本发明属于调制载波技术领域，尤其涉及一种同相正交不平衡的盲补偿方法、无线通信系统。
背景技术：
目前，业内常用的现有技术是这样的：随着通信系统对低成本低功耗接收机的需求日益增长，同相正交(i/q)不平衡问题受到越来越多的关注。i/q不平衡是由同相支路和正交支路幅度相位不匹配引起的，这种不平衡在通信系统的实现中是无法避免的。i/q不平衡往往会引起非常严重的性能损失，特别是在一些的低成本的射频前端中。因为i/q不平衡造成的性能损失不会随着snr的提高而缓解，因此，在数字域对i/q不平衡进行补偿就显得十分重要。通常，i/q不平衡可以用模型来描述。设解调信号为y(t)＝[yc(t)，ys(t)]t，发送信号为r(t)＝[rc(t)，rs(t)]t，本振信号zlo，c(t)＝(1+α)cos(ωt+θ)，zlo，s(t)＝(1-α)sin(ωt-θ)，其中，幅度不平衡参数为α，相位不平衡参数为θ，则：
可以看出，i/q不平衡导致的结果就是本来应该独立的i路信号和q路信号中各自混入了另一支路的传输信号，导致接收信号出现了严重的失真情况。i/q不平衡补偿算法的目标就是从接收信号y(t)中解出原始发送信号r(t)。一方面，目前大多数针对i/q不平衡的估计或补偿算法都采用数据辅助模式，最小均方估计方法、面向判决(dd)算法、期望最大化(em)的方法等。数据辅助算法借助了已知数据，通过在接收端将接收数据与已知发送数据对比，估计干扰的存在情况。这种算法的优势是往往具有较快的收敛速度，但这些使用数据辅助的方法也占用原本用于传输数据的载荷，导致单位时间内能够传输的数据量相对减小，造成频谱的浪费，降低频谱效率。随着频谱资源越来越珍贵，这一问题也变得越发突出。另一方面，针对i/q不平衡的盲估计算法相对较少，比较经典的有基于盲源分离(bss)的补偿算法、基于共轭信号模型的补偿算法、基于直接判决(dd)的补偿算法等。其中，盲补偿算法复杂度极高，会占用大量资源和处理时间，不适合硬件实现，直接判决法依赖于多次迭代来最小化判决误差，同样需要较大的资源和时间消耗。基于共轭信号模型的方案复杂度较低，但经过大量仿真实验表明，这一算法对幅度不平衡较为敏感，随着幅度不平衡的加剧或调制阶数的提高，系统的误比特率(ber)性能会急剧下降，在幅度不平衡完完全不存在的情况下，补偿效果最好。
综上所述，现有技术存在的问题是：
(1)补偿算法复杂度极高，会占用大量资源和处理时间，不适合硬件实现。
(2)基于共轭信号模型的方法则对幅度不平衡较为敏感，随着幅度不平衡的加剧或调制阶数的提高，系统的误比特率(ber)性能会急剧下降。
解决上述技术问题的难度和意义：由于同相正交不平衡的产生由于硬件的不理想而产生的，在实际的通信系统实现中无法避免，而这种不平衡又会对系统性能造成十分重要的影响。因此对i/q不平衡的估计和补偿具有十分重要的意义。当前已有的盲估计算法复杂度较高，在实际应用时会占用较多的资源，而复杂度较低的共轭信号模型法则受到幅度不平衡的影响较大，这些都制约了i/q不平衡问题的解决。本发明通过一种复杂度较低的算法消除幅度不平衡对共轭信号模型法的性能影响，改进了原有的共轭信号模型法，实现了一种复杂度低、性能良好的用于i/q不平衡补偿的盲估计补偿方案。
技术实现要素：
针对现有技术存在的问题，本发明提供了一种同相正交不平衡的盲补偿方法、无线通信系统。
本发明是这样实现的，一种同相正交不平衡的盲补偿方法，所述同相正交不平衡的盲补偿方法使用一阶统计量估计出信号的幅度不平衡参数；使用补偿矩阵对信号进行幅度补偿，使信号的幅度不平衡得到补偿；使用特征值分解得到相位不平衡补偿参数，并用参数构造出相位不平衡补偿矩阵对原信号进行补偿。
进一步，所述同相正交不平衡的盲补偿方法包括以下步骤：
(1)借助一定数量的接收符号，求出等效幅度不平衡参数；
(2)根据计算的幅度不平衡参数，进一步构造幅度不平衡补偿矩阵；
(3)利用幅度不平衡矩阵，对接收信号进行幅度不平衡补偿，得到新的信号矩阵；
(4)对新的接收信号矩阵的相关矩阵进行特征值分解，得到酉矩阵u和对角矩阵λ；
(5)使用已经计算得到的酉矩阵u和对角矩阵λ，构造相位不平衡补偿矩阵；
(6)对新的接收信号矩阵进行相位不平衡补偿，得到最终补偿后的信号。
进一步，所述(1)中接收信号为yc＝[yc，1，yc，2…yc，n]，ys＝[ys，1，ys，2…ys，n]，接收信号矩阵表示为y=[yc，ys]t，求出等效幅度不平衡参数：
