CN110636602A - NB-IoT downlink synchronization method and related operation module thereof - Google Patents
NB-IoT downlink synchronization method and related operation module thereof Download PDFInfo
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Abstract
The invention provides an NB-IoT downlink synchronization method and a correlation operation module thereof, aiming at actual sampling points, a local down-sampling NPSS signal is pre-generated for each symbol; moving a sliding window in sampling points bit by bit, and performing cross-correlation operation on each symbol of a received signal and each different symbol of a pre-generated local down-sampling NPSS in the moving process; and carrying out autocorrelation calculation on the cross-correlation value by using an autocorrelation operation module. The calculation method can obtain more accurate cross-correlation data, improves the precision of coarse synchronization, and provides more accurate data for subsequent symbol-level autocorrelation and calculation cost functions.
Description
Technical Field
The invention relates to the field of narrow-band Internet of things communication, in particular to a synchronization method of a downlink of a narrow-band Internet of things and a related operation module thereof.
Background
The Narrow-Band Internet of Things (NB-IoT) becomes an important branch of the Internet of everything. NB-IoT is focused on the low power wide coverage (LPWA) internet of things (IoT) market, an emerging technology that is widely applicable worldwide. The system has the characteristics of wide coverage, multiple connections, low speed, low cost, low power consumption, excellent architecture and the like, so that the system can be widely applied to various vertical industries, such as remote meter reading, asset tracking, intelligent parking, intelligent agriculture and the like.
An Orthogonal Frequency Division Multiplexing (OFDM) technology is adopted for an NB-IoT downlink to divide a Frequency band into a plurality of subcarriers, two adjacent subcarriers satisfy an Orthogonal relationship, can be close to each other and overlapped for transmission, and can demodulate an original signal at a receiving end. OFDM systems are very sensitive to time and frequency errors, and in practical communication systems, signals are transmitted via multiple paths with different delays in each path, which can introduce time offset and cause inter-symbol interference if not corrected in time. Because the clock frequencies of the signal transmitting end and the signal receiving end are not completely consistent, the frequency offset of the signals can be caused, adjacent subcarriers are not orthogonal any more, and subcarrier interference is generated. In order to eliminate time offset and frequency offset and ensure the accuracy and reliability of the whole communication system, the receiver front end must perform signal synchronization. While the synchronization process is generally divided into two steps: coarse synchronization and fine synchronization, wherein the fine synchronization is to further synchronize data with higher sampling rate on the basis of the coarse synchronization, and finally finish the synchronization of a downlink. In order to obtain more accurate and precise synchronization data, coarse synchronization is particularly important, and thus the importance of coarse synchronization as downlink synchronization is shown.
In the prior art, a coarse Synchronization process is performed on downlink Signal data which is down-sampled to 240KHZ, and Signal Synchronization is achieved mainly by using good autocorrelation and cross-correlation characteristics of a main Synchronization Signal (hereinafter referred to as "NPSS"). Firstly, subframe data divided by a section of received signals in a unit of 10ms is received, 11 pieces of subframe data are received, then OFDM symbol cross correlation is calculated with the same locally generated NPSS signal in a sliding mode, then symbol level autocorrelation is calculated, and finally a cost function is calculated to obtain frequency offset and time offset.
Symbol is a data unit, data before modulation is in bit, and data after modulation is in Symbol. For different modulation modes, the number of bits corresponding to each symbol is different. For example, QPSK modulation scheme, symbol: bits is 1: 2; modulation scheme of 8PSK, symbol: bits 1:3, etc.
The traditional coarse synchronization calculation OFDM symbol cross-correlation is multiplied by only a locally generated set of 17 NPSS signals, that is, the collected 11 sub-frame data are multiplied by the local set of 17 NPSS signals of symbol. As shown in fig. 1, for a certain time τ, 11 cross-correlation values y are calculated, i.e. 16 samples per OFDM symbol (plus cyclic prefix, 18 or 17 samples per symbol, as shown in the above figure) are cross-correlated with NPSS. And sliding one point and continuing to do the process until all sampling points of the 11 subframes are completely cross-correlated. The calculation formula is as follows:
this cross-correlation calculation method affects the final coarse tuning result in two cases:
in the first case, there are two cases, 17 and 18, because the sampling points of each symbol are different, which results in a deviation of the cross-correlation values calculated at symbol for 18 sampling points.
