Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

Trends in mobile network communication and telematics in 2020

46 visualizaciones

Publicado el

International Journal of Mobile Network Communications & Telematics (IJMNCT) is an open access peer-reviewed journal that addresses the impacts and challenges of mobile communications and telematics. The journal also aims to focus on various areas such as ecommerce, e-governance, Telematics, Telelearning nomadic computing, data management, related software and hardware technologies, and mobile user services. The journal documents practical and theoretical results which make a fundamental contribution for the development of mobile communication technologies.

Publicado en: Ingeniería
  • Sé el primero en comentar

  • Sé el primero en recomendar esto

Trends in mobile network communication and telematics in 2020

  1. 1. TRENDS IN MOBILE NETWORK COMMUNICATIONS & TELEMATICS IN 2020 INTERNATIONAL JOURNAL OF MOBILE NETWORK COMMUNICATIONS & TELEMATICS (IJMNCT) ISSN: 1839-5678 https://wireilla.com/ijmnct/index.html
  2. 2. INVESTIGATING THE PERFORMANCE OF VARIOUS CHANNEL ESTIMATION TECHNIQUES FOR MIMO-OFDM SYSTEMS USING MATLAB Woud M. Abed and Raghad K. Mohammed, College of Dentistry University of Baghdad, Iraq ABSTRACT This paper simulates and investigates the performance of four widely-used channel estimation techniques for MIMO-OFDM wireless communication systems; namely, super imposed pilot (SIP), comb-type, space-time block coding (STBC), and space-frequency block coding (SFBC) techniques.The performance is evaluated through a number of MATLab simulations, where the bit-error rate (BER) and the mean square error (MSE) are estimated and compared for different levels of signal-to-noise ratio (SNR). The simulation results demonstrate that the comb-type channel estimation and the SIP techniques overwhelmed the performance of the STFC and STBC techniques in terms of both bit-error rate (BER) and mean square error (MSE). KEYWORDS MIMO-OFDM, pilot-based channel estimation, pilot allocation, SIP, comb-type, STBC, SFBC, MATLab. FULL TEXT: https://wireilla.com/papers/ijmnct/V9N5/9519ijmnct01.pdf ABSTRACT URL: https://wireilla.com/ijmnct/article/9519ijmnct01.html VOLUME URL: https://wireilla.com/ijmnct/vol9.html
  3. 3. REFERENCES [1] Yong Soo Cho, Jaekwon Kim, Won Young Yang, Chung G. Kang. MIMO-OFDM Wireless Communications with MATLab, John Wiley & Sons, August 2010. [2] Sunho Park, Byonghyo Shim, and Jun Won Choi. Iterative Channel Estimation Using Virtual Pilot Signals for MIMO-OFDM Systems. IEEE Transactions on Signal Processing, Vol. 63, Issue: 12, pp. 3032 - 3045, June 2015. [3] Nisha Achra, Garima Mathur, R. P. Yadav. Performance Analysis of MIMO-OFDM System for Different Modulation Schemes under Various Fading Channels. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 5, May 2013. [5] V. Sivanagaraju and P. Siddaaih. Comprehensive Analysis of BER and SNR in OFDM Systems. International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), Volume 2, Issue 2, February 2014. [6] Nitin Kumar Chourasiya and Aman Saraf. A Review Channel Estimation Technique for MIMOOFDM Wireless Systems. International Journal on Emerging Technologies, Vol. 8, No. 1, pp. 20- 24, 2017. [7] Vishal Sharma and Harleen Kaur. On BER Evaluation of MIMO-OFDM Incorporated Wireless System. International Journal for Light and Electron Optics, Vol. 127, Issue 1, pp. 203-205, January 2016. [8] Vijaykumar Katgi. Comparison of MIMO OFDM System with BPSK and QPSK Modulation. International Journal on Emerging Technologies, Vol. 6, No. 2, pp. 188-192, 2015. [9] Sharief Nasr Abdel-Razeq, Areej Salah Al-Azzeh, Rawan Yousef Ayyoub. Study of QPSK Modulator and Demodulator in Wireless Communication System Using MATLab. International Journal of Interactive Mobile Technologies (iJIM), Vol. 7, No. 2, pp. 4-8, 