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Radio Access Technology (RAT) selection, Joint Radio Resource Management (JRRM), Heterogeneous Wireless Networks.
(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 5, May 2011 An Overview on Radio Access Technology (RAT) Selection Algorithms for Heterogeneous Wireless Networks J.Preethi Dr.S.Palaniswami Assistant Professor, Professor, Department of Computer Science and Engineering Department of Electrical and Electronics Engineering Anna University of Technology, Coimbatore Government College of Technology India India Abstract—Next generation wireless networks (NGWN) will accurately as possible into its original wave form by the base be heterogeneous in nature where different radio access station. technology coexist in the same coverage area. The The second generation was implemented to improve coexistence of different RATs require a need for Joint transmission quantity system capacity and network coverage. Radio Resource Management (JRRM) to support the In second generation systems (e.g., Personal communication provision of quality of service and efficient utilization of systems (PCS), Global System for Mobile Communication radio resources. The Joint Radio Resource Management (GSM), Code Division Multiple Access (CDMA), Time (JRRM) manages dynamically the allocation and Division Multiple Access (TDMA) and Orthogonal Frequency deallocation of radio resources between different Radio Division Multiplexing (OFDM)) are based on Digital Access Technology (RAT). The homogenous Call Transmission. Admission Control (CAC) algorithms do not provide a In digital systems, more efficient use of the available single solution to address the heterogeneous architectures spectrum is achieved by digital encoding of the speech data. which characterize next generation wireless networks. This Due to the transition from 2G to 3G, a number of standards limitation of homogeneous CAC algorithms necessitates have been developed, which are categorized as 2.5G. These the development of RAT selection algorithms for are add-ons to the 2G standards and mainly focus on heterogeneous wireless network. The goal is to select the deployment of efficient IP connectivity within the mobile most suitable RAT for each user. This paper investigates networks. ten different approaches for selecting the most appropriate 2.5G is a stepping stone between 2G and 3G cellular Radio Access Technology (RAT) for incoming calls among wireless technologies, invented for marketing purposes only. the Heterogeneous Wireless Networks. This RAT selection 2.5G implements a packet switched domain which includes works in two steps; the first step is to select a suitable GPRS (General Packet Radio Service), EDGE (Enhanced Data combination of cells among the different RATs. The rates for GSM). second step chooses the most appropriate RAT to which The objective of the third generation (3G) is to provide the users can be attached and to choose the suitable fairly high speed wireless communications to support bandwidth to allocate for the users. multimedia, data and video in addition to voice. 3G includes Universal Mobile Telecommunications systems (UMTS) , CDMA2000 based on W-CDMA technologies which provides Keywords- Radio Access Technology (RAT) selection, Joint Radio services like wireless access to the Internet and high data rate Resource Management (JRRM), Heterogeneous Wireless Networks. applications like real time video transmission. To cope with these, high bandwidth services and the enormous increase in I. INTRODUCTION the number of users, a more efficient use of radio spectrum is In the recent years, mobile communication systems have required . experienced a significant change leading to the introduction of In turn, the perspective of beyond 3G systems is that of the heterogeneous wireless networks. The first generation heterogeneous networks, which provides wireless services mobile communications systems (e.g. Nordic Mobile independently of its location in a completely transparent way Telephony (NMT) and Advanced Mobile Phone System . The user terminal should be able to pick the best access (AMPS)) are based on analog transmission techniques. Analog technology such as Wireless Local Area Network (WLAN), signals are radio transmissions sent in a wave-like form. A the Universal Mobile Telecommunication Systems (UMTS) mobile device sends the waves to a base station where they are and the Global System for Mobile Telecommunication processed to determine the signals next destination (i.e. (GSM)/Enhanced Data rate for GSM Evolution (EDGE) Radio another base station, mobile phone, land line phone etc.,) Once Access Network (GERAN) at its current location and use the the destination is determined, the signal is reconstructed as access technology seamlessly for the provision of desired service. This leads to the introduction of new Radio Resource 1 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 5, May 2011 Management (RRM) techniques referred to as (JRRM) Joint III. BENEFITS OF HETEROGENEOUS JOINT RADIO Radio Resource Management algorithms which manages RESOURCE MANAGEMENT ALGORITHMS dynamically the allocation and deallocation of radio resources Each Radio Access Network (RAN) differs from the others between different Radio Access Technology (RAT). That is, by the air interface technology, cell size, services supported, instead of performing the