CN114745236A - Data preprocessing method and device for transform domain channel estimation and communication equipment - Google Patents
Data preprocessing method and device for transform domain channel estimation and communication equipment Download PDFInfo
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Abstract
A data preprocessing method and device for transform domain channel estimation, and a communication device, the method includes: obtaining a channel estimation sequence H prior to domain transformationLS(k) The channel estimation sequence HLS(k) Comprises a plurality of sample points; the channel estimation sequence HLS(k) Adding N to the left and right edges ofextraValue, and N added to the left and right edgesextraAll or part of the values plus a transition window, NextraNot less than 1. By using the scheme of the invention, the performance of noise reduction in the transform domain can be improved.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a data preprocessing method and apparatus for transform domain channel estimation, and a communication device.
Background
Channel estimation is a process of estimating model parameters of a certain channel model to be assumed from received data, and for a demodulation process, residual noise on a channel estimation sequence is multiplicative noise, which deteriorates demodulation performance, so that the residual noise on the channel estimation sequence is reduced as much as possible. The commonly used noise reduction algorithm for the channel estimation sequence is: a sliding window filtering method, an MMSE (Minimum Mean square Error) noise reduction filtering method, a transform domain filtering method, and the like. The principle of the transform domain filtering method is as follows: orthogonal transforms, such as DFT (Discrete Fourier Transform)/IDFT (Inverse Discrete Fourier Transform), are used for the original channel estimation sequence, so that the signal energy is compressed to some intervals, and noise interference is considered outside the intervals, and is subjected to energy reduction.
The existing transform domain noise reduction algorithm mainly processes the sequence edge values as follows: the method comprises the steps of not processing the edge value of a channel estimation sequence before orthogonal transformation, repeating the edge value of the channel estimation sequence before orthogonal transformation, extrapolating the edge value of the channel estimation sequence before orthogonal transformation, and symmetrically expanding the edge value of the channel estimation sequence before orthogonal transformation. The processing of the sequence edge values before the noise reduction of the transform domain results in energy dispersion of signal energy in the transform domain during the subsequent transform domain processing, that is, the signal energy cannot be well compressed to some intervals, thereby affecting the noise reduction performance of the transform domain.
Disclosure of Invention
The embodiment of the invention provides a data preprocessing method and device for transform domain channel estimation and communication equipment, which are used for improving the performance of noise reduction in a transform domain.
Therefore, the embodiment of the invention provides the following technical scheme:
in one aspect, an embodiment of the present invention provides a data preprocessing method for transform domain channel estimation, where the method includes:
obtaining a channel estimation sequence H prior to domain transformationLS(k) The channel estimation sequence HLS(k) Comprises a plurality of sample points;
the channel estimation sequence HLS(k) Adding N to the left and right edges ofextraValue, and N added to the left and right edgesextraAll or part of the values plus a transition window, Nextra≥1。
Optionally, the channel estimation sequence HLS(k) Adding N to the edge ofextraThe values include any of:
the channel estimation sequence H is repeated by an edge valueLS(k) Adding N to the left and right edges ofextraA value;
the channel estimation sequence H is obtained by means of edge value extrapolationLS(k) Adding N to the left and right edges ofextraA value;
the channel estimation sequence H is processed by an edge value symmetric extension modeLS(k) Adding N to the left and right edges ofextraA value.
Optionally, the transition window is any one of: rectangular window, raised cosine window, triangular window, Hamming window, Hanning window, Blackman window, Chebyshev window.
Optionally, the channel estimation sequence H is repeated by an edge valueLS(k) Adding N to the left and right edges ofextraThe values include:
number of ports and N multiplexed according to code division multiplexing groupextraDetermining the channel estimation sequence HLS(k) The number of sampling points and the number of repetition times contained in the subsequences to be repeated at the left edge and the right edge of the frame;
respectively estimating the channel estimation sequence H according to the repetition timesLS(k) The left and right edges of the sub-sequence to be repeated are repeatedly added.
Optionally, the channel estimation sequence H is obtained by extrapolating edge valuesLS(k) Adding N to the left and right edges ofextraThe values include: the channel estimation sequence HLS(k) Respectively extrapolate the left and right edges of NextraAnd (5) sampling points.
Optionally, the channel estimation sequence H is processed by edge value symmetric spreadingLS(k) Adding N to the left and right edges ofextraThe values include: the channel estimation sequence HLS(k) N of left and right edges ofextraThe sampling points are symmetrically expanded respectively.
Optionally, the method further comprises: for the channel estimation sequence HLS(k) The linear phase of (a) is pre-compensated.
Optionally, the method further comprises: n added to the left and right edges respectivelyextraValues are added linearly in phase.
Optionally, the domain is transformed into any of: transform from frequency domain to time domain, transform from time domain to frequency domain, transform from spatial domain to beam domain, and transform from beam domain to spatial domain.
