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信道预测对自适应调制系统性能的影响分析 被引量：2

Influence analysis of channel prediction on adaptive modulation system
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摘要 自适应调制系统通常根据当前信道条件调整未来的调制和编码方式来提高频谱效率,存在的问题是信道的不断变化使得这样的调整不准确,反而导致性能恶化。本文主要研究了信道变化对自适应调制系统性能的影响,调整方式有以下两种:使用当前信道估计值来判断下一帧调制方式;采用MMSE信道预测算法对下一帧信道值进行预测,再根据预测值判断调制方式。同时推导出两种情况下正确选择调制方式的概率公式。数值结果表明以当前值代替未来值的准确程度是和多普勒与帧长的乘积成反比的,且和平均信噪比距离信噪比界值的程度相关,而信道预测时选择调制方式的正确概率和多普勒与帧长的乘积也成反比,且和预测采用的阶次成反比。 Modulation and encoding schemes are adjusted depending on channel conditions in adaptive modulation systems.The existing problem is that such adjustment will be incorrect with the variance of channel so that system performance will become worse.In this paper influence of channel variance on adaptive modulation is analyzed over a fading channel in theory.There are two cases： one is to adjust modulation scheme using current channel estimation;the other is to adjust the modulation scheme using channel estimation predicted by MMSE rules.Then the probability equation is derived for the two cases.Numerical results show that the probability without channel prediction is inversely proportional to the multiplication of Doppler frequency and packet length.And it is relevant to the distance to the SNR threshold.The probability with MMSE channel prediction is inversely proportional to the multiplication of Doppler frequency and packet length,and it is inversely proportional to the prediction order.
作者 方昕 王君 冯忠娜 应娜 李光球
机构地区 杭州电子科技大学通信工程学院
出处 《电路与系统学报》 CSCD 北大核心 2010年第4期65-69,共5页 Journal of Circuits and Systems
基金 浙江省自然科学基金(Y106179) 国家自然科学基金(60772067)
关键词 自适应调制 信道预测 多普勒 帧长 adaptive modulation channel prediction doppler packet
分类号 TN929.533 [电子电信—通信与信息系统]
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参考文献9

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同被引文献30

  • 1HEIDARI A, KHANDANI A K, MCAVOY D. Adaptive modelling and long-range prediction of mobile fading channels [ J]. IET Com- munications, 2010, 4(1) : 39 - 50.
  • 2FERNANDEZ-PLAZAOLA U, MARTOS-NAYA E, PARIS J F, et al. Adaptive modulation for MIMO systems with channel predic- tion errors [ J]. IEEE Transactions on Wireless Communications, 2010, 9(8) : 2516 - 2527.
  • 3ABDALLAH S, BLOSTEIN S D. Rate adaptation using long range channel prediction based on discrete prolate spheroidal sequences [ C] // Proceedings of the 20t4 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications. Piscat- away: IEEE, 2014:479 -483.
  • 4KIM J, MOON S H, SUNG C K, et al. A new SNR prediction method for MIMO-OFDM systems with maximum likelihood detector [ C]// Proceedings of the 2011 IEEE International Conference on Communications. Piscataway: IEEE, 2011:1-5.
  • 5LEE H C, CHIU J C, LINK H. Two-dimensional interpolation-as- sisted channel prediction for OFDM systems [ J]. IEEE Transactions on Broadcasting, 2013, 59(4) : 648 - 657.
  • 6MUNOZ MORALES C, ESLAVA G S. Linear and non-linear chan- nel prediction performance for a MIMO-OFDM system[ C]// Pro- ceedings of the 2014 IEEE 5th Latin American Symposium on Cir- cuits and Systems. Piscataway: IEEE, 2014:1 -4.
  • 7DING T, HIROSE A. Fading channel prediction based on combina- tion of complex-valued neural networks and Chirp Z-transform [ J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(9) : 1686 - 1695.
  • 8LEE K, CHO J, PARK Y. Channel prediction-based noise reduc- tion algorithm for dual-microphone mobile phones [ J]. IEEE Trans- actions on Consumer Electronics, 2014,60(3) : 393 -401.
  • 9JIA T, DUEL-HAI,I,EN A, HALLEN H. Data-aided noise reduction for long-range fading prediction in adaptive modulation systems [ J]. IEEE Transactions on Vehicular Technology, 2013, 62(5) : 2358 - 2362.
  • 10XING X, JING T, HUO Y, et al. Channel quality prediction based on Bayesian inference in cognitive radio networks [ C]//Pro- ceedings of the 2013 IEEE International Conference on Computer Communications. Piscataway: IEEE, 2013:1465-1473.

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电路与系统学报
2010年 第4期

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