CN111030958B - Method for analyzing and processing air interface system message in NB-IoT system - Google Patents
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Abstract
The invention relates to a method for accurately and quickly retrieving system messages by an NB _ IoT system in an air interface complex electromagnetic environment, which comprises the following steps: extracting the sampled MIB baseband data, and performing Fourier transform to obtain frequency domain data; performing channel estimation through NRS to recover a transmitting end signal; performing resource de-mapping; performing de-layer mapping and pre-coding; de-QPSK modulation; performing descrambling calculation; performing rate de-matching; viterbi decoding; descrambling and CRC (cyclic redundancy check) checking; and successfully decoding to obtain the MIB message. Further determining the position of SIB1 according to MIB, extracting data and transforming to frequency domain; using NRS to estimate the channel; obtaining SIB1 information through similar processes of resource mapping, precoding and layer mapping, QPSK modulation, scrambling code, rate matching, viterbi decoding, CRC check, etc. The method for analyzing and processing the air interface system message in the NB _ IoT system has good restoration and error correction performance, and enhances the real-time performance and the practicability of algorithm implementation.
Description
Technical Field
The invention relates to the field of mobile communication research and development and testing, in particular to the field of NB _ IoT system receiving, and specifically relates to a method for analyzing and processing air interface system messages in an NB _ IoT system.
Background
The internet of things is applied to aspects of production and life, and the demands of services on network transmission rate are different. The high-rate service mainly uses 3G and 4G technologies, such as a monitoring camera and the like; medium rate services mainly use GPRS technology, such as POS machines. Low rate services are not currently satisfied by good cellular technologies, and in many cases can only be supported marginally using GPRS technology. With the development of the internet of things, low-rate services gradually become the main market development direction of the cellular internet of things in the future, and the industry generalizes the market into an LPWAN (Low Power Wide Area Network) market, that is, a Low-Power Wide Area Network. The GPRS technology has the problems of high power consumption of the terminal, insufficient coverage capability and the like, and cannot meet the market demand of the LPWAN. As the third wave of the development of the information industry, the Internet of things is applied from the concept, and the industries continuously explore and mine the maximum value of the Internet of things, so that technical power is provided for the global economic resuscitation. From the technical aspect, as an emerging technology which is widely applied in the global scope, the NB-IoT has the characteristics, and the inherent advantages provide possibility for ubiquitous terminal access of the Internet of things.
The physical layer design of NB-IoT is modified on the basis of the E-UTRAN physical layer as follows: only one PRB is used per NB-IoT carrier; the downlink only supports 15kHz subcarrier spacing in E-UTRAN; single-tone transmission (single-tone transmission) is introduced in the uplink, and 3.75kHz subcarrier spacing is additionally introduced on the basis of 15kHz subcarrier spacing. In the case of a 3.75kHz subcarrier spacing, the narrow band time slot (NB-slot) length is defined as 2ms (while the subframe and frame concepts of E-UTRAN no longer apply); multi-tone transmission (multi-tone transmission) is introduced into the uplink, and the subcarrier interval of 15kHz is supported; only normal CP is supported and only FDD is supported. The UE only supports a half-duplex mode and supports three operation modes of LTEin-band, LTEguard-band and standby.
The currently used parsing method of NB _ IoT system air interface system messages is realized through an inverse coding process, which can well realize channel estimation and data recovery under the condition of good channel environment or direct connection between points to points, but under the condition of air interface transmission, there is an air interface signal interference signal, the channel environment becomes poor, and it is difficult to correctly decode the system messages only by performing the coding process in the inverse direction due to instability of the transmission channel. The invention provides an analysis method of NB _ IoT system air interface system messages in a complex electromagnetic environment, which adopts channel estimation with interpolation, and has good restoration and error correction performances under the condition that transmitted data is interfered by noise through LMMSE channel estimation and viterbi decoding. Meanwhile, the sampling rate is reduced, and the calculation speed is increased on the basis of meeting the application requirement by adopting 128-point FFT. When the system frame number is detected in a blind mode, rate de-matching is further carried out by using redundant data, the decoding success rate is improved on the premise that the use of small hardware resource overhead is met, the real-time performance and the practicability of algorithm implementation are enhanced, and the environment using capacity is strong.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for analyzing and processing air interface system messages in an NB _ IoT system, which has the advantages of good real-time performance, high calculation speed, low resource overhead and high decoding success rate.
