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Waliullah, G. M.
- Study the Performance of Capacity for SISO, SIMO, MISO and MIMO in Wireless Communication
Abstract Views :410 |
PDF Views:0
Authors
Diponkor Bala
1,
G. M. Waliullah
1,
Mst. Ashrafunnahar Hena
2,
Md. Ibrahim Abdullah
1,
Mohammad Alamgir Hossain
1
Affiliations
1 Department of Computer Science & Engineering, Islamic University, Kushtia, BD
2 Department of Electrical and Electronic Engineering, Islamic University, Kushtia, BD
1 Department of Computer Science & Engineering, Islamic University, Kushtia, BD
2 Department of Electrical and Electronic Engineering, Islamic University, Kushtia, BD
Source
Journal of Network and Information Security, Vol 8, No 1&2 (2020), Pagination: 01-06Abstract
Due to the rapid development of the wireless communication system, it is highly required a reliable system which can provide higher channel capacity and higher data transmission rates for the users. These are obtained by the Multiple Input Multiple Output (MIMO) systems because the MIMO systems allow the spatial diversity and spatial multiplexing technique due to its multiple antennas at both transmitter and receiver side. The aim of this paper is to discuss and show the capacity performance between SISO, SIMO, MISO and MIMO systems. In this paper, we will mainly be focused on the MIMO system due to its higher capacity and higher data transmission rates properties. For these properties of the MIMO systems, it will be perfectly suitable for modern communication technology.Keywords
Channel Capacity, MIMO System, MISO system, SIMO System, SISO System, Wireless Communication.References
- D. Tse, and P. Viswanath, Fundamentals of Wireless Communication, Cambridge University Press, USA, 2005.
- T. Rapaport, Wireless Communications: Principles and Practice (2nd ed.), Prentice Hall PTR, USA, 2001.
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- A. Robertcalderbank, A. Constantinioles, A. Goldsmith, A. Paulraj, and H. Vincent Poor, Wireless Communication System, Cambridge University Press, 2007.
- F. Tramarin, S. Vitturi, M. Luvisotto, and A. Zanella, “The IEEE 802.11n wireless LAN for real-time industrial communication,” 2015 IEEE World Conference on Factory Communication Systems (WFCS), Palma de Mallorca, pp. 1-4, 2015, doi: 10.1109/WFCS.2015.7160568.
- A. Khan, and R. Vesilo, “A tutorial on SISO and MIMO channel capacities,” 2006.
- A. K. Sarangi, and A. Datta, “Capacity comparison of SISO, SIMO, MISO & MIMO systems,” 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), pp. 798-801, Erode, 2018, doi: 10.1109/ICCMC.2018.8488147.
- K. Sengar, and N. Rani, “Study and capacity evaluation of SISO, MISO and MIMO RF wireless communication systems,” International Journal of Engineering Trends and Technology, vol. 9, no. 9, March 2014.
- A. F. Molisch, M. Z. Win, Y.-S. Choi, and J. H. Winters, “Capacity of MIMO systems with antenna selection,” In IEEE Transactions on Wireless Communications, vol. 4, no. 4, pp. 1759-1772, July 2005, doi: 10.1109/TWC.2005.850307.
- A. Goldsmith, S. A. Jafar, N. Jindal, and S. Vishwanath, “Capacity limits of MIMO channels,” In IEEE Journal on Selected Areas in Communications, vol. 21, no. 5, pp. 684-702, June 2003, doi: 10.1109/JSAC.2003.810294.
- S. Taruna, and I. Kaur, “Performance analysis of MIMO for various antenna configurations,” 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), Chennai, 2013, pp. 90-93, doi: 10.1109/ICGCE.2013.6823406.
- M. Tan, and J. Chen, “Comparison and analysis of MIMO channel capacity,” 2007 International Conference on Wireless Communications, Networking and Mobile Computing, pp. 299-301, Shanghai, 2007, doi: 10.1109/WICOM.2007.81.
- Y. S. Cho, J. Kim, W. Y. Yang, and C. G. Kang, MIMO-OFDM Wireless Communication with MATLAB, Wiley Publishing, 2010.
- T. K. Roy, “Capacity and performance analysis of Rayleigh Fading MIMO Channels using CSI at the transmitter side,” IJAR-CSIT, vol. 1, no. 3, July 2012.
- P. Rayi, and S. Chandra Ch, “Performance evaluation of channel capacity in MIMO system,” International Journal of Engineering Research and Applications (IJERA), vol. 1, no. 4, pp. 1871-1878, 2011.
