Nonlinear error modified equalization algorithm in high-order modulation of satellite communication

被引：0
|
作者：
Tang C.-K. [1 ]
Zhang L.-L. [2 ]
Lian B.-W. [1 ]
机构：
[1] School of Electronics and Information, Northwestern Polytechnical University, Xi'an
[2] School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an
来源：
| 1600年 / Editorial Board of Journal on Communications卷 / 38期
基金：
中国国家自然科学基金;
关键词：
Equalization; Feedback; Nonlinear error; Satellite communication;
D O I：
10.11959/j.issn.1000-436x.2017014
中图分类号：
学科分类号：
摘要：
Aiming at the larger computation and the slower speed of nonlinear error compensation of traditional equalization algorithms, the fast nonlinear error equalization algorithm with amplitude and phase separation was constructed. It combined the feedback equalization to reduce the computational complexity respectively. In order to eliminate nonlinear interference and memory interference, the algorithm utilized the Volterra model to modify the error and it was adapted to update the feedback equalization parameters. The theoretical analysis and simulation compared proposed algorithm and existing conventional equalization algorithms from the bit error rate, convergence speed and computational complexity. The results show that the computational complexity is only equivalent to 14.1%~24.9% of other algorithms and proposed method can cancel the nonlinear interference quickly. © 2017, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：117 / 125
页数：8
相关论文
共 13 条
  • [1] Tang C.K., Lian B.W., Zhang L.L., An algorithm to eliminate self-interference of bidirectional relaying for satellite communication systems, Journal of Xi'an Jiaotong University, 49, 2, pp. 74-79, (2015)
  • [2] Xu C., John P., Wilson S.G., Achievable information rates for nonlinear satellite channels in unidirectional and bidirectional relaying, IEEE Latin-America Conference on Communications, pp. 1-4, (2012)
  • [3] Park J., Ha Y., Chung W., Reduced complexity MLSD equalizers based on bidirectional DFEs, IEICE Transactions on Communications, 95, 11, pp. 3432-3438, (2012)
  • [4] Arik S.O., Askarov D., Kahn J.M., Adaptive frequency-domain equalization in mode-division multiplexing systems, Journal of Lightwave Technology, 32, 10, pp. 1841-1852, (2014)
  • [5] Tang C., Lian B., Zhang L., Post predistortion of 16APSK modulation with memory and nonlinear effect in satellite communication, Journal of Northwestern Polytechnical University, 32, 6, pp. 34-39, (2014)
  • [6] Geng Y., Zhang L., Sun Y., Research on ant colony algorithm optimization neural network weights blind equalization algorithm, International Journal of Security and its Applications, 10, 2, pp. 95-104, (2016)
  • [7] Meyer S., Real-time bit error rate analysis of blind equalization algorithms, Signal Processing: Algorithms, Architectures, Arrangements, and Applications, pp. 268-273, (2013)
  • [8] Sugiura S., Hanzo L., Frequency-domain equalization aided iterative detection of faster-than-nyquist signalling, IEEE Transactions on Vehicular Technology, 66, 9, pp. 1-4, (2014)
  • [9] Shu X., Wang J., Wang H., Hypothesis feedback equalization for M-ary parallel combinatory communication in deep water, 2014 12th International Conference on Signal Processing (ICSP), pp. 1649-1653, (2014)
  • [10] Rafati A., Lou H., Xiao C., Soft-decision feedback turbo equalization for LDPC-coded MIMO underwater acoustic communications, IEEE Journal of Oceanic Engineering, 39, 1, pp. 90-99, (2014)
12