CN110651453B - Data merging method, device and equipment - Google Patents
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- CN110651453B CN110651453B CN201880000707.9A CN201880000707A CN110651453B CN 110651453 B CN110651453 B CN 110651453B CN 201880000707 A CN201880000707 A CN 201880000707A CN 110651453 B CN110651453 B CN 110651453B
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Abstract
The invention provides a data merging method, a data merging device and data merging equipment. The method comprises the following steps: acquiring a channel estimation result of a subframe; determining the confidence of the received data in the subframe according to the channel estimation result; carrying out equalization processing on the data received in the subframe to obtain corresponding equalization data; acquiring intermediate data of the subframe, wherein the intermediate data refers to data obtained by multiplying the equalization data by the confidence coefficient; and merging the intermediate data of all the subframes to obtain merged data. Therefore, the optimization of the merged data in the NB-IoT scene is realized, and the demodulation performance of the low signal-to-noise ratio data is improved.
Description
Technical Field
The application relates to the technical field of communication, in particular to a data merging method, device and equipment of a narrow-band internet of things (NB-IoT) based on a honeycomb.
Background
With the development of communication technology, everything interconnection has become an inevitable trend, however, the current 4G network is far from meeting the connection requirement between things. The 3rd Generation Partnership Project (3 GPP) has formally established a cellular-based narrowband Internet of Things (NB-IoT) standard in 2016 to improve network coverage performance.
At present, since NB-IoT technology needs to combine a large number of subframes, and the time span is large, channel fading has a large impact on the combination, and deep fading of the channel can severely limit the accuracy of channel estimation and equalization performance. For the problem, in the prior art, a confidence optimization channel estimation algorithm is adopted to improve the accuracy of channel estimation.
However, the channel estimation value at the trough under the low snr is often inaccurate, so that the equalized data is severely distorted, thereby affecting the demodulation performance of the fading channel with the low snr.
Disclosure of Invention
The invention provides a data merging method, a data merging device and data merging equipment, which are used for optimizing merged data in an NB-IoT scene and improving the demodulation performance of low signal-to-noise ratio data.
In a first aspect, the present invention provides a data merging method, including:
acquiring a channel estimation result of a subframe;
determining the confidence of the received data in the subframe according to the channel estimation result;
carrying out equalization processing on the data received in the subframe to obtain corresponding equalization data;
acquiring intermediate data of the subframe, wherein the intermediate data refers to data obtained by multiplying the equalization data by the confidence coefficient;
and merging the intermediate data of all the subframes to obtain merged data.
Optionally, the obtaining a channel estimation result of the subframe includes:
receiving all pilot signals within the subframe;
performing channel estimation on all pilot signals in the subframe to obtain the channel response power of all pilot signals;
determining a confidence level of the data received in the subframe according to the channel estimation result, including:
summing the channel response power of all the pilot signals in the subframe to obtain a combined power value of the channel response power of all the pilot signals;
taking the associated value of the combined power value as the confidence of the data received in the subframe, wherein the associated value of the combined power value comprises: the square of the combined power value, the cube of the combined power value.
Optionally, the obtaining a channel estimation result of the subframe includes:
acquiring the channel response power of the sub-carrier in the sub-frame;
determining a confidence level of the data received in the subframe according to the channel estimation result, including:
taking an associated value of channel response power of a subcarrier of the subframe as a confidence level of data received in the subframe, wherein the associated value of channel response power comprises: the square of the channel response power, the cube of the channel response power.
Optionally, the obtaining a channel estimation result of the subframe includes:
acquiring a pilot signal sent by a sending end, a pilot signal received by a receiving end and noise superposed on a channel;
calculating the channel response of the subframe according to the pilot signal sent by the sending end, the pilot signal received by the receiving end and the noise superposed on the channel;
determining a confidence level of the data received in the subframe according to the channel estimation result, including:
setting a confidence level of the data received in the subframe according to the size of the channel response of the subframe.
In a second aspect, the present invention provides a data merging apparatus, including:
an obtaining module, configured to obtain a channel estimation result of a subframe;
a determining module, configured to determine a confidence level of data received in the subframe according to the channel estimation result;
the equalizing module is used for equalizing the data received in the subframe to obtain corresponding equalized data;
the processing module is used for acquiring intermediate data of the subframe, wherein the intermediate data refers to data obtained by multiplying the equalization data by the confidence coefficient;
and the merging module is used for merging the intermediate data of all the subframes to obtain merged data.