2013. http://dx.doi.org/10.3991/ijim.v7i2.2239. [10] Mathuranathan Viswanathan. Digital Modulations using MATLab: Build Simulation Models from Scratch. E-book, June, 2017. [11] Sagar Somashekar, Naveen K.D. Venkategowda, and Aditya K.Jagannatham. Bandwidth Efficient Optimal Superimposed Pilot Design for Channel Estimation in OSTBC-Based MIMO–OFDM Systems. Journal of Physical Communication, Vol. 26, pp. 185-195, February 2018. [12] Umesha G B and M N Shanmukha Swamy. Comb-Type Pilot Arrangement Based Channel Estimation for Spatial Multiplexing MIMO-OFDM Systems. International Research Journal of Engineering and Technology (IRJET), Vol. 5, Issue 2, pp. 641-645, February 2018. [13] Gaurav Maurya and Pramod Patel. An Extensive Review on STBC for MIMO-OFDM System. International Journal of Emerging Technology and Advanced Engineering (IJETAE), Vol. 4, Issue-8, pp. 352-357, August 2014. [14] Neeraj Shrivastava and Aditya Trivedi. Combined Beamforming with Space-Time-Frequency Coding for MIMO–OFDM Systems. AEU - International Journal of Electronics and Communications, Vol. 69, Issue 6, pp. 878-883, June 2015. [15] Madhvi Jangalwa. Performance Analysis of MIMO-OFDM system with Zero Forcing Receiver. International Journal of Multidisciplinary and Current Research, September-October Issue, 2013.
  4. 4. [16] Mamunur Rashid and Saddam Hossain. A Potent MIMO–OFDM System Designed for Optimum BER and its Performance Analysis in AWGN Channel. International Journal of Engineering Science Invention (IJESI), Vol. 4, Issue 8, pp. 44-50, August 2015. [17] Atul Kumar Pandey, Santosh Sharma, and Neetu Sikarwar. Improvement of BER with The Help of MIMO-OFDM using STBC Code Structure. International Journal of Engineering Research & Technology (IJERT), Vol. 2, Issue 5, May 2013. [18] Niharika Sethy and Subhakanta Swain. BER Analysis of MIMO-OFDM System in Different Fading Channe. International Journal of Application or Innovation in Engineering and Management, Vol. 2, Issue 4, pp. 405-409, April 2013. [19] B. Siva Kumar Reddy and B. Lakshmi. BER Analysis with Adaptive Modulation Coding in MIMOOFDM for WiMAX using GNU Radio. International Journal Wireless and Microwave Technologies, November 2014. [20] Srishtansh Pathak and Himanshu Sharma. Channel Estimation in OFDM Systems. International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol.3, No.3, pp. 312-327, 2013. [21] S. Patil and A. N. Jadhav. Channel Estimation Using LS and MMSE Estimators. KIET International Journal of Communications & Electronics, Vol. 2, No.1, pp. 51-55, April 2014. [22] Himanshi Jain and Vikas Nandal. A Comparison of Various Channel Estimation Techniques to Improve Fading Effects in MIMO over Different Fading Channels. International Journal of Current Engineering and Technology (IJCET), Vol. 6, No. 4, pp. 1382-1386, 2016. [23] Omar Daoud, Qadri Hamarsheh, and Wael Al-Sawalmeh. MIMO-OFDM Systems Performance Enhancement Based Peaks Detection Algorithm. International Journal of Interactive Mobile Technologies (iJIM), Vol. 7, No. 3, pp. 4-8, 2013. http://dx.doi.org/10.3991/ijim.v7i3.2302. [24] Omar Daoud and Omar Alani. Robotic Mobile System's Performance-Based MIMO-OFDM Technology. International Journal of Interactive Mobile Technologies (iJIM), Vol. 3, pp.12-17, 2009. http://online-journals.org/index.php/i-jim/article/view/923. [25] Nimay Ch. Giri, Anwesha Sahoo, J. R. Swain, P. Kumar, A. Nayak, P. Debogoswami. Capacity and Performance Comparison of SISO and MIMO System for Next Generation Network (NGN). International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 3, Issue 9, pp. 30131-3035, 2014. [26] O. Longoria-Gandara, R. Parra-Michel, R.Carrasco-Alvarez, and E. Romero-Aguirre. Iterative MIMO Detection and Channel Estimation Using Joint Superimposed and Pilot-Aided Training. Journal of Mobile Information Systems, Vol. 2016, Article ID 3723862, 2016.
