Trends in mobile network communication and telematics in 2020

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

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

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.

[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.

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.

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.

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.

[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.

[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.

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.

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.

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

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.

[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

Trends in mobile network communication and telematics in 2020

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à Trends in mobile network communication and telematics in 2020

Similaire à Trends in mobile network communication and telematics in 2020 (20)

Dernier

Dernier (20)

Trends in mobile network communication and telematics in 2020