Patent Publication Number: US-11641293-B1

Title: Systems and method for distortion compensation

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Application No. 63/008,325 filed Apr. 10, 2020 and titled “Method of Interweaved Look-Up Table for Signal Distortion Compensation in Coherent Systems,” hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Increasing the quantity of data that may be transmitted over a communication networks is an ongoing effort. To fuel the development of future ultra-high-speed optical transport and access technology, higher-order modulation formats are an indispensable technology. These formats bring about higher-spectral efficiency and lower power consumption per bit. However, demodulating ultra-high baud rate (ultra-HB) higher-order (HO) quadrature amplitude modulation (UHH-QAM) in coherent systems may experience a number of challenges. In one aspect, because of an increased density of constellation points, HB-HO-QAM may be sensitive toward channel penalties such as intensity noise, phase noise, inter-symbol interference (ISI), and nonlinear distortions. In another aspect, HB-HO-QAM also suffers more from imperfections of the system, such as the skew, timing offset, imbalance between in-phase (I) and quadrature (Q) components, and quadrature error. Therefore, transmitters using high speed coherent modules will be strictly calibrated before use and this strict standard may significantly reduce the product yield rate. Moreover, the matured signal recovery DSP (Digital Signal Processing) in a receiver is less effective with higher-order QAM. For example, in modulation formats such as 64-QAM and 256-QAM, skews and nonlinearities become harder to be eliminate completely as compared with simpler modulation formats like QPSK. This results in stronger residual distortions and heavier penalties on signal quality. A way to address distortions in a higher order QAM system includes pre-distorting the signal to absorb the accumulated residual signal distortions at various steps in a HB-HO-QAM coherent transmission system. 
     SUMMARY OF THE EMBODIMENTS 
     In a first aspect, a method of compensating for signal distortion in a data transmission payload sent over a transmission link includes receiving a sequence of training symbols S i ; decomposing each symbol S i  into real-valued in-phase I i  and quadrature Q i  components; composing symbol chips c Ii  and c Qi  from n consecutive symbols, wherein c Ii  comprises n I i  components and at least one Q i  component, and wherein c Qi  comprises n Q i  components and at least one I i  component; storing symbol chips c Ii  in an I branch array and symbol chips c Qi  in a Q branch array; processing the I branch array and Q branch array symbol chips to calculate an input-output data map; and performing signal compensation of a data transmission payload using the input-output data map. 
     In a further embodiment, c Ii =[I i−(n−1)/2 , . . . , I i , . . . , I i+(n−1)/2 , Q i ], c Qi =[Q i−(n−1)/2 , . . . , Q i , . . . , Q i+(n−1)/2 , I i ] and n is an odd integer. 
     In any of the above embodiments, n=3. 
     In another embodiment, the input-output data map comprises an interweaved look-up table (ILUT) comprising an I look-up table (LUT) and a Q LUT; 
     In any of the above embodiments, the transmission link uses dual polarization, I and Q denote orthogonal phases and X and Y denote orthogonal polarizations, and wherein the ILUT comprises an XI LUT, an XQ LUT, a YI LUT and a YQ LUT. 
     In any of the above embodiments, symbol chips are composed from n consecutive symbols, wherein
     c XI =[XI i−(n−1)/2 , . . . , XI i , . . . , XI i+(n−1)/2 , XQ i ],   c XQ =[XQ i−(n−1)/2 , . . . , XQ i , . . . , XQ i+(n−1)/2 , XI i ],   c YI =[YI i−(n−1)/2 , . . . , YI i , . . . , YI i+(n−1)/2 , YQ i ] and   c YQ =[YQ i−(n−1)/2 , . . . , YQ i , . . . , YQ i+(n−1)/2 , YI i ].   