Optionally, the obtaining module is specifically configured to:
receiving all pilot signals within the subframe;
performing channel estimation on all pilot signals in the subframe to obtain the channel response power of all pilot signals;
the determining module is specifically configured to:
summing the channel response power of all the pilot signals in the subframe to obtain a combined power value of the channel response power of all the pilot signals;
taking the associated value of the combined power value as the confidence of the data received in the subframe, wherein the associated value of the combined power value comprises: the square of the combined power value, the cube of the combined power value.
Optionally, the obtaining module is specifically configured to:
acquiring the channel response power of the sub-carrier in the sub-frame;
the determining module is specifically configured to:
taking an associated value of channel response power of a subcarrier of the subframe as a confidence level of data received in the subframe, wherein the associated value of channel response power comprises: the square of the channel response power, the cube of the channel response power.
Optionally, the obtaining module is specifically configured to:
acquiring a pilot signal sent by a sending end, a pilot signal received by a receiving end and noise superposed on a channel;
calculating the channel response of the subframe according to the pilot signal sent by the sending end, the pilot signal received by the receiving end and the noise superposed on the channel;
the determining module is specifically configured to:
setting a confidence level of the data received in the subframe according to the size of the channel response of the subframe.
In a third aspect, the present invention provides a data merging apparatus, including:
a memory for storing a program;
a processor for executing the program stored by the memory, the processor being configured to perform the method of any of the first aspects when the program is executed.
In a fourth aspect, the present invention provides a computer-readable storage medium comprising: instructions which, when run on a computer, cause the computer to perform the method of any one of the first aspects.
According to the data merging method, the data merging device and the data merging equipment, the channel estimation result of the subframe is obtained; determining the confidence of the received data in the subframe according to the channel estimation result; carrying out equalization processing on the data received in the subframe to obtain corresponding equalization data; acquiring intermediate data of the subframe, wherein the intermediate data refers to data obtained by multiplying the equalization data by the confidence coefficient; and merging the intermediate data of all the subframes to obtain merged data. Therefore, the optimization of the merged data in the NB-IoT scene is realized, and the demodulation performance of the low signal-to-noise ratio data is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive exercise.
FIG. 1 is a schematic diagram of channel fading of the extended pedestrian channel model EPA 5;
FIG. 2 is a flowchart of a data merging method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram showing comparison results of Block Error Rate (BLER) performance when a confidence method and a direct combination method are used to combine data of an NPBCH channel of the extended pedestrian channel model EPA 1;
fig. 4 is a schematic structural diagram of a data merging device according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of a data merging device according to a third embodiment of the present invention.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate concepts presented by the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, article, or apparatus.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Hereinafter, some terms in the present application are explained to facilitate understanding by those skilled in the art.
1) Long Term Evolution (LTE) is a Long Term Evolution of UMTS (Universal Mobile Telecommunications System) technology standard established by The 3rd Generation Partnership Project (3 GPP) organization. The LTE system introduces key transmission technologies such as OFDM (Orthogonal Frequency Division Multiplexing) and MIMO (Multi-Input and Multi-Output), which significantly increases spectrum efficiency and data transmission rate, and supports multiple bandwidth allocation: 1.4MHz, 3MHz, 5MHz, 10MHz, 15MHz, 20MHz and the like, and supports the global mainstream 2G/3G frequency band and some newly-added frequency bands, thereby the frequency spectrum allocation is more flexible, and the system capacity and the coverage are also obviously improved.
2) The cellular-based narrowband Internet of Things (NB-IoT) is constructed in a cellular network, only consumes about 180KHz of bandwidth, and can be directly deployed in a GSM network, a UMTS network or an LTE network so as to reduce the deployment cost and realize smooth upgrade. NB-IoT is an emerging technology in the IoT domain that supports cellular data connectivity for low power devices over wide area networks, also known as low power wide area networks (LPWA). NB-IoT supports efficient connection of devices with long standby time and high requirements for network connectivity.
3) Least Squares (LS), a mathematical optimization technique, finds the best functional match of the data by minimizing the sum of squares of the errors; unknown data can be easily obtained by the least square method, and the sum of squares of errors between these obtained data and actual data is minimized.
4) Zero Forcing (ZF) algorithm, which is a linear equalization algorithm.
5) A Narrowband Physical Broadcast Channel (NPBCH) for carrying the most basic, most important cell information of the NB-IoT network.