- Performance Analysis of Zero Forcing and MMSE Equalizer on MIMO System in Wireless Channel
Abstract Views :318 |
PDF Views:0
Authors
G. M. Waliullah
1,
Diponkor Bala
1,
Mst. Ashrafunnahar Hena
2,
Md. Ibrahim Abdullah
1,
Mohammad Alamgir Hossain
1
Affiliations
1 Department of Computer Science & Engineering, Islamic University, Kushtia, BD
2 Department of Electrical and Electronic Engineering, Islamic University, Kushtia, BD
1 Department of Computer Science & Engineering, Islamic University, Kushtia, BD
2 Department of Electrical and Electronic Engineering, Islamic University, Kushtia, BD
Source
Journal of Network and Information Security, Vol 8, No 1&2 (2020), Pagination: 19-25Abstract
In wireless communication research multiple communication antennas are one of the major contexts. At present wireless communication is moving fast and the best example is MIMO. Wireless transmission is suffering from fading and interference effects which may be combated with equalizer. As a result of fading and interference, it creates a problem for signal recovery in wireless communication. The MIMO system uses Multiple Transmit and Multiple Receive antennas which take advantages of multipath propagation during a high distraction environment. This paper analyses the performance of Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) equalizer for 2×2 and 4×4 MIMO wireless channels. By using MATLAB toolbox version 2015a simulation results can be got to the RF processing lab. The Bit Error Rate (BER) features for various communication antennas is simulated in the MATLAB toolbox and many merits and demerits of the system are discussed. The simulation results show that the equalizer based zero-forcing receiver is helpful for noise-free channel and is successful in removing ISI, but MMSE is an optimal choice than ZF in terms of BER characteristics.Keywords
ISI, Bit Error Rate (BER), BPSK, Maximal Ratio Combining (MRC), 2×2 MIMO channel, MIMO system, MMSE Equalizer, Signal to Noise Ratio (SNR), ZF Equalizer.References
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- X. Zhang, and S. Kung, “Capacity analysis for parallel and sequential MIMO equalizers,” IEEE Transactions on Signal Processing, vol. 51, pp. 2989-3002, 2003.
- I. E. Telatar, “Capacity of multi-antenna Gaussian channels,” European Transactions on Telecommunications, vol. 10, no. 6, pp. 585-595, November-December 1999.
- V. Tarokh, A. Naguib, N. Seshadri, and A. R. Calderbank, “Space-time codes for high data rate wireless communication: Performance criteria in the presence of channel estimation errors, mobility, and multiple paths,” IEEE Transactions on Communications, vol. 47, no. 2, pp. 199-207, 1999.
- J. Meiyan, J. Qian, Y. Li, G. Tan, and X. Li, “Comparison of multiuser MIMO systems with MF, ZF and MMSE receivers,” 2013 IEEE Third International Conference on Information Science and Technology (ICIST), 2013.
- T. Abdessalem, et al., “Performance analysis of ZF and MMSE equalizers for MIMO systems,” 7th International Conference on Design & Technology of Integrated Systems in the Nanoscale Era, IEEE, 2012.
- B. M. Hochwald, and T. L. Marzetta, “Unitary space-time modulation for multiple-antenna communication in Rayleigh flat fading,” IEEE Transactions on Information Theory, vol. 46, pp 543-564, March 2000.
- D. Malik, and D. Batra, “Comparison of various detection algorithms in a MIMO wireless communication receiver,” International Journal of Electronics and Computer Science Engineering, vol. 1, no. 3 pp. 1678-1685, 2012.
- A. J. Paulraj, D. Gore, R. Nabar, and H. Bolcskei, “An overview of MIMO communications – A key to gigabit wireless,” Proceedings of the IEEE, vol. 92, pp. 198-218, February 2004.
- J. Hoydis, C. Hoek, T. Wild, and S. ten Brink, “Channel measurements for large antenna arrays,” Proceedings of International Symposium on Wireless Communication Systems (ISWCS), pp. 811-815, August 2012.
- G. J. Foschini, D. Chizhik, M. Gans, C. Papadias, and R. A. Valenzuela, “Analysis and performance of some basic space-time architectures,” IEEE Journal on Selected Areas in Communications, vol. 21, pp. 303-320, April 2003.
