International Journal of Advance Foundation and Research in Computer (IJAFRC) Volume 2, Issue 5, May ISSN

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1 A Spectrum Selection Framework for Mobility Management in Cognitive Radio Cellular Network Mr. Pritam B. Salwe 1 Prof. Mrs. Dimple Chaudhari 2 Yadavrao Tasgaonkar Institute Of Engineering and Technology, Chandhai, Karjat Department of Electronics & Telecommunication Engineering pritam.salwe@gmail.com 1, dimple.chaudhari@gmail.com 2 A B S T R A C T Cognitive radio (CR) offer solution to the spectrum inefficiency by using the spectrum holes in space without introducing any sort of interference to the primary user. It also solves the spectrum inefficiency and spectrum scarcity problem. This exploits potential opportunities for use of spectrum without any interference. It has main tasks like Spectrum Sensing, Spectrum Analysis and Spectrum Allocation. In this paper, A spectrum selection framework for mobility management in cognitive radio cellular network is discussed. First the Spectrum decision is done to determine a set of available spectrum bands by considering the dynamic nature of spectrum band & users need. A handoff process for each spectrum is characterized by jointly considering primary user activity in cell area and spectrum sensing process. On this basis, a dynamic resource management scheme is modeled to accommodate the spectrum decision adaptively dependent on the timevarying cognitive radio network capacity. This mobility management framework improves total capacity & throughput of system. Index Terms Cognitive Radio, Cognitive Radio Architecture, Resource Management, Spectrum Selection, Handoff Schemes, Inter Cell Resource Management, Spectrum Selection Method. I. INRODUCTION Wireless spectrums are under government policies & assigned to the service providers on lease for long period. Cognitive radio (CR) technology used to realize dynamic spectrum access which permits an unlicensed user (or, secondary user) to adaptively adjust its operating parameters and exploit the spectrum which is not used by licensed users (or) primary users in an opportunistic manner. The CR technology permits secondary users (SUs) to search and make use of spectrum holes in a time and location-varying radio environment devoid of causing any interference to primary users (PUs). This opportunistic use of the spectrum tends to new challenges to make the network protocols adaptive to the varying available spectrum [8]. In CR (cognitive radio) network to proposed spectrum decision framework, using DSA algorithm is to provide the efficient bandwidth utilization, improving the service quality and reduce the spectrum inefficiency problem [1], [2]. In DSA scenario where cognitive workstation having multi-hoping potential, dynamically access frequency bands assigned to different radio access networks has been considered, this gain achieved to 10 to 40% [3], [10]. CR arises to be enticing a key to the spectral congestion problem. Co-operative sensing & External sensing algorithm are proposed, as a solution to hidden primary user problem & it can decrease the sensing time & also reduce the probability of misdetection. The developing algorithm for prediction into the future using past information can be considered as some of the open research areas [4], [6]. Propose a spectrum assignment policy having a tradeoff between the minimization of the call blocking rate of ongoing call for smooth handoff, this process required a two phases are, [i]. A clash in between two calls for spectrum band allocation is resolved, [ii]. Spectral and allocation among a no of call is considered [5]. The load , IJAFRC All Rights Reserved

2 balancing ng scheme may increase the number of satisfied users, but may not always increase the overall system throughput, and To improve global resource utilization and reduce regional congestion given asymmetric arrivals and a goal requiring load balancing among multiple cells. Decrease the blocking rate of MS arrivals among the whole system by 50% to 100% or even more, it also maximizes system exploitation and obstructs regional clogging [10]. We propose the proactive spectrum access approach & two channel Selection & switching technique, That are overcome the frequent disruptions to operation of both primary as well as secondary users, because of existing proposal take a reactive sense. To minimize disruptions to primary user and maintains reliable communication at secondary users. The practical approach successfully decrease the obstruction to primary user by up to 30% & significantly decreases throughput jitters at secondary users [7]. In cooperative sensing algorithm the opportunistic use of under-utilized utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detect However, since the required sensitivity is very demanding any individual Radio might face a deep fade[9], [15].in decentralized MAC(Media Access control) opportunity to minimizes the interference caused to neighboring cells, reduces their transmission chances, leading to the so-called sensing efficiency problem [19]. Fig 1: cognitive radio network II. SYSTEM MODEL A. Handoff Technique Related work on CR (cognitive radio) based spectrum handoff falls into two categories based on the moment when SUs (secondary user) carry out spectrum handoffs. One approach is that SUs (Secondary user) perform spectrum switching and radio frequency (RF) front-end reconfiguration after detecting a PU (Primary user) namely the reactive approach. Although the concept of this approach is intuitive, there is a non-negligible negligible sensing and reconfiguration delay which causes unavoidable disruptions to both the PU (Primary user) and the SU (Secondary user) transmissions. Handoff type is related to different mobility event, and its performance is mainly dependent on both network and user conditions, such as resource availability, network capacity, user location, etc. Thus, Cognitive radio networks require a unified mobility management scheme to exploit different handoff types adaptively to the dynamic nature of underlying spectrum bands. B. Spectrum Decision Making First, novel spectrum organization functionalities such as spectrum sensing, spectrum sharing, and spectrum decision, and spectrum mobility are introduced. A particular prominence is given to cross-layer , IJAFRC All Rights Reserved