其中[·]t表示矩阵的转置操作。
进一步，所述(2)中构造幅度不平衡补偿矩阵：
5、如权利要求2所述的同相正交不平衡的盲补偿方法，其特征在于，所述(3)中对接收信号进行幅度不平衡补偿，得到新的信号矩阵：
进一步，所述(4)中对新的接收信号矩阵的相关矩阵进行特征值分解，得到酉矩阵u和对角矩阵a：
cy＇＝e[(y＇c+j*y＇s)(y＇c+j*y＇s)h]＝uuh，
其中，e[.]表示求期望，j是虚数单位。
进一步，所述(5)中构造相位不平衡补偿矩阵w：
其中，[·]h表示矩阵的共轭转置。
进一步，所述(6)中对新的接收信号矩阵进行相位不平衡补偿，得到初步的估计信号：
y″＝w11(y′c+j*y′c)+w12(y′c-j*y′s)。
本发明的另一目的在于提供一种应用所述同相正交不平衡的盲补偿方法的无线通信系统。
综上所述，本发明的优点及积极效果为
一、收敛速度快，能够利用较少的接收信号构造出有效的补偿矩阵并还原出未受i/q不平衡损伤的原始信号，这一特点可由附图7进行说明。在qpsk调制方案，幅度不平衡参数α为0.2，相位不平衡参数θ为10°的场景下，本发明所述算法借助100个符号进行i/q不平衡估计和补偿，即可将系统误比特率降低到10-3以下，在借助400个符号进行i/q不平衡估计和补偿时，即可使系统误比特率基本达到无i/q不平衡的理想标准。随着用于估计的符号数的增大，系统性能最终可以达到无i/q不平衡的理想性能；
二、针对原有共轭信号模型的算法性能增益明显，特别是在高阶qam调制及幅度不平衡较高时，这一特性可由附图8进行说明，当幅度不平衡参数α为0.2，相位不平衡参数θ为10°时，本发明所述算法借助足够多的接收符号进行i/q不平衡估计和补偿，可以使系统性能达到无i/q不平衡的理想性能，而传统基于共轭对称模型的算法在同样的场景下无法达到理想性能，且性能恶化情况随着调制阶数的提高而显著加剧。如使用256-qam调制，其他参数同上时，传统基于共轭对称模型的算法在信噪比snr为30db时只能达到高于1e-2的误比特率，而本发明所述算法可以达到无i/q不平衡情况下的理想误比特率2e-4。
三、相对于其它盲估计算法复杂度较低。例如传统的基于盲源分离的补偿算法往往需要高维矩阵的构造以及迭代更新，这将大量占用系统资源并造成较高的延时。基于直接判决的方法需要进行迭代运算和误差估计，同样需要大量资源和时间的消耗。而本发明提出的方法，不需要依赖迭代来优化性能，不需要计算误差函数，也不需要构造高维数的矩阵来存储中间结果，因此在复杂度上具有较为明显的优势。
附图说明
图1是本发明实施例提供的同相正交不平衡的盲补偿方法流程图。
图2是本发明实施例提供的同相正交不平衡的盲补偿方法实现流程图。
图3是本发明实施例提供的i/q不平衡的产生原理示意图。
图4是本发明实施例提供的发送信号的星座图。
图5是本发明实施例提供的受到i/q不平衡影响的接收信号星座图。
图6是本发明实施例提供的经过补偿算法补偿后的信号星座图。
图7是本发明实施例提供的在16-qam调制、awgn信道、α＝0.3、θ＝10°情况下随用于估计补偿的符号数量变化的性能曲线示意图。
图8是本发明实施例提供的与传统csm算法α＝0.3，θ＝10°时不同阶数qam调制下性能对比示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
如图1所示，本发明实施例提供的同相正交不平衡的盲补偿方法包括以下步骤：
s101：求出等效幅度不平衡参数；
s102：构造幅度不平衡补偿矩阵；
s103：对接收信号进行幅度不平衡补偿，得到新的信号矩阵；
s104：对新的接收信号矩阵的相关矩阵进行特征值分解，得到酉矩阵u和对角矩阵λ；
s105：构造相位不平衡补偿矩阵；
s106：对新的接收信号矩阵进行相位不平衡补偿，得到补偿后的信号。
下面结合具体实施例对本发明的应用原理作进一步的描述。
以一般的i/q不平衡信号模型为例，按照本发明的方法进行实现。各参数设置为，幅度不平衡参数，α＝0.3，相位不平衡参数θ＝10°。调制方式为16-qam。归一化后的发射信号的复基带等效形式如下：
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本发明的具体实现步骤如下
1)求出等效幅度不平衡参数α
