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Resource allocation in MIMO systems specific to radio communication

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The performance of the multi-input multi-output (MIMO) systems can be improved by spatial modulation. By using spatial modulation, the transmitter can select the best transmit antenna based on the channel variations using channel state information (CSI). Also, the modulation helps the transmitter to select the best modulation level such that the system has the best performance in all situations. Hence, in this paper, two issues are considered including spatial modulation and information modulation selection. For the spatial modulation, an optimal solution for obtaining the probability of selecting antenna is calculated and then Huffman coding is used such that the transmitter can select the best transmit antenna to maximize the channel capacity. For the information modulation, a multi quadrature amplitude modulation (MQAM) strategy is used. In this modulation, the modulation size is changed based on the channel state variations; therefore, the best modulation index is used for transmitting data in all channel situations. In simulation results, the optimal method is compared with Huffman mapping. In addition, the effect of modulation on channel capacity and a bit error rate (BER) is shown.
Rocznik
Strony
91--100
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wz.
Twórcy
  • Department of Electrical Engineering, Shiraz Branch Islamic Azad University Shiraz, Iran
  • Department of Electrical Engineering, Shiraz Branch Islamic Azad University Shiraz, Iran
Bibliografia
  • [1] Khederzadeh R., Farrokhi H., Adaptive rate and power transmission in spectrum-sharing systems with statistical interference constraint, IET Communications, vol. 6, pp. 870–877 (2014).
  • [2] Sreesudha P., Malleswari B.L., Design of Multi-Carrier CDMA-MIMO System by Various Spreading Strategies, IEEE 7th International Advance Computing Conference (IACC), Hyderabad, India, pp. 326–329 (2017).
  • [3] Cover T.M., Thomas J.A., Elements of Information Theory, 2nd edition, New York, NY, USA, Wiley (2006).
  • [4] Mohammadi A., Ghannouchi F.M., Single RF front-end MIMO transceivers, IEEE Communication Magazine, vol. 49, no. 12, pp. 104–109 (2011).
  • [5] Renzo M.D., Haas H., Ghrayeb A., Sugiura S., Hanzo L., Spatial modulation for generalized MIMO: Challenges, opportunities, and implementation, Proceedings of the IEEE, vol. 102, no. 1, pp. 56–103 (2016).
  • [6] Di-Renzo M., Haas H., Ghrayeb A., Sugiura S., Hanzo L., Spatial modulation for generalized MIMO: challenges, opportunities and implementation, Proc. IEEE, vol. 102, no. 1, pp. 56–103 (2014).
  • [7] Maleki M., Bahrami H. R., Beygi S., Kafashan M., Tran N. H., Space modulation with CSI: Constellation design and performance evaluation, IEEE Transactions on Vehicular Technology, vol. 62, no. 4, pp. 1623–1634 (2013).
  • [8] Lee M. C., Chung W. H., Lee T. S., Generalized precoder design formulation and iterative algorithm for spatial modulation in MIMO systems with CSIT, IEEE Transactions on Communications, vol. 63, no. 4, pp. 1230–1244 (2015).
  • [9] Yang P., Guan Y. L., Xiao Y., Renzo M. D., Li S., Hanzo L., Transmit precoded spatial modulation: Maximizing the minimum Euclidean distance versus minimizing the bit error ratio, IEEE Transactions on Wireless Communications, vol. 15, no. 3, pp. 2054–2068 (2016).
  • [10] Wang W., Zhang W., Huffman Coding-Based Adaptive Spatial Modulation, IEEE Transactions on Wireless Communications, vol. 16, no. 8, pp. 5090–5101 (2017).
  • [11] Anwar Hosen M., Khosravi A., Nahavandi S., Creighton D., Improving the quality of prediction intervals through optimal aggregation, IEEE Transactions on Industrial Electronics, vol. 62, no. 7, pp. 4420–4429 (2015).
  • [12] Precup R. E., David R. C., Petriu E.M., Grey wolf optimizer algorithm-based tuning of fuzzy control systems with reduced parametric sensitivity, IEEE Transactions on Industrial Electronics, vol. 64, no. 1, pp. 527–534 (2017).
  • [13] Saadat J., Moallem P.,Koofigar H., Training echo state neural network using harmony search algorithm, International Journal of Artificial Intelligence, vol. 15, no. 1, pp. 163–179 (2017).
  • [14] Vrkalovic S., Teban T. A., Borlea I. D., Stable Takagi-Sugeno fuzzy control designed by optimization, International Journal of Artificial Intelligence, vol. 15, no. 2, pp. 17–29 (2017).
  • [15] Mesleh R., Haas H., Sinanovic S., Ahn C. W., Yun S., Spatial modulation, IEEE Trans. Veh. Technol., vol. 57, no. 4, pp. 2228–2241 (2008).
  • [16] Elias P.,Universal codeword sets and representations of the integers, IEEE Transactions on Information Theory, vol. 21, no. 2, pp. 194–203 (1975).
  • [17] Huffman D. A., A method for the construction of minimum redundancy codes, Proceedings of the Institute of Radio Engineers (IRE), vol. 40, pp. 1098–1101 (1952).
  • [18] Khederzadeh R., Farrokhi H., Optimal and Suboptimal Adaptive Algorithms for Rate and Power Transmission in OFDM-Based Cognitive Radio Systems, Computers & Electrical Engineering Journal, vol. 42, pp. 168–177 (2015).
  • [19] Fazel K., Kaiser S., Multi-Carrier and Spread Spectrum Systems, Wiley & sons press (2003).
  • [20] WangW., Zhang W., Adaptive Spatial Modulation Using Huffman Coding, IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA (2016).
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d7887b08-ace3-425c-9b51-b73196f2bea4
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