handoff based on RSS (Nasıf Ekiz, et.al., 2005) The conventional RSS based handoff method selects the Base station (BS) with strongest received signal at all times. In figure 1 the hand off would occur at point A (Nasıf Ekiz, et.al., 2005). This method is observed many unnecessary handoffs even when the signal strength of the current BS is still at an acceptable level, which results poor quality of service (QOS) of the whole system. 3. Proposed fuzzy Logic based Handoff Controller Figure 2 shows the structure of the proposed fuzzy inference system for designing the handoff controller. The three input parameters which we have considered are: Distance between Base station (BS) & Mobile station (MS), Received Signal Strength (RSS) and Network Load. The only output parameter of the fuzzy inference system is Handoff Decision. In our proposed model the range for distance between base station and mobile station is taken 0 to 8 km., the range for received signal strength is taken 0 to 10 mw and the range for network load i.e. number of users in the cell is taken 0 to 15. The membership functions of input parameters for the proposed fuzzy logic controlled handoff mechanism are shown in 53

3 figure 3, 4 and 5. The output parameter i.e. fuzzy handoff decision (FHD) is divided in four levels: Handoff, Be careful, Wait and No handoff. Distance between BS & MS Received Signal Strength (RSS) FUZZIFIER FUZZY INFERENCE ENGINE DE FUZZIFIER Handoff Decision Network Load FUZZY RULE BASE Figure 2: Block diagram proposed fuzzy logic based handoff controller In order to design a fuzzy logic system the following steps are used: 1. Identify the inputs and outputs using linguistic variables. In this step we have to define the number of inputs and output terms linguistically. 2. Assign membership functions to the variables. In this step we will assign membership functions to the input and output variables. 3. Build a rule base. In this step we will build a rule base between input and output variables. The rule base in a fuzzy system takes the form of IF AND OR, THEN with the operations AND, OR, etc. Figure 3: Membership functions of Distance between BS & MS 54

4 Figure 4: Membership functions of Received Signal Strength (RSS) Figure 5: Membership functions of Network Load Data to the fuzzy system is first applied to the fuzzifier, which takes the inputs and fuzzified the information [2].The fuzzified information is then passed to the fuzzy Inference Engine. The Inference Engine will take the fuzzified input and perform operations on it according to the Fuzzy Rules. These operations will produce output fuzzy sets for each fired rule. The Output of Inference Engine will be passed to the Defuzzifier. The Defuzzifier will compute a crisp value, i.e., converts the fuzzy domain back to the real world domain. There are several methods for defuzzification such as left max operation, right max operation, center of gravity etc. The Center of Gravity (COG) technique is mostly used method for defuzzification. The fuzzy rule base (FRB) for the proposed Mamdani s FIS model for handoff mechanism is shown in Table 1. Total 27 rules are formulated based on the different combinations of the 3 input parameters and 1 output parameter. The rules have the form like: IF Condition 55

5 THEN Control action. The fuzzy IF THEN rules provide knowledge base to the system and decides whether handoff is necessary or not. Table 1: Fuzzy Rule Base for Proposed Handoff Controller Rule No. DISTANCE RSS NETWORK LOAD HANDOFF STATUS 1 Near Strong High Wait 2 Near Strong Medium No Handoff 3 Near Strong Low No Handoff 4 Near Medium High Wait 5 Near Medium Medium No Handoff 6 Near Medium Low No Handoff 7 Near Weak High Handoff 8 Near Weak Medium Wait 9 Near Weak Low Wait 10 Medium Strong High Be Careful 11 Medium Strong Medium No Handoff 12 Medium Strong Low No Handoff 13 Medium Medium High Handoff 14 Medium Medium Medium Wait 15 Medium Medium Low No Handoff 16 Medium Weak High Handoff 17 Medium Weak Medium Be Careful 18 Medium Weak Low Wait 19 Far Strong High Handoff 20 Far Strong Medium Be Careful 21 Far Strong Low No Handoff 22 Far Medium High Handoff 23 Far Medium Medium Be Careful 24 Far Medium Low Wait 25 Far Weak High Handoff 26 Far Weak Medium Handoff 27 Far Weak Low Handoff 56

6 Figure 6: Rule Viewer for the proposed system Figure 7: Surface curve between Distance, RSS & Handoff Decision Figure 8: Surface curve between Network Load, RSS & Handoff Decision 57