2)求出幅度不平衡补偿矩阵g，即
3)对信号进行幅度不平衡补偿，得到新的信号矩阵y′
其中，y′c的值如下
-0.31,-0.25,-0.83,0.77,-0.72,-1.2,0.79,1.01,-0.75,0.030,-0.49,-1.14,-0.73,-0.44,-0.28,0.33,1.15,0.27,-0.48,-0.75,-0.78,-1.28,-0.41,0.72,-1.1,-0.13,0.81,-0.23,-0.43,-1.04,-0.48,0.15,0.11,-0.53,0.94,-0.16,0.31,0.78,-1.13,-0.93,0.99,-0.12,-0.66,-1.28,-0.92,0.75,0.36,0.42,-0.050,0.12,1.04,-0.77,-0.42,1.16,0.75,-0.40,0.44,-1.05,0.33,0.38,-0.42,1.03,-0.16,-0.74,1.14,0.41,0.44,0.18,-0.32,-0.57,-1.16,-0.14,1.04,1.11,-0.98,-0.94,-0.49,0.41,-1.13,1.13,-1.16,0.36,0.61,1.19,-0.49,-0.43,0.14,0.10,-0.45,0.93,0,0.98,0.41,0.88,-0.54,-1.09,1.01,-1.08,-1.04,0.29,-1.33,0.75,-0.46,1.01,0.76,-0.10,-0.70,0.31,-0.25,0.81,0.16,-0.080,0.23,0.50,0.39,0.73,1.04,-0.56,0.22,1.09,0.47,0.24,0.46,0.27,-0.43,0.43,-0.26,-0.95,-0.30,-0.67,0.31,-0.24,0.80,0.38,0.89,0.66,-0.88,-0.23,0.040,-0.91,-0.97,-0.14,0.45,-0.30,1.07,-1.18,-0.94,0.13,-0.89,0.23,0.22,-0.82,0.80,-0.82,-0.40,1.18,0.49,0.87,-0.11,0.11,0.46,1.07,0.11,-0.32,-0.10,0.96,-1.04,-0.66,-1.08,0.43,0.84,0.74,0.13,0.25,1.11,-0.99,-1,1.07,0.27,0.37,0.26,0.41,-0.22,-0.35,0.51,-0.90,0.35,0.16,-0.37,-0.39,0.84,0.92,0.44,0.84,-0.76,1.05,-0.70,0.54,0.67,1,-0.94,0.81,0.84,-0.22,-0.90,0.29,0.58,1.09,0.92,0.45,-0.41,0.11,0.93,-0.16,-0.19,0.23,-0.46,-1.08,0.19,0.86,0.24,-0.85,0.32,-0.40,1.02,-0.70,0.87,0.52,-0.40,-0.77,1.21,-0.83,0.32,1.06,0.81,0.68,0.96,0.11,-1.06,-0.44,0.23,-0.36,0.51,-1.14,0.90,0.19,-1.02,-0.52,-0.75,-1.05,0.91,0.30,-0.37,-0.82,0.080,-0.48,0.050,-0.10,0.88,-0.86,1.22,-0.37,-0.12,0.25,0.41,-0.14,0.73,-0.45,1.07,0.27,0.93,-1.31,0.29,0.90,-0.81,-1.08,-0.060,-0.48,1.24,-0.020,0.33,0.48,-0.11,1.09,-0.89,0.73,-0.25,0.64,-0.20,0.68,-0.50,-0.52,-0.90,0.90,0.25,0.16,0.99,-0.97,1.21,-1.04,-0.45,0.48,-0.58,0.23,-0.32,0.97,0.95,-0.76,-0.38,0.080,-0.62,-0.51,-0.94,-0.97,-0.27,0.31,0.93,0.32,-0.15,-0.57,0.35,-1,-1,0.87,0.080,-0.80,0.71,-1.03,0.80,0.87