In another aspect, an embodiment of the present invention further provides a data preprocessing apparatus for transform domain channel estimation, where the apparatus includes:
a data acquisition module for acquiring the channel estimation sequence H before domain transformationLS(k) The channel estimation sequence HLS(k) Comprises a plurality of sample points;
a pre-processing module for converting the channel estimation sequence HLS(k) Respectively adding N to the left and right edges ofextraValue, and N added to the left and right edgesextraAll or part of the values plus a transition window, Nextra≥1。
Optionally, the preprocessing module comprises any one or more of the following units:
a first processing unit for repeating the channel estimation sequence H by an edge valueLS(k) Adding N to the left and right edges ofextraA value;
a second processing unit for extrapolating the channel estimation sequence H by means of an edge valueLS(k) Adding N to the left and right edges ofextraA value;
a third processing unit, configured to apply the channel estimation sequence H in an edge value symmetric spreading mannerLS(k) Adding N to the left and right edges ofextraA value.
Optionally, the apparatus further comprises: a phase compensation module for estimating the channel estimation sequence HLS(k) Is pre-compensated.
Optionally, the apparatus further comprises: a phase adding module for adding N to the left and right edges respectivelyextraValues are added linearly in phase.
In another aspect, an embodiment of the present invention further provides a communication device, where the communication device includes the data preprocessing apparatus for transform domain channel estimation as described above.
In another aspect, an embodiment of the present invention further provides a computer-readable storage medium, which is a non-volatile storage medium or a non-transitory storage medium, and has a computer program stored thereon, where the computer program is executed by a processor to perform the steps of the above method.
In another aspect, an embodiment of the present invention further provides a communication device, which includes a memory and a processor, where the memory stores a computer program executable on the processor, and the processor executes the steps of the method when executing the computer program.
The data preprocessing method, the data preprocessing device and the communication equipment for transform domain channel estimation provided by the embodiment of the invention aim at the process of adopting a transform domain to carry out channel estimation, carry out edge processing on a channel estimation sequence before orthogonal transform, and particularly carry out edge processing on a channel estimation sequence HLS(k) Adding N to the left and right edges ofextraValue and estimate a sequence H for the channelLS(k) N added to the left and right edges of (1)extraAnd the transition window is added to all or part of the values, so that the noise reduction performance of the transform domain can be effectively improved.
Further, the addition of the edge data value of the channel estimation sequence in the edge processing can be performed in various ways: the repetition of the sequence edge data values, the extrapolation of the sequence edge data values, the symmetric expansion of the sequence edge data values and the like can be conveniently and flexibly selected.
Further, the transition window may be, but is not limited to: rectangular window, raised cosine window, triangular window, Hamming window, Hanning window, Blackman window, Chebyshev window, etc. to increase the flexibility of implementation of the scheme.
Drawings
FIG. 1 is a flow chart of a data pre-processing method for transform domain channel estimation according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a pilot architecture of Type1 for the configuration of 5GNR PUSCHHDMRS;
fig. 3 is a schematic diagram of frequency domain response amplitude before adding and windowing channel estimation sequence edge values under 1 port in the embodiment of the present invention;
FIG. 4 is a schematic diagram of frequency domain response amplitude after addition and windowing of edge values of a channel estimation sequence for 2 ports according to an embodiment of the present invention;
FIG. 5 is another flow chart of a data pre-processing method for transform domain channel estimation according to an embodiment of the present invention;
FIG. 6 is another flow chart of a data pre-processing method for transform domain channel estimation according to an embodiment of the present invention;
FIGS. 7-10 are schematic diagrams of MSE results of channel estimation simulation tests using the method of the present invention;
FIGS. 11 and 12 are diagrams illustrating MSE results for each RE when the signal-to-noise ratio of the TDLB channel and the TDLC channel is 30dB in the channel estimation simulation test, respectively;
FIG. 13 is a schematic diagram of a data preprocessing apparatus for transform domain channel estimation according to an embodiment of the present invention;
FIG. 14 is a schematic diagram of another structure of a data preprocessing apparatus for transform domain channel estimation according to an embodiment of the present invention;
fig. 15 is a schematic diagram of another structure of a data preprocessing apparatus for transform-domain channel estimation according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures are described in detail below.
Aiming at the problem that the noise reduction performance of a transform domain is influenced because the signal energy is subjected to energy dispersion in the transform domain due to the existing transform domain noise reduction algorithm, the embodiment of the invention provides a data preprocessing method and a data preprocessing device for the transform domain channel estimationLS(k) Adding N to the left and right edges ofextraValues and estimates a sequence H for the channelLS(k) N added to the left and right edges ofextraAll or part of the values are windowed to improve transform domain noise reduction performance.
The data preprocessing method and device for transform domain channel estimation provided by the embodiment of the invention can be used for various different transform domains, such as: transform from frequency domain to time domain, transform from time domain to frequency domain, transform from spatial domain to beam domain, transform from beam domain to spatial domain, and so on. For convenience of description, the following embodiments are illustrated by way of example of frequency domain to time domain transformation.
