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  3. Investigating routing algorithms in cognitive radio networks and providing a method to improve network performance

Investigating routing algorithms in cognitive radio networks and providing a method to improve network performance

Number of pages: 128 File Format: word File Code: 32165
Year: 2014 University Degree: Master's degree Category: Electrical Engineering
Tags/Keywords: Bandwidth - cognitive radio networks - CRAHN - Network output - radiology - Routing algorithms - Telecommunication networks - The technique of multiple submissions
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  • Summary of Investigating routing algorithms in cognitive radio networks and providing a method to improve network performance

    Master thesis in electrical engineering, system communication

    Abstract:

    Investigation of routing algorithms in radiological networks

    and providing a method to improve network output

    Radio science technology was first expressed by Dr. Mitola in 1999 and in recent years has created an emerging evolution in the field of radio communication that can provide faster and more reliable wireless services by using available spectrum resources intelligently and effectively. Of course, this dynamic use of the spectrum is associated with many complications in the field of designing communication protocols in different layers. In this thesis, we have reviewed the studies conducted on effective routing methods in radio cognitive networks and the basic ideas that led to the presentation of these algorithms. Considering the shortcomings in the previous designs as well as the effective factors in an optimal design, we will derive an efficient routing algorithm for CRAHN [1] based on AODV [2], which uses two techniques of multi-channel and multi-path transmissions to deal with the variable activity with the location and frequency of PUs [3]. In this method, knowing the changes in network topology, local information related to frequency holes and statistical characteristics of the performance pattern of PUs, we choose the best part of the bandwidth. We also combine the performance improvement metric with the appropriate channel selection strategy to increase efficiency as much as possible. In the following, a realistic simulation for contingent radiological networks is presented and a realistic and flexible model of the activity of primary users as well as the radiological cycle (detection, mobility and spectrum sharing) performed by each SU [4] has been implemented. Also, this appendix provides the possibility of inter-layer data exchange between protocols of different network layers. The ideas related to the proposed method have been investigated through NS2 software [5]. The simulations show a significant improvement in increasing the end-to-end efficiency compared to the previous method.

    Key words: radio cognitive technology, CRAHN, routing algorithm, multiple transmission technique

     

     

    1-1- General

     

    Due to the increasing demand for more capacity, telecommunication networks and available wireless resources such as spectrum (broadband) should be used more efficiently. Network design patterns and new communication technologies such as radio cognitive networks have emerged in recent years that have the ability to use spectrum resources in an intelligent and effective way. Radio cognitive technology was first described by Dr. Mitola in 1999 [1]. And in recent years, it has created an emerging development in the field of radio communication that can provide faster and more reliable wireless services by efficiently using the available spectrum resources. The significant difference between radiological networks and conventional wireless networks of the past is that the users of these networks must be aware of the radio space around them and adjust their internal parameters such as transmitted power, transmitted frequency, and modulation type to it. In general, the approach of common sharing and spectrum management mechanisms in the past was based on the assumption that all network users cooperate unconditionally in a fixed space, which is not possible in a radiological network. Extensive measurements show that fixed frequency allocation results in underutilization of licensed spectrum of about 6% most of the time [2]. rtl;">In radiological networks, spectrum bands are shared between primary users ([1]PUs) and secondary users (SU[2]s) in a prioritized manner, they are also smart users..

    Figure 1-1 Frequency spectrum usage diagram, [2]

    in radiological networks spectrum bands between primary users ([1] PUs (secondary users (SU[2])) in a prioritized manner Also, users are intelligent and have the ability to monitor, learn and perform optimally in order to increase their efficiency. If they belong to different domains and pursue different goals, full cooperation with other users will not be beneficial for them. And information exchange is constantly evolving.

    1-2- Radiocognitive systems technology

    Dynamic methods of spectrum acquisition are realized through radiocognitive technologies. These systems have the ability to share wireless channels opportunistically with the primary user; In heterogeneous network structures, they can provide more bandwidth to users, this goal can only be realized through efficient spectrum management techniques and dynamic methods. Also, due to the dynamic nature of access to spectrum resources and also considering the quality of different services depending on different applications, CRN networks face challenges. In order to face these challenges, each SU in the network must:

    determine frequency slots.

    choose the best available channel.

    compete with other users to access these channels.

    vacate that channel in case of sudden presence of PUs. choose another part of the spectrum for transmission.

    These capabilities are realized through spectrum management structures that address the four main challenges of spectrum detection, spectrum decision-making, spectrum sharing and mobility.

    Decision making in a CR network is done in the form of Figure 2:

    Figure 1-2 Block diagram of a SU that has the ability to recognize coordination and learn from the environment. [4]

    In this section, the structures and challenges in managing the spectrum of this type of networks, especially the development of smart networks where there is no need to change the shape of the primary networks, are stated. An intelligent radio system can adjust the parameters of its transmitter based on the changes in its surrounding environment, so two definitions can be defined in the main characteristic and structure of CRN: 1-2-1-Intelligence ability. It can be determined by detecting momentary changes in radio environments where parts of the spectrum have not been used at a specific time or position. This capability is not easily achieved by reviewing the power of some frequency bands, but requires more complex methods to determine temporary spatial frequency changes in these environments. With this capability, frequency holes can be determined at a specific time or position and, as a result, the best part of the spectrum can be selected with more suitable operating parameters.