,-0.76,0.38,0.16,-0.47,-0.45,-0.72,-0.88,1.05,0.86,-0.12,0.99,-0.67,0.30,-0.83,-0.15,0.77,1.11,1.09,-0.47,0.45,-0.19,0.65,0.20,-0.30,0.28,0.37,-1.02,-1.05,-0.49,0.81,0.89,0.72,-0.72,-0.83,0.45,0.40,0.86,0.34,0.080,-1.07,1.17,0.44,1.05,0.77,0.98,0.84,-0.82,-0.32,-0.93,-0.16,1.06,0.90,-0.48,1.13,-1.11,-0.93,-0.28,-0.15,-1.02,-0.26,0.020,1.01,0.37,0.89,-0.13,-0.98,1.17,-0.41,0.80,0.67,-0.45,1.09,-0.90,0.27,1.21,-0.16,-0.95,0.11,0.80,0.95,0.74,0.43,1,0.89,0.10,-0.58,-0.96,-0.13,0,0.86,0.33,-0.44,0.71,-0.92,0.15,-0.34,1.17,0.53,-0.35,0.69,-0.32,-0.090,0.83,-0.48,-0.81,-0.95,0.24,0.89,0.97,-0.16,-0.80,0.83,-0.81,-0.16,-1.18,-0.26,0.59,0.78,-0.31,-0.10,-0.82,-1.13,0.84,-0.89,0.29,-0.58,-0.75,0.82,0.29,-0.21,0.62,0.82,0.96,-0.54,-0.60,-0.38,-0.56,-1,-0.21,-0.74,0.98,-0.70,0.37,0.27,-0.22,0.28,0.25,0.34,-1.11,-0.81,-1.11,-0.23,-0.91,0.50,-0.99,-0.38,-0.17,-0.30,0.24,-0.70,-1.15,-0.41,0.31,-1.06,0.12,0.28,-0.52,0.11,-0.84,0.44,0.98,1.01,-0.36,-0.99,-1.07,0.82,0.42,0.80,-0.29,0.28,0.85,-0.88,0.69,-0.060,0.84,0.24,-0.030,0.020,0.91,-0.84,-0.92,0.11,-0.32,0.050,0.48,-0.84,0.49,0.68,0.38,0.92,0.70,-1.11,-0.95,-0.62,-0.44,-0.15,-0.18,-0.45,-1.09,0.10,0.30,0.92,-0.75,0.14,0.80,-0.88,0.19,-0.39,0.52,-0.44,1.08,-0.39,1.17,-0.96,0.98,-1.16,-0.26,1.13,-0.38,0.38,0.19,0.33,0.84,-0.90,-0.83,-0.87,1,0.52,-0.30,0.86,0.13,-0.22,-0.12,-0.99,1.18,0.37,-0.10,0.49,-0.53,1.14,0.22,0.97,0.31,-0.99,0.070,-0.88,-0.87,0.59,-0.28,-0.19,0.50,-0.040,0.98,-0.42,-0.31,0.63,1,-0.81,-0.080,-1.11,1.06,-1.25,-0.45,0.83,0.19,0.54,-0.84,0.19,0.15,1.05,0.22,-0.24,0.16,-0.74,-0.31,-1.09,-0.26,-1.2,1.01,0.83,-0.31,-0.73,-0.71,-0.49,-0.93,-0.80,0.23,0.74,0.52,-0.74,-0.91,-0.17,0.13,0.23,0.18,-0.35,-0.23,-1.04,0.85,0.89,-1.04,-0.68,-0.79,0.82,0.59,0.21,0.46,-0.48,-0.28,-0.80,0.50,-0.98,-0.74,-0.10,0.090,0.83,-0.37,0.33,0.72,0.83,-0.36,0.32,0.25,-0.90,-1.16,-0.38,1.09,-0.10