In the second case, the sampling point obtained by down-sampling from the high sampling frequency of 1.92MHZ to 240KHZ does not correspond to the locally generated NPSS signal at a symbol 17 point, and thus the calculated cross-correlation value is biased, as shown in fig. 2. The location of each symbol extraction point is different. In other words, the actual sampling point does not correspond to the locally generated symbol 17 point because the actual positions of the sampling point and the symbol 17 point are different.
Therefore, if only 17 NPSSs in one symbol are multiplied by the received signal in the conventional method, it is obvious that a deviation of the final cross-correlation calculation is generated, thereby resulting in a deviation of the final coarse synchronization result.
Disclosure of Invention
In view of this, the present invention provides an NB-IoT downlink synchronization method and a related operation module thereof, which can solve the problem of cross-correlation calculation deviation in the prior art, improve the accuracy of coarse synchronization, and reduce the synchronization time of the entire downlink.
In a first aspect, the present invention provides a synchronization method for NB-IoT downlink, comprising the following steps:
step 1, aiming at actual sampling points, generating a local down-sampling NPSS signal for each symbol in advance;
step 2, performing cross-correlation operation on a received symbol signal and each different symbol of the local down-sampled NPSS at a first sampling point to obtain a cross-correlation value;
step 3, moving a sliding window in a sampling point bit by bit, and performing cross-correlation operation on each symbol of the received signal and each different symbol of the pre-generated local down-sampling NPSS in the moving process;
step 4, judging whether each sampling point finishes sliding cross-correlation calculation, if so, entering step 5, and if not, returning to step 2;
the sliding cross-correlation calculation means that a sliding window is shifted bit by bit in the down-sampled baseband data, and the cross-correlation calculation is performed bit by bit during the shifting.
And 5, completing the self-correlation calculation of symbol level.
According to a specific implementation manner of the embodiment of the present disclosure, the step 1 is implemented such that the obtained original baseband data is data with a sampling frequency of 1.92MHz, and the original baseband data is down-sampled by 16 times, so as to obtain down-sampled baseband data with a sampling frequency of 240 KHz; a 16-fold down-sampling is also used for the local NPSS signal, and a good local down-sampled NPSS signal is pre-generated for each symbol of each sample point.
According to a specific implementation manner of the embodiment of the present disclosure, the calculation formula of the cross-correlation values in step 2 and step 3 is:
wherein y is a cross-correlation value, τ is a sampling time point, NPSS is a local 1/16 downsampled NPSS signal, and k is 11 symbols of 0-10.
According to a specific implementation manner of the embodiment of the present disclosure, the calculation formula of the autocorrelation in step 5 is:
m is 1, and represents conjugate multiplication and accumulation of two adjacent cross-correlation values;
m-2, representing the multiplication and accumulation of two conjugates separated by one;
and m is 3, and represents that two conjugates separated by two are multiplied and accumulated.
In a second aspect, a correlation operation module for NB-IoT downlink includes a local downsampling NPSS signal acquisition module, a cross-correlation operation module, and an autocorrelation operation module, and is characterized in that: and the local down-sampling NPSS signal acquisition module is used for pre-generating a local down-sampling NPSS signal aiming at each symbol of each sampling point.
According to a specific implementation manner of the embodiment of the present disclosure, in the cross-correlation operation module, a sliding window is shifted bit by bit in a sampling point, and in the shifting process, cross-correlation operation is performed on each symbol of a received signal and each different symbol of a pre-generated local down-sampling NPSS.
According to a specific implementation manner of the embodiment of the present disclosure, the calculation formula of the cross-correlation operation is:
wherein y is a cross-correlation value, τ is a sampling time point, NPSS is a local 1/16 downsampled NPSS signal, and k is 11 symbols of 0-10.
According to a specific implementation manner of the embodiment of the present disclosure, the autocorrelation operation module performs autocorrelation calculation on the cross-correlation value.
