Improving MIMO system throughput using power transmission scheduling

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The main idea of this paper is to select the best power allocation throughput model by simulating the different power allocation theoretical modules in MIMO arbitrary multipath environment to address the effects of channel parameters on throughput. In general, power allocation techniques are used for minimizing the overall Bit Error Rate (BER). In this process, the channel estimation is usually done at the receiver by accessing the Channel State Information (CSI). The optimized system can be designed with respect to channel parameters so that it can be suitable for transmitter side during power allocation. The simulation analysis is carried out in NI LabVIEW and it is observed from the studies that the throughput results are as a function of received power. Under static system parameters, the relative throughput of the water filling (WF) power allocation model is found to be high efficient 15.74% when it is compared with the open loop zero-forcing (ZF) and it is 4.45 % with respect to inverse singular value decomposition (SVD) equal power allocation models.

     

     


  • Keywords


    Multiple-Input-Multiple-Output (MIMO); Bit-Error-Rate (BER), Channel-State-Information (CSI); Water-Filling (WF), Zero-Forcing (ZF); Singular-Value-Decomposition (SVD.

  • References


      [1] D. Hassan, Y. Kirsal & S. Redif, "Channel capacity improvement for cooperative MIMO wireless sensor networks via adaptive MIMO-SVD" , HONET-ICT, 2016, pp. 49-53, https://doi.org/10.1109/HONET.2016.7753449.

      [2] Mittu, Sangeeta, Avinash Manu & Aravindan, “Optimal Power Allocation Techniques using SVD for MIMO-OFDM Multimedia Mobile Networks”, International Journal of Engineering Trends and Technology (IJETT), Vol.26, No.5,2015, pp.239-245. https://doi.org/10.14445/22315381/IJETT-V26P242.

      [3] J. Zhang & J. S. Lehnert, "Throughput Optimal Precoding and Rate Allocation for MISO Systems with Noisy Feedback Channels", IEEE Transactions on Information Theory, Vol. 54, No.5, 2008, pp.2139-2155, https://doi.org/10.1109/TIT.2008.920342.

      [4] J. Choi, J. Park & B. L. Evans, "Spectral Efficiency Bounds for Interference-Limited SVD-MIMO Cellular Communication Systems", IEEE Wireless Communications Letters,Vol. 6,No. 1, 2017,pp. 46-49, https://doi.org/10.1109/LWC.2016.2629474.

      [5] F. Ito, F. Uzawa, K. Mitsuyama & N. Iai, "Performance evaluation of TDD-SVD-MIMO system with feedback delays", International Symposium on Antennas and Propagation (ISAP), 2017, https://doi.org/10.1109/ISANP.2017.8228736.

      [6] X. Chen, B. Þ. Einarsson & P. S. Kildal, "Improved MIMO Throughput With Inverse Power Allocation Study Using USRP Measurement in Reverberation Chamber", IEEE Antennas and Wireless Propagation Letters, Vol. 13, 2014, pp. 1494-1496, https://doi.org/10.1109/LAWP.2014.2342217.

      [7] W. P. Hunek, "New SVD-based matrix H-inverse vs. minimum-energy perfect control design for state-space LTI MIMO systems", 20th International Conference on System Theory Control and Computing (ICSTCC), 2016, https://doi.org/10.1109/ICSTCC.2016.7790633.

      [8] X. Chen, P. S. Kildal & M. Gustafsson, "Characterization of Implemented Algorithm for MIMO Spatial Multiplexing in Reverberation Chamber", IEEE Transactions on Antennas and Propagation, Vol. 61, No. 8, 2013, pp. 4400-4404, https://doi.org/10.1109/TAP.2013.2259459.

      [9] SK. Mohammed & E. G. Larsson, "Improving the Performance of the Zero-Forcing Multiuser MISO Downlink Precoder Through User Grouping", IEEE Transactions on Wireless Communications, Vol. 15, No. 2, 2016, pp. 811-826, https://doi.org/10.1109/TWC.2015.2478878.

      [10] P. He, L. Zhao & B. Venkatesh, "Novel Water Filling for Maximum Throughput of Power Grid, MIMO, and Energy Harvesting Coexisting System With Mixed Constraints", IEEE Transactions on Communications, Vol. 65, No. 2, 2017, pp. 827-838, https://doi.org/10.1109/TCOMM.2016.2638907.

      [11] L. Zhao, Y. Wang & P. Chargé, "Efficient iterative water-filling power allocation method in MU-MIMO broadcast channels", Military Communications and Information Systems Conference (MCC), 2013.

      [12] Qi, Qilin & Yang, Yaoqing Lamar, " An Efficient Water-Filling Algorithm for Power Allocation in OFDM-Based Cognitive Radio Systems", International Conference on Systems and Infor-matics (ICSAI2012), 2012, Pages: 2069 - 2073, https://doi.org/10.1109/ICSAI.2012.6223460.

      [13] Chin-Wei Hsu, Ming-Fu Tang & Borching Su," Power allocation for downlink path based precoding in multiuser FDD massive systems without CSI feedback", IEEE 50th Asilomar Conference on Signals, Systems and Computers, 2016, pp: 198 - 202, https://doi.org/10.1109/ACSSC.2016.7869023.

      [14] Chen, Xiaoming, Kildal, Per-Simon & Gustafsson Mattias, " Comparing throughputs of 2×2 spatial multiplexing MIMO systems with and without CSI at the transmit side in rich isotropic multipath envirments ", 2014, pp.3493-3494, https://doi.org/10.1109/EuCAP.2014.6902582.

      [15] P.-S. Kildal et al, “Threshold receiver model for throughput of wireless devices with MIMO and frequency diversity measured in reverberation chamber”, IEEE Antennas Wireless Propag.Lett, Vol.10, 2011, pp. 1201–1204, https://doi.org/10.1109/LAWP.2011.2172909.

      [16] V. D. Nehete & K. Ziri-Castro, “A study of 2 x 2 MIMO system with Alamouti STBC in SDR with LabVIEW and NI-USRP 2953R”, IEEE 85th Vehicular Technology Conference (VTC 2017), 2017.


 

View

Download

Article ID: 13097
 
DOI: 10.14419/ijet.v7i3.13097




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.