In order to achieve the above object, the method for parsing an air interface system message in an NB _ IoT system of the present invention is as follows:
the method for analyzing and processing the air interface system message in the NB _ IoT system is mainly characterized by comprising the following steps of:
(1) Extracting the sampled NPBCH baseband data, and performing Fourier transform to obtain frequency domain data;
(2) Performing channel estimation through NRS to recover a signal at a transmitting end;
(3) Performing resource de-mapping;
(4) Performing de-layer mapping and pre-coding according to the port number of the antenna;
(5) Demodulating each complex-valued symbol into a pair of bit data by QPSK modulation;
(6) Performing descrambling calculation;
(7) Performing rate de-matching;
(8) Performing Viterbi decoding;
(9) A CRC check is performed.
Preferably, the method further comprises the steps of:
(10) Decoding MIB, acquiring information, and judging the position of SIB1;
(11) Extracting NPDSCH baseband data, and performing Fourier transform to obtain frequency domain data;
(12) Knowing the port number of the antenna, performing channel estimation through NRS, and recovering a signal at a sending end;
(13) Decoding the SIB1 in the steps (3) to (9) in this order.
Preferably, the step (1) specifically comprises the following steps:
(1.1) sampling at the rate of 1.92MHz, removing a cyclic prefix, extracting baseband data on a subframe 0, and acquiring complete MIB data and related pilot frequency data on a time domain;
(1.2) performing 128-point Fourier transform to generate frequency domain data of the subframe.
Preferably, the step (2) specifically comprises the following steps:
(2.1) performing blind detection on the antenna port number p =2000 or p =2001, and initializing the antenna port number to be 2000;
(2.2) calculating NRS mapping positions (k, l) corresponding to antenna port numbers, extracting NRS signals, and performing channel estimation by using NRS sequences generated locally and NRS signals in received data; and performing minimum mean square error estimation, and recovering the signal at the transmitting end by using the obtained estimation matrix.
Preferably, the step (2.2) of calculating the minimum mean square error estimation channel matrix specifically includes:
the minimum mean square error estimated channel matrix is calculated according to the following formula:
wherein H LMMSE Is a linear minimum mean square error estimation channel matrix, R hh Is the channel impulse response autocorrelation matrix, beta is the constellation factor, SNR is the average signal-to-noise ratio, I is the identity matrix, H LS Is the LS channel estimation matrix.
Preferably, the step (3) specifically includes the following steps:
(3.1) eliminating resource lattices occupied by reference signals LTE-CRS and NRS, and sequentially taking out complex value symbols from positions (k, l) of the resource lattices;
(3.2) carrying out blind detection on the 8 th frame of the frame in 64 frames of the NPBCH period, and sequentially taking the 80ms sub-block indexes from 0 to 7; then divided by the coefficient theta corresponding to the sub-block index of 80ms f (i) To obtain 100A complex-valued symbol.
Preferably, the step (7) specifically comprises the following steps:
(7.1) dividing 200-bit data into a group of every 50 bits, rearranging the previous three groups according to the block set in the step (3.2) to form 150 bits with a correct sequence, and performing rate de-matching;
(7.2) if the data fails in CRC check and the CRC is close to a correct value, selecting a fourth group of 50 bits to replace the redundant data of the previous 3 groups, and performing de-rate matching operation again; otherwise, continue step (8).
Preferably, the step (8) specifically comprises the following steps:
(8.1) initializing a state register, a state transfer register and a path register;
(8.2) circulating each state after the initial state, and calculating the Hamming distance between the state and the first two possible states;
(8.3) comparing and selecting a path with a smaller Hamming distance until finding out a path with a maximum probability value;
and (8.4) backtracking the path transfer to generate decoding data.
Preferably, the step (10) specifically comprises the following steps:
(10.1) extracting a system frame number, a deployment mode and SIB1 deployment information from the MIB, acquiring the information of the SIB1 such as the repetition times in 2560ms, the initial frame number containing the SIB1 information, the start from the number of symbols in a subframe, the bit length of the SIB1 transmission block and the like, and calculating that the currently read data contains several groups of decodable SIB1 data;
(10.2) a group of complete data is taken to start decoding, whether the CRC passes the check or not is judged, and if yes, the decoding is successful; otherwise, the next set of data is taken to decode SIB1.