     In any of the above embodiments, processing the symbol chips further comprises, for each of the I and Q branches classifying symbol chips of a respective branch array into one of a plurality of clusters C k , each cluster comprising all symbol chips with a minimal Euclidean distance to a kth extended symbol basis; calculating a mean error for each cluster and storing it in the look-up table of the respective branch; and calculating an extended symbol basis and storing it in the look-up table of the respective branch. 
     In any of the above embodiments, performing signal compensation further comprises pre-compensating the data transmission payload after binary data for transmission is mapped into vector symbols by: clustering the vector symbols into different basis categories using an original-basis list from the ILUT; and pre-distorting the clustered symbols by deducing a mean error from a second symbol in each chip and a mean error from the ILUT; and forming the data transmission payload from the pre-distorted symbols. 
     In any of the above embodiments, performing signal compensation further comprises compensating for signal distortion in a decision stage of a receiver by composing received symbols into arrays of symbol chips; and applying a maximum likelihood detection (MLD) to find a minimal Euclidean distance between a received symbol chip and an extended symbol basis from the ILUT. 
     In any of the above embodiments, performing signal compensation further comprises post-compensating the data transmission payload after signal recovery in a receiver by composing received symbols into arrays of symbol chips; applying a maximum likelihood detection (MLD) to find a minimal Euclidean distance between a received symbol chip and an extended symbol basis from the ILUT; and subtracting a corresponding mean error from the ILUT from a second element of the received symbol chip. 
     In a further embodiment, the transmission link is a coherent optical link using dual-polarization and high baud rate higher-order quadrature amplitude modulation (HB-HO-QAM). 
     In another aspect, a system for compensating for signal distortion in a data transmission payload sent by a transmitter over a transmission link to a receiver comprising at least one processing element to receiving a sequence of training symbols S i ; decomposing each symbol S i  into real-valued in-phase L and quadrature Q i  components; composing symbol chips c Ii  and c Qi  from n consecutive symbols, wherein c Ii  comprises n I i  components and at least one Q′ component, and wherein c Qi  comprises n Q i  components and at least one I i  component; storing symbol chips c Ii  in an I branch array and symbol chips c Qi  in a Q branch array; processing the I branch array and Q branch array symbol chips to calculate an input-output data map; and performing signal compensation of a data transmission payload using the input-output data map. 
     In a further embodiment, C Ii =[I i−(n−1)/2 , . . . , I i , . . . , I i+(n−1)/2 , Q i ], c Qi =[Q i−(n−1)/2 , . . . , Q i , . . . , Q i+(n−1)/2 , I i ] and n is an odd integer. 
     In any of the above embodiments, n=3. 
     In any of the above embodiments, the input-output data map comprises an interweaved look-up table (ILUT) comprising an I look-up table (LUT) and a Q LUT; 
     In any of the above embodiments, the at least one processing element, for each of the I and Q branches, further processes the I branch array and Q branch array symbol chips by classifying symbol chips of a respective branch array into one of a plurality of clusters C k , each cluster comprising all symbol chips with a minimal Euclidean distance to a kth extended symbol basis; calculating a mean error for each cluster and storing it in the look-up table of the respective branch; and calculating an extended symbol basis and storing it in the look-up table of the respective branch. 
     In any of the above embodiments, the at least one processing element further pre-compensates the data transmission payload after binary data for transmission is mapped into vector symbols, by clustering the vector symbols into different basis categories using an original-basis list from the ILUT; pre-distorting the clustered symbols by deducing a mean error from a second symbol in each chip and a mean error from the ILUT; and forming the data transmission payload from the pre-distorted symbols. 
     In any of the above embodiments, the at least one processing element further post-compensates the data transmission payload, by composing received symbols into arrays of symbol chips; and applying a maximum likelihood detection (MLD) to find a minimal Euclidean distance between a received symbol chip and an extended symbol basis from the ILUT. 
     In any of the above embodiments, the at least one processing element further post-compensates the data transmission payload, by composing received symbols into arrays of symbol chips; applying a maximum likelihood detection (MLD) to find a minimal Euclidean distance between a received symbol chip and an extended symbol basis from the ILUT; and subtracting a corresponding mean error from the ILUT from a second element of the received symbol chip. 
     In a further embodiment, the transmission link is a coherent optical link. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG.  1    depicts a flow diagram of a data transmission system incorporating an interweaved look-up table (ILUT), in an embodiment. 
         FIG.  2 A  is a flow diagram of a method of training an ILUT, in an embodiment. 
         FIG.  2 B  is a flow diagram of a method of training an ILUT in a dual-polarization transmission link, in an embodiment. 
         FIG.  3 A  is a block diagram of an implementation of an ILUT for signal pre-compensation, in embodiments. 
         FIGS.  3   b   - 3 C are block diagrams of an implementation of an ILUT for signal post-compensation, in embodiments. 