6) The ratio of the Block Error Rate (BLER) of the Block with Error to the total number of blocks received by the digital circuit is to determine the demodulation performance of the system by detecting the cyclic redundancy on each transport Block to determine the ratio of the correct data Block received after channel de-interleaving and decoding.
7) Extended Pedestrian channel model (Extended period a model 5Hz, EPA5), doppler shift 5Hz, one of the most common channels of NB-IoT.
8) Additive White Gaussian Noise (AWGN), amplitude follows a normal distribution with a mean value of 0, and power spectral density follows a uniformly distributed Noise signal.
The NB-IoT-based data merging method can be applied to honeycomb-based narrowband IoT NB-IoT, NB-IoT standard is used as an evolution IoT protocol branch of LTE, and a receiver algorithm is very similar to that of LTE. However, if NB-IoT is completely applied to the LTE algorithm processing flow, the channel estimation result is poor and can only meet the minimum requirements of the protocol, one reason is that the number of pilot signals of NB-IoT is much smaller than that of LTE, so a large number of subframes are often needed for data combining, the time span is large, and the influence of fading is far greater than that of LTE. Deep fading of fading channels can cause limited channel estimation and equalization performance, and even cause interference under severe conditions, thereby greatly increasing the number of repeated combining, causing low data transmission efficiency and large power consumption, and causing adverse effects on the NB-IoT terminal. Fig. 1 is a schematic diagram of channel fading of the EPA5 extended pedestrian channel model, and as shown in fig. 1, the peak and trough of the power curve of a signal passing through the EPA5 channel are different by more than 9 dB. Under the high signal-to-noise ratio, the wave trough and the wave crest are directly combined according to the traditional mode, and the noise influence can be effectively reduced. However, under a low signal-to-noise ratio, the channel estimation value at the trough position is not accurate any more, and the equalized data is seriously distorted, so that the quality of the demodulated data is influenced.
The invention provides a data merging method, which aims to solve the problems in the prior art.
The technical solution of the present invention and how to solve the above technical problems will be described in detail with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of a method of a data merging method according to an embodiment of the present invention, as shown in fig. 2, the method in this embodiment may include:
s101, obtaining a channel estimation result of a subframe.
In this embodiment, data sent by a sending end is transmitted in sub-frames, and one sub-frame includes multiple frequency points, and referring to fig. 1, a channel response power value at each frequency point can be known, and especially in a low signal-to-noise ratio region, channel fading is obvious. Therefore, in order to improve the quality of the combined data, it is necessary to reduce the influence of the channel estimation values at the troughs. In this embodiment, channel estimation is performed on data of a subframe, and a corresponding channel estimation result is obtained. The most common channel estimation method may be the least squares method; however, it should be noted that the present embodiment does not limit the specific way of channel estimation, and all existing channel estimation methods can be applied to the present embodiment.
Optionally, all pilot signals within the subframe may be received;
and performing channel estimation on all pilot signals in the subframe to obtain the channel response power of all pilot signals.
In this embodiment, all pilot signals in the subframe may be selected for channel estimation, and the corresponding channel response power of the pilot channel may be obtained. Specifically, assuming that one subframe includes 8 pilot signals, the channel response powers of the 8 pilot signals can be obtained respectively. It should be noted that the number of the selected pilot signals is not limited in this embodiment.
Optionally, the channel response power of the sub-carriers in the sub-frame may be obtained.
In this embodiment, interpolation may be performed on the channel estimation results of all pilot signals in the subframe to obtain the channel estimation results of all subcarriers in the subframe and obtain the channel response power of the subcarriers in the subframe, where the channel response power value of the default subcarrier is accurate, so that pilot frequency combination is not required to reduce the influence of noise on the estimation results.
Optionally, a pilot signal sent by the sending end, a pilot signal received by the receiving end, and noise superimposed on the channel may also be obtained;
and calculating the channel response of the subframe according to the pilot signal sent by the sending end, the pilot signal received by the receiving end and the noise superposed on the channel.
In this embodiment, the channel response of the subframe may be calculated through a pilot signal sent by the sending end, a pilot signal received by the receiving end, and noise superimposed on a channel. Taking an Orthogonal Frequency Division Multiplexing (OFDM) system model as an example, the following relationship exists between a pilot signal transmitted by a transmitting end, a pilot signal received by a receiving end, noise superimposed on a channel, and a channel response:
Y＝XH+W
in the formula: h is the channel response, X is the pilot signal sent by the sender, Y is the pilot signal received by the receiver, and W is the AWGN vector superimposed on the pilot subchannel. With reference to fig. 1, the channel response is small at the trough position, indicating that the influence of noise is relatively large here, and therefore the obtained channel estimation result is relatively unreliable; and the channel response at the peak position is larger, which shows that the influence of noise is relatively small, and the obtained channel estimation result is relatively reliable.
