CN111030958A - 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 MIB baseband data after sampling, and performing Fourier transform to obtain frequency domain data; performing channel estimation through NRS to recover a signal at a transmitting end; 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 decoding successfully 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; the SIB1 message is obtained through similar processes of resource mapping, precoding and layer mapping, QPSK modulation, descrambling, rate matching, viterbi decoding, CRC check, and the like. 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 currently not satisfied by the well-established 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 the lpwan (low Power Wide Area network) market, i.e., a low-Power Wide Area network. The GPRS technology has the problems of high terminal power consumption, 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 rise, 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 the 15kHz subcarrier spacing in the 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 narrowband 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 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 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 SIB 1;
(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) the SIB1 is decoded by sequentially performing the steps (3) to (9).
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) a 128-point Fourier transform is performed 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 of 2000 or p of 2001, and initializing the antenna port number of 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 of 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 HLMMSEIs a linear minimum mean square error estimation channel matrix, RhhIs the channel impulse response autocorrelation matrix, β is the constellation factor, SNR is the average signal-to-noise ratio, I is the identity matrix, HLSIs 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 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 80msf(i) Resulting in 100 complex-valued symbols.
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, continuing to 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 a path with a maximum probability value is found out;
and (8.4) backtracking the path transfer to generate decoded data.
Preferably, the step (10) specifically comprises the following steps:
(10.1) extracting a system frame number, a deployment mode and the deployment information of the SIB1 from the MIB, acquiring the information of the number of times of repeating SIB1 in 2560ms, the starting frame number containing SIB1 information, the starting from the number of symbols in a subframe, the bit length of SIB1 transport blocks 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 SIB 1.
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 detected in a blind mode, for signals with CRC values close to the correct values, rate de-matching is carried out again by adopting redundant data, the use of smaller hardware resource overhead and a larger decoding success rate are balanced, MIB messages are decoded independently on any frame on any 80ms subblock, the real-time performance and the practicability of algorithm implementation are enhanced, and the method has stronger environment using capacity.
Drawings
Fig. 1 is a flowchart of a method for performing parsing processing on 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 the application of the MIB to perform the air interface analysis 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 signal at a transmitting end;
(2.1) performing blind detection on the antenna port number p of 2000 or p of 2001, and initializing the antenna port number of 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 80msf(i) Obtaining 100 complex value symbols;
(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 de-QPSK modulation;
(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, continuing to 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 a path with a maximum probability value is found out;
(8.4) backtracking the path transfer to generate decoded data;
(9) performing CRC;
(10) decoding MIB, acquiring information, and judging the position of SIB 1;
(10.1) extracting a system frame number, a deployment mode and the deployment information of the SIB1 from the MIB, acquiring the information of the number of times of repeating SIB1 in 2560ms, the starting frame number containing SIB1 information, the starting from the number of symbols in a subframe, the bit length of SIB1 transport blocks 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, take down a set of data decoding SIB 1;
(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) the SIB1 is decoded by sequentially performing the steps (3) to (9).
As a preferred embodiment of the present invention, the step (2.2) calculates NRS mapping positions (k, l), specifically:
the NRS mapping position (k, l) is calculated according to the following formula:
k＝6m+(v+vshift)mod6
where l is an OFDM symbol index in one slot, k is a subcarrier index on a frequency domain, p is a corresponding antenna port number, and m is 0, 1. v + vshiftValue ofTo be related to the cell ID, the offset of different cells in the frequency domain is indicated.
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 HLMMSEIs a linear minimum mean square error estimation channel matrix, RhhIs a channel impulse response autocorrelation matrix, β is a constellation factor (QPSK, & lttt translation = β "& ttt & gtt & 16QAM, <tt translation & β & lttt/t & 17/9), SNR is an average signal-to-noise ratio, I is a unit matrix, HLSIs 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,
is a modulated bit, b (i) is a descrambled bit, and c (i) is a scrambled bit.
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 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 NPBCH sending end signals are restored after channel equalization and relevant processing. Then, the correct 34-bit MIB data is obtained through processes of resource mapping, precoding and layer mapping, demodulation, descrambling, rate matching, viterbi decoding, descrambling, CRC (cyclic redundancy check) and the like. Next, 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 transmitting 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 check. 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 cell search, 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 (4) using NRS to carry out channel estimation 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 + vshiftThe 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 performed on the antenna port number p of 2000 or p of 2001, and the initialized antenna port number is 2000. And selecting NRS at the corresponding position and locally generated NRS signals according to the assumed antenna port to carry out channel estimation.
In order to obtain channel estimation of the whole NPBCH time-frequency resource grid, channel impulse response of the resource grid where the NRS is located on the subframe 0 is obtained by a complex point division method, a complete NPBCH channel estimation matrix is formed through linear interpolation of inter-row channel impulse response and linear interpolation of inter-symbol channel impulse response, and then minimum mean square error estimation is carried out, so that NPBCH data of a transmitting end are recovered. Minimum mean square error estimation channel matrix calculation reference formula (2):
wherein HLMMSEIs a linear minimum mean square error estimation channel matrix, RhhIs a channel impulse response autocorrelation matrix, β is a constellation factor (QPSK, & lttt translation = β "& ttt & gtt & 16QAM, <tt translation & β & lttt/t & 17/9), SNR is an average signal-to-noise ratio, I is a unit matrix, HLSIs the LS channel estimation matrix.