    1-2-2- Ability to reshape

    Intelligence can reshape spectral information into Dynamically and considering adaptation to the environment to plan for communication on different frequencies, also a SU can use different access methods supported by its hardware design. The ultimate goal of radiological systems is to select the best spectrum through intelligent and reshaping capabilities, since most of the spectrum has already been allocated, the most important challenge is to share the allowed spectrum without creating annoying interference for PUs, as shown in Figure 3. Smart radio has the possibility of opportunistic use of spectrum holes, so the best part of free bandwidth can be selected and shared with other SUs and used without causing interference to primary users [3].

  • Contents & References of Investigating routing algorithms in cognitive radio networks and providing a method to improve network performance

    List:

    Chapter One: Introduction

    1-1- Generalities. 2

    1-2- Technology of radiological systems. 4

    1-2-1-the ability to be intelligent. 5

    1-2-2-ability to reshape. 5

    1-3- Physical architecture of radiological networks. 6

    1-4 - radiological networks. 7

    1-4-1- network components. 7

    1-4-2- spectrum heterogeneity. 10

    1-4-3- spectrum management framework. 11

    1-4-4- spectrum sharing. 12

    1-5-The difference of CRN with the common multi-radio and multi-channel networks of the past. 13

    1-6- Classification of routing algorithms. 14

    1-6-1- Classification of routing methods in contingent radio cognitive networks. 16

     

    Chapter Two: An overview of past activities in response to routing challenges

    2-1- The solutions provided in response to the challenges of radiological networks. 18

    2-1-1- Methods based on interference and transmitted power. 20

    2-1-2- Methods based on delay 21

    2-1-3- Methods based on path stability 22

    2-1-4- Methods based on maximizing output 23

    2-2- Common quantitative criteria of routing in contingency networks. 26

    2-2-1- Classification of quantitative routing criteria. 27

    2-2-2- selected first group, HOP. 28

    2-2-3- Round trip time to hop (RTT) 30

    2-2-4- Expected transmission frequency (ETX) 31

    2-2-5- Expected transmission time (ETT) 33

    2-2-6- Expected exclusive transmission time (EETT) 34

    2-2-7- Implementation of four criteria Choice in AODV algorithm. 36

    2-2-8- Important points in the design of optimal quantitative criteria. 39

    2-3-channel selection strategies in CRN. 40

    2-3-1- Classification of channel selection strategies. 41

     

    Chapter three: Deriving an efficient routing algorithm using dual diversity techniques in the NS2-CRAHN simulator. 44

    3-1- An introduction to the challenges facing CRAHN. 44

    3-2- Assumptions and system model. 46

    3-2-1- PUs activity pattern 48

    3-2-2- SUs performance basis 49

    3-3- CRAHN modeling using NS2 simulator. 51

    3-3-1- File related to PU activity 53

    3-3-2- File related to channel events. 53

    3-3-3-management of spectrum resources. 54

    3-3-4- Activities of SUs 57

    3-4- Presenting an efficient CRAHN routing algorithm based on the technique of multi-path and multi-channel transmissions 58

    3-4-1- AODV protocol. 59 3-5- Presentation of an efficient routing algorithm using the method of double transmissions in contingent radiological networks 67 3-5-1-Algorithm of the RREQ stage. 68

    3-5-2-RREP step algorithm. 70

    3-5-3- Path maintenance process 71

    Chapter four: Virtualization

    4-1- Comparison of the efficiency of AODV, D2CARP and the proposed algorithm. 74

    4-2- The effect of the performance pattern of PUs on network efficiency. 78

    4-2-1- Performance analysis 85

    4-3- Spectrum heterogeneity analysis. 89

    4-4- Performance comparison of the two proposed methods and D2CARP in terms of spectrum detection time. 91

    4-5- Performance comparison of the two proposed methods and D2CARP in terms of node movement speed 93

    4-6- Performance comparison of the two proposed methods and D2CARP in terms of RREQ packet rates. 94

    Chapter Five: Conclusion and Suggestions

    5-1- Conclusion. 97

    5-2- Suggestions. 100

    List of sources and sources: 101

    Source:

    [1] J. Mitola III, "Cognitive radio: an integrated agent architecture for software defined radio," Ph.D. thesis, KTH Royal Institute of Technology, Stockholm, Sweden, 2000.