  5. 5. AUTHORS Woud M. Abed is a member of academic staff at the Department of Basic Sciences, College of Dentistry, University of Baghdad (Baghdad, Iraq). She received her B.Sc degree in Computer Science from the Department of Computer Science, Alrafidain University College (Baghdad, Iraq) in 2003, and her M.Sc degree in Computer Networks, Informatics Institute for Higher Studies, University of Technology (Baghdad, Iraq) in 2005. Her research interests include: robotic, genetic algorithms, cryptography and steganography, image processing, and computer security. Raghad K. Mohammed is serving as a member of academic staff at the Department of Basic Sciences, College of Dentistry, University of Baghdad (Baghdad, Iraq). She received her B.Sc degree in Computer Science from the Department of Computer science, Alrafidain University College (Baghdad, Iraq) in 2003, and her M.Sc degree in Computer Networks, Informatics Institute for Higher Studies, University of Technology (Baghdad, Iraq) in 2005. Her research interests include cryptography and steganography, image processing, and information and network security.
  6. 6. SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING FUZZY OPTIMIZATION H. Hamdy1 , M. R. Hassan1 , M. Eid1 and M. Khalifa2 , 1 Aswan University, Egypt and 2 South Valley University, Egypt ABSTRACT Optimal components assignment problem subject to system reliability, total lead-time, and total cost constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the presented problem. The optimal solution found by the proposed approach is characterized by maximum reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different examples taken from the literature to illustrate its efficiency in comparison with other previous methods. KEYWORDS Components Assignment Problem, Stochastic-Flow Networks, Network Reliability, Fuzzy Multi-Objective Linear Programming, Genetic Algorithms. FULL TEXT: https://wireilla.com/papers/ijmnct/V9N3/9319ijmnct01.pdf ABSTRACT URL: https://wireilla.com/ijmnct/article/9319ijmnct01.html VOLUME URL: https://wireilla.com/ijmnct/vol9.html
  7. 7. REFERENCES [1] Y. K. Lin & Huang Cheng-Fu, (2013) “Stochastic Flow Network Reliability With Tolerable Error Rate”, Quality Technology And Quantitative Management, Vol. 10, No. 1, Pp. 57-73. [2] Http://Etd.Ohiolink.Ed,Xuinying Wu,(2014),Master Of Science , “Heuristic For Multi-Type Component Assignment Problems Through The Birnbaum Importance”,The Faculty Of The Russ College Of Engineering And Technology, Ohio University. [3] Y. K. Lin (2010) “Reliability Of K Separate Minimal Paths Under Both Time And Budget Constraints”, Ieee Trans. Reliability, 59: 183-190. [4] Y.K. Lin & C. T. Yeh , ( 2011) “Maximizing Network Reliability For Stochastic Transportation Networks Under A Budget Constraint By Using A Genetic Algorithm”, International Journal Of Innovative Computing Information And Control, 7(12): 7033-50. [5] Y.K. Lin & C.T.Yeh, ( 2011) “Multistate Components Assignment Problem With Optimal Network Reliability Subject To Assignment Budget”, Applied Math. Comput., 217: 10074-10086. [6] Y.K. Lin & C.T. Yeh, (2012) “Multi-Objective Optimization For Stochastic Computer Networks Using Nsga-Ii And Topsis”, European Journal Of Operational Research, Vol. 218, No. 3, Pp. 735- 746. [7] Y.K. Lin & C.T.Yeh, ( 2013) “A Two-Stage Approach For A Multi-Objective Component Assignment Problem For A