,-0.41,-0.59,-1.14,0.29,-0.32,0.39,-0.77,-0.37,-0.79,-0.40,0.34,0.31,-1.25,1.04,-0.18,0.15,-0.90,0.34,-1.13,0.72,-0.86,-1.03,-0.96,0.32,-0.51,0.64,-0.90,-0.93,0.37,-0.31,-0.84,-0.65,0.25,0.47,-0.11,-0.84,0.40,-0.56,1.31,-1.02,-1.23,0.18,0.26,-0.55,0.22,0.17,0.18,0.35,0.67,-1.11,-0.78,-0.49,-0.70,-0.21,0.090,-0.38,1.05,1,0.14,-0.43,1.05,-0.93,-0.90,1.01,0.97,-0.12,1.14,-0.14,0.51,-0.36,0.75,0.67,0.34,-0.99,1.16,-0.91,1.08,0.32,-0.67,-0.17,0.89,0.31,1.07,0.22,-0.43,-0.070,-0.20,-1.02,-1.19,-0.070,0.47,0.95,0.24,0.45,1.01,-0.36,-0.17,0.43,-0.78,-0.76,0.90,-0.25,1.08,-0.19,0.83,-0.25,0.95,0.26,-0.85,-0.40,-0.86,0.70,0.26,-0.81,1.02,0.87,0.91,-0.16,1.07,-0.96,0.81,0.99,-0.32,-1.26,-0.42,0.96,-0.22,0.38,-0.86,-0.20,0.20,0.65
y′s的值如下：
-0.26,-0.34,0.79,-0.68,0.67,-1.21,-0.78,1.13,0.69,0.84,-0.94,-1.07,0.21,-0.32,0.33,0.21,1.11,0.37,-0.53,0.77,0.85,-1.25,0.23,-0.23,-1.03,0.78,-0.86,0.96,0.26,-0.97,-0.91,-0.82,-0.87,-1.11,-0.74,0.88,-0.21,0.35,-0.49,0.28,0.42,0.88,0.19,-1.2,0.76,-0.75,0.76,1.15,1,-0.78,0.55,0.73,0.12,1.06,-0.73,-0.21,0.89,-0.93,-0.33,0.43,0.22,0.31,1.08,0.06,1.03,0.33,0.94,-0.25,0.27,-0.82,-0.98,0.35,1.02,1.18,-1.09,0.11,-0.94,0.26,-1.3,1.06,-1.17,0.43,0.98,0.62,-1.03,-0.33,-0.84,-0.85,-0.46,1.06,-0.91,0.42,0.94,-0.79,-0.42,-0.57,0.37,-1.14,-0.51,0.43,-1.18,-0.64,-1.1,0.63,-0.16,0.69,0.83,0.16,0.28,-0.57,-0.89,0.92,-0.21,0.35,1.03,-0.81,1.15,-1.08,0.34,0.46,0.91,-0.25,0.85,0.2,-0.5,0.3,0.32,-0.46,-0.25,0.86,0.31,0.74,-0.76,0.93,-0.64,-0.85,-0.32,-0.32,-0.86,-0.52,-0.56,0.85,0.97,0.12,0.49,-1.17,0.17,1,-0.62,-0.92,-0.89,0.56,0.21,0.61,-0.9,1.24,0.33,-0.79,0.89,-0.81,1.05,0.69,-0.84,0.14,0.47,0.38,0.05,-0.94,-1.08,0.97,-0.16,-0.89,-0.99,0.2,1.15,0.65,-0.02,1.12,-0.28,0.25,-0.83,-0.2,0.3,0.18,0.95,-0.5,-0.23,-0.83,0.37,0.18,-0.21,-0.16,1.07,0.41,0.24,1.01,0.7,1.14,-0.71,0.52,-0.47,-0.21,-0.06,0.35,0.59,0.25,0.96,1.21,