S102, according to the channel estimation result, determining the confidence degree of the data received in the subframe.
In this embodiment, the confidence of the data received in the subframe is set according to the channel estimation result, and the confidence is also a weight value of the data received in the subframe, where the larger the weight value is, the larger the influence of the data received in the subframe on the total data (merged data) is, and the smaller the weight value is, the smaller the influence of the data received in the subframe on the total data (merged data) is.
Corresponding to the channel estimation mode, the channel response powers of all pilot signals in the subframe can be summed to obtain the combined power value of the channel response powers of all pilot signals;
taking the associated value of the combined power value as the confidence of the data received in the subframe, wherein the associated value of the combined power value comprises: the square of the combined power value, the cube of the combined power value.
And correspondingly to the channel estimation mode, taking the associated value of the channel response power of the sub-carrier of the sub-frame as the confidence of the data received in the sub-frame, wherein the associated value of the channel response power comprises: the square of the channel response power, the cube of the channel response power.
Corresponding to the channel estimation mode, the confidence of the data received in the subframe may be set according to the size of the channel response of the subframe, and the larger the channel response is, the larger the corresponding confidence is.
S103, equalizing the data received in the subframe to obtain corresponding equalized data.
In this embodiment, a zero forcing algorithm may be used to perform data equalization processing, but it should be noted that this embodiment does not limit a specific manner of data equalization, and all existing data equalization methods may be applied in this embodiment.
S104, acquiring intermediate data of the subframe, wherein the intermediate data refers to data obtained by multiplying the equalization data by the confidence coefficient.
In this embodiment, the equalization data obtained by equalizing the data received in the subframe is multiplied by the confidence to obtain the intermediate data of the subframe.
And S105, merging the intermediate data of all the subframes to obtain merged data.
In this embodiment, the intermediate data of the sub-frames may be combined with the intermediate data of a plurality of sub-frames, or with the intermediate data of a plurality of sub-carriers in one sub-frame. Specifically, when a data block is transmitted by multiple subframes, data of the multiple subframes needs to be combined, and when the data block can be transmitted by one subframe, intermediate data of multiple subcarriers in the subframe is combined.
Specifically, taking 8 intermediate data to be merged as an example, the following calculation formula can be used:
in the formula: y is the merged data, x (i) is the ith intermediate data, and w (i) is the confidence corresponding to the ith intermediate data.
In this embodiment, a channel estimation result of a subframe is obtained; determining the confidence of the received data in the subframe according to the channel estimation result; carrying out equalization processing on the data received in the subframe to obtain corresponding equalization data; acquiring intermediate data of the subframe, wherein the intermediate data refers to data obtained by multiplying the equalization data by the confidence coefficient; and merging the intermediate data of all the subframes to obtain merged data. Therefore, the optimization of the merged data in the NB-IoT scene is realized, and the demodulation performance of the low signal-to-noise ratio data is improved.
Fig. 3 is a schematic diagram illustrating comparison results of Block Error Rate (BLER) performance when a confidence method and a direct combination method are used to combine data of an NPBCH of the extended pedestrian channel model EPA 1. As shown in fig. 3, the data combining of the NPBCH channel of EPA1 by using the confidence method has better BLER performance than the way of directly combining the channel data; the improvement of BLER performance is particularly significant, especially for low signal-to-noise ratio channels. Taking the fading channel model of EPA1 as an example, the 99% NPBCH detection success rate performance is improved by more than 4db compared with direct combination, it can be seen that the method provided by the embodiment of the present invention can effectively reduce retransmission times and power consumption, improve traffic, and can meet the deep coverage with higher requirements.
Fig. 4 is a schematic structural diagram of a data merging device according to a second embodiment of the present invention, and as shown in fig. 3, the device in this embodiment may include:
an obtaining module 10, configured to obtain a channel estimation result of a subframe;
a determining module 20, configured to determine, according to the channel estimation result, a confidence level of the data received in the subframe;
the equalizing module 30 is configured to perform equalization processing on the data received in the subframe to obtain corresponding equalized data;
a processing module 40, configured to obtain intermediate data of the subframe, where the intermediate data is data obtained by multiplying the equalized data by the confidence level;
and a merging module 50, configured to merge the intermediate data of all the subframes to obtain merged data.