3 design looms from the viewpoint of both infrastructure-based network requiring central network entities and ad-hoc networks based on distributed coordination. The main challenge in CR networks is to integrate these functions in the layers of the protocol stack, so that the CR users can communicate reliably over a dynamic spectrum environment. Thus, the persuade of these functions on the recital of the upper layer protocols, such as the network layer, and transport layer protocols are investigated, and open research issues in these areas are also outlined. In fig 1, see the infrastructure-based CR networks consist of multiple cells. Each cell has a single BS and its CR users. In this architecture, CR users observe their radio environments and report the results to the BS (base station). Accordingly, the BS determines proper action in support of a upper-level control node, Such as the mobility management entity (MME) in 3GPP. CR users have a single wideband RF transceiver that can sense multiple contiguous spectrum bands at the same time without RF reconfiguration. In this technique occurring two main problem that are, 1. Resource allocation: In resource allocation there is lack of memory, large number of users can not be accommodate. 2. Handoff technique: In our spectrum selection method is convince to the radio network and sensor network not worth able for mobile based network. Fig 2: Cognitive Radio Architecture (Wiley Publication) III. ALGORITHM DESCRIPTION A. Dynamic spectrum algorithm In DSA (Dynamic spectral access) algorithm, The Spectrum availability in CR networks varies over time and space, making it more difficult to provide seamless and reliable communications to mobile users traversing across multiple cells. For efficient mobility management, CR networks need to mitigate this dynamic spectrum availability by performing mobility management adaptively dependent on the heterogeneous network conditions. In DSA (Dynamic spectral access) scenario where cognitive terminal having multi-homing capability, dynamically access frequency bands assigned to different radio access networks has been considered. To provide the opportunities to both user and operator and then improve the user experience & increased network capacity. Compared to SINR (Signal interference to noise ratio) based hand over algorithm, the DSA algorithm has very large gain in network capacity. This gain equal to 10-40% for different value of traffic rate per cognitive terminal, also increase this gain the traffic rate also increased per cognitive terminal. B. Intercell Resource Allocation Since each cell has time-varying wireless resource because of the dynamic nature of underlying spectrum in CR networks, it cannot have a permanent extended spectrum. Furthermore, as explained in Section 2, , IJAFRC All Rights Reserved

4 the extended spectrum of the current cell cannot be used in its extended neighbors, leading to decrease in their capacity. As a result, CR networks necessitate an intercell resource allocation scheme to select and maintain the extended spectrum. Although global optimization in every spectrum change achieves optimal allocation, it requires a huge computational complexity and also causes a high communication overhead due to frequent spectrum switching. Instead, we consider the stochastic characteristics of spatial and temporal spectrum availabilities, and develop a distributed intercell resource allocation method, which improves total network capacity as well as mobility support, i.e., the availability of the extended spectrum. The following are the procedures of the proposed method: 1. Initially, all available spectrum bands in current cell i are considered as basic spectrum bands. 2. CR users can access the spectrum band only when all PU activity regions in the BA of the current cell are idle. Thus, the expected capacity of spectrum band j at cell i is defined as follows: =, C. An algorithm for combined cell-site selection and power control to maximize cellular spread spectrum capacity There is vast attention in spread spectrum wireless mobile communications and in meticulous the subject of spread spectrum wireless capability. We portray spread spectrum cellular capacity and provide a combined power control, cell-site selection algorithm that facilitates this capacity is to be reach. The algorithm acclimatizes users' transmitter power levels and alters them among the cell-sites, and it is revealed that the algorithm flocks to an allocation of users to cells that is optimal in the sense that interference is minimized. The algorithm is decentralized, and can be considered as a mechanism for cellsite diversity and handover. IV. PROPOSED WORK We propose a spectrum-sentient mobility management plan for CR cellular networks. First, we propose a narrative CR cellular network design based on the spectrum grouping concept, which alleviates the heterogeneous spectrum availability. Based on this structural design, a cohesive mobility management framework is defined to maintain assorted mobility events in CR networks, made up of inter-cell resource allocation, spectrum mobility management, and user mobility management functions. Through inter-cell resource allotment, every cell regulates its spectrum configuration to better mobility as well as overall capacity. To back spectrum mobility while keeping optimum cell capacity, the spectrum mobility management regulates a appropriate handoff type and target cell for CR users encounters PU activities by taking into consideration both spectrum utilization and stochastic connectivity models. On the other hand, user mobility management mainly focuses on spectrum heterogeneity in space, and offers a switching cost-based handoff decision mechanism to minimize service quality degradation in mobile users. A. Algorithm- Load Balancing Base Open Access Spectral Selection Method Depending upon this, a least variance based spectrum decision is proposed for real-time functions, which reduces the capacity variance of the desired spectrum bands subject to the capacity constraints. Load balancing base open access spectral selection method was proposed for CR spectral selection, And it provide the opportunities to improve user experience & increase network capacity. Network topology includes seven wireless access nodes. Each wireless access node has Omni-directional antenna. Each wireless access node is assigned different frequency band. So, there is no interference between different wireless access nodes , IJAFRC All Rights Reserved