According to a specific implementation manner of the embodiment of the present disclosure, the calculation formula of the autocorrelation operation is:
m is 1, and represents conjugate multiplication and accumulation of two adjacent cross-correlation values;
m-2, representing the multiplication and accumulation of two conjugates separated by one;
and m is 3, and represents that two conjugates separated by two are multiplied and accumulated.
The method has the advantages that more accurate cross-correlation data are obtained through the correlation calculation module and the synchronization method, the precision of coarse synchronization is improved, and more accurate data are provided for subsequent symbol-level autocorrelation and calculation cost functions.
Drawings
FIG. 1 is a schematic diagram of cross-correlation downsampled data acquisition in the prior art;
FIG. 2 is a schematic diagram illustrating a sampling point from high sampling to low sampling in the prior art;
FIG. 3 is a flow chart of a synchronization method of the present invention;
FIG. 4 is a schematic diagram of a related computing module according to the present invention;
FIG. 5 is a screenshot comparing the results of a simulation test performed with matlab software.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The invention discloses a synchronization method of NB-IoT downlink, which mainly improves the precision of coarse synchronization by improving a cross-correlation calculation method. As shown in fig. 3, the method comprises the following steps:
step 1, aiming at actual sampling points, generating a local down-sampling NPSS signal for each symbol in advance;
in this embodiment, the obtained original baseband data is data with a sampling frequency of 1.92MHz, and the original baseband data is down-sampled by 16 times, so that down-sampled baseband data with a sampling frequency of 240KHz can be obtained. A 16-fold down-sampling is also used for the local NPSS signal, and a good local down-sampled NPSS signal is pre-generated for each symbol of each sample point. Subframe data obtained by dividing a segment of a received signal by 10ms is received as 11 pieces of subframe data, that is, NPSS signal data of 11 symbols is generated in advance.
Step 2, performing cross-correlation operation on a received symbol signal and each different symbol of the local down-sampled NPSS at a first sampling point to obtain a cross-correlation value;
in this embodiment, NPSS signal data of 11 symbols are generated in advance, that is, 11 cross-correlation values can be obtained by cross-correlation operation.
Step 3, moving a sliding window in sampling points bit by bit, and in the moving process, performing cross-correlation operation on each symbol of the received signal and each different symbol of the pre-generated local down-sampling NPSS, and obtaining 11 cross-correlation values for each sampling point;
in this embodiment, using the length of 11 1/16 downsampled OFDM symbol durations as a sliding window, moving bit by bit in the downsampled baseband data, and performing cross-correlation operation on each symbol of the received signal and the NPSS signal data of 11 pre-generated symbols to obtain a cross-correlation value y at each position.
Specifically, the calculation formula of the cross-correlation value between the local down-sampled NPSS signal in step 2 and step 3 and the down-sampled baseband data in the sliding window is as follows:
in the above formula, y is a cross-correlation value, τ is a sampling time point, NPSS is a local 1/16 downsampled NPSS signal, and k is 11 symbols of 0-10.
Step 4, judging whether each point of the subframe data completes sliding cross-correlation calculation, if so, entering step 5, and if not, returning to step 2;
and 5, completing the symbol-level autocorrelation operation.
The symbol-level autocorrelation calculation means that autocorrelation calculation is performed when the data unit is symbol, and specifically, the autocorrelation calculation formula is as follows:
m is 1, and represents conjugate multiplication and accumulation of two adjacent cross-correlation values;
m-2, representing the multiplication and accumulation of two conjugates separated by one;
and m is 3, and represents that two conjugates separated by two are multiplied and accumulated.
The invention also discloses a relevant operation module of the NB-IoT downlink, which is shown in figure 4. The device comprises a local down-sampling NPSS signal acquisition module, a cross-correlation operation module and an autocorrelation operation module.
In the local down-sampling NPSS signal obtaining module in this embodiment, a local down-sampling NPSS signal is generated in advance for each symbol of each sampling point.
The cross-correlation operation module in this embodiment moves a sliding window in sampling points bit by bit, and performs cross-correlation operation on each symbol of a received signal and each different symbol of a pre-generated local down-sampling NPSS during the moving process.
The autocorrelation operation module in this embodiment performs autocorrelation calculation on the cross-correlation value.