Optionally, the obtaining module 10 is specifically configured to:
receiving all pilot signals within the subframe;
and performing channel estimation on all pilot signals in the subframe to obtain the channel response power of all pilot signals.
Optionally, the obtaining module 10 is specifically configured to:
and acquiring the channel response power of the sub-carrier in the sub-frame.
Optionally, the obtaining module 10 is specifically configured to:
acquiring a pilot signal sent by a sending end, a pilot signal received by a receiving end and noise superposed on a channel;
and calculating the channel response of the subframe according to the pilot signal sent by the sending end, the pilot signal received by the receiving end and the noise superposed on the channel.
Optionally, the determining module 20 is specifically configured to:
summing the channel response power of all the pilot signals in the subframe to obtain a combined power value of the channel response power of all the pilot signals;
taking the associated value of the combined power value as the confidence of the data received in the subframe, wherein the associated value of the combined power value comprises: the square of the combined power value, the cube of the combined power value.
Optionally, the determining module 20 is specifically configured to:
taking an associated value of channel response power of a subcarrier of the subframe as a confidence level of data received in the subframe, wherein the associated value of channel response power comprises: the square of the channel response power, the cube of the channel response power.
Optionally, the determining module 20 is specifically configured to:
and setting the confidence degree of the data received in the subframe according to the size of the channel response of the subframe, wherein the larger the channel response is, the larger the corresponding confidence degree is.
The data merging device in this embodiment may execute the method shown in fig. 2, and for specific implementation processes and technical principles of the method, reference is made to relevant descriptions in the method shown in fig. 2, which is not described herein again.
Fig. 5 is a schematic structural diagram of a data merging device according to a third embodiment of the present invention, and as shown in fig. 5, the data merging device 60 in this embodiment includes:
a processor 61 and a memory 62; wherein:
a memory 62 for storing executable instructions, which may also be a flash (flash memory).
A processor 61 for executing the executable instructions stored in the memory to implement the steps of the method according to the above embodiments. Reference may be made in particular to the description relating to the preceding method embodiment.
Alternatively, the memory 62 may be separate or integrated with the processor 61.
When the memory 62 is a device independent of the processor 61, the data merging apparatus 60 may further include:
a bus 63 for connecting the memory 62 and the processor 61.
In addition, embodiments of the present application further provide a computer-readable storage medium, in which computer-executable instructions are stored, and when at least one processor of the user equipment executes the computer-executable instructions, the user equipment performs the above-mentioned various possible methods.
Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In addition, the application specific integrated circuit may be located in the user equipment. Of course, the processor and the storage medium may reside as discrete components in a communication device.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as Read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and so on.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for merging data, comprising:
acquiring a channel estimation result of a subframe;
determining the confidence of the received data in the subframe according to the channel estimation result;
carrying out equalization processing on the data received in the subframe to obtain corresponding equalization data;
acquiring intermediate data of the subframe, wherein the intermediate data refers to data obtained by multiplying the equalization data by the confidence coefficient;
and merging the intermediate data of all the subframes to obtain merged data.
2. The method of claim 1, wherein obtaining the channel estimation result of the subframe comprises:
receiving all pilot signals within the subframe;
performing channel estimation on all pilot signals in the subframe to obtain the channel response power of all pilot signals;
determining a confidence level of the data received in the subframe according to the channel estimation result, including:
summing the channel response power of all the pilot signals in the subframe to obtain a combined power value of the channel response power of all the pilot signals;
taking the associated value of the combined power value as the confidence of the data received in the subframe, wherein the associated value of the combined power value comprises: the square of the combined power value, the cube of the combined power value.
3. The method of claim 1, wherein obtaining the channel estimation result of the subframe comprises:
acquiring the channel response power of the sub-carrier in the sub-frame;
determining a confidence level of the data received in the subframe according to the channel estimation result, including:
taking an associated value of channel response power of a subcarrier of the subframe as a confidence level of data received in the subframe, wherein the associated value of channel response power comprises: the square of the channel response power, the cube of the channel response power.
4. The method of claim 1, wherein obtaining the channel estimation result of the subframe comprises:
acquiring a pilot signal sent by a sending end, a pilot signal received by a receiving end and noise superposed on a channel;
calculating the channel response of the subframe according to the pilot signal sent by the sending end, the pilot signal received by the receiving end and the noise superposed on the channel;
determining a confidence level of the data received in the subframe according to the channel estimation result, including:
setting a confidence level of the data received in the subframe according to the size of the channel response of the subframe.