    [2] B. Wang, K. J. Ray Liu, "Advances in Cognitive Radio Networks: A Survey", IEEE Journal of selected topics in signal processing, VOL. 1, February 2011. [3] I. F. Lee, M. C. Vuran, and S. Mohantly, "Next generation/dynamic spectrum access/cognitive radio network: a survey,” Elsevier Computer Networks, Vol. 50. 2127-2159, Sept.2006.

    [4] Z. Han, “Game theory for Cognitive Radio Networks”, IEEE GLOBECOM'09, Honolulu, Hawaii, USA 4th December, 2009.

    [5] Perkins, C., Ad-hoc on-demand distance vector routing. In Proceedings of the Second IEEEIn Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications, Washington, DC, USA, 1999, pp. 90, Washington, DC, USA: IEEE Computer Society.

    [6] Johnson, D.B. Maltz, D.A. Broch, J. DSR: The Dynamic Source Routing for Multihop Ad Hoc networks. In Ad hoc Networking, Boston, MA, USA, 2001, pp. 139-172, Addison-Wesley Longman Publishing Co., Inc: Boston, MA, USA.

    [7] Q. Wang, H. Zheng, Route and spectrum selection in dynamic spectrum networks, in: Proceeding. of IEEE CCNC 2006, Las Vegas, USA, 2006, pp. 625–629. [8] M. Cesana, C. Francesca, and E. Eylem. "Routing in cognitive radio networks: Challenges and solutions." Ad Hoc Networks 9.3 (2011): 228-248.

    [9] Y. Shi, Y. Hou, “A distributed optimization algorithm for multi-hop cognitive radio networks”, in: The 27th IEEE Conference on Computer Communications, INFOCOM 2008

    [10] M. Xie, Z. Wei, and W. Kai-Kit. "A geometric approach to improve spectrum efficiency for cognitive relay networks." Wireless Communications, IEEE Transactions on 9.1 (2010): 268-281. [11] Chowdhury, Kaushik R., and M. D. Felice. "Search: A routing protocol for mobile cognitive radio ad-hoc networks." Computer Communications 32, no. 18 (2009): 1983-1997.

    [12] L. Ding,"Rosa: distributed joint routing and dynamic spectrum allocation in cognitive radio ad hoc networks." Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems. ACM, 2009

    [13] A. Sampath, "High throughput spectrum-aware routing for cognitive radio networks." Proc. of IEEE Crowncom (2008).

    [14] Pefkianakis, Ioannis, Starsky HY Wong, and Songwu Lu. "SAMER: spectrum aware mesh routing in cognitive radio networks." New Frontiers in Dynamic Spectrum Access Networks, 2008. DySPAN 2008. 3rd IEEE Symposium on. IEEE, 2008.

    [15] L. Ding, T. Melodia, S. Batalama, M.J. Medley, Rosa: distributed joint routing and dynamic spectrum allocation in cognitive radio ad hoc networks, in: MSWiM ’09: Proceedings of the 12th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2009. [16] S.L Nxumalo, N. Ntlatlapa, P. Mudali, M.O. Adigun, 2009. Performance Evaluation of Routing Metrics for Wireless Networks. In Proceedings of South African Telecommunication Networks and Applications Conference, Royal Swazi Spa, Swaziland, 30 August – 2 September 2009

    [17] De Couto, Douglas SJ, "A high-throughput path metric for multi-hop wireless routing." Wireless Networks 11.4 (2005): 419-434.

    [18] Dijkstra, W. Edsger "A note on two problems in connection with graphs." Numerische mathematik 1, no. 1 (1959): 269-271.

    [19] A. Adya, "A multi-radio unification protocol for IEEE 802.11 wireless networks." Broadband Networks, 2004. BroadNets 2004. Proceedings. First International Conference on. IEEE, 2004

    [21] R. Draves, P. Jitendra, and Z. Brian. "Routing in multi-radio, multi-hop wireless mesh networks." In Proceedings of the 10th annual international conference on Mobile computing and networking, pp. 114-128.ACM, 2004.

    [22] Q. Xiao, Y. Li, M. Zhao, S. Zhou, and J. Wang, “Opportunistic channel selection approach under collision probability constraint in cognitive radio systems,” Computer Communications, vol. 32 (18), pp. 1914–1922, 2009.

    [23] L. Yang, L. Cao, and H. Zheng, “Proactive channel access in dynamic spectrum networks,” Elsevier Physical Communications Journal, vol (1), pp. 103–111, 2008.

    [24] G. D. Nguyen and S. Kompella, “Channel sharing in cognitive radio networks,” in MILCOM, 2010

    [25] I.F. Akyildiz, L. Won-Yeol, and K.R. Chowdhury. "CRAHNs: Cognitive radio ad hoc networks." Ad Hoc Networks 7.5 (2009): 810–836.

    [26] K.R. Chowdhury, I.F. Akyildiz, OFDM based common control channel design for Cognitive Radio Ad HoC Networks, IEEE Transactions on Mobile Computing (2010) (in press).

    [27] A.

Investigating routing algorithms in cognitive radio networks and providing a method to improve network performance
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