Stochastic-Flow Network”, Eng. Optimiz., 45: 265-285. Doi: 10.1080/0305215x.2012.669381. [8] S. G. Chen, (2014) “Optimal Double-Resource Assignment For The Robust Design Problem In Multistate Computer Networks”, Applied Math. Model., 38: 263-277. Doi: 10.1016/J.Apm.2013.06.020. [9] M.R.Hassan, (2015) “Solving A Component Assignment Problem For A Stochastic Flow Network Under Lead-Time Constraint”,Indian Journal Of Science And Technology, Vol. 8(35), Doi: 10.17485/Ijst/2015/V8i35/70455. [10] M.R.Hassan& H.Abdou, (2018) “Multi-Objective Components Assignment Problem Subject To Leadtime Constraint”,Indian Journal Of Science And Technology, Vol. 11(21), Doi: 10.17485/Ijst/2018/V11i21/100080. [11] A. Aissou, A. Daamouche & M.R.Hassan , (2019) “Optimal Components Assignment Problem For Stochastic Flow Network “,Journal Of Computer Science , Doi:10.3844/Jcssp. [12] R. E. Bellman& L. A.Zadeh, (1970) “Decision-Making In A Fuzzy Environment”, Management Science, Vol. 17, No. 4, Pp. 141-164. [13] H. J. Zimmermann, (1978) “Fuzzy Programming And Linear Programming With Several Objective Functions, Fuzzy Sets And Systems, Vol. 1, No.1, Pp. 45-56. [14] M.K.Luhandjula , (1989)”Fuzzy Optimization: An Appraisal”, Fuzzy Sets And Systems, Vol. 30, Pp. 257-282. [15] D. Wang, (1995) ”An Inexact Approach For Linear Programming With Fuzzy Objective And Resources”, Fuzzy Sets And Systems, Vol. 1, No. 24, Pp. 261-281. [16] J. Tang& D.Wang, (1997) ”An Interactive Approach Based On A Ga For A Type Of Quadratic Programming Problem With Fuzzy Objective And Resources”, Computers And Operations Research, Vol. 24, Pp. 413-422.
  8. 8. [17] H. R. Maleki &M. Mashinchi,(2001) “A Method For Solving A Fuzzy Linear Programming”, Journal Of Applied Mathematics And Computing, Doi: 10.1007/Bf02941971. [18] P. M. Vasant, (2005) “Solving Fuzzy Linear Programming Problems With Modified S-Curve Membership Function”, International Journal Of Uncertainty, Fuzziness And Knowledge-Based Systems, Vol. 13, No. 01, Pp. 97-109. [19] H. R. Maleki & Mashaallah Mashinchi, (2008) “A New Method For Solving Fuzzy Linear Programming By Solving Linear Programming”, Applied Mathematical Sciences, Vol. 2,No. 50,Pp. 2473 – 2480. [20] C.Veeramani, C.Duraisamy & A.Nagoorgani, (2011) “Solving Fuzzy Multi-Objective Linear Programming Problems With Linear Membership Functions”, Australian Journal Of Basic And Applied Sciences, 5(8): 1163-1171. [21] J. L. Verdegay & B. Kheirfam, (2013) “Optimization And Reoptimization In Fuzzy Linear Programming Problems”, The 8th Conference Of The European Society For Fuzzy Logic And Technology (Eusflat), Doi: 10.2991/Eusflat.2013.80. [22] M.Kiruthiga & C.Loganathan, (2015)“Fuzzy Multi-Objective Linear Programming Problem Using Fuzzy Programming Model”, International Journal Of Science, Engineering And Technology Research, Vol. 4, Issue 7. [23] S. K. Das, (2017)” Modified Method For Solving Fully Fuzzy Linear Programming Problem With Triangular Fuzzy Numbers” ,International Journal Of Research In Industrial Engineering,Vol. 6, No. 4 (2017) 293–311,Doi: 10.22105/Riej.2017.101594.1024. [24] S. Kumar & T. Mandal, (2017) ”A New Model For Solving Fuzzy Linear Fractional Programming Problem With Ranking Function”, Journal Of Applied Research On Industrial Engineering,Vol. 4, No. 2 (2017) 89–96. [25] S. H. Nasseri & H. Zavieh, (2018) “A Multi-Objective Method For Solving Fuzzy Linear programming Based