- Y. Jiang, M. K. Varanasi, and J. Li, “Performance analysis of ZF and MMSE for MIMO systems: An in-depth of the high SNR regime,” IEEE Transactions on Information Theory, vol. 57, no. 4, April 2011.
- A. Kumar, and P. Vardhan, “Design, simulation & concept verification of 4×4, 8×8 MIMO with ZF, MMSE and BF detection schemes,” Electrical, Control and Communication Engineering, vol. 13, no. 1, pp. 69-74, 2017.
- N. Prasad, and M. K. Varanasi, “Analysis of decision feedback detection for MIMO Rayleigh fading channels and the optimization of rate and power allocations,” IEEE Transactions on Information Theory, vol. 50, pp. 1009-1025, June 2004.
- Efficient Classification Techniques of Human Activities from Smartphone Sensor Data using Machine Learning Algorithms
Abstract Views :91 |
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Authors
Affiliations
1 Department of Computer Science & Engineering, Islamic University, Kushtia, BD
1 Department of Computer Science & Engineering, Islamic University, Kushtia, BD
Source
International Journal of Knowledge Based Computer System, Vol 8, No 1&2 (2020), Pagination: 1-10Abstract
Increasing use of accelerometer and protractor sensors in recent years has created a field of study for the definition of human activities. This issue is tried to be solved by using machine learning methods. For this, it is solved by extracting different properties from the obtained signals, obtaining the characteristics specific to the activity and classifying these properties. In this study, the time and frequency domain properties of 4 different human activities were extracted, then a pre-treatment step was applied in accordance with the obtained feature set, and then the size was reduced with PCA and Fisher ‘LDA methods. The k-NN classifier and perceptron classifiers were designed for the obtained feature set and the classification process was performed. In this study, the classification success of these methods using different parameters has been examined and the results are shown.Keywords
Feature Extraction, Fisher’s LDA, Gradient Descent Method, Human Activity Identification, Kessler’s Reconstruction, k-NN Classifier, PCAReferences
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- Analysis the Performance of MIMO-OFDM for Various Modulation Techniques over AWGN, Rayleigh Fading and Rician Fading Channel
Abstract Views :127 |
PDF Views:0
Authors
Diponkor Bala
1,
G. M. Waliullah
1,
Md. Hafizur Rahman
1,
Md. Ibrahim Abdullah
1,
Mohammad Alamgir Hossain
2
Affiliations
1 Department of Computer Science and Engineering, Islamic University, Kushtia, BD
2 Department of Computer Science and Engineering, Islamic University, Kushtia,, BD
1 Department of Computer Science and Engineering, Islamic University, Kushtia, BD
2 Department of Computer Science and Engineering, Islamic University, Kushtia,, BD
Source
Journal of Network and Information Security, Vol 9, No 2 (2021), Pagination: 1-8Abstract
Nowadays, we are living in the era of modern communication technology. The number of the mobile users is increasing tremendously day by day all over the world. Due to the increasing of the mobile users, the wireless communication systems are highly required a communication system that provides data transmissions rates and more reliability to the users. Multiple Input and Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is being considered to get those facilities. MIMO-OFDM has the ability to serve a large number of users with an enormous data transmission speed communication as well as utilizing the bandwidth efficiently. The multicarrier modulation technique is cap-able of reducing the inter symbol interference and multipath fading problems. In this paper, we mainly focused on analysis the performance of MIMO-OFDM systems and the performance of MIMO-OFDM has been measured in terms of the Bit Error Rate and Signal to Noise Ratio based on different channels such as- AWGN, Rayleigh fading, Rician fading and different modulation techniques such as- BPSK, QPSK, M-PSK, D-BPSK, D-QPSK, DPSK and QAM. All the simulations are performed by MATLAB framework.Keywords
BPSK, D-BPSK, DPSK, DQPSK, M-PSK, MIMO- OFDM, OFDM, QAM, QPSK.References
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- D. Bala, G. M. Waliullah, Mst. A. Hena, Md. I. Abdullah, and Md. A. Hossain, “Study the performance of capacity for SISO, SIMO, MISO and MIMO in wireless communication,” Journal of Network and Information Security, vol. 8, no. 1-2, pp. 1-6, 2020.
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- G. M. Waliullah, D. Bala, Mst. A. Hena, Md. I. Abdullah, and Md. A. Hossain, “Performance analysis of zero forcing and MMSE equalizer on MIMO system in wireless channel,” Journal of Network and Information Security, vol. 8, no. 1-2, pp. 19-25, 2020.