The method for analyzing and processing the air interface system message in the NB _ IoT system adopts channel estimation with interpolation, and has good restoration and error correction performance under the condition that transmitted data is interfered by noise through LMMSE channel estimation and viterbi decoding. Meanwhile, the sampling rate is reduced, and the calculation speed is increased on the basis of meeting the application requirement by adopting 128-point FFT. When the system frame number is blindly detected, for a signal with a CRC value close to the correct value, rate de-matching is carried out again by adopting redundant data, so that the low hardware resource overhead and the decoding success rate of a larger degree are balanced, and the MIB message is independently decoded on any frame on any 80ms subblock, thereby enhancing the real-time performance and the practicability of algorithm realization and having strong environment use capability.
Drawings
Fig. 1 is a flowchart of a method for parsing an air interface system message in an NB _ IoT system according to the present invention.
FIG. 2 is a diagram of NRS time-frequency resource mapping using different antenna ports according to the present invention.
FIG. 3 is a diagram illustrating MIB message parsing according to the present invention.
FIG. 4 is a schematic diagram of an application of MIB empty parsing according to the present invention.
Detailed Description
In order to more clearly describe the technical contents of the present invention, the following further description is given in conjunction with specific embodiments.
The method for analyzing and processing the air interface system message in the NB _ IoT system comprises the following steps:
(1) Extracting the sampled NPBCH baseband data, and performing Fourier transform to obtain frequency domain data;
(1.1) sampling at the rate of 1.92MHz, removing a cyclic prefix, extracting baseband data on a subframe 0, and acquiring complete MIB data and related pilot frequency data on a time domain;
(1.2) performing 128-point Fourier transform to generate frequency domain data of the subframe;
(2) Performing channel estimation through NRS to recover a transmitting end signal;
(2.1) performing blind detection on the antenna port number p =2000 or p =2001, and initializing the antenna port number to be 2000;
(2.2) calculating NRS mapping positions (k, l) corresponding to antenna port numbers, extracting NRS signals, and performing channel estimation by using NRS sequences generated locally and NRS signals in received data; performing minimum mean square error estimation, and recovering a signal of a transmitting end by using an obtained estimation matrix;
(3) Performing resource de-mapping;
(3.1) eliminating resource lattices occupied by reference signals LTE-CRS and NRS, and sequentially taking out complex value symbols from the positions (k, l) of the resource lattices;
(3.2) carrying out blind detection on the 8 th frame of the frame in the 64 frames of the NPBCH period, and sequentially taking the 80ms sub-block indexes from 0 to 7; then divided by the coefficient theta corresponding to the sub-block index of 80ms f (i) Obtaining 100 complex value symbols;
(4) Performing de-layer mapping and pre-coding according to the port number of the antenna;
(5) de-QPSK modulating, demodulating each complex value symbol into a pair of bit data;
(6) Performing descrambling calculation;
(7) Performing rate de-matching;
(7.1) dividing 200-bit data into a group of every 50 bits, rearranging the previous three groups according to the block set in the step (3.2) to form 150 bits with a correct sequence, and performing rate de-matching;
(7.2) if the data fails in CRC check and the CRC is close to a correct value, selecting a fourth group of 50 bits to replace the redundant data of the previous 3 groups, and performing de-rate matching operation again; otherwise, continue step (8).
(8) Performing Viterbi decoding;
(8.1) initializing a state register, a state transfer register and a path register;
(8.2) circulating each state after the initial state, and calculating the Hamming distance between the state and the first two possible states;
(8.3) comparing and selecting a path with a smaller Hamming distance until finding out a path with a maximum probability value;
(8.4) backtracking the path transfer to generate decoding data;
(9) Performing CRC;
(10) Decoding MIB, acquiring information, and judging the position of SIB1;
(10.1) extracting a system frame number, a deployment mode and SIB1 deployment information from the MIB, acquiring the information of the SIB1 such as the repetition times in 2560ms, the initial frame number containing the SIB1 information, the start from the number of symbols in a subframe, the bit length of the SIB1 transmission block and the like, and calculating that the currently read data contains several groups of decodable SIB1 data;
(10.2) a group of complete data is taken to start decoding, whether the CRC passes the check or not is judged, and if yes, the decoding is successful; otherwise, taking down a group of data to decode SIB1;
(11) Extracting NPDSCH baseband data, and performing Fourier transform to obtain frequency domain data;
(12) Knowing the port number of the antenna, performing channel estimation through NRS, and recovering a signal at a sending end;
(13) Decoding the SIB1 in the steps (3) to (9) in this order.