         FIGS.  4 A- 4 D  are graphs illustrating representative mean-error distribution patterns using an ILUT. 
         FIGS.  5 A- 5 H  illustrate symbol constellations that have experienced various types of distortion and corresponding rectified symbol constellation after compensation using an ILUT. 
         FIGS.  6 A- 6 D  are graphs illustrating post compensation using an ILUT, in embodiments. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     In embodiments, a data transmission system  100  incorporating an input/output data map or an interweaved dataset or data array, such as but not limited to an interweaved look-up table (ILUT), is represented in a flow diagram in  FIG.  1   . A transmitter  102  transmits data signals over a transmission link  104  to a receiver  106 . In embodiments, transmission link  104  is a coherent optical link but embodiments disclosed herein may be used with any transmission link that experiences distortion including, for example, wireless, wired and satellite communication links. In addition, embodiments disclosed herein may be used in coherent and non-coherent systems. Data signals are transmitted over transmission link  104  using, for example, quadrature amplitude modulation (QAM). In embodiments, transmission link  104  uses higher-order QAM, such as 64-QAM or 256-QAM. In this modulation format, data signals are transmitted as a sequence of symbols, which may provide greater spectral efficiency and lower power consumption per bit. However, the data signals transmitted may be vulnerable to distortions from a variety of sources. For this reason, data transmission system  100  includes functionality for compensating for distortion in the system. In particular, a sequence of training symbols is sent through transmission link  104  and used to train an ILUT  108 , which is then used to compensate for distortion in data payload signals  112 . Although embodiments of a coherent optical link both with and without dual-polarization are disclosed herein, ILUT  108  may be used to rectify any two-dimensional signal degradations. 
     After transmission to receiver  106 , received training symbols  110  are extracted and used to calculate ILUT  108 . In embodiments, the longer the sequence of training symbols, the more precise the ILUT  108 . The training sequence may include, for example, anywhere from hundreds to thousands of symbols. The terms “training sequence” and “payload” do not have a defined relationship in time or quantity. For some transmission link types and distortions, training symbols may be sent infrequently, such as once a day or less. In situations with rapidly changing distortions, training symbols may be sent more frequently, as necessary to respond to changing link circumstances. 
     There are several methods of using ILUT  108  in signal compensation. One is to pre-compensate  114  the signals at transmitter  102  by adding the inverse of the distortions. Another is to post-compensate  116  the signal at receiver  106  by deducting the estimated distortions, resulting in recovered signal  118 . 
       FIG.  2 A  is a flow diagram illustrating a method  200  of training ILUT  108 . A series of training symbols  202  are received at receiver  106 . Training symbols  202  have the form S i =I i +jQ i  representing a QAM signal, where subscript i indexes and differentiates each training symbol of training symbols  110 .  FIG.  2    illustrates method  200  for training symbols in an X polarization. 
     Step  204  includes normalizing training symbols  202 . In an example of step  204 , training symbols  202  that were processed at different parts of transmission link  104  are normalized to the same unified amplitude reference. 
     Step  206  includes composing the signal into a matrix of symbol chips according to the extended signal basis in ILUT  108 . As used herein “symbol chips” refers generally to a unit or collection of data, not an integrated circuit device manufactured on a substrate such as silicon. To reduce computational complexity, the pre- or post-compensation is only applied to one-dimensional real-valued signals. Symbols S i  decomposed into real-valued coefficients on the dimensions of I and Q, which denote two orthogonal phases. In embodiments, ILUT  108  includes look-up tables for an I branch and a Q branch. In an embodiment, each symbol S i  will be decomposed into real-valued components I i , Q i , where I and Q denote two orthogonal phases. In embodiments, for an M-QAM signal with √{square root over (M)} levels in the I or Q branch and the size of the basis as L, the total number of basis in one dimension would be K=(√{square root over (M)}) L . Embodiments discussed herein refer to 64-QAM where M=64, L=4, and K=4096. These embodiments are for purposes of illustration only and other orders of modulation, and values of K and L, may be used. 
     In embodiments, an LUT in each branch includes (i) the mean error and (ii) the extended symbol basis. The extended symbol basis is an expansion of an original standard symbol basis s k =[S k,1 , S k,2 , S k,3 , S k,4 ] through the incorporation of channel distortions, where k=1:K and S k,i ∈{−√{square root over (M)}+1, −√{square root over (M)}+3, . . . , √{square root over (M)}−3, √{square root over (M)}−1}. In embodiments, the extended symbol basis is calculated from symbol chips composed from the real-valued components I i , Q i , of training symbols  202 . A structure of the symbol chip is shown in  FIG.  2    at I branch array  208  and Q branch array  209 , where each row represents a symbol chip that is stored in the respective array. A symbol chip c Ii  and c Qi  is composed from n consecutive pulse-amplitude-modulated (PAM) symbols with either I or Q in one polarization and one PAM symbol in the another I/Q branch of the same polarization. The general forms for a symbol chip are
 
 c   Ii =[ I   i−(n−1)/2   , . . . , I   i   , . . . , I   i+(n−1)/2   , Q   i ] and   (1)
 
 c   Qi =[ Q   i−(n−1)/2   , . . . , Q   i   , . . . , Q   i+(n−1)/2   , I   i ]  (2)
 
for the I and Q branches, respectively. Although a symbol chip composed of three symbols is illustrated and discussed herein, embodiments are not limited to three symbols. For example, a symbol chip composed from five symbols or any odd number of symbols may be used. For any odd number of symbols, the last coefficient and the center coefficient in the odd number of symbols will correspond to the I and Q components of one original QAM symbol.
 
     Such an interweaved configuration of the symbol chips brings several benefits. For one, it accounts for the memory effect in the residual distortions since adjacent symbols are included in a symbol chip. Additionally, the interweaved configuration takes account of the distortion penalty across I and Q branches since one reference symbol from the opposite I/Q branch is introduced. 
     Steps  210  and  212  include classification of symbol chips. In an example of steps  210  and  212 , after re-arranging the received symbols  202  into arrays of symbol chips  208  and  209 , the next step is to classify the chips into different clusters. A cluster, denoted as C k , includes all the symbol chips with the minimal Euclidean distance to the kth Extended Symbol Basis. It is worth noticing that the classification could be blind or with the knowledge from training. When blind classification is used, a decision feedback scheme is needed to update the value of different extend symbol basis. 
     Steps  214  and  216  include calculating errors between each symbol chip and the original basis. In an example of steps  214  and  216 , the errors between the symbol chip and the original basis in the I and Q branches are calculated using the equations (3)-(4):
 
 e _ I   k,l   =S   k,2   −I   i   (3)
 
 e _ Q   k,m   =S   k,2   −Q   m   (4)
 
where l, m, p and q are indices into the symbol chips within the cluster.
 
     Steps  218  and  220  include calculating a mean error for each cluster. In an example of steps  218  and  220 , the mean error for each cluster is calculated for each cluster using the equations (5)-(6):
 
 E _ I   k =mean l ( e _ I   k,l )   (5)
 
 E _ q   k =mean m ( e _ Q   k,m )   (6)
 
     The kth extended symbol basis of one dimension can be obtained through calculating the vector average for all the symbol chips contained by the kth cluster in the same dimension using equations (7)-(8): 
     
       
         
           
             
               
                 
                   
                     
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     Based on the above calculations, ILUT  108  includes a look-up table (LUT) for each of the I and Q branches which maps one mean error and one extended symbol basis to one original basis. ILUT  108  may be used to perform signal compensation on a data transmission payload. It may be used pre-compensate the payload by deducing the mean error from the distorted symbol chips or post-compensate the payload through mapping the received distorted symbol chips back to the original symbol basis. 
     In embodiments, system  100  may be use dual polarization Symbols  250  as shown in  FIG.  2 B  are S Xi =XI i +jXQ i  and S Yi =YI i +jYQ i .  FIG.  2 B  illustrates method  200 B for training an ILUT  108  in a dual polarization system. 
       FIG.  2 B  is a flow diagram illustrating a method  200 B of training ILUT  108  in a dual polarization system. A series of training symbols  250  are received at receiver  106 . Training symbols  250  have the form are S Xi =XI i +jXQ i  and S Yi =YI i +jYQ i . representing a QAM signal, where subscript i indexes and differentiates each training symbol of training symbols  250 . 
     Step  252  includes normalizing training symbols  250 . In an example of step  252 , training symbols  250  that were processed at different parts of transmission link  104  are normalized to the same unified amplitude reference. 
     Step  254  includes re-arranging the signal into a matrix of symbol chips according to the extended signal basis in ILUT  108 . To reduce computational complexity, the pre- or post-compensation is only applied to one-dimensional real-valued signals. As noted above, embodiments disclosed herein may be used with coherent or non-coherent systems, and with single or dual polarization. For purposes of illustration, a coherent dual-polarization system is not described. Training symbols  250  are decomposed in to four real-valued coefficients on the dimensions of XI, XQ, YI, and YQ, where X and Y denote two orthogonal polarizations; I and Q denote two orthogonal phases. In embodiments, an M-QAM signal with √{square root over (M)} levels in the I or Q tributary and the size of the basis as L, the total number of basis in one dimension would be K=(√{square root over (M)}) L  . In total, a dual polarization format includes four LUTs covering XI, XQ, YI, and YQ and each LUT contains K units. Embodiments discussed herein refer to 64-QAM where M=64, L=4, and K=4096. These embodiments are for purposes of illustration only and other orders of modulation, and values of K and L, may be used. 
     In embodiments, an LUT in each tributary includes (i) the mean error and (ii) the extended symbol basis. The extended symbol basis is an expansion of an original standard symbol basis s k =[S k,1 , S k,2 , S k,3 , S k,4 ] through the incorporation of channel distortions, where k=1: K and S k,i ∈{−√{square root over (M)}+1, −√{square root over (M)}+3, . . . , √{square root over (M)}−3, √{square root over (M)}−1}. In embodiments, the extended symbol basis is calculated from symbol chips composed from the real-valued components XI i , XQ i , YI i  and YQ i  of training symbols  250 . A structure of the symbol chip is shown in  FIG.  2 B  at XI branch array  256 , XQ branch array  266 , YI branch array  276  and YQ branch array  286 , where each row represents a symbol chip that is stored in the respective array. As discussed above for  FIG.  2 A , each symbol chip is composed of n consecutive pulse-amplitude-modulated (PAM) symbols with either I or Q in one polarization and one PAM symbol in the another I/Q tributary of the same polarization. The general forms for a symbol chip are
 
 c   XI =[ XI   i−(n−1)/2   , . . . , XI   i   , . . . , XI   i+(n−1)/2   , XQ   i ],   (9)
 
 c   XQ =[ XQ   i−(n−1)/2   , . . . , XQ   i   , . . . , XQ   i+(n−1)/2   , XI   i ],   (10)
 
 c   YI =[ YI   i−(n−1)/2   , . . . , YI   i   , . . . , YI   i+(n−1)/2   , YQ   i ] and   (11)
 
 c   YQ =[ YQ   i−(n−1)/2   , . . . , YQ   i   , . . . , YQ   i+(n−1)/2   , YI   i ].   (12)
 
for signals distributed on XI, XQ, YI, and YQ branches, respectively. Although a symbol chip composed of three symbols is illustrated and discussed herein, embodiments are not limited to three symbols. For example, a symbol chip composed from five symbols or any odd number of symbols may be used. For any odd number of symbols, the last coefficient and the center coefficient in the odd number of symbols will correspond to the I and Q components of one original QAM symbol.
 
     Such an interweaved configuration of the symbol chips brings several benefits. For one, it accounts for the memory effect in the residual distortions since adjacent symbols are included in a symbol chip. Additionally, the interweaved configuration takes account of the distortion penalty across I and Q tributaries since one reference symbol from the opposite I/Q tributary is introduced. 
     Steps  258 ,  268 ,  278  and  288  include classification of symbol chips. In an example of steps  258 ,  268 ,  278  and  288 , after re-arranging the received symbols  250  into arrays  256 ,  266 ,  276  and  286  of symbol chips, the next step is to classify the chips into different clusters. A cluster, denoted as C k , includes all the symbol chips with the minimal Euclidean distance to the kth Extended Symbol Basis. It is worth noticing that the classification could be blind or with the knowledge from training. When blind classification is used, a decision feedback scheme is needed to update the value of different extend symbol basis. 
     Steps  260 ,  270 ,  280  and  290  include calculating errors between each symbol chip and the original basis. In an example of steps  260 ,  270 ,  280  and  290 , the errors between the symbol chip and the original basis in the dimensions of XI, XQ, YI, and YQ are calculated using the equations (13)-(16):
 
 e _ XI   k,l   =S   k,2   −XI   l   (13)
 
 e _ XQ   k,m   =S   k,2   −XQ   m   (14)
 
 e _ YI   k,p   =S   k,2   −YI   p   (15)
 
 e _ YQ   k,q   =S   k,2   −YQ   q .   (16)
 
where l, m, p and q are indices into the symbol chips within the cluster.
 
     Steps  218  and  220  include calculating a mean error for each cluster. In an example of steps  218  and  220 , the mean error for each cluster is calculated for each cluster using the equations (17)-(20):
 
 E _ XI   k =mean l ( e _ XI   k,l )   (17)
 
 E _ XQ   k =mean m ( e _ XQ   k,m )   (18)
 
 E _ YI   k =mean p ( e _ YI   k,p )   (19)
 
 E _ YQ   k =mean q ( e _ YQ   k,q )   (20)
 
     The kth extended symbol basis of one dimension can be obtained through calculating the vector average for all the symbol chips contained by the kth cluster in the same dimension using equations (21)-(24): 
     
       
         
           
             
               
                 
                   
                     
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     Based on the above calculations, ILUT  108  includes a look-up table (LUT) for each tributary which maps one mean error and one extended symbol basis to one original basis. The ILUT  108  may be used to perform signal compensation on a data transmission payload. It may be used pre-compensate the payload by deducing the mean error from the distorted symbol chips or post-compensate the payload through mapping the received distorted symbol chips back to the original symbol basis. 
       FIGS.  3 A- 3 C  are block diagrams of an implementation of ILUT  108  module for signal pre- and post-compensations in coherent signal generation and recovery procedures. Although specific components are shown, these are for purposes of illustration and similar components that accomplish the transmission and reception of data over a transmission link may be used. Although various components are shown as separate blocks, this is not limiting and some or all components may be combined into fewer or separated into components. In embodiments, the block diagrams of  FIGS.  3 A- 3 C  may be implemented in one or more processing elements, one or more digital signal processors adapted to execute instructions stored in a non-transitory computer readable medium to perform the methods disclosed herein. In further embodiments, any of the methods and processes described herein may also be performed in a chip-level device, such as an ASIC (application-specific integrated circuit) or an FPGA (field-programmable gate array), for example. 
       FIG.  3 A  is a block diagram of a representative signal generation apparatus in, for example, transmitter  102 . Binary data generation component  302  provides binary data for transmission over transmission link  312 , which is mapped into vector symbols in a modulation component  304 . In embodiments, pre-compensation is performed using components  306  and  308 . Clustering component  306  clusters the vector symbol chips into different basis categories using the original-basis list provided by the ILUT  108 . Pre-distortion component  308  adjusts the coefficients of each symbol chip by deducing the mean error from every second symbol in each chip, where the mean error is accessed from ILUT  108 . Pre-distorted symbols are further processed by representative component  310  before being sent over transmission link  312 . As explained above, embodiments disclosed herein are not limited a particular transmission medium or format. 
       FIG.  3 B  is a block diagram of a representative signal recovery apparatus in, for example, receiver  106 . Receiving component  314  receives a signal from transmission link  312 . Depending on the type of transmission link, the received signal is further processed to recover the signal by component  316 . Following signal recovery, the received symbols are arranged into an array of chips and sent to decision stage  318 . Decision stage  318  includes a 4-tap maximum likelihood sequence detector (MLSD) that executes a maximum likelihood detection (MLD) algorithm to find the minimal Euclidean distance between the received symbol chip and extended symbol basis. The list of extended symbol basis may be provided by ILUT  108 . 
     In embodiments, 4-tap MLSD  320  may also be used as a pre-decision stage in a decision feed-forward signal post compensation as shown in  FIG.  3 C . After signal recovery component  316 , MLSD  320  executes MLD algorithm to find the minimal Euclidean distance between the received symbol chip and extended symbol basis as a pre-decision, then the corresponding mean error listed in ILUT  108  is deducted from the second element of each symbol chip in post-compensation component  322  before it is sent to final decision stage  318 . Compared with ILUT enhanced decisioning, ILUT assisted post-compensation has a higher computational complexity but it may be used in scenarios where constellations have to be used such as error vector magnitude (EVM) evaluation and strength of distortion estimation. 
     In a coherent optical system, because of the blind DSP and disturbance from phase noise, signal recovery penalties may be stronger than in intensity-modulation and direct detection schemes. In many cases, there are some residual distortions on the symbols, so the ILUT  108  is provided to remove these distortions. Unlike an LUT that includes coefficients from only one tributary and thus, only compensates for one-dimensional distortions, the system and method disclosed herein combines the symbols from both I and Q tributaries, so it can be used to rectify two-dimensional signal degradations. A transmission link may be subject to many different types of distortions, and these result in different unique distribution patterns of the errors.  FIGS.  4 A- 4 D  are graphs illustrating representative mean-error distribution patterns while using ILUT  108 . A 64-QAM modulation format is applied and the signal-to-noise ratio is set around 25 dB. For the nonlinear distortions, a sinusoidal shape transfer function S out =sin(0.6 S in ) is introduced in both I and Q dimensions. IQ imbalance is introduced as
 
 S   out =real( S   in )+ j α×imag( S   in ),   (25)
 
where α=0.8 is the imbalance coefficient. The quadrature error is introduced as
 
                       S     o   ⁢   u   ⁢   t       =       S     o   ⁢   u   ⁢   t       -       d   2     ⁢       (     1   +   j     )     [       real   (     S     i   ⁢   n       )     +     imag   ⁡   (     S     i   ⁢   n       )       ]           ,           (   26   )               
where d=0.2 is the quadrature mismatch coefficient.
 
     The mean-error distributions versus index for the regular signals without distortions is shown in  FIG.  4 A . The mean-error distributions under the influence of nonlinear distortions, IQ imbalance, and quadrature error are shown in  FIGS.  4 B- 4 D , respectively, where periodic patterns on the mean-error distributions are observed. In embodiments, the unique distributions of the mean errors under the influence of different factors are evaluated using machine learning algorithms to recognize and quantize the various channel penalties. This information may be used to evaluate the system performance and proactively impose corresponding pre-distortion or post-distortion techniques at the transmitter or receiver site respectively. 
     ILUT  108  is effective to restore symbol constellations from two dimensional distortions.  FIGS.  5 A,  5 C,  5 E and  5 G  illustrate signal constellations in a 64-QAM modulation scheme that are affected by residual inter-symbol interference, nonlinear distortions, IQ imbalance, and quadrature error, respectively. The rectified constellations after applying ILUT post compensation as shown in  FIG.  3 C  are also displayed in  FIGS.  5 B,  5 D,  5 F and  5 H , respectively. These figures show that the distorted constellations of  FIGS.  5 A,  5 C,  5 E and  5 G  have been transformed into clear and balanced constellations using ILUT  108 . 
       FIGS.  6 A- 6 D  are graphs illustrating post compensation using ILUT  108 . Received data captured from an optical modulation analyzer in dual-polarization 64-QAM systems suffered from residual channel distortions. The graphs in  FIGS.  6 A- 6 D  plot bit-error rates (BER) at baud rates of 4-GBd and 60-GBd. BER without using an LUT is shown with square data points. BER with a prior art LUT-based method with a 3-tap memory depth is shown with circle data points. BER with 4-tap ILUT  108  as disclosed herein is shown with triangle data points. It can be observed that, for both bit-error rates (BER) and error-vector magnitude (EVM), 4-tap ILUT  108  performs better than the 3-tap LUT. There could be several reasons for this result. For one, 4-tap ILUT  108  greatly expands the possible scenarios of data combinations. Additionally, by interweaving the symbols from the opposite I/Q components, the performance is better against two-dimensional distortions including nonlinear effect, IQ imbalance, and quadrature errors. 
     Changes may be made in the above methods and systems without departing from the scope hereof. For example, various methods and systems disclosed herein may incorporate machine learning or neural networks. It should thus be noted that the matter contained in the above description or shown in the accompanying drawings should be interpreted as illustrative and not in a limiting sense. Herein, and unless otherwise indicated: (a) the adjective “exemplary” means serving as an example, instance, or illustration, and (b) the phrase “in embodiments” is equivalent to the phrase “in certain embodiments,” and does not refer to all embodiments. The following claims are intended to cover all generic and specific features described herein, as well as all statements of the scope of the present method and system, which, as a matter of language, might be said to fall therebetween.