management of radio resources bit rate capabilities, coverage, mobility support etc., therefore independently for each RAT, some form of overall and global the heterogeneous characteristics offered by the network is management of the pool of radio resources can be envisaged. considered. As a result, RAT provide further flexibility in the The coexistence of different RATs require a need for Joint way how radio resources can be managed. This lead to the Radio Resource Management (JRRM) to support the provision introduction of RRM. The basic function of Call Admission of quality of service and efficient utilization of radio Control (CAC) algorithm is to decide whether a new handoff resources. In heterogeneous wireless networks, different RATs call can be accepted into a RAT without violating service coexist in the same coverage area. The goal is to select the commitments . CAC has been used in wired networks and most suitable RAT for each user. In this paper, a in homogenous wireless networks such as GSM, UMTS, comprehensive survey of different RAT selection algorithms WLAN, Satellite network etc., However, homogenous CAC for a heterogeneous wireless network is proposed. Section II algorithms do not provide a single solution to address the explains about the architecture of heterogeneous wireless heterogeneous architecture. This limitation of homogenous network. Section III presents the benefits of Joint Radio CAC algorithm necessitates the development of RAT selection Resource Management algorithms and Section IV presents the algorithm for heterogeneous wireless network. comprehensive survey of RAT selection algorithms Section V Joint Call Admission Control algorithm is one of the JRRM gives the proposed work and lastly the conclusions are algorithms. Within the JRRM, the initial RAT selection, i.e the presented in Section VI. allocation of connections to specific RANs at session initiation II. HETEROGENEOUS WIRELESS NETWORK BEYOND 3G and the Vertical Handover (VHO) i.e the capability to switch ongoing connections from one RAN to another. These are the Next generation networks will be heterogeneous where key enablers to properly manage the heterogeneous radio different radio access networks such as WLAN, UMTS, access network and become the key for the JRRM functions. WiMax and satellite networks which is illustrated in the The benefits of Heterogeneous Joint Radio Resource Figure 1. In order to provide the mobile users with the Management Algorithms are requested multimedia services and corresponding quality of Efficient utilization of radio resources, service (QoS) requirements, these radio access technologies Consistent provisioning of QoS across different will be integrated to form a heterogeneous wireless access RATs, network. Such a network will consist of a number of wireless Overall stability of network, network and will form the fourth generation (4G) or next Increase in Operator‟s revenue and generation of wireless networks. However, each access network provides different levels of QoS, in terms of Enhancement of users satisfaction. bandwidth, mobility, coverage area and cost to the mobile users. Internet RRM WLAN Node AP Node WLAN WIMAX Cellular / GSM/ GPRS/ UMTS/3G/B3G User Equipment /Mobile Terminal Fig.1. Integration of Heterogeneous Wireless Access Network 2 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 5, May 2011 IV. AN OVERVIEW ON RAT SELECTION ALGORITHMS namely WLAN and WWAN. Four Fuzzy logic controllers are The section describes different RAT selection algorithms for used separately for selecting the best access network and initial RAT selection and Vertical Handover. References Particle swarm optimization is used to optimize the fuzzy , presents the good review of RAT selection algorithms. logic controllers and Genetic algorithm is used to optimize the In Random based RAT selection, when a new call or vertical weight coefficients. handover arrives, any of the available RATs will be selected randomly. In Load balancing based RAT selection, the main D) Soft Computing techniques based RAT Selection objective is to uniformly distribute the load among all the available RATs in heterogeneous wireless network Soft computing techniques based RAT selection admits an ,,. In Policy based RAT selection, it allocates users incoming call based on applying soft computing techniques for to the RAT based on some specific rules specified by the the RAT selection. The soft computing techniques applied for network. A simple policy has been proposed in , which RAT selection are discussed includes Voice GERAN (VG) policy, Voice UTRAN (UV) Fuzzy logic , focuses on the issues related to policy and Indoor (IN) policy. Service-class based RAT mobility management in future mobile admits calls into a particular RAT based on class of service communication scenario where a multi segment such as voice, video streaming, real-time video, web browsing access network is integrated into an IP core network. etc., . Service cost based RAT selection admits incoming  Proposes a new approach to handover call into the least expensive RAT in order to reduce the service management by applying a fuzzy logic concept to a cost incurred by the users. Path loss based RAT selection heterogeneous environment. For handover initiation, algorithm makes call admission algorithm based on path-loss parameters considered are network coverage, measurements taken in the cells of each