Fig. 1 shows a flow chart of a data preprocessing method for transform domain channel estimation according to an embodiment of the present invention, which includes the following steps:
The channel estimation sequence HLS(k) The channel estimation sequence after removing the phase influence caused by the pilot frequency takes the frequency domain channel estimation as an example, and the number of frequency domain samples is
Namely have
N added to left and right edgesextraValue plus transition window, assuming a length of N for the transition windowWinThen N isWin≤Nextra。
It is noted that, among others, the sequence H is estimated for the channelLS(k) The addition of the sequence edge value can be performed in the following ways:
(1) repetition of sequence edge data values, i.e. the channel estimation sequence H is repeated by means of edge valuesLS(k) Respectively adding N to the left and right edges ofextraA value.
(2) Extrapolation of sequence edge data values, i.e. extrapolating the channel estimation sequence H by means of edge value extrapolationLS(k) Adding N to the left and right edges ofextraA value.
(3) Symmetric extension of sequence edge data value, namely, estimating the channel by the edge value symmetric extension modeLS(k) Respectively left and right edges ofAddition of NextraA value.
Of course, in practical application, there may be other addition modes, which may be selected according to application requirements, and the embodiment of the present invention is not limited.
The transition window may be any one of: a rectangular window, a raised cosine window, a triangular window, a hamming window, a hanning window, a brakman window, a chebyshev window, etc., which are not limited in the embodiments of the present invention.
In 5G (5th Generation Mobile Communication Technology, fifth Generation Mobile Communication Technology) NR (New Radio), DMRS (Demodulation Reference Signal) is introduced for downlink and uplink channels, and is used for a receiving end (base station side or UE (User Equipment) side) to perform channel estimation to demodulate a related physical channel, the UE determines mapping of the DMRS to the physical resource according to a high-layer configuration parameter DMRS-downlink configuration ═ maxLength, that is, a maximum number of pre-DMRS symbols, and uses Single-Symbol (Single-Symbol) DMRS if no high-layer DMRS-downlink configuration ═ maxLength is configured; if the higher layer parameter maxLength is configured as "len 2", the UE determines whether it is a single Symbol or a Double Symbol (Double-Symbol) according to the received DCI (Downlink Control Information). When the configuration type is type1, a single symbol supports 4 ports (ports) at maximum, and a double symbol supports 8 ports at maximum; when the configuration type is type2, a single symbol supports 6 ports at maximum and a double symbol supports 12 ports at maximum.
Taking the 5GNR PUSCH (Physical Uplink Shared Channel) DMRS (Demodulation Reference Signal) configuration Type1 as an example, the pilot architecture is shown in fig. 2.
DMRSs corresponding to ports in one CDM group (Code Division Multiplexing group) share the same re (resource element) resource. As shown in fig. 2, for a single symbol DMRS, Port0 and Port1 share the same RE resource, both ports in CDM group 0; port2 and Port3 share the same RE resource, both ports in CDM group 1. For a dual-symbol DMRS, Port0, Port1, Port4, and Port5 share the same RE resource, all of which are ports in CDM group 0; port2, Port3, Port6, and Port7 share the same RE resource, all of which are ports in CDM group 1.
For convenience of description, for a CDM group0 single-symbol DMRS, if only Port0 or only Port1 data is transmitted, it is referred to as transmitting 1 Port or N Focc1; for CDM group0 single-symbol DMRS, if Port0 and Port1 data are transmitted simultaneously, the DMRS is said to transmit 2 ports or NFocc＝2。
The same is true for CDM group 1.
Accordingly, regarding the three different methods for adding the sequence edge values, which sample values are added needs to consider the number of ports sharing the same RE resource, that is, the added sample values are different according to the number of ports sharing the same RE resource, and the above N is used belowFocc1 and NFoccThe three types of sequence edge value addition and transition window addition are exemplified as two cases 2. And so on, and correspondingly, if more than two ports share the same RE resource, the same applies.
For the sequence edge value adding method of the (1) th type:
the number of ports and N that need to be multiplexed according to CDM group firstextraDetermining a channel estimation sequence HLS(k) The left edge and the right edge of the channel estimation sequence H, and then respectively estimating the channel estimation sequence H according to the repetition timesLS(k) The left and right edges of (a) are repeatedly added to the subsequence to be repeated. Such as:
if N is presentFoccIf 1, the channel estimation sequence H is first obtainedLS(k) Respectively repeats N for a left edge and a right edge of a value subsequenceextraSecondly:
k＝0，...，Nextraat the time of-1, Hextra(k)＝HLS(0)；
if N is presentFoccRepeating the left edge and right edge of the channel estimation sequence by N respectivelyextra2 times:
k＝0，2，...，Nextraat-2, Hextra(k)＝HLS(0)；
k＝1，3，...，NextraAt the time of-1, Hextra(k)＝HLS(1)；
for the sequence edge value adding method of the above (2):
this approach requires that the channel estimation sequence H be appliedLS(k) Respectively extrapolate the left and right edges of NextraAnd (5) sampling points. Such as:
if N is presentFocc1, the channