CN113300995A - Channel estimation algorithm for IM/DD-OFDM/OQAM-PON system - Google Patents

Channel estimation algorithm for IM/DD-OFDM/OQAM-PON system Download PDF

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CN113300995A
CN113300995A CN202110634705.9A CN202110634705A CN113300995A CN 113300995 A CN113300995 A CN 113300995A CN 202110634705 A CN202110634705 A CN 202110634705A CN 113300995 A CN113300995 A CN 113300995A
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oqam
ofdm
channel estimation
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symbol
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梁思远
王雪粉
赵芳利
赵峰
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Xian University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
    • H04L27/2695—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation with channel estimation, e.g. determination of delay spread, derivative or peak tracking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2668—Details of algorithms
    • H04L27/2673—Details of algorithms characterised by synchronisation parameters
    • H04L27/2675—Pilot or known symbols
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
    • H04L27/2691—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation involving interference determination or cancellation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2697—Multicarrier modulation systems in combination with other modulation techniques
    • H04L27/2698—Multicarrier modulation systems in combination with other modulation techniques double density OFDM/OQAM system, e.g. OFDM/OQAM-IOTA system

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Abstract

The invention provides a channel estimation algorithm for an IM/DD-OFDM/OQAM-PON system, which is characterized in that the channel estimation is carried out for the first time by an interference approximation algorithm to complete initialization, then the channel estimation performance is improved by continuous recursion, the influence of IMI on the system performance can be effectively inhibited, the system error rate is optimized, and an IAM (inter-frequency interference model) can utilize a pseudo-random OFDM/OQAM pilot frequency symbol to avoid the higher PAPR (peak-to-average power ratio) of a signal. The RLS channel estimation algorithm of the invention uses the pseudo data reconstructed in real time at the receiver end as the pseudo pilot frequency, thus completing the RLS self-adaptive channel estimation. The invention can obtain better channel estimation precision.

Description

Channel estimation algorithm for IM/DD-OFDM/OQAM-PON system
Technical Field
The invention belongs to the field of Optical communication, and relates to an algorithm for realizing channel estimation by Recursive Least Squares (RLS) in a Passive Optical Network (PON) system of Orthogonal Frequency Division Multiplexing/Offset Quadrature Amplitude Modulation (OFDM/OQAM) of Direct Detection (IM/DD).
Background
Compared with the OFDM, the OFDM/OQAM relaxes the complex domain orthogonality condition to the real domain, and improves the spectrum utilization rate by introducing a set of shaping filters with good Time Frequency focusing (TFL) characteristics instead of rectangular filters, and because the out-of-band attenuation of the shaping filter function in both the Time domain and the Frequency domain is more ideal, the OFDM/OQAM system has stronger ability to resist Inter Symbol Interference (ISI) and Inter Carrier Interference (ICI).
OFDM/OQAM relaxes the orthogonality condition between subcarriers from complex domain orthogonality to real domain orthogonality, and when signals are transmitted in a channel, Imaginary Interference (IMI) may be generated, which affects the accuracy of restoring the data of a receiving end to the complex domain. The OFDM/OQAM system needs to perform channel estimation and corresponding channel equalization at the receiving end to offset the impact of the IMI.
The most commonly used channel estimation algorithm at present is mainly a Least Square (LS) algorithm, and the method is simple to implement and does not consider the interference of noise. The original RLS algorithm initial condition is initialized to be all 0, the interference of symbols around data to the RLS algorithm is not considered, and the pilot symbols of the OFDM/OQAM system are IAM-R, IAM-I, IAM-new type, have high Peak-to-Average Power Ratio (PAPR), can not effectively inhibit the influence of IMI on the system performance, and have low channel estimation precision.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a channel estimation algorithm for an IM/DD-OFDM/OQAM-PON system, which can solve the influence of IMI on the system and obtain better channel estimation precision.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
a) initial channel estimation of RLS algorithm using IAM at pilot symbols beginning of OFDM/OQAM frame
Figure BDA0003105059250000021
Where M is 0, 1., M-1, M is the number of subcarriers in each OFDM/OQAM symbol, r ism，0For received values at pilot, cm，0Is a pseudo pilot frequency;
b) initialization of Xm，0、Xm，1And
Figure BDA00031050592500000217
Xm，0＝Xm，1＝|cm，0|2，
Figure BDA0003105059250000022
wherein Xm，0、Xm，1Represents cm，0The square of the modulus of the pseudo pilot,
Figure BDA0003105059250000023
is the receiving end am，1Reconstructed OQAM symbols;
c) setting n to 2;
d) derived using a zero forcing algorithm
Figure BDA0003105059250000024
Wherein N is 0, 1s-1，NsIs the number of the baseband symbols,
Figure BDA0003105059250000025
is the real part of the complex signal,
Figure BDA0003105059250000026
is a baseband symbol am，nAn estimated value of (d);
e) by OQAM demodulator pair
Figure BDA0003105059250000027
Demodulating, and reconstructing corresponding OQAM data symbol by OQAM modulator
Figure BDA0003105059250000028
f) Use of
Figure BDA0003105059250000029
And first order time frequency domain
Figure BDA00031050592500000210
Reconstructing all of the dummy data
Figure BDA00031050592500000211
g) Is provided with
Figure BDA00031050592500000212
Finding Xm，n-1And
Figure BDA00031050592500000213
Figure BDA00031050592500000214
h) adding 1 to the value of n;
i) repeating steps d) to h) until N ═ NsStopping iteration to obtain system function
Figure BDA00031050592500000215
Thereby recovering the original data
Figure BDA00031050592500000216
Said step a) inserts only one OFDM-OQAM pilot symbol at the beginning of the frame, followed by one all-zero symbol, and the remaining symbols are used for transmitting data.
The invention has the beneficial effects that: the channel estimation is performed for the first time by an Interference Approximation Method (IAM) algorithm to complete initialization, and then the channel estimation performance is improved through continuous recursion, so that the influence of IMI on the system performance can be effectively inhibited, and the system error rate is optimized. Where IAM may utilize pseudo-random OFDM/OQAM pilot symbols to avoid high signal PAPR. The RLS channel estimation algorithm of the invention is different from the traditional idea, and the RLS self-adaptive channel estimation is completed by using the pseudo data reconstructed in real time at the receiver end as the pseudo pilot frequency.
In general OQAM, the real and imaginary parts of a respective QAM symbol are mapped to the same subcarrier of two adjacent OFDM/OQAM symbols. However, in the present invention, the real and imaginary parts are mapped to two adjacent subcarriers of the same OFDM/OQAM symbol, which only affects the recursive operation in the proposed RLS adaptive channel estimation scheme, without affecting any system performance.
Only one OFDM-OQAM pilot symbol is inserted at the beginning of the frame followed by an all-zero symbol to prevent most of the inherent IMI to the pilot. The remaining symbols are used to transmit data.
Drawings
Fig. 1 is a transmission schematic diagram of an OFDM/OQAM-PON.
Figure 2 is a diagram of neighbor symbol interference.
FIG. 3 is a diagram consisting of M sub-carriers and NsA frame structure consisting of OFDM-OQAM symbols.
Fig. 4 is a block diagram of a RLS algorithm channel estimation scheme.
Fig. 5 is a simulation configuration of an OFDM/OQAM-PON system.
FIG. 6 shows the dispersion coefficient of the fiber at 16.75ps.nm for the FDLS algorithm and the RLS algorithm-1.km-1And the BER curve of the bit error rate with the transmission distance when the line width of the laser is 0.1 MHz.
FIG. 7 shows the dispersion coefficients of the FDLS algorithm and the RLS algorithm at 16.75ps.nm, respectively, for the optical fiber-1.km-1、25ps.nm- 1.km-1Graph of BER of error rate versus transmission distance.
FIG. 8 is a graph of the error rate versus transmission distance for different laser linewidths for different fiber dispersion coefficients in the RLS algorithm, where (a) is the fiber dispersion coefficient of 16.75ps.nm-1.km-1And (b) the dispersion coefficient of the optical fiber is 25ps.nm- 1.km-1。
Detailed Description
The present invention will be further described with reference to the following drawings and examples, which include, but are not limited to, the following examples.
As shown in fig. 1, the signal is modulated on a specific subcarrier by Inverse Fast Fourier Transform (IFFT), then the information is modulated on an optical carrier by an optical intensity modulator, and the OFDM/OQAM signals on each channel are coupled and sent to an optical fiber line for transmission. After the optical signal is transmitted through a standard single mode fiber, the optical signal is received at a receiving end, and the signal is received and demodulated in an IM-DD mode. At the transmitting end, the input binary data stream is encoded into baseband symbols am，n，m＝0, 1, 2, …, M-1, where M is the number of subcarriers. After QAM mapping, extracting the real part and the imaginary part of a data symbol, converting the data into a serial data stream through IFFT (inverse fast Fourier transform) and a prototype filter bank, and further obtaining a signal sent by an OFDM/OQAM-PON (orthogonal frequency division multiplexing/optical amplitude modulation-passive optical network) system at a transmitting end, wherein the expression is
Figure BDA0003105059250000031
Wherein M is the number of subcarriers in each OFDM/OQAM symbol and is generally an even number, and NsIs the number of baseband symbols, am，nIndicating that the data symbol transmitted on the mth subcarrier in the nth OFDM/OQAM symbol has a real value. To satisfy the real-valued orthogonality condition, am，nIs extracted from the real or imaginary part of the original complex signal,
Figure BDA0003105059250000041
showing the extraction process. gm，n(t) represents the filter function at the time-frequency coordinate point (m, n), and g (t) is the basis function of the prototype filter. Upsilon is0And τ0The sub-carrier interval and the time interval of the transmitted signal of the OFDM/OQAM system are respectively satisfied
Figure BDA0003105059250000042
After transmission over optical fiber, the received OFDM/OQAM symbols at FT point (m, n) can be demodulated to
Figure BDA0003105059250000043
Wherein h (t), h (u) are optical channel responses, symbols
Figure BDA0003105059250000044
Which represents a convolution operation, is a function of,
Figure BDA0003105059250000045
is a conversion of convolution and integration, u being only oneThe variable, η (t), is Additive White Gaussian Noise (AWGN), and the maximum delay spread Δ of the channel is usually small when the PON transmits at a distance of 20-100km, and therefore the maximum delay spread Δ is obtained
Figure BDA0003105059250000046
From (3) can be obtained
Figure BDA0003105059250000047
Then (4) is brought into (2) to obtain
Figure BDA0003105059250000048
In the formula
Figure BDA0003105059250000049
In order to be a noise term, the noise term,
Figure BDA00031050592500000410
is a channel response. I ism，nIs an imaginary interference term introduced for the system and extracted from the real part or the imaginary part of the original complex signal, and when a prototype filtering function of TFL characteristic is used, the interference term Im，nThe symbols mainly come from around the FT point (m, n), called "neighbor symbols" of the time-frequency point (m, n).
In FIG. 2, the symbol set
Figure BDA00031050592500000411
Represents the "first-order neighbors" of the FT point (m, n).
As shown in fig. 3, considering that the prototype filter has excellent time-frequency localization characteristics, it is further assumed that the subcarrier spacing is reasonably distributed so that the channel is in the middle of the time
Figure BDA0003105059250000051
Remains constant within the range, then equation (5) mayTo simplify as
rm，n＝Hm，n(am，n+jum，n)+ηm，n＝Hm，ncm，n+ηm，n (6)
Wherein a ism，nRepresenting real numbers, jum，nThe representation is a purely imaginary interference term, which can be approximated as
Figure BDA0003105059250000052
cm，n＝am，n+jum，n (8)
cm，nThis is referred to as a dummy transmit symbol, and when (m, n) is located at a pilot position, it is also referred to as a "dummy pilot" symbol. ju (jet)m，nIs the sum of the weighted products of the 'neighbor symbols' around the symbol and the prototype filter function after time-frequency offset shift, the imaginary term
Figure BDA0003105059250000053
Is the interference weight of the prototype filter, the correlation degree of the OFDM/OQAM symbol at (m, n) and (p, q). In order to facilitate the analysis of the imaginary part interference, the inner product result of the prototype filter after the shift in the time-frequency domain is called as the "interference weight coefficient", and then, for a specific prototype filter, the interference weight coefficient in the range of the (m, n) neighbor symbol of the time-frequency grid point is used as the interference weight coefficient<gm，n，gp，q>May be pre-calculated. Moreover, for any prototype filter g (t), the interference weight coefficients have a specific pattern, and the interference weight coefficient matrix is
Figure BDA0003105059250000054
Here, the matrix W corresponds to an interference weight matrix in the range of "first-order neighbors" of the FT point (m, n), the vertical direction being the frequency axis and the horizontal direction being the time axis. Wherein, alpha is 0.5644, beta is 0.2393, and gamma is 0.2058.