On Semi-Infinite Model”,Fuzzy Information And Engineering,Vol. 10, No. 1, 91– 98. [26] C.Malathi1 & P.Umadevi,(2018) “A New Procedure For Solving Linear Programming Problems In An Intuitionistic Fuzzy Environment”, International Conference On Applied And Computational Mathematics, Iop Conf. Series: Journal Of Physics: Conf. Series 1139 (2018) 012079, Doi:10.1088/1742- 6596/1139/1/012079. [27] D. S. Dinagar & M. M. Jeyavuthin,(2018) “Fully Fuzzy Integer Linear Programming Problems Underrobust Ranking Techniques”,International Journal Of Mathematics And Its Applications, 6(3)(2018), 19-25. [28] Z. Gong,W. Zhao&K. Liu,(2018) “A Straightforward Approach For Solving Fully Fuzzy Linear Programming Problem With Lr-Type Fuzzy Numbers”,Journal Of The Operations Research Society Of Japan,Vol. 61, No. 2, Pp. 172-185. [29] S. M. Ingle,(2019) “Solving Fflpp Problem With Hexagonal Fuzzy Numbersby New Ranking Method”,International Journal Of Applied Engineering Research,Vol. 14, No. 1,Pp.97-101. [30] M. Ranjbar &S. Effati,(2019) “Symmetric And Right-Hand-Side Hesitant Fuzzy Linear Programming”, Ieee Transactions On Fuzzy Systems,Doi: 10.1109/Tfuzz.2019.2902109. [31] A. Kabiraj, P. K. Nayak & S. Raha, (2019) “Solving Intuitionistic Fuzzy Linear Pogramming Problem”, International Journal Of Intelligence Science, Doi: 10.4236/Ijis.2019.91003.
  9. 9. [32] D. Wang, (1996) ”Modeling And Optimization For A Type Of Fuzzy Nonlinear Programming Problems In Manufacture Systems”, Proceeding Of Ieee Conference On Decision And Control, Vol. 4, Pp. 4401- 4405. [33] M. Mutingi, (2014),” System Reliability Optimization: A Fuzzy Multi-Objective Genetic Algorithm Approach”, Maintenance And Reliability, 16 (3): 400–406. [34] H. Y. Chang, Y. J. Tzang, C. H. Tzang & C. Y. Huang, (2015) “An Application Of Fuzzy Multiobjective Linear Programming For Components Design Of Games Or Animated Characters”, First International Conference On Computational Intelligence Theory, Systems And Applications ,Doi 10.1109/Ccitsa.2015.39. [35] D. Stephen Dinagar & S. Kamalanathan, (2017) ”International Journal Of Applications Of Fuzzy Sets And Artificial Intelligence”,Vol. 7 (2017), 281-292. [36] M. K. Sinha, A. P. Burnwal & C. Singh,(2018) “Fuzzy Multi-Objective Linear Programming Approach For Solving Problem Of Food Industry”, International Journal Of Students’ Research In Technology & Management,Vol 6, No 2, Pp. 13-19. [37] I. H. V. Gue, A. T. Ubando, K. B. Aviso & R. R. Tan,(2019) “Optimal Design Of A Trigeneration Plant Using Fuzzy Linear Programming With Global Sensitivity Analysis On Product Price Uncertainty”, Science Directenergy Procedia 158 (2019) 2176–2181. [38] S. G. Chen & Y. K. Lin, (2012) “Search For All Minimal Paths In A General Large Flow Network”, Ieee Transactions On Reliability, 61(4), 949-956. Doi:10.1109/Tr.2012.2220897 AUTHORS Heba Hamdy Ahmed Is A Demonstrator In Computer Science Branch, Department Of Mathematics, Faculty Of Science, Aswan University, Aswan, Egypt. Motamad Refaat Hassan Is An Assistant Professor In Computer Science Branch, Department Of Mathematics, Faculty Of Science, Aswan University, Aswan, Egypt. Mohamed Eid Mohamedis A Lecture In Computer Science Branch, Department Of Mathematics, Faculty Of Science, Aswan University, Aswan, Egypt. Mosa Khalifa Ahmed Is An Assistant Professor In Department Of Mathematics, Faculty Of Science, South Valley University, Qena, Egypt.