In order to verify that the cross-correlation algorithm of the invention uses matlab commercial mathematical software to perform a test, the results of coarse synchronization time offset and frequency offset obtained by the cross-correlation algorithm of the prior art and the cross-correlation algorithm of the invention are compared. The simulation generates received signal data with parameters set to time offset 40 and frequency offset 850 Hz. The same input data, results of the prior art and the present invention are shown in comparison to fig. 5.
As can be seen from fig. 5, the frequency offset of the present invention is 850-; the prior art frequency offset is 850-. It is therefore clear that both the time offset and the frequency offset of the present invention are smaller than the prior art. In other words, the time offset and the frequency offset of the method for performing cross-correlation calculation used in the present invention are closer to the actual frequency offset and time offset value.
The above description is for the purpose of illustrating embodiments of the invention and is not intended to limit the invention, and it will be apparent to those skilled in the art that any modification, equivalent replacement, or improvement made without departing from the spirit and principle of the invention shall fall within the protection scope of the invention.
Claims (9)
1. A method for synchronization of NB-IoT downlink, comprising the steps of:
step 1, aiming at actual sampling points, pre-generating a local down-sampling NPSS signal for each symbol;
step 2, performing cross-correlation operation on a received symbol signal and each different symbol of the local down-sampled NPSS at a first sampling point to obtain a cross-correlation value;
step 3, moving a sliding window in a sampling point bit by bit, and performing cross-correlation operation on each symbol of the received signal and each different symbol of the pre-generated local down-sampling NPSS in the moving process;
step 4, judging whether each sampling point finishes sliding cross-correlation calculation, if so, entering step 5, and if not, returning to step 2;
and 5, completing the self-correlation calculation of symbol level.
2. The synchronization method of NB-IoT downlink according to claim 1, characterized by: the step 1 is realized by acquiring original baseband data with a sampling frequency of 1.92MHz, and performing 16-time down-sampling on the original baseband data to acquire down-sampled baseband data with a sampling frequency of 240 KHz; a 16-fold down-sampling is also used for the local NPSS signal, and the local down-sampled NPSS signal is pre-generated for each symbol at each sample point.
3. The synchronization method of NB-IoT downlink according to claim 1, characterized by: the calculation formula of the cross correlation values in the step 2 and the step 3 is as follows:
wherein y is a cross-correlation value, τ is a sampling time point, NPSS is a local 1/16 downsampled NPSS signal, and k is 11 symbols of 0-10.
4. The synchronization method of NB-IoT downlink according to claim 1, characterized by: the calculation formula of the autocorrelation in the step 5 is as follows:
m is 1, and represents conjugate multiplication and accumulation of two adjacent cross-correlation values;
m-2, representing the multiplication and accumulation of two conjugates separated by one;
and m is 3, and represents that two conjugates separated by two are multiplied and accumulated.
5. A correlation operation module of NB-IoT downlink comprises a local down-sampling NPSS signal acquisition module, a cross-correlation operation module and an autocorrelation operation module, and is characterized in that: and the local down-sampling NPSS signal acquisition module is used for pre-generating a local down-sampling NPSS signal aiming at each symbol of each sampling point.
6. The NB-IoT downlink correlation operation module according to claim 5, wherein: in the cross-correlation operation module, a sliding window is moved in sampling points bit by bit, and in the moving process, each symbol of a received signal and each different symbol of a pre-generated local down-sampling NPSS are subjected to cross-correlation operation.
7. The NB-IoT downlink correlation operation module according to claim 6, wherein: the calculation formula of the cross-correlation operation is as follows:
wherein y is a cross-correlation value, τ is a sampling time point, NPSS is a local 1/16 downsampled NPSS signal, and k is 11 symbols of 0-10.
8. The NB-IoT downlink correlation operation module according to claim 5, wherein: and the autocorrelation operation module carries out autocorrelation calculation on the cross-correlation value.
9. The NB-IoT downlink correlation operation module as claimed in claim 8, wherein: the calculation formula of the autocorrelation operation is as follows:
m is 1, and represents conjugate multiplication and accumulation of two adjacent cross-correlation values;
m-2, representing the multiplication and accumulation of two conjugates separated by one;
and m is 3, and represents that two conjugates separated by two are multiplied and accumulated.
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