FIG. 3 at the beginning of a frameInserting only one OFDM-OQAM pilot symbol a0Followed by an all-zero symbol a1To prevent most of the inherent IMI to the pilot. The rest symbols
Figure BDA0003105059250000055
For transmitting data, wherein an＝(a0，n，a1，n，...，aM-1，n)T，n＝0，.，...，Ns-1。
Fig. 4 is mainly composed of IAM initial channel estimation, RLS adaptive channel estimation, channel equalization, OQAM demodulation, OQAM modulation, and recombined dummy data, and by using the real-time reconstructed dummy data as dummy pilots in the RLS adaptive channel estimation, the RLS adaptive channel estimation algorithm not only improves the channel estimation performance, but also realizes high transmission efficiency.
As shown in table 1, in general OQAM, real and imaginary parts of a corresponding QAM symbol are mapped to the same subcarrier of two adjacent OFDM/OQAM symbols. However, in this context, the real and imaginary parts are mapped to two adjacent subcarriers of the same OFDM/OQAM symbol, which only affects the recursive operation in the proposed RLS adaptive channel estimation scheme, without affecting any system performance.
TABLE 1 QAM and OQAM modulation symbol Table
Figure BDA0003105059250000061
As shown in fig. 5, the polarization rotator divides one laser into two lasers in polarization directions, and the generated OFDM/OQAM signals are respectively subjected to digital-to-analog conversion (DAC), used for IM of the lasers, and then combined by a Polarization Combiner (PC). After transmission through the optical fiber, the received OFDM/OQAM signal is detected by a polarization beam splitter (PS) by two photodiodes respectively.
As shown in table 2, in the transmitter, 20Gbaud baseband OFDM/OQAM signals are generated in MATLAB, and then the virtual-real separation is converted into two analog streams by digital-to-analog converters (DACs), respectively. The OFDM/OQAM signal is converted to a Double Sideband (DSB) optical signal using a Mach-Zehnder modulator (MZM). The two optical signals are combined by the PC. An Isotropic Orthogonal Transformation Algorithm (IOTA) is used as a prototype filter and a filter bank is constructed. The pulse length of the prototype filter is 4, M is 1024, the total number of subcarriers is 256, and the number of symbols of each subcarrier is 40. And then through Standard Single Mode Fiber (SSMF). At the receiving end, the DSB optical OFDM/OQAM signal is divided by PS and detected by two photodiodes respectively. The analog signal is then converted to a discrete digital signal using an analog-to-digital converter (ADC). In MATLAB, the received electrical OFDM/OQAM signal is decoded offline.
TABLE 2 simulation System parameter Table
Parameter(s) Value of Parameter(s) Value of
Number of subcarriers 256 DAC/ADC ratio 20Gsample/s
Prototype filter IOTA Bandwidth of 20GHz
Laser output power 0dBm Transmission distance 20 30 40 50 60 70 80 90 100km
Symbol rate 20Gbaud Optical fiber dispersion 16.75ps/km/nm 25ps/km/nm
CP Length
0 Optical fiber attenuation 0.2dB/km
Modulation system 4QAM Differential group delay 0.2ps/km
Reference wavelength 1550nm Coefficient of PMD 0.5ps/km0.5
In fig. 6, as the length of the optical fiber increases, the BER gradually increases, meaning that Chromatic Dispersion (CD) and Polarization Mode Dispersion (PMD) will also increase in channel interference. After SSMF transmission, the RLS algorithm is obviously superior to the FDLS method in optimizing the channel performance. This is because the RLS algorithm can effectively overcome the dispersion caused by the channel, and improves the robustness of the channel estimation to the IMI effect. Compared with the FDLS method, the channel estimation performance of the RLS algorithm can be significantly optimized under the same transmission distance, for example, the BER of the FDLS algorithm and the RLS algorithm is 0.0731 and 0.006 respectively when the transmission distance is 30 km. Obviously, the BER of the CE algorithm based on RLS is an order of magnitude better than LS; when the transmission distance is 80km, the FDLS algorithm and the RLS algorithmThe BER of the method is 0.1020, 0.0466, respectively. Obviously, the BER of the CE algorithm of FDLS is about 45% lower than that of the RLS algorithm; when the transmission distance is 100km, the BER of the LS algorithm and the BER of the RLS algorithm are respectively 0.1508 and 0.0964, and the BER of the CE algorithm of the RLS algorithm is improved by about 60 percent compared with that of the FDLS algorithm; when the error rate of the IM/DD system is 10-1In time, the fiber transmission distance of the RLS algorithm can be increased by about 40km compared with the transmission distance of the FDLS method. The RLS can effectively suppress the IMI and can obtain more accurate channel estimation.