  10. 10. CODING SCHEMES FOR ENERGY CONSTRAINED IOTDEVICES Mais Sami Ali and Abdulkareem Abdulrahman Kadhim Al-Nahrain University, Iraq ABSTRACT This paper investigates the application of advanced forward error correction techniques mainly: low-density parity checks (LDPC) code and polar code for IoT networks. These codes are under consideration for 5G systems. Different code parameters such as code rate and a number of decoding iterations are used to show their effect on the performance of the network. LDPC is performed better than polar code, over the IoT network scenario considered in the work, for the same coding rate and the number of decoding iterations. Considering bit error rate (BER) performance, LDPC with rate1/3 provided an improvement of up to 2.6 dB for additive white Gaussian noise (AWGN) channel, and 2 dB for SUI-3 (frequency selective fading channel model). LDPC code gives an improvement in throughput of about 12% as compared to polar code with a coding rate of 2/3 over AWGN channel. The corresponding values over SUI-3 channel are about 10%. Finally, in comparison with LDPC, polar code shows better energy saving for large number of decoding iterations and high coding rates. KEYWORDS IoT, LDPC, Polar, Energy consumption. FULL TEXT: https://wireilla.com/papers/ijmnct/V9N2/9219ijmnct01.pdf ABSTRACT URL: https://wireilla.com/ijmnct/article/9219ijmnct01.html VOLUME URL: https://wireilla.com/ijmnct/vol9.html
  11. 11. REFERENCES [1] Z. Abbas and W. Yoon, "A survey on energy conserving mechanisms for the internet of things: Wireless networking aspects", Sensors, Vol.15, Issue.10, 2015. [2] O. Eriksson, “Error Control in Wireless Sensor Networks a Process Control Perspective”, Master Thesis, Faculty of Science and Technology, Uppsala University, Sweden, 2011. [3] I. Imad, M. Arioua, A. El-Oualkadi, and Y. Al-Assari, "Joint FEC/CRC coding scheme for energy constrained IOT devices", In Proceedings of the International Conference on Future Networks and Distributed Systems, No.25, pp. 1-8, 2017. [4] S. Kraijak and P. Tuwanut, "A Survey on IoT architectures, protocols, applications, security, privacy, real-world implementation, and future trends",11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015), pp 6-6, 2015. [5] I. Zazi, M. Arioua, A. Oualkadi and P. Lorenz, "A Hybrid Adaptive Coding and Decoding Scheme for Multi-hop Wireless Sensor Networks", Wireless Personal Communications, Springer Verlag, Vol.94, Issue.4, pp.1-17, 2016. [6] H. Vangala, E. Viterbo and Yi.Hong, "A comparative study of polar code constructions for the AWGN channel", arXiv preprint arXiv:1501.02473 , 2015. http://arxiv.org/pdf/1501.02473.pdf, [7] A. Kadhim, M. Abboud, "Joint Forward Error Correction and Network Coding for Wireless Sensor Networks", International Journal of Innovations in Engineering and Technology (IJIET), Vol 6. Issue 2, December 2015. [8] A.Kadhim, A. Al-Joudi, and H. Al-Raweshidy, "Error Correction Scheme for Wireless Sensor Networks" Journal of telecommunication", Vol. 29, Issue .2, February 2015. [9] M.Arioua, Y. El-Ansari, I. Ez-Zazi, and A. El-Oualkadi, "Multi-hop cluster based routing approach for wireless sensor networks". Procedia Computer Science,Vol. 83, No.4, pp 584-591, 2016. [10 Y. Sankarasubramaniam, I. Akyuildiz, S. McLaughin, "Energy efficiency based packet size optimization in wireless sensor networks",Proceedings of IEEE International Workshop on Sensor Network Protocols and Applications SNPA, Vol.3, pp.1-8, 2003. [11] M. Ali,” Coding Scheme for Energy Constrained IoT Devices”, M.Sc. Thesis submitted to the Department of Computer Engineering, Al- Nahrain University, Iraq, Jan.2019. [12] P. Mohapatra, S. Panigrahi, and J. Mishra, “Performance Comparison of Linear and Decision Feedback Equalizers in Flat and Frequency Selective Fading Channel”, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, No. 10, October 2013. [13] K. Hari, D. Baum, A. Rustako, R. Roman, and D. Trinkwon, "Channel models for fixed wireless applications", IEEE 802.16 Broadband wireless access working group, Vol.29, 2003.