7 4. Implementation of Proposed Fuzzy Handoff Controller on FPGA 4.1 Overview The application of fuzzy technologies into real time control problems demands the development of efficient hardware implementations of fuzzy inference mechanisms (Mamun Bin Ibne Reaz and Md. Saiur Rahman, 2002). A Field Programmable Gate Array (FPGA) may be a good solution for it. FPGA is a digital integrated circuit that can be programmed to do any type of digital function. There are three main advantages of an FPGA over a microprocessor chip for fuzzy systems (Michael Mc Kenna and B.M. Wilamowski, 2001): (1) An FPGA has the ability to be reprogrammed on the site (2) An FPGA used as a fuzzy controller will be semicustom hardware (3) The FPGA will operate faster than a microprocessor chip. FPGAs are programmed using support software and Once they are programmed, they can be disconnected from the computer and will retain their functionality until the power is removed from the chip. A Read Only Memory (ROM) type of a chip that is connected to the FPGA s programmable inputs can also program the FPGA upon power up (Mamun Bin Ibne Reaz and Md. Saiur Rahman, 2002 and Michael Mc Kenna, B.M. Wilamowski, 2001).The figure 9 shows the block diagram of the process of implementing the designed fuzzy handoff controller on FPGA. Distance RSS Network Load Distance Fuzzifier RSS Fuzzifier Network Load Fuzzifier Near Medium Far Weak Medium Strong FPG A Low Medium Rule Matrix Fuzzifier (on FPGA) R Handoff Be Carefull Wait NO Handoff High 3 June 2011 D.C.SATI (09 ECE 305) VHDL Model Figure 9: Block diagram of fuzzy controller on FPGA There are many ways for writing VHDL Model viz. Behavior Model, Data Flow Model and Structure Model. We have used Behavior Model for writing the VHDL code for the Fuzzy Logic Based Handoff Controller for Wireless Mobile Networks. The VHDL Code of the system is available with the authors. 58

8 4.3 Simulation The functionality of the proposed system is verified from the waveform generated by the simulation tool. The simulation tool used for the simulation is ISE Simulator (VHDL/Verilog). Simulated waveform of the system which is obtained after experiment is shown in Figure 10. Figure 10: Simulated waveform of the proposed fuzzy handoff controller 4.3 Synthesis For the designated hardware realization, the VHDL code is synthesized. Xilinx s XST Tool is used for the purpose of synthesis. Technology schematic generated after synthesis is shown in Figure 11. Figure 11: Technology schematic of the proposed fuzzy handoff controller 4.4 Implementation 59

9 The Target device (FPGA) used for implementation of the proposed system is XILINX s SPARTAN 3 XC3S200 FT256 5.Result of a Place & Route Tool after placement and routing of various logic blocks in XILINX s SPARTAN 3 XC3S200 5 FT256 is shown in figure 12. This work is done at the VLSI and ECAD Laboratory of B.R.C.M. CET, Bahal, Haryana(India).The lab set up for the implementation of fuzzy logic based handoff controller in XILINX s SPARTAN 3. C3S200 5 FT256 is shown in Figure 13. Figure 12: Architecture view of proposed system on FPGA. 5. Results Figure 13: Lab setup for the FPGA implementation of the proposed system The device utilization summary and timing report for implementation of proposed fuzzy logic based handoff controller is given below: 5.1 Device utilization summary: Optimization Goal Selected Device : Speed : 3s200ft256 5 Number of Slices : 20 out of % Number of 4 input LUTs : 36 out of % Number of bonded IOBs : 13 out of 173 6% IOB Flip Flops : Timing Report Speed Grade : 5 Minimum period Minimum input arrival time before clock : No path found : 6.153ns Maximum output required time after clock : 6.141ns Maximum combinational path delay : No path found 60

10 5.3 Total memory used : kilobytes 6. Conclusion The need of the day is to develop the systems with flexibility in programming. The advantage of designing with VHDL is that when in future technology advances then same design can be used because VHDL is a technology independent language. Here we have designed, simulate and finally implement the proposed fuzzy logic based handoff controller on FPGA. The proposed system can be reconfigured and additional features can be incorporated with increasing need of the end users and providing better quality of service, by adding additional parameters. 7. References 1. Chandrasekhar G. Patil, Mahesh T. Kolte (January June 2011), An Approach for Optimization of Handoff Algorithm Using Fuzzy Logic System, International Journal of Computer Science and Communication 2(1), pp George Edwards and Ravi Shankar (1995), Handoff using Fuzzy Logic, /95 IEEE Proceeding. 3. L.A. Zadeh(1965), Fuzzy sets, Information and Control 8, pp Leonard Barolli, Fatos Xhafa, Arjan Durresi, Akio Koyama (2008), A Fuzzybased Handover System for Avoiding Ping Pong Effect in Wireless Cellular Networks International Conference on Parallel Processing, IEEE DOI /ICPP W Mamun Bin Ibne Reaz and Md. Saiur Rahman (26 28 December 2002), FPGA Realization of Fuzzy Based Subway Train Braking System ICECE Meriem Kassar, Brigitte Kervella, and Guy Pujolle. (2008), An Intelligent Handover Management System for Future Generation Wireless Networks, EURASIP Journal on Wireless Communications and Networking, Article ID Michael Mc Kenna and B.M. Wilamowski. (2001), Implementing a Fuzzy System on a Field Programmable Gate Array, IEEE / N. Nasser, A. Hasswa, H. Hassanein, "Handoffs in Fourth Generation Heterogeneous Networks", IEEE Communication Magazine, 44(10). 9. Nasıf Ekiz, Tara Salih, Sibel Kuçukoner, and Kemal Fidanboylu, 2005, An Overview of Handoff Techniques in Cellular Networks, World Academy of Science, Engineering and Technology. 10. Y. Kinoshita and Y. Omata (1992), Advanced Handoff Control Using Fuzzy Inference for Indoor Radio Sytems, IEEE 4th VTC, 2, pp

11 11. Dibyayan Das Sharma, Anshu Kumari and Pushplata, (2011), FPGA Based hardware Realization of DCT II Independent Update Algorithm for Image Processing Applications, International Journal of Computational Engineering & Management, 13, pp