3) And (5) resource mapping is solved. When the resource mapping is removed, firstly, the resource lattices occupied by the reference signals LTE-CRS and NRS are removed, and the resource lattices 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 existAssume that NB- IoT antenna ports 0 and 1 both exist, and calculate the frequency offset v of the CRSshiftFor use at night
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 the SIB1 is decoded, since the number of antenna ports and the number of antenna ports are known, the position where the symbol starts 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 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 sub-blocks which are equal in length and can be decoded by self, each sub-block is 200bit, the MIB information is mapped to a frame which satisfies SFNmod8 which is 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 indexf(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)]TThen the complex-valued symbol d after de-precoding and layer mapping(0)Reference may be made to equations (3) and (4). NPDSCH and NPBCH with SIB1 use the same antenna port.
For antenna port 2000:
for antenna port 2001:
wherein,
5) and (6) 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 pseudo-random scrambling sequence of 1600 bits, and selecting 200 bits from the sub-block of 80ms in the NPBCH period according to the frame assumed in the step 3) to descramble. When the SIB1 is dissolved, the unwinding is repeated a few times within the cycle. The descrambling formula refers to formula (5).
Wherein,
is a modulated bit, b (i) is a descrambled bit, and c (i) is a scrambled bit.
7) And (4) rate matching is performed. For the descrambled 200 bits, the position of every 50 bits after descrambling is adjusted according to the sub-block of the frame which is supposed to be positioned in the 80ms in the NPBCH period in the step 3), 150 bits in the correct sequence are taken out, and rate de-matching is carried out. 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 with constraint length of 7 and code rate of 1/3 before tail-biting convolutional coding. 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 assumed 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
a0DA+15+a1DA+14+...+aA-1D16+p0D15+p1D14+...+p14D1+p15Whether or not it can be represented by a 16-bit polynomial gCRC16(D)＝[D16+D12+D5+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 further includes, after step 9): a method for analyzing SIB1 system messages of NB _ IoT system air interface PDSCH channels comprises the following steps:
10) decoding MIB, obtaining information, and judging the position of SIB 1. And extracting information such as system frame number, deployment mode, SIB1 deployment and the like from the MIB. Acquiring the information of the SIB1 such as the repetition number in 2560ms, the starting frame number containing SIB1 information, the starting from the several symbols in a subframe, the bit length of SIB1 transport block and the like, and calculating that the currently read data contains 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 SIB 1;
11) extracting NPDSCH baseband data, and performing Fourier transform to obtain frequency domain data;
12) and knowing the port number of the antenna, performing channel estimation through NRS, and recovering the signal at the transmitting end. 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) the SIB1 is decoded by sequentially performing the steps (3) to (9).
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 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 (9)
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) demodulating each complex-valued symbol into a pair of bit data by de-QPSK modulation;
(6) performing descrambling calculation;
(7) performing rate de-matching;
(8) performing Viterbi decoding;
(9) a CRC check is performed.
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 SIB 1;
(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) the SIB1 is decoded by sequentially performing the steps (3) to (9).
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 analyzing and processing air interface system messages in an NB _ IoT system according to claim 1, wherein the step (2) specifically includes the following steps:
(2.1) performing blind detection on the antenna port number p of 2000 or p of 2001, and initializing the antenna port number of 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 of the transmitting end by using the obtained estimation matrix.
5. The method for parsing an air interface system message in an NB _ IoT system according to claim 4, wherein the step (2.2) of calculating the minimum mean square error estimation channel matrix specifically comprises:
the minimum mean square error estimated channel matrix is calculated according to the following formula:
wherein HLMMSEIs a linear minimum mean square error estimation channel matrix, RhhIs the channel impulse response autocorrelation matrix, β is the constellation factor, SNR is the average signal-to-noise ratio, I is the identity matrix, HLSIs the LS channel estimation matrix.
6. 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 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 80msf(i) Resulting in 100 complex-valued symbols.
7. The method for analyzing and processing air interface system messages in an NB _ IoT system according to claim 1, wherein the step (7) specifically includes 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, continuing to step (8).
8. The method for analyzing and processing air interface system messages in an NB _ IoT system according to claim 1, wherein the step (8) 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.
9. The method for analyzing air interface system messages in an NB _ IoT system according to claim 2, wherein the step (10) specifically includes the following steps:
(10.1) extracting a system frame number, a deployment mode and the deployment information of the SIB1 from the MIB, acquiring the information of the number of times of repeating SIB1 in 2560ms, the starting frame number containing SIB1 information, the starting from the number of symbols in a subframe, the bit length of SIB1 transport blocks 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 SIB 1.
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