Application of Soft Computing Techniques for Handoff Management in Wireless Cellular Networks

Transcription

1 International Journal of Engineering and Management Research, Vol.-2, Issue-6, December 2012 ISSN No.: Pages: Application of Soft Computing Techniques for Handoff Management in Wireless Cellular Networks Dayal C. Sati Assistant Professor in Department of Electronics and Communication Engineering BRCM College of Engineering & Technology, Bahal, Bhiwani, Haryana,INDIA ABSTRACT Handoff process enables a cellular system to provide continuation of an active call when user moves from one cell to another. The modern cellular industry is using smaller cell sizes in order to increase the system capacity by using frequency reuse. Conventional handoff decisions are normally signal strength based. To make a better handoff and keep Quality of service (QoS) in wireless networks several handoff algorithms, based on soft computing techniques can be used. This paper highlights the basic handoff mechanism and a brief description about some of the soft-computing techniques which can be applied for handoff management in modern cellular networks. At last I have proposed a Fuzzy Logic based handoff technique using Fuzzy tool of MATLAB Keywords - Handoff, Soft Computing, QoS, Fuzzy Logic, ANN. I. INTRODUCTION During the last few years wireless networks have been a very active research area [3]. 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. II. CONVENTIONAL HANDOFF ALGORITHMS Normally signal strength based measurements are considered due to its simplicity and effective performance. 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)[2]. However the fluctuations of signal strength cause ping-pong effect. Some of the main signal strength metrics 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. Figure1: Conventional handoff based on RSS [2] 1

2 The conventional RSS based handoff method selects the Base station (BS) with strongest received signal at all times. All the above techniques initiate handoff before point D, which is called Receiver Threshold [2]. Receiver threshold is the minimum acceptable RSS for call continuation [T2 in figure 1]. If RSS is dropped below receiver threshold the ongoing call is dropped. 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. This problem can be minimized using soft computing techniques for hand off decisions. III. VARIOUS SOFT COMPUTING TECHNIQUES Some of the basic Soft Computing methods, which promise a global optimum or nearly so, such as expert system (ES), artificial neural network (ANN), genetic algorithm (GA),fuzzy logic (FL), etc. have been emerged in recent years[8]. These methods are also known as artificial intelligence (AI) in several works. Many investigations have addressed different handoff algorithms for cellular communication systems. There are many criteria may be used to support handoff decisions, such as Received signal strength(rss),signal to Interference Ratio (SIR),Velocity of MS, Distance between the MS and BS, Traffic Load etc [3][4][5][6]. However it becomes very complex to make handoff decision considering multiple criteria for handoff. Sometimes, the trade-off of some criteria may be considered. The timing of the handoff initiation is also important. There can be deleterious effects on link quality and interference if the initiation is too early or too late. A timely handover algorithm is one which initiates handoffs neither too early nor too late. Because of large-scale and small-scale fades are frequently encountered in mobile environment, it is very difficult for handover algorithm to make an accurate and timely decision. Handover algorithms operating in real time have to make decisions without the luxury of repeated uncorrelated measurements or the future signal strength information. It should be noted that some of handover criteria information can be inherently imprecise, or the precise information is difficult to obtain. For this reason, the soft computing-based approach, which can operate with imprecision data and can model nonlinear functions with arbitrary complexity may provide the solution of the problem.. Some of the soft computing techniques that can be applied for intelligent handoff decisions are described here: III (A). GENETIC ALGORITHMS Genetic algorithm (GA) is an optimization method based on the mechanics of natural selection and natural genetics. Its fundamental principle is the fittest member of population has the highest probability for survival.the most familiar conventional optimization techniques fall under two categories viz. calculus based method and enumerative schemes. Though well developed, these techniques possess significant drawbacks. Calculus based optimization generally relies on continuity assumptions and existence of derivatives. Enumerative techniques rely on special convergence properties and auxiliary function evaluation. The genetic algorithm, on the other hand, works only with objective function information in a search for an optimal parameter set. The GA can be distinguished from other optimization methods by following four characteristics: (i) The GA works on coding of the parameters set rather than the actual parameters. (ii) The GA searches for optimal points using a population of possible solution points, not a single point. This is an important characteristic which makes GA more powerful and also results into implicit parallelism. (iii) The GA uses only objective function information. No other auxiliary information (e.g. derivatives, etc.) is required. (iv) The GA uses probability transition rules, and not the deterministic rules. III (B). ARTIFICIAL NEURAL NETWORKS An artificial neural network (ANN) is an information processing system that tries to simulate biological neural networks, ANN are distributed, adaptive, generally nonlinear learning machines built from many different processing elements (PE). Each PE receives connections from other PE and/or itself. The interconnectivity defines the topology. The signals flowing on the connections are scaled by adjustable parameters called weights. Neural networks are typically arranged in layers. Each layer in a layered network is an array of processing elements or neurons. Information flows through each element in an input-output manner. III (C). FUZZY LOGIC Fuzzy logic (FL) was developed by Zadeh in 1964 to address uncertainty and imprecision, which widely exist in the engineering problems [1]. Fuzzy set theory can be considered as a generalization of the 2