-0.11,1.08,0.18,-0.84,0.4,0.71,0.89,-0.86,0.15,-1.14,-0.34,-0.71,-0.35,-0.4,0.33,-0.39,0.4,0.7,0.39,-0.89,-0.43,0.71,0.98,0.6,0.09,0.52,-0.24,-0.76,0.43,-0.21,-1.05,-0.26,0.36,-0.32,0.95,-1.18,-0.29,-0.98,-0.93,-1.1,0.81,-0.48,-0.68,0.33,0.21,0.8,-0.91,-0.37,-0.77,0.33,-0.2,0.72,1.22,0.08,0.3,-0.13,0.96,0.29,-0.69,-0.78,0.56,-0.3,0.46,-1.19,0.35,-0.35,0.07,-1.04,0.92,-1.1,1.15,0.91,0.44,0.99,0.79,0.41,0.04,-0.76,0.32,-0.83,0.83,-0.83,-1.17,-1.1,-0.5,-0.12,0.27,-0.98,0.97,-0.4,0.53,-0.47,-0.91,0.45,-1.06,-1.01,0.12,-0.06,0.5,0.05,-1,-0.89,0.8,-1,-0.54,-0.38,0.21,-0.93,0.89,0.84,0.5,-0.45,-0.12,-1.12,0.21,-0.2,-0.95,0.19,-0.76,-1.02,-0.21,-0.7,0.7,1.03,-0.22,-0.96,0.13,0.63,-0.51,0.49,0.32,0.92,-0.59,0.74,-0.26,0.06,0.26,-0.84,1.17,1.15,-0.96,0.95,1,-0.97,-0.84,0.3,0.35,1.07,0.05,-1.08,-0.87,-0.84,-0.64,-0.93,0.75,-0.93,1.02,0.36,-0.13,-0.32,-1.01,-0.66,1.15,0.81,1.11,0.28,1.11,-0.13,0.17,-0.39,-0.36,0.34,0.97,-0.19,-0.95,1.06,-1,-0.57,0.89,-0.27,-0.45,0.87,-1.01,0.61,-0.25,0.34,0.79,,1.02,-0.32,-0.19,1.03,-0.91,0.43,-0.4,-0.27,1.08,0.89,-0.48,-0.33,-0.02,-0.13,-0.77,0.93,0.35,1.15,-0.97,-0.99,-0.46,0.97,-0.92,-0.22,0.31,-0.97,-0.79,0.61,-0.79,-0.4,1.31,0.98,0.21,-0.77,0.26,0.92,-0.54,-1.15,0.07,-0.53,-0.24,-0.18,-0.68,0.15,0.85,0.46,0.78,0.76,-0.56,-0.33,0.93,-0.7,0.19,0.81,0.19,-1.17,-0.07,0.01,-0.91,-0.83,0.09,0.4,0.37,0.29,1.25,-0.04,0.42,-0.94,-0.89,-0.97,-1.2,-0.5,-0.45,0.81,0.46,0.86,0.27,0.44,0.16,-0.13,-0.25,-0.14,-1.22,0.82,-0.55,0.13,-0.36,1.01,-0.49,-0.35,0.82,0.13,-1.04,0.63,-1.14,-0.92,0.34,-0.48,-0.92,-0.23,-0.92,-0.35,0.04,1.02,0.41,0.28,-0.35,-0.42,-1.03,-0.73,0.96,-0.75,-0.19,-0.25,0.41,-0.34,-0.68,0.43,-0.08,-0.21,1,-0.91,-0.17,0.08,-0.32,-0.68,-0.27,-0.94,0.88,0.67,0.98,-0.89,0.23,0.33,-0.74,-0.07,-0.43,0.82,-0.47,0.19,0.81,-0.44,-1.02,-0.9,-0.32,0.5,0.85,-1.02,-0.66,0.06,-0.43,0.23,1.08,-0.28,0.45,-0.31