采用最小角回归的稀疏MIMO均衡器设计方法
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摘要
为减少均衡器非零抽头数,降低计算复杂度,该文将多进多出系统稀疏有限冲激响应判决反馈均衡器设计问题转化为l1范数最小化问题,并提出利用最小角回归算法迭代计算稀疏判决反馈均衡器非零抽头位置和权重。仿真结果表明,在给定较小的性能损失下,相比最小均方误差准则的非稀疏最优均衡器,在相同的误比特率下,所提方法设计的稀疏判决反馈均衡器在车载移动A信道中的最大信噪比损失约为0.3dB,而其非零抽头数目减少超过70%,达到了性能与计算复杂度的有效权衡。
A new scheme for designing sparse finite impulse response(FIR)decision feedback equalizers(DFE)in multiple input multiple output(MIMO)systems based on the Least Angle Regression(LARS)algorithm is proposed.To decrease the number of nonzero taps for FIR DFE and reduce computational complexity,the problem of designing sparse FIR DFE is transformed into an l1-norm minimization approach,and the proposed design scheme is applied to compute the locations and weights of the nonzero taps for sparse FIR DFE iteratively.Simulation results show that when compared with the optimum Minimum Mean Square Error(MMSE)non-sparse solution for a small given performance loss,the number of nonzero taps for the proposed sparse equalizer design is reduced by more than 70%,while the maximum SNR loss for the proposed sparse equalizer is just about 0.3 dB in the Vehicular A channel,which results in an effective trade-off between performance and computational complexity.
A new scheme for designing sparse finite impulse response(FIR)decision feedback equalizers(DFE)in multiple input multiple output(MIMO)systems based on the Least Angle Regression(LARS)algorithm is proposed.To decrease the number of nonzero taps for FIR DFE and reduce computational complexity,the problem of designing sparse FIR DFE is transformed into an l1-norm minimization approach,and the proposed design scheme is applied to compute the locations and weights of the nonzero taps for sparse FIR DFE iteratively.Simulation results show that when compared with the optimum Minimum Mean Square Error(MMSE)non-sparse solution for a small given performance loss,the number of nonzero taps for the proposed sparse equalizer design is reduced by more than 70%,while the maximum SNR loss for the proposed sparse equalizer is just about 0.3 dB in the Vehicular A channel,which results in an effective trade-off between performance and computational complexity.
引文
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[2]TIMMINS I J,O'YOUNG S.Marine Communications Channel Modeling Using the Finite-Difference Time Domain Method[J].IEEE Transactions on Vehicular Technology,2009,58(6):2626-2637.
[3]AL-DHAHIR N,SAYED A H.Finite-length Multi-input Multi-output MMSE-DFE[J].IEEE Transactions on Signal Processing,2000,48(10):2921-2936.
[4]MELVASALO M,JANIS P,KOIVUNEN V.Sparse Equalization in High Data Rate WCDMA Systems[C]//Proceedings of the 2007IEEE Workshop on Signal Processing Advances in Wireless Communications.Piscataway:IEEE,2007:4401362.
[5]DONOHO D L.Compressed Sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[6]赵永红,张林让,刘楠,等.采用协方差矩阵稀疏表示的DOA估计方法[J].西安电子科技大学学报,2016,43(2):58-63.ZHAO Yonghong,ZHANG Linrang,LIU Nan,et al.DOA Estimation Method Based on the Covariance Matrix Sparse Representation[J].Journal of Xidian University,2016,43(2):58-63.
[7]AL-ABBASI A O,HAMILA R,BAJWA W U,et al.Design and Analysis of Sparsifying Dictionaries for FIR MIMOEqualizers[J].IEEE Transactions on Wireless Communications,2017,16(4):2576-2586.
[8]PINTO R G D C P,MERCHED R.A Compressed Sensing Approach to Block-Iterative Equalizers[J].IEEETransactions on Signal Processing,2018,66(4):1007-1022.
[9]KUTZ G,RAPHAELI D.Determination of Tap Positions for Sparse Equalizers[J].IEEE Transactions on Communications,2007,55(9):1712-1724.
[10]GOMAA A,AL-DHAHIR N.A New Design Framework for Sparse FIR MIMO Equalizers[J].IEEE Transactions on Communications,2011,59(8):2132-2140.
[11]VLACHAOS E,LALOS A S,BERBERIDIS K.Stochastic Gradient Pursuit for Adaptive Equalization of Sparse Multipath Channels[J].IEEE Journal on Emerging and Selected Topics in Circuits and Systems,2012,2(3):413-423.
[12]EFRON B,HASTIE T,JOHNSTONE I,et al.Least Angle Regression[J].The Annals of statistics,2004,32(2):407-499.
[13]AL-DHAHIR N,CIOFFI J M.MMSE Decision-feedback Equalizers:Finite-length Results[J].IEEE Transactions on Information Theory,1995,41(4):961-975.