As a preferred embodiment of the present invention, the step (2.2) of calculating NRS mapping positions (k, l) specifically includes:
the NRS mapping position (k, l) is calculated according to the following formula:
k＝6m+(v+v shift )mod6
where l is the OFDM symbol index in one slot, k is the subcarrier index in the frequency domain, p is the corresponding antenna port number, and m is 0,1.v + v shift Is mainly related to cell ID and represents the offset of different cells in frequency domain.
As a preferred embodiment of the present invention, the minimum mean square error estimation channel matrix calculation formula in step (2.2) is as follows:
wherein H LMMSE Is a linear minimum mean square error estimation channel matrix, R hh Is the channel impulse response autocorrelation matrix, β is the constellation factor (QPSK, β =1, 1694am, β = 17/9), SNR is the average signal-to-noise ratio, I is the identity matrix,H LS is the LS channel estimation matrix.
As a preferred embodiment of the present invention, the complex-valued symbol d after layer de-mapping and precoding in the step (4) is (0) Specifically, the calculation is based on the following formula:
for antenna port 2000:
for antenna port 2001:
wherein y (i) is output, and y (i) = [ y (0) (i) y (1) (i)] T ，
As a preferred embodiment of the present invention, the calculating of the descrambling code in step (6) specifically includes:
descrambling calculation is performed according to the following formula:
wherein,
for modulated bits, b (i) is descrambled bits, and c (i) is scrambled bits.
The specific implementation of the invention can be used in an algorithm for obtaining system messages by NB _ IoT system air interface signals, and relates to the field of mobile communication research and development and testing. Aiming at the problem that an NB _ IoT system air interface analyzes system messages, baseband data containing NPBCH channel data are extracted, converted into a frequency domain through 128-point Fourier transform, channel estimation is carried out through NRS and locally generated NRS data, and an NPBCH sending end signal is restored after channel equalization and correlation processing. Then, the correct 34-bit MIB data is obtained through the processes of resource mapping, precoding and layer mapping, demodulation, descrambling, rate matching, viterbi decoding, descrambling, CRC (cyclic redundancy check) and the like. Then, the SIB1 message of the NPDSCH channel is further solved according to the received MIB information, the time-frequency position of the SIB1 is determined, the baseband data of the SIB1 is obtained and converted to the frequency domain through Fourier transform, channel estimation and equalization are performed through NRS, the NPDSCH sending end information is obtained, and the SIB1 message is obtained through resource de-mapping, pre-coding and layer mapping, demodulation, descrambling, rate de-matching, viterbi decoding and CRC checking. The invention provides a method for accurately and quickly retrieving system messages in an air interface complex electromagnetic environment.
The invention discloses a method for analyzing and processing air interface system messages in an NB _ IoT system, which comprises the following steps:
1) After the cell search, the time-frequency synchronization between the cell and the base station is completed, and the cell ID and the position of the frame in 8 frames are also known. At this time, the baseband data on the sub-frame 0 after sampling is extracted to ensure that complete MIB data and related pilot data on the time domain can be acquired, and the frequency domain data of the sub-frame is generated by performing 128-point fourier transform.
2) And using NRS to estimate the channel and recovering the signal at the transmitting end. The mapping position of the NRS on the resource grid is related to an antenna port number, a cell ID, and the like. NRS mapping position (k, l) refers to formula (1):
where l is the OFDM symbol index in one slot, k is the subcarrier index in the frequency domain, and for a normal CP,
p is the corresponding antenna port number. v + v shift The value of (a) is mainly related to the cell ID and indicates the offset of different cells in the frequency domain. When the antenna port is unknown, blind detection is carried out on the antenna port number p =2000 or p =2001, and the initialized antenna port number is 2000. And selecting NRS and locally generated NRS signals at corresponding positions according to the assumed antenna ports to carry out channel estimation.