5. A data merging apparatus, comprising:
an obtaining module, configured to obtain a channel estimation result of a subframe;
a determining module, configured to determine a confidence level of data received in the subframe according to the channel estimation result;
the equalizing module is used for equalizing the data received in the subframe to obtain corresponding equalized data;
the processing module is used for acquiring intermediate data of the subframe, wherein the intermediate data refers to data obtained by multiplying the equalization data by the confidence coefficient;
and the merging module is used for merging the intermediate data of all the subframes to obtain merged data.
6. The apparatus of claim 5, wherein the obtaining module is specifically configured to:
receiving all pilot signals within the subframe;
performing channel estimation on all pilot signals in the subframe to obtain the channel response power of all pilot signals;
the determining module is specifically configured to:
summing the channel response power of all the pilot signals in the subframe to obtain a combined power value of the channel response power of all the pilot signals;
taking the associated value of the combined power value as the confidence of the data received in the subframe, wherein the associated value of the combined power value comprises: the square of the combined power value, the cube of the combined power value.
7. The apparatus of claim 5, wherein the obtaining module is specifically configured to:
acquiring channel response power in the subframe;
the determining module is specifically configured to:
taking an associated value of channel response power of a subcarrier of the subframe as a confidence level of data received in the subframe, wherein the associated value of channel response power comprises: the square of the channel response power, the cube of the channel response power.
8. The apparatus of claim 5, wherein the obtaining module is specifically configured to:
acquiring a pilot signal sent by a sending end, a pilot signal received by a receiving end and noise superposed on a channel;
calculating the channel response of the subframe according to the pilot signal sent by the sending end, the pilot signal received by the receiving end and the noise superposed on the channel;
the determining module is specifically configured to:
setting a confidence level of the data received in the subframe according to the size of the channel response of the subframe.
9. A data merging device, comprising:
a memory for storing a program;
a processor for executing the program stored by the memory, the processor being configured to perform the method of any of claims 1-4 when the program is executed.
10. A computer-readable storage medium, comprising: instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1-4.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2018/084935 WO2019205111A1 (en) | 2018-04-27 | 2018-04-27 | Method for merging data, apparatus, and device |
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| Publication Number | Publication Date |
|---|---|
| CN110651453A CN110651453A (en) | 2020-01-03 |
| CN110651453B true CN110651453B (en) | 2022-04-26 |
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| CN104718781A (en) * | 2012-08-17 | 2015-06-17 | 瑞典爱立信有限公司 | Methods, systems and devices for obtaining system information in a wireless network |
| WO2015117532A1 (en) * | 2014-08-21 | 2015-08-13 | 中兴通讯股份有限公司 | Doa-bf weight estimation method and device for dual-polarization antenna system |
| CN105024950A (en) * | 2014-04-16 | 2015-11-04 | 普天信息技术有限公司 | Data detection method |
| CN106603137A (en) * | 2015-10-19 | 2017-04-26 | 中兴通讯股份有限公司 | Method and device for combining antennas |
| CN106972915A (en) * | 2017-03-30 | 2017-07-21 | 苏州维特比信息技术有限公司 | A kind of method for transmitting signals and Narrowband wireless terminals |
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| US20070189151A1 (en) * | 2006-02-10 | 2007-08-16 | Interdigital Technology Corporation | Method and apparatus for performing uplink transmission in a multiple-input multiple-output single carrier frequency division multiple access system |
| US8612820B2 (en) * | 2009-04-11 | 2013-12-17 | Qualcomm Incorporated | Apparatus and methods for interleaving in a forward link only system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104718781A (en) * | 2012-08-17 | 2015-06-17 | 瑞典爱立信有限公司 | Methods, systems and devices for obtaining system information in a wireless network |
| CN105024950A (en) * | 2014-04-16 | 2015-11-04 | 普天信息技术有限公司 | Data detection method |
| WO2015117532A1 (en) * | 2014-08-21 | 2015-08-13 | 中兴通讯股份有限公司 | Doa-bf weight estimation method and device for dual-polarization antenna system |
| CN106603137A (en) * | 2015-10-19 | 2017-04-26 | 中兴通讯股份有限公司 | Method and device for combining antennas |
| CN106972915A (en) * | 2017-03-30 | 2017-07-21 | 苏州维特比信息技术有限公司 | A kind of method for transmitting signals and Narrowband wireless terminals |
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