In fig. 7, when the dispersion coefficient of the optical fiber becomes large, the RLS algorithm proposed herein has higher channel estimation accuracy than the FDLS algorithm, for example, below 80km, the dispersion coefficient is 25ps-1.km-1The BER of the time RLS algorithm is 16.75ps.nm compared with the dispersion coefficient-1.km-1The BER of the FDLS algorithm is low, which shows that the RLS algorithm can improve the tolerance to the dispersion coefficient of the optical fiber.
In fig. 8, when the laser linewidth is enlarged, the RLS algorithm proposed herein has higher channel estimation accuracy compared to the FDLS algorithm, for example, at 40km, the BER of 0.0127 for the laser linewidth of 0.1MHZ and the BER of 0.0289 for the laser linewidth of 5MHZ, which illustrates that the RLS algorithm can improve the tolerance for the laser linewidth.
To facilitate the application of the RLS adaptive algorithm to per-subcarrier channel estimation for OFDM/OQAM systems, the following reasoning is first given.
Let the t-th observed signal at the m-th subcarrier be rm，t＝cm，tHm+ηm，tWherein t is 0, 1s-1，HmRepresenting the parameter to be estimated, cm，tAnd ηm，tRespectively representing known data and gaussian noise, the cost function of the RLS adaptive algorithm is defined as:
Figure BDA0003105059250000081
wherein 0 < lambda < 1 represents a forgetting factor (lambda is 1 in a static multipath scene, and 0 < lambda < 1 in a dynamic multipath scene). Since the fiber is a quasi-static channel, λ is 1.
Definition of rm(n)＝[rm，0，rm，1，...，rm，n]TAnd cm(n)＝[cm，0，cm，1，...，cm，n]TThe cost function can be written as
J(m，n)＝[rm(n)-cm(n)Hm]H[rm(n)-cm(n)Hm] (11)
Wherein, (.)TAnd (·)HRepresenting transpose and conjugate transpose, respectively, by using a Weighted Least Squares (WLS) algorithm, the following estimator is obtained:
Figure BDA0003105059250000082
the upper label (·)-1Representing the reciprocal operator. Definition of Xm，n＝cH m(n)cm(n), which can be calculated by a recursive method as follows:
Figure BDA0003105059250000083
| represents the modulo operator by the above formula
Figure BDA0003105059250000084
Can be further calculated as
Figure BDA0003105059250000085
According to the above formulae and Xm，nThe recursive initial condition can be obtained,
Figure BDA0003105059250000086
and Xm，0＝|cm，0|2. For the mth subchannel, the recursion formula of the RLS adaptive algorithm can be written as
Xm，n＝Xm，n-1+|cm，n|2 (15)
Figure BDA0003105059250000091
Wherein the initial condition of the recursive operation can be set to
Figure BDA0003105059250000092
(see first step of Algorithm step) and Xm，0＝|cm，0|2。
Then, by using the pseudo data reconstructed in real time in step 6 as pseudo pilot
Figure BDA0003105059250000093
And with the above theorem, the recursive formula of RLS-per-subcarrier adaptive channel estimation can be given by
Figure BDA0003105059250000094
Figure BDA0003105059250000095
Wherein the initial condition can be set to
Figure BDA0003105059250000096
(see step 1 of the Algorithm) and Xm，0＝Xm，1＝|cm，0|2. In the formula (I), the compound is shown in the specification,
Figure BDA0003105059250000097
which can be regarded as "innovation" of the FFT output signal from the FT point (m, n-1), is the channel frequency domain response estimate of m subchannels after n-1 recursions, which can be used as the channel frequency domain response for the next channel equalization in step 3.