In order to obtain channel estimation of the whole NPBCH time-frequency resource grid, the channel impulse response of the resource grid where the NRS is located on the subframe 0 is obtained by a complex number point division method, a complete NPBCH channel estimation matrix is formed through linear interpolation of inter-row channel impulse responses and linear interpolation of inter-symbol channel impulse responses, and then minimum mean square error estimation is carried out, so that NPBCH data of a sending end are recovered. The minimum mean square error estimation channel matrix calculation reference formula (2):
wherein H LMMSE Is a linear minimum mean square error estimation channel matrix, R hh Is the channel impulse response autocorrelation matrix, β is the constellation factor (QPSK, β =1, 1694am, β = 17/9), SNR is the average signal-to-noise ratio, I is the identity matrix, H is the average signal-to-noise ratio LS Is the LS channel estimation matrix.
3) And (5) solving the resource mapping. When the resource mapping is removed, firstly, the resource grids occupied by the reference signals LTE-CRS and NRS are removed, and the resource grids occupied by the first three OFDM symbols and the reference signals are removed, wherein when the position of the reference signals is calculated, the antenna ports 0-3 of the CRS are assumed to exist, the antenna ports 0 and 1 of the NB-IoT are assumed to exist, and the frequency offset v of the CRS is calculated shift For using at the same time
Instead of the former
Then, the complex symbols are sequentially taken out from (k, l) in the order of increasing k first and l second from the 4 th symbol. When decoding SIB1, the symbols are known due to the number of antenna ports and the number of antenna portsThe starting position is known, the resource lattices occupied by the reference signals LTE-CRS and NRS are directly removed, and the complex value symbols are sequentially taken out from the 1 st symbol or the fourth symbol.
The MIB is transmitted in an NPBCH channel, the data transmission period of the NPBCH channel is 64 frames, one piece of MIB information outputs 1600bit data after rate matching, then the MIB information is divided into 8 self-decodable subblocks with equal length, each subblock is 200bit and is mapped to a frame which meets SFNmod8=0 in 64 frames, and the data of the frame is copied in the next 7 frames. Therefore, blind detection is carried out on the 8 th frame of the frame in the 64 frames of the NPBCH period, and the 80ms sub-block indexes are sequentially valued from 0 to 7; divided by the coefficient theta corresponding to the hypothetical 80ms sub-block index f (i) Resulting in 100 complex-valued symbols.
4) De-precoding and layer mapping. NPBCH uses a maximum of 2 antenna ports, possibly using antenna ports 2000 and 2001. When 1 antenna port is adopted, the signal can be regarded as being directly transmitted. When 2 antenna ports are used, a transmission diversity scheme is employed. The output is y (i) = [ y = (0) (i) y (1) (i)] T Then the complex-valued symbol d after de-precoding and layer mapping (0) Reference may be made to equations (3) and (4). NPDSCH and NPBCH including SIB1 use the same antenna port.
For antenna port 2000:
for antenna port 2001:
wherein,
5) And (4) demodulating. de-QPSK modulation, demodulating every two complex-valued symbols into a pair of bit data
The conversion relationship is as in the following table.
6) And (5) descrambling. Generating a 1600-bit pseudo-random scrambling sequence, and selecting 200 bits from the 80ms sub-blocks in the NPBCH period to descramble according to the frame assumed in the step 3). When the SIB1 is released, the unwinding is repeated a few times within the cycle. The descrambling formula refers to formula (5).
Wherein,
for modulated bits, b (i) is descrambled bits, and c (i) is scrambled bits.
7) And (5) rate matching is carried out. For the descrambled 200 bits, adjusting the position of every 50 bits after descrambling according to the sub-block of 80ms of the frame in the NPBCH period assumed in the step 3), taking out 150 bits in the correct sequence, and performing rate de-matching. And calculating the position of the NULL symbol, inserting the NULL symbol, then putting a matrix with 32 columns according to the columns, performing inverse intercolumn replacement, taking out the matrix according to rows, and separating the data to obtain 3 groups of 50-bit data for de-channel coding. If the data fails after CRC check and the CRC is close to a correct value, a 4 th group of 50 bits is selected to replace a certain group of redundant data in the previous 3 groups, and the rate de-matching operation is carried out again to improve the decoding success rate.