3 classical set theory. In classical set theory, an element of the universe either belongs to or does not belong to the set. Thus, the degree of association of an element is crisp. In a fuzzy set theory, the association of an element can be continuously varying. Mathematically, a fuzzy set is a mapping (known as membership function) from the universe of discourse to the closed interval. Membership function is the measure of degree of similarity of any element in the universe of discourse to a fuzzy subset. Triangular, trapezoidal, piecewise-linear and Gaussian functions are most commonly used membership functions. The membership function is usually designed by taking into consideration the requirement and constraints of the problem. Fuzzy logic implements human experiences and preferences via membership functions and fuzzy rules. Due to the use of fuzzy variables, the system can be made understandable to a non-expert operator. In this way, fuzzy logic can be used as a general methodology to incorporate knowledge, heuristics or theory into controllers and decision makers. IV. PROPOSED FUZZY LOGIC BASED HANDOFF TECHNIQUE Figure. 2 shows the structure of the proposed fuzzy inference system. In order to design a fuzzy logic system the following steps are used: Identify the inputs and outputs using linguistic variables. In this step we have to define the number of inputs and output terms linguistically Assign membership functions to the variables. In this step we will assign membership functions to the input and output variables. 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 2. Controller Proposed fuzzy logic based Handoff The three input parameter which we have considered are: Change of the signal strength of present base station (CSSP), Signal Strength from Neighbor base station (SSN), Velocity of Mobile station (VEL). while the output linguistic parameter taken is Handoff Decision (HD). The term sets of CSSP, SSN, VEL and HD are defined as : T(CSSP) = [Small Change, No Change, Big Change] = [SC, NC, BG] ; T(SSN) = [Weak, Normal, Strong] = [WK, NOR, STRG] ; T(VEL) = [Low, Medium, High] = [LO, MD,HG]; T(HD) = [ No Handoff, Wait, Handoff] = [NH,WT,HO] The membership functions of input parameters for the proposed fuzzy logic controlled handoff mechanism are shown in figure 3,4 and 5. 3

4 Figure 5. Membership function of Velocity of Mobile Terminal (VEL) in km/h The fuzzy rule base (FRB) for the proposed Figure 3. Membership functions of Change in signal strength of current BS (CSSP) in DB Figure 4. Membership function of Signal strength from neighbor BS (SSP) in DB Rule CSSP SSN VEL HD No. (DB) (DB) (km/h) 1 -VE WK LOW NH 2 -VE WK MD NH 3 -VE WK HG WT 4 -VE NOR LOW NH 5 -VE NOR MD NH 6 -VE NOR HG WT 7 -VE STRG LOW NH 8 -VE STRG MD NH 9 -VE STRG HG WT 10 NC WK LOW NH 11 NC WK MD WT 12 NC WK HG WT 13 NC NOR LOW NH 14 NC NOR MD NH 15 NC NOR HG WT 16 NC STRG LOW WT 17 NC STRG MD HD 18 NC STRG HG HD 19 +VE WK LOW NH 20 +VE WK MD WT 21 +VE WK HG HD 22 +VE NOR LOW WT 23 +VE NOR MD HD 24 +VE NOR HG HD 25 +VE STRG LOW HD 26 +VE STRG MD HD 27 +VE STRG HG HD handoff technique is shown in Table 1 and has =27 rules. The rules have the following form: IF Condition THEN Control action. 4

5 Table 1 : Fuzzy Rule Base for proposed Fuzzy logic based handoff system Figure 8. Surface curve between Change in signal strength(cssp), Received signal strength from neighbor BS(SSN) and Handoff decision (HD). Figure 6. Rule Viewer for the proposed system Figure 9. Surface curve between Change in signal strength(cssp), Velocity of MS (VEL) and Handoff decision (HD). V. CONCLUSION AND FUTURE WORK Figure 7. Surface curve between, Velocity of MS (VEL), Received signal strength from neighbor BS(SSN) and Handoff decision (HD). In this paper I have proposed a fuzzy logic based soft computing technique to find out the handoff decision of the mobile terminals in wireless cellular networks. The result shows that the handoff decisions are taken in appropriate positions so that the load at base stations and Mobile switching centre (MSC) is reduced. In future I will implement it on FPGA and also design a handoff mechanism based on another soft computing technique: Artificial 5

6 Neural Network (ANN), taking the same input parameters and a comparison of the results of these two mechanisms will be performed. REFERENCES [1] L.A. Zadeh, Fuzzy sets, Information and Control 8, , [2] Nasıf Ekiz, Tara Salih, Sibel Kuçukoner, and Kemal Fidanboylu, An Overview of Handoff Techniques in Cellular Networks.,World Academy of Science, Engineering and Technology [3] Leonard Barolli, Fatos Xhafa, Arjan Durresi, Akio Koyama, A Fuzzy-based Handover System for Avoiding Ping-Pong Effect in Wireless Cellular Networks International Conference on Parallel Processing, / , IEEE. [4] N. Nasser, A. Hasswa, H. Hassanein, "Handoffs in Fourth Generation Heterogeneous Networks", IEEE Communication Magazine, Vol. 44, No. 10. [5] Meriem Kassar, Brigitte Kervella, and Guy Pujolle, An Intelligent Handover Management System for Future Generation Wireless Networks, EURASIP Journal on Wireless Communications and Networking Volume 2008, Article ID [6] Y. Kinoshita and Y. Omata," Advanced Handoff Control Using Fuzzy Inference for Indoor Radio Sytems", IEEE 4th VTC, vol.2, pp , [7] Chandrashekhar G. Patil, Mahesh T. Kolte, An Approach for Optimization of Handoff Algorithm Using Fuzzy Logic System, International Journal of Computer Science and Communication Vol. 2, No. 1, January-June 2011, pp [8] Arthur K.Kordon, Future Trends in Soft Computing Industrial Applications,IEEE International Conference on Fuzzy Systems,2006. [9] P.P. Bhattacharya, Application of Artificial Neural Netowrks in Cellular Handoff Management International Conference on Computational Intelligence and Multimedia Applications, IEEE