,1.09,-1.08,0.56,-1.08,-0.27,1.13,-0.42,0.41,-0.28,-0.35,-0.14,-0.46,0.7,-0.44,0.4,-0.65,0.11,-0.88,-0.79,0.79,0.82,-1.13,1.17,0.37,0.92,0.82,-1.14,1.11,-0.42,0.03,-0.22,0.23,-1.04,0.79,0.18,-0.7,0.22,0.15,0.92,0.84,0.44,-0.39,0.86,0.58,0.4,0.1,1.03,-0.89,1.02,-0.68,-0.34,-0.22,-0.28,0.98,0.18,-0.38,-0.26,0.44,-0.39,0.24,-0.96,1,-0.35,-1.1,0.23,-1.12,0.57,-0.87,0.18,0.85,0.81,0.79,0.25,0.75,-0.18,-0.17,1.13,0.83,0.19,0.79,-0.92,-0.23,-0.25,0.11,0.88,-1.05,-0.3,-0.25,-0.36,-1.07,0.66,-0.79,1.1,0.37,0.78,-1.03,0.11,0.7,0.22,0.18,0.85,0.95,-0.2,-0.65,-1.03,0.31,-0.89,0.27,-0.44,-0.36,0.34,0.7,-1.07,-0.45,0.51,0.89,-0.51,-1.17,-1.3,-0.31,0.25,0.41,0.68,-1,-0.03,0.38,-0.31,0.38,-1.25,1.03,0.94,-0.98,0.18,0.85,-1.04,-0.14,-0.46,-0.33,-0.61,-0.2,-0.84,-0.76,0.8,-0.48,0.43,-0.51,0.65,-0.51,0.24,0.58,1.02,-0.5,0.4,-0.54,1.06,-1.1,-1.09,-0.77,-0.21,-1,0.44,-0.29,-0.82,0.53,-0.88,-1.12,-0.52,-1.12,0.86,0.82,-0.43,0.14,0.56,0.52,-0.87,-1.11,-0.16,-0.55,0.25,0.41,-0.06,1.02,0.61,0.32,0.87,0.43,-0.28,-0.84,-0.24,-0.41,0.52,0.07,1,0.22,0.74,0.24,-0.16,0.32,1.12,-0.28,-1.03,0.86,-0.2,-1.07,-1.02,0.85,0.92,0.58,-0.21,0.92,0.59,-0.39,0.86,0.96,0.15,0.19,-0.27,0.3,0.28,0.86,-0.25,0.29,-0.22,-0.19,0.09,-0.45,0.17,-0.21,-0.9,0.11,1.14,1,-0.75,0.82,0.47,-0.54,-0.72,-0.11,-0.29,-1.1,-0.95,0.48,0.39,0.35,0.11,0.88,-0.3,-0.9
4)对新的接收信号矩阵的相关矩阵进行特征值分解，得到酉矩阵u和对角矩阵λ，即
cy′＝e(y′y′h)＝uauh；
其中：
5)构造相位不平衡补偿矩阵w，即：
6)对新的接收信号矩阵进行相位不平衡补偿，得到最终的估计信号：
y＝w11(y′c+j*y′c)+w12(y′c-j*y′s)；
最终估计信号如下：
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通常通过对i/q不平衡的纠正，最终估计信号可直接送入下一模块进行其它操作。这里，为了描述i/q不平衡补偿算法的工作效果，直接对估计信号进行功率还原和解调，并将解调后信号与原始发送信号进行对比。图6给出了本发明所用算法在16-qam调制方式、18db信噪比情况下，随着估计符号的增加，误比特率性能的变化曲线。其中，除i/q不平衡和加性高斯白噪声外，其它失真不予考虑，虚线部分为信噪比18db的加性高斯白噪声情况下理论的误比特率取值。可以看出，本发明提出的方法能够达到较快的收敛速度，在用于估计的符号足够多的情况下，可以达到理论的误比特率性能。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。