  12. 12. EXTENDED LINEAR MULTICOMMODITY MULTI COST NETWORK AND MAXIMAL CONCURENT FLOW PROBLEMS Ho Van Hung1 and Tran Quoc Chien2 1 Quangnam University, Vietnam and 2 The University of Education, University of Danang, Vietnam ABSTRACT Graph is a powerful mathematical tool applied in many fields as transportation, communication, informatics, economy, … In ordinary graph the weights of edges and vertexes are considered indepently where the length of a path is the sum of weights of the edges and the vertexes on this path. However, in many practical problems, weights at a vertex are not the same for all paths passing this vertex, but depend on coming and leaving edges. In the article [2], a kind of weights, called switch cost, is defined. The papers [3-6] study multicomodity flow problems in ordinary networks. The papers [3-6] study multicomodity flow problems in extended networks, where switch costs are defined for mixed graphs. The papers [12,13] develops a model of extended linear multicommodity multicost network and studies the maximal linear multicomodity multicost flow problems. The papers [14,15] study the maximal multicomodity multicost flow limited cost problems. Model of extended linear multicommodity multicost network can be applied to modelize many practical problems more exactly and effectively. The presented paper studies the maximal concurent linear multicomodity multicost flow problems, that are modelized as implicit linear programming problems. On the base of dual theory in linear programming an effective aproximate algorithms is developed. KEYWORDS Graph, Network, Multicommodity Multicost Flow, Optimization, Linear Programming FULL TEXT: https://wireilla.com/papers/ijmnct/V9N1/9119ijmnct01.pdf ABSTRACT URL: https://wireilla.com/ijmnct/article/9119ijmnct01.html VOLUME URL: https://wireilla.com/ijmnct/vol9.html
  13. 13. REFERENCES [1] Naveen Garg, Jochen Könemann: Faster And Simpler Algorithms For Multicommodity Flow And Other Fractional Packing Problems, SIAM J. Comput, Canada, 37(2), 2007, Pp. 630-652. [2] Xiaolong Ma, Jie Zhou: An Extended Shortest Path Problem With Switch Cost Between Arcs, Proceedings Of The International Multiconference Of Engineers And Computer Scientists 2008 Vol IIMECS 2008, 19-21 March, 2008, Hong Kong. [3] Tran Quoc Chien: Linear Multi-Channel Traffic Network, Ministry Of Science And Technology, Code B2010DN-03-52. [4] Tran Quoc Chien, Tran Thi My Dung: Application Of The Shortest Path Finding Algorithm To Find The Maximum Flow Of Goods.Journal Of Science & Technology, University Of Danang, 3 (44) 2011. [5] Tran Quoc Chien: Application Of The Shortest Multi-Path Finding Algorithm To Find The Maximum Simultaneous Flow Of Goods Simultaneously.Journal Of Science & Technology, University Of Danang, 4 (53) 2012. [6] Tran Quoc Chien: Application Of The Shortest Multi-Path Finding Algorithm To Find The Maximal Simultaneous Flow Of Goods Simultaneously The Minimum Cost.Journal Of Science & Technology, Da Nang University, 5 (54) 2012. [7] Tran Quoc Chien: The Algorithm Finds The Shortest Path In The General Graph, Journal Of Science & Technology, University Of Da Nang, 12 (61) / 2012, 16-21. [8] Tran Quoc Chien, Nguyen Mau Tue, Tran Ngoc Viet: The Algorithm Finds The Shortest Path On The Extended Graph.Proceeding Of The 6th National Conference On Fundamental And Applied Information Technology (FAIR), Proceedings Of The Sixth National Conference On Scientific Research And Application, Hue, 20-21 June 2013.Publisher Of Natural Science And Technology. Hanoi 2013. P.522- 527. [9] Tran Quoc Chien: Applying The Algorithm To Find The Fastest Way To Find The Maximum Linear And Simultaneous Minimum Cost On An Extended Transportation Network, Journal Of Science & Technology, University Of Da Nang . 