在采用多天线高阶QAM的MIMO通信系统中,现有基于信道分组并行检测算法虽然接近最优检测性能但以牺牲计算效率为代价.针对这一问题,本文提出一种MMSE准则下基于信道分组的并行检测算法,不但有效降低计算复杂度,而且仍保证检测性能.该算...在采用多天线高阶QAM的MIMO通信系统中,现有基于信道分组并行检测算法虽然接近最优检测性能但以牺牲计算效率为代价.针对这一问题,本文提出一种MMSE准则下基于信道分组的并行检测算法,不但有效降低计算复杂度,而且仍保证检测性能.该算法采用MMSE准则下格归约算法改进分组后条件较好子信道矩阵特性,并在消除参考信号基础上利用改进的子信道矩阵对剩余信号以非线性方式进行检测.仿真结果表明:对4@4和6@6MIMO系统,该算法检测性能达到最优,对于8@8 MIMO系统,比最优算法所需信噪比提高约1dB.复杂度分析表明:相比现有信道分组检测算法,相同检测性能下该算法在6@6 M IMO系统中复杂度降低90%以上,在8@8 MIMO系统中复杂度降低98%以上.展开更多
针对采用最小均方误差(minimum mean square error,MMSE)检测算法在MIMO系统接收端进行检测时,需要进行大量伪逆运算导致检测复杂度增加的问题,提出了用一种基于迭代QR分解的MMSE V-BLAST算法,避免了伪逆运算,有效地降低了检测算法的复...针对采用最小均方误差(minimum mean square error,MMSE)检测算法在MIMO系统接收端进行检测时,需要进行大量伪逆运算导致检测复杂度增加的问题,提出了用一种基于迭代QR分解的MMSE V-BLAST算法,避免了伪逆运算,有效地降低了检测算法的复杂度,使系统检测性能得到了明显改善.在多散射物无线通信环境下进行仿真实验,结果表明,与传统的算法相比,提案算法在保证相同信噪比,误码率没有显著变化的前提下,系统检测复杂度明显改善.理论分析证明,系统中有效天线数目越多,所提出的算法优越性越明显.展开更多
非线性码间干扰是影响卫星通信的重要因素之一,需要有效消除或降低这种影响。在用Volterra级数分解表示非线性信道基础上,提出了基于线性MMSE(Minimum Mean Square Error)的Turbo均衡算法,以解决非线性码间干扰问题。通过对基于线性MMSE...非线性码间干扰是影响卫星通信的重要因素之一,需要有效消除或降低这种影响。在用Volterra级数分解表示非线性信道基础上,提出了基于线性MMSE(Minimum Mean Square Error)的Turbo均衡算法,以解决非线性码间干扰问题。通过对基于线性MMSE的Turbo均衡算法作无先验信息和低复杂度的MMSE近似处理,在不降低均衡性能的前提下,既能同时消除线性和非线性干扰,又能大大降低计算复杂度。仿真验证了该算法的有效性。展开更多
Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power consumption.The power consumption at the receiver ra...Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power consumption.The power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low resolution.In this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician fadings.We start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in radar.We also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the system.We emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining algorithm.We also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable rates.We emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.展开更多
Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which ent...Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.展开更多
文摘在采用多天线高阶QAM的MIMO通信系统中,现有基于信道分组并行检测算法虽然接近最优检测性能但以牺牲计算效率为代价.针对这一问题,本文提出一种MMSE准则下基于信道分组的并行检测算法,不但有效降低计算复杂度,而且仍保证检测性能.该算法采用MMSE准则下格归约算法改进分组后条件较好子信道矩阵特性,并在消除参考信号基础上利用改进的子信道矩阵对剩余信号以非线性方式进行检测.仿真结果表明:对4@4和6@6MIMO系统,该算法检测性能达到最优,对于8@8 MIMO系统,比最优算法所需信噪比提高约1dB.复杂度分析表明:相比现有信道分组检测算法,相同检测性能下该算法在6@6 M IMO系统中复杂度降低90%以上,在8@8 MIMO系统中复杂度降低98%以上.
文摘针对采用最小均方误差(minimum mean square error,MMSE)检测算法在MIMO系统接收端进行检测时,需要进行大量伪逆运算导致检测复杂度增加的问题,提出了用一种基于迭代QR分解的MMSE V-BLAST算法,避免了伪逆运算,有效地降低了检测算法的复杂度,使系统检测性能得到了明显改善.在多散射物无线通信环境下进行仿真实验,结果表明,与传统的算法相比,提案算法在保证相同信噪比,误码率没有显著变化的前提下,系统检测复杂度明显改善.理论分析证明,系统中有效天线数目越多,所提出的算法优越性越明显.
文摘非线性码间干扰是影响卫星通信的重要因素之一,需要有效消除或降低这种影响。在用Volterra级数分解表示非线性信道基础上,提出了基于线性MMSE(Minimum Mean Square Error)的Turbo均衡算法,以解决非线性码间干扰问题。通过对基于线性MMSE的Turbo均衡算法作无先验信息和低复杂度的MMSE近似处理,在不降低均衡性能的前提下,既能同时消除线性和非线性干扰,又能大大降低计算复杂度。仿真验证了该算法的有效性。
文摘Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power consumption.The power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low resolution.In this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician fadings.We start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in radar.We also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the system.We emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining algorithm.We also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable rates.We emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.
基金supported by National Natural Science Foundation of China(62371225,62371227)。
文摘Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.