1) Pilot symbols at the beginning of an OFDM/OQAM frame, using IAM to obtain RLS algorithmInitial channel estimation of (1):
Figure BDA0003105059250000098
wherein M is 0, 1,., M-1, as an initial value of the RLS algorithm. r ism，0For received values at pilot, cm，0Are dummy pilots.
2) Initialization of Xm，0，Xm，1And
Figure BDA0003105059250000099
Xm，0＝Xm，1＝|cm，0i and
Figure BDA00031050592500000910
wherein M is 0, 1.
Figure BDA00031050592500000911
Is the receiving end am，1Reconstructed OQAM symbols;
3) derived using zero forcing ZF algorithm
Figure BDA00031050592500000912
Figure BDA00031050592500000913
Wherein M is 0, 1, M-1, N is 0, 1, Ns-1，
Figure BDA00031050592500000914
Is the real part of the complex signal,
Figure BDA00031050592500000915
is am，nAn estimated value of (d);
4) demodulating by OQAM demodulator, and reconstructing corresponding OQAM data symbol by OQAM modulator
Figure BDA00031050592500000916
Wherein M is 0, 1, M-1, N is 0, 1, Ns-1；
5) Use of
Figure BDA00031050592500000917
And first order FT domain
Figure BDA00031050592500000918
Reconstructing all of the dummy data
Figure BDA00031050592500000919
The calculation is carried out by the formulas (7) and (8);
6) by the formula
Figure BDA0003105059250000101
And
Figure BDA0003105059250000102
finding Xm，n-1And
Figure BDA0003105059250000103
7) if N is equal to NsStopping iteration; otherwise repeating steps 3) to 6).
On the basis of the specific implementation mode, the algorithm is realized by carrying out joint simulation on Matlab and Optisystem software, the performance of the algorithm is evaluated by simulation, and the feasibility of the algorithm is analyzed and proved.

Claims (2)

1. A channel estimation algorithm for an IM/DD-OFDM/OQAM-PON system, comprising the steps of:
a) initial channel estimation of RLS algorithm using IAM at pilot symbols beginning of OFDM/OQAM frame
Figure FDA0003105059240000011
Where M is 0, 1., M-1, M is the number of subcarriers in each OFDM/OQAM symbol, r ism，0For received values at pilot, cm，0Is a pseudo pilot frequency;
b) initialization of Xm，0、Xm，1And
Figure FDA0003105059240000012
Xm，0＝Xm，1＝|cm，0|2，
Figure FDA0003105059240000013
wherein Xm，0、Xm，1Represents cm，0The square of the modulus of the pseudo pilot,
Figure FDA0003105059240000014
is the receiving end am，1Reconstructed OQAM symbols;
c) setting n to 2;
d) derived using a zero forcing algorithm
Figure FDA0003105059240000015
Wherein N is 0, 1s-1，NsIs the number of the baseband symbols,
Figure FDA0003105059240000016
is the real part of the complex signal,
Figure FDA0003105059240000017
is a baseband symbol am，nAn estimated value of (d);
e) by OQAM demodulator pair
Figure FDA0003105059240000018
Demodulating, and reconstructing corresponding OQAM data symbol by OQAM modulator
Figure FDA0003105059240000019
f) Use of
Figure FDA00031050592400000110
And first order time frequency domain
Figure FDA00031050592400000111
Reconstructing all of the dummy data
Figure FDA00031050592400000112
g) Is provided with
Figure FDA00031050592400000113
Finding Xm，n-1And
Figure FDA00031050592400000114
Figure FDA00031050592400000115
h) adding 1 to the value of n;
i) repeating steps d) to h) until N ═ NsStopping iteration to obtain system function
Figure FDA00031050592400000116
Thereby recovering the original data
Figure FDA00031050592400000117
2. The channel estimation algorithm for IM/DD-OFDM/OQAM-PON system according to claim 1, wherein said step a) inserts only one OFDM-OQAM pilot symbol at the beginning of the frame, followed by one all-zero symbol, and the remaining symbols are used for transmitting data.
CN202110634705.9A 2021-06-08 2021-06-08 Channel estimation algorithm for IM/DD-OFDM/OQAM-PON system Pending CN113300995A (en)

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