8) Viterbi decoding. And decoding by a viterbi algorithm to obtain 50bit data before tail-biting convolutional coding with the constraint length of 7 and the code rate of 1/3. The idea of decoding is to find a maximum likelihood decoding based on the received sequence. Initializing a state register, a state transfer register and a path register, circulating each state after the initial state, calculating the Hamming distance between the state and the previous two possible states, comparing and selecting a path with smaller Hamming distance until finding out a path with the maximum probability value, and finally backtracking the path transfer to generate decoding data. viterbi decoding can achieve better error correction capability.
9) A CRC check is performed. The 50-bit data contains 34-bit mib data and 16-bit CRC check data, and the 16-bit CRC check bits are scrambled by different scrambling sequences. Firstly, according to the number of the antenna ports supposed before, different scrambling codes are selected, and the CRC check bit is subjected to descrambling operation. If the number of the antenna ports assumed in the step 2) is 2000, sequentially selecting 1 and 2 antenna ports for descrambling at the moment; if the number of the antenna ports assumed in step 2) is 2001, only 1 antenna port needs to be selected for descrambling at this time. The descrambling formula may refer to formula 5). Then using the descrambled 16-bit CRC data to judge whether the data is decoded correctly, namely, by judging a polynomial
a 0 D A+15 +a 1 D A+14 +...+a A-1 D 16 +p 0 D 15 +p 1 D 14 +...+p 14 D 1 +p 15 Whether or not it can be represented by a 16-bit polynomial g CRC16 (D)＝[D 16 +D 12 +D 5 +1]And (4) removing completely. If the remainder is 0, the 34 bits of MIB data are successfully decoded by checking.
The NB _ IoT system air interface system message parsing method, after the step 9), further includes: a method for analyzing SIB1 system messages of an NB _ IoT system air interface PDSCH channel comprises the following steps:
10 Decoding the MIB, obtaining the information, and determining the location of SIB1. And extracting information such as system frame number, deployment mode, SIB1 deployment and the like from the MIB. Acquiring the information of the SIB1 in 2560ms, the starting frame number containing SIB1 information, the starting from the several symbols in the subframe, the bit length of the SIB1 transmission block and the like, and calculating the data currently read containing several groups of decodable SIB1 data; a complete set of data is taken to start decoding. If the CRC does not pass, taking down a group of data decoding SIB1;
11 Extracting NPDSCH baseband data, and performing Fourier transform to obtain frequency domain data;
12 Antenna port number is known, and a signal at the transmitting end is recovered by performing channel estimation through NRS. At the moment, the number of the antenna ports and the number of the antenna ports are known, and channel estimation is directly carried out according to the antenna ports detected blindly;
13 Carry out the decoding of SIB1 from step (3) to step (9) in order.
The method for analyzing and processing the air interface system message in the NB _ IoT system adopts the channel estimation with interpolation, and has good restoration and error correction performances under the condition that the transmitted data is interfered by noise by LMMSE channel estimation and viterbi decoding. Meanwhile, the sampling rate is reduced, and the calculation speed is increased on the basis of meeting the application requirement by adopting 128-point FFT. When the system frame number is detected in a blind mode, rate de-matching is further carried out by using redundant data, the decoding success rate is improved on the premise that the use of small hardware resource overhead is met, the real-time performance and the practicability of algorithm implementation are enhanced, and the environment using capacity is strong.
In this specification, the invention has been described with reference to specific embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
Claims (8)
1. A method for analyzing and processing air interface system messages in an NB _ IoT system is characterized by comprising the following steps:
(1) Extracting the sampled NPBCH baseband data, and performing Fourier transform to obtain frequency domain data;
(2) Performing channel estimation through NRS to recover a signal at a transmitting end;
(3) Performing resource de-mapping;
(4) Performing de-layer mapping and pre-coding according to the port number of the antenna;
(5) de-QPSK modulating, demodulating each complex value symbol into a pair of bit data;
(6) Performing descrambling calculation;
(7) Performing rate de-matching;
(8) Performing Viterbi decoding;
(9) Performing CRC;
the step (4) is specifically as follows:
when the antenna ports 2000 and 2001 are employed, the complex-valued symbol d after de-precoding and layer mapping is obtained according to the following formula (0) ：
For antenna port 2000:
for antenna port 2001:
wherein y (i) is output, and y (i) = [ y (0) (i)y (1) (i)] T ，
The step (2) specifically comprises the following steps:
(2.1) performing blind detection on the antenna port number p =2000 or p =2001, and initializing the antenna port number to be 2000;
(2.2) calculating NRS mapping positions (k, l) corresponding to the antenna port numbers, extracting NRS signals, and performing channel estimation by using NRS sequences generated locally and the NRS signals in the received data; and performing minimum mean square error estimation, and recovering the signal at the transmitting end by using the obtained estimation matrix.