10 (71) 2013, 85-91. [10] Tran Ngoc Viet, Tran Quoc Chien, Nguyen Mau Tue: Optimized Linear Multiplexing Algorithm On Expanded Transport Networks, Journal Of Science & Technology, University Of Da Nang. 3 (76) 2014, 121-124. [11] Tran Ngoc Viet, Tran Quoc Chien, Nguyen Mau Tue: The Problem Of Linear Multi-Channel Traffic Flow In Traffic Network. Proceedings Of The 7th National Conference On Fundamental And Applied Information Technology Research (FAIR'7), ISBN: 978-604-913-300-8, Proceedings Of The 7th National Science Conference "Fundamental And Applied Research IT ", Thai Nguyen, 19-20 / 6/2014. Publisher Of Natural Science And Technology. Hanoi 2014. P.31-39. [12] Tran Quoc Chien, Ho Van Hung: Extended Linear Multicommodity Multicost Network And Maximal Flow Finding Problem. Proceedings Of The 7th National Conference On Fundamental And Applied Information Technology Research (FAIR'10), ISBN: 978-604-913-614-6, P.385-395. Publisher Of Natural Science And Technology. Hanoi 2017.
  14. 14. [13] Tran Quoc Chien, Ho Van Hung: Applying Algorithm Finding Shortest Path In The Multiple Weighted Graphs To Find Maximal Flow In Extended Linear Multicomodity Multicost Network, EAI Endorsed Transactions On Industrial Networks And Intelligent Systems, 12.2017, Volume 4, Issue 11, Pp 1-6. [14] Tran Quoc Chien, Ho Van Hung: Extended Linear Multi-Commodity Multi-Cost Network And Maximal Flow Limited Cost Problems, The International Journal Of Computer Networks & Communications (IJCNC), Volue 10, No. 1, January 2018, Pp 79-93 [15] Ho Van Hung, Tran Quoc Chien, Applying Algorithm Finding Shortest Path In The Multiple- Weighted Graphs To Find Maximal Flow Limited Cost In Extended Linear Multicomodity Multicost Network, International Conference On Electrical, Electronics, Computers, Communication, Mechanical And Computing (EECCMC), 2018. Accepted And To Be Published. AUTHORS Ass. Prof. DrSc. Tran Quoc Chien (http://scv.ued.udn.vn/ly_lich/chi_tiet/275).He has 14 papers in SCIE of Journal (http://www.kybernetika.cz/contact.html). Born in 1953 in Dien Ban, Quang Nam, Vietnam. He graduated from Maths_IT faculty. He got Ph.D Degree of maths in 1985 in Charles university of Prague, Czech Republic and hold Doctor of Science in Charles university of Prague, Czech Republic in 1991. He received the tittle of Ass. Pro in 1992. He work for university of Danang, Vietnam. His main major: Maths and computing, applicable mathematics in transport, maximum flow, parallel and distributed process, discrete mathemetics, graph theory, grid Computing, distributed program M.Si. Ho Van Hung (http://qnamuni.edu.vn/viewLLKH.asp?MaGV=134). Born in 1977 in Thang Binh, Quang Nam, and Vietnam. He graduated from Faculty of Information Technology –College of Sc iences – Hue University in 2000. He got Master of Science (IT) at Danang University of technology. His main major: Applicable mathematics in transport, maximum flow, parallel and distributed process, graph theory and distributed programming

×