RAT. perceived QoS and Signal Strength (SS). The framework for JRRM algorithm [18-19] based on Some of the other RAT selection algorithms are discussed in fuzzy neural mechanism as explained in Fig.2 this section. consists of three main blocks namely fuzzy based decision, reinforcement learning and multiple A) Network layer based RAT selection algorithm objective decision making. The inputs for the fuzzy based decision block are signal strength of each RAT, Network layer based RAT selection algorithm admits calls resource availability of each RAT and mobile speed into a particular layer. If the layer cannot accommodate the of the user. The Fuzzy based decision consists of call, this algorithm tries to admit the call into the next layer. three parts namely fuzzifier, inference engine and These algorithms are very simple but can lead to highly defuzzifier. The fuzzifier allocates a value from 0 to 1 unbalanced network load. Network layer based RAT selection for each input. In the inference engine, for each of the algorithm is explained in . The objective of this algorithm fuzzy subset defined in the fuzzifer, fuzzy rules are is to minimize new call blocking probability while associated to indicate if it is suitable to be selected. guaranteeing a hard constraint on handoff call dropping The output of the inference engine is a value that probability. varies between Y(yes), N (no), PY(probably yes) and PN (probably no). The defuzzifer will convert the B) Utility/cost function based RAT selection output of the inference engine into fuzzy selected decision (FSD). The subjective and techno-economic Utility/Cost function based RAT selection admits the criteria in the form of user preferences (UP) and incoming calls into a particular RAT based on some utility or operator preferences (OP) are inputs to the multiple cost function derived from a number of criteria. It considers objective decision making. The outputs of the fuzzy user time constraints, and estimates the complete file delivery neural algorithm are cell/RAT selection and amount time for each available access network and then selects the of bit rate allocated for the selected RAT. most promising access network. These algorithms are very Fuzzy MADM (Multiple Attribute Decision Making) efficient but are very complex and incur high computational method [20-22] operates in two steps. The first step is overhead.  present the utility based RAT selection to convert the imprecise fuzzy variables to crisp algorithms for selecting the RAT. numbers. The second step is to use classical MADM technique to determine the ranking order of the C) Mobile based RAT selection algorithm candidate networks. The highest ranking RAT is then selected for the call. This algorithm uses mobile terminal measurements from Using Fuzzy logic controllers, genetic algorithms and different radio access technology for the initial RAT selection particle swarm optimization for decision making of . The inputs to this algorithm are speed of the mobile user, radio access technology selection for the next signal strength, quality of service and service cost. This generation wireless networks under given input algorithm uses fuzzy logic controllers, genetic algorithms and criteria on user velocity, type of service and service particle swarm optimization for decision making under given parameters, Quality of service and service costs of the input criteria. In this, two access networks are considered mobile user . 3 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 5, May 2011 REINFORCEMENT LEARNING Bandwidth Signal strength MULTIPLE Allocated INFERENCE Fuzzy Resource FUZZIFIER DEFUZZIFIER OBJECTIVE Bandwidth Selected availability ENGINE DECISION RAT MAKING Mobile speed RAT User Selected Fuzzy based Decision Preferences Operators Preferences Fig.2. Block Diagram of the Fuzzy Neural System (Figure taken from ) This algorithm uses mobile terminal measurements G) Network Controlled Cell Breathing RAT selection from different radio access technologies within a given time interval, with aim to obtain information The Network controlled cell breathing RAT for multi criteria decision making between different selection algorithm is applied for the Initial RAT access networks available to the terminal. selection and Vertical handover between CDMA/TDMA based systems . In E) Hopfield Neural network RAT selection FDMA/TDMA systems like GSM/GPRS, there is no Intra cell interference, but there will be inter cell Here the RAT selection mechanism is based on the interference for a single user exchanging the Hopfield neural networks model . The input information between the cells. In case of CDMA parameters to the HRM model are gathered from based systems like UMTS, there are both Intra cell different layers of the protocol stack. This approach and Inter cell interferences. CDMA systems are more allows improvement in terms of service retainability, sensitive for multi user interference than decreased packet loss, jitter. The main advantage of FDMA/TDMA systems. By effectively controlling HRM is smaller complexity. The smaller complexity the cell radius of the CDMA RAT, the capacity of the results in smaller CPU load. CDMA systems is increased and target coverage area is assured. F) Operator RAT selection based on the Fittingness Factor H) Performance Analysis of Radio Access Technology Selection Algorithms using 2-Dimensional Markov The initial RAT selection