2. The method for parsing over the air interface system message in the NB _ IoT system according to claim 1, wherein the method further comprises:
(10) Decoding MIB, acquiring information, and judging the position of SIB1;
(11) Extracting NPDSCH baseband data, and performing Fourier transform to obtain frequency domain data;
(12) Knowing the port number of an antenna, performing channel estimation through NRS, and recovering a signal at a transmitting end;
(13) And (4) decoding the SIB1 in the steps (3) to (9) in sequence.
3. The method for analyzing and processing air interface system messages in an NB _ IoT system according to claim 1, wherein the step (1) specifically includes the following steps:
(1.1) sampling at the rate of 1.92MHz, removing a cyclic prefix, extracting baseband data on a subframe 0, and acquiring complete MIB data and related pilot frequency data on a time domain;
and (1.2) performing 128-point Fourier transform to generate frequency domain data of the subframe.
4. The method for parsing an air interface system message in an NB _ IoT system according to claim 1, wherein the step (2.2) of calculating a minimum mean square error estimation channel matrix specifically comprises:
the minimum mean square error estimation channel matrix is calculated according to the following formula:
wherein H LMMSE Is a linear minimum mean square error estimation channel matrix, R hh Is the channel impulse response autocorrelation matrix, beta is the constellation factor, SNR is the average signal-to-noise ratio, I is the identity matrix, H LS Is the LS channel estimation matrix.
5. The method for analyzing and processing air interface system messages in an NB _ IoT system according to claim 1, wherein the step (3) specifically includes the following steps:
(3.1) eliminating resource lattices occupied by reference signals LTE-CRS and NRS, and sequentially taking out complex value symbols from positions (k, l) of the resource lattices;
(3.2) carrying out blind detection on the 8 th frames in the 64 frames in the NPBCH period, and sequentially taking the values of the 80ms sub-block indexes0 to 7; then divided by the coefficient theta corresponding to the sub-block index of 80ms f (i) Resulting in 100 complex-valued symbols.
6. The method for analyzing and processing air interface system messages in an NB _ IoT system according to claim 5, wherein the step (7) specifically includes the following steps:
(7.1) dividing 200bit data into one group every 50 bits, rearranging the previous three groups according to the block set in the step (3.2) to form 150 bits with correct sequence, and performing rate de-matching;
(7.2) if the data fails in CRC check and the CRC is close to a correct value, selecting a fourth group of 50 bits to replace the redundant data of the previous 3 groups, and performing de-rate matching operation again; otherwise, continuing to step (8).
7. The method according to claim 1, wherein the step (8) of parsing the air interface system message in the NB _ IoT system specifically includes the following steps:
(8.1) initializing a state register, a state transfer register and a path register;
(8.2) circulating each state after the initial state, and calculating the Hamming distance between the state and the first two possible states;
(8.3) comparing and selecting a path with a smaller Hamming distance until a path with a maximum probability value is found out;
and (8.4) backtracking the path transfer to generate decoded data.
8. The method according to claim 2, wherein the step (10) of parsing the air interface system message in the NB _ IoT system specifically includes the following steps:
(10.1) extracting a system frame number, a deployment mode and SIB1 deployment information from the MIB, acquiring the number of times of SIB1 repetition in 2560ms, a starting frame number containing SIB1 information, bit length information of SIB1 transmission blocks starting from the number of symbols in a subframe, and calculating that the currently read data contains several groups of decodable SIB1 data;
(10.2) a group of complete data is taken to start decoding, whether the CRC passes the check or not is judged, and if yes, the decoding is successful; otherwise, the next set of data is taken to decode SIB1.
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