5 B. Structural Design Model In fig2, explain the concept of the cognitive network, the mobility management technique to track the where the subscriber allow the call and then delivered to other service. In spectrum pool is a set of licensed spectrum band, each of its spectrum consist of multiple channel. Resource allocation it depends on the spectrum availability, first to find the available spectrum band after to allocate user in the white space spectrum. We use the spectrum handoff technique, first to sense the spectrum, after to detect the primary user, Then to find who are currently active. Based on this information spectrum holes or white space will be recognized. In spectrum selection method to use the algorithm of load balancing spectral selection method it is used to find the available spectrum band after allocate the user it depends upon the capacity allocation scheme to resolve the collision between the two calls, After to avoid the spectral inefficiency problem. C. Basic Model In this paper, we consider infrastructure-based CR network having number of cells. Each cell has a single BS and its CR users. In this architecture, CR users observe their radio environments and report the results to the BS. Accordingly, the BS determines proper actions in support of a upper-level control node, such as the mobility management entity (MME) in 3GPP LTE [15]. CR users has a single wideband RF transceiver that can sense multiple contiguous spectrum bands at the same time without RF reconfiguration. Each CR user m needs Km channels to satisfy its QoS requirement. All spectrum bands are assumed to be licensed to different primary networks. Furthermore, each spectrum can have multiple primary networks that are operated independently in the different region, called PU activity region. For example, the cell coverage of each BS is considered as the PU activity region. Since most of primary networks such as cellular networks or TV broadcasting networks have a fixed service coverage, the PU activity region is generally assumed to be fixed.generally, each PU activity region in the licensed band has ON and OFF states where ON (Busy) state represents the period used by primary users and an OFF (Idle) state represents the unused period. In this paper, we assume that the length of ON and OFF periods at PU activity region k in spectrum band j is exponentially distributed with means 1= j/ kþ and 1= j/ kþ, respectively. If < + ρ, CR users using!"# %& $ ( channels in EA are not preferred and shifted to the adjacent cells. Since the state transitions ' are detected by periodic sensing operations in every interval of length Ft, the PU activity in the licensed band is modeled as a two-state Markov chain. V. SIMULATION RESULT In fig 3, the infrastructure-based CR (cognitive radio) network is developed. In the network topology includes 20 wireless access nodes. Fig.4 shows data packet transfer. Each wireless access node has its own Omni-directional antenna. It has assigned various frequency band. In CR network is composed of single base-station and multiple mobile users. Each user is evenly separated over the network coverage with the radius of 2 km. The CR network is supposed to maneuver in 20 licensed spectrum bands consisting of four VHF/UHF TV, four AMPS, four GSM, four CDMA, and four WCDMA bands. The bandwidth of the bands are 5.5 MHz (TV), 30 khz (AMPS), 200 khz (GSM), 1.25 MHz (CDMA), and 4 MHz (WCDMA), respectively. Fig.4 shows data packet transfer , IJAFRC All Rights Reserved

6 Fig3: Handoff process Fig4: Data packet transfer Fig5: Graph for no. of delivered packets Vs User capacity Fig6: Graph for network lifetime Vs User capacity In fig 3, MR initiates the handoff process in within the network. In graphical representation of fig.6, shows, if the user capacity is augmented means drop rate is also increased in the network, and also cell capacity will bedecreased. VI. CONCLUSION Cognitive radio (CR) technology is proficient to resolve the issues in wireless networks due to the inadequate vacant spectrum bandwidth and the incompetence in the spectrum usage by make use of the existing wireless spectrum opportunistically. CR networks, outfitted with the inherent potentials of the cognitive radio, will endow an vital spectrum-aware communication prototype in wireless communications. CR networks, on the other hand, compel distinguishing challenges for the reason that the lofty variation in the available spectrum in addition to miscellaneous quality-of-service (QoS) requirements. These key distinguishing factors necessitates a rethinking of the existing solutions developed for classical wireless networks. Simulation results shows that the proposed methodology efficiently gives rise in mobility management. For the PU activity, spectrum mobility management is developed where the network decides a proper spectrum band and target cell as per both current spectrum utilization and stochastic connectivity model. In user mobility management, the switching cost-based handoff decision mechanism is proposed so as to minimize quality degradation caused by user mobility. Simulation results show that the proposed method provides optimum cell capacity while delivering very less quality degradation in mobile users. VII. REFERENCES [1] Won-Yeol Lee, Student Member, IEEE, and Ian F. Akyildiz, Fellow, IEEE, Spectrum-Aware Mobility Management in Cognitive Radio Cellular Networks, IEEE Transaction on Mobile Computing, Vol. 11, No. 4, April [2] Won-yeol-lee, A spectrum decision framework for cognitive radio network, IEEE Transaction On Mobile Computing, Vol. 10, No. 2, feb , IJAFRC All Rights Reserved

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