and vertical handover is Model done based on measuring the fittingness factor depending on network and terminal capabilities for The performance analysis of Radio Access each RAT . The fittingness factor is calculated by Technology selection is made using Markov model three factors namely the capabilities of the terminal, . To evaluate the performances metrics, Mobility User-centric suitability and network centric suitability based algorithm and Always WLAN algorithm are of the overall RAT perspective. For Initial RAT considered. Authors Porjazoski and Borislav selection, fittingness factor is calculated for each Popovski considered the RAT selection algorithms RAT. The RAT with the highest fittingness factor is performances, like user mobility, call arrival rate, call selected first and if admission is not possible, select duration, size of RAT cells. The results explained that the next RAT in the decreasing order with respect to Always WLAN algorithm have higher rate of vertical fittingness factor. For Vertical handover, the handover than Mobility based algorithm. Mobility fittingness factor of the serving RAT and the new based RAT selection algorithm shows better RAT are measured and the RAT with the highest performances in the means of blocking probability for fittingness factor is given the call. higher percentage of vehicular users. For smaller percentage of vehicular users and for very high traffic loads, Always WLAN algorithm performs better then mobility based algorithm. 4 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 5, May 2011 V PROPOSED WORK  W.Zhang, “Performan ce of Real-time and Data traffic in Heterogeneous Overlay Wireless Networks”, 19th International Teletraffic Congress Applying optimizing methodologies like Fuzzy logic, (ITC 19), Beijing, China, August 29- September 2, 2005. Genetic algorithm, Neural Networks, Particle Swarm  J.Perez-Romero, O.Sallent, R.Agusti, “Enhanced Radio Access optimization and Simulated Annealing for the RAT selection Technology Selection exploiting Path loss information”, 17th annual IEEE Symposium on Personal, Indoor and Mobile Radio of the heterogeneous wireless network and comparing these Communication (PIMRC’06). algorithms and finding out the best access network.  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The coexistence of different RATs Technology Selection for the Next Generation Wireless Networks” in requires a need for Joint Radio Resource Management International Journal of Research and Reviews in Next Generation (JRRM) to support the provision of quality of service and Networks, Vol. 1, No. 1, March 2011 efficient utilization of radio resources. Hence this paper  P.M.L. Chan, R.E.Sheriff, Y.F.Hu, P.Conforto, C.Tocci, “Mobility presents the architecture for heterogeneous wireless networks management incorporating fuzzy logic for a heterogeneous IP environement”, IEEE Communications Magazine 39(12) (2001) 42-51. and benefits for Joint radio resource management algorithms.  L. Giupponi, R. Agustí, J. Pérez-Romero, and O. Sallent, “A novel Then an overview of Radio Access Technology selection for approach for joint radio resource management based on fuzzy neural the Heterogeneous wireless networks is discussed. 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J.Perez-Romero, O.Sallent and R.Agusti, “Policy-based Initial RAT selection algorithms in HeterogeneousNetworks”, The 7th IFIP International Conference on Mobile and Wireless Communications Networks (MWCN 2005), Marrakech, Morocco, September 19-21, 2005. 5 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 5, May 2011 AUTHORS PROFILE S. Palaniswami received the B.E. degree in electrical and electronics engineering from the Govt., college of Technology, Coimbatore, University of Madras, Madras, India, in 1981, the M.E. degree in electronics and communication engineering (Applied Electronics) from the Govt., college of Technology, Bharathiar University, Coimbatore, India, in 1986 and the Ph.D. degree in electrical engineering from the PSG Technology, Bharathiar University, Coimbatore, India, in 2003. Coimbatore, India, in 2003. He was the Registrar of Anna University Coimbatore, Coimbatore, India, from May 2007 to May 2010. Currently he is heading the Department of Electrical and Electronics Engineering, His research interests include Control systems, Communication and Networks, Fuzzy logic and Networks, AI, Sensor Networks. . He has about 25 years of teaching experience, since 1982. He has served as lecturer, Associate Professor, Professor, Registrar and the life Member of ISTE, India. J.Preethi received the B.E. degree in Computer Science and Engineering from Sri Ramakrishna Engineering College, Coimbatore, Anna University, Chennai, India, in 2003, the M.E. degree in Computer Science and Engineering from the Govt. college of Technology, Anna University, Chennai, India, in 2007 and she is currently pursuing the part time Ph.D. degree in the Department of Computer Science and Engineering from the Anna University Coimbatore, Coimbatore, India. Currently, she works as a Assistant Professor in the Department of Computer Science and Engineering, Anna University Coimbatore. Her research interests include Mobile adhoc networks, Mobile Communication systems especially in Radio Access Technology selection, Fuzzy logic and Neural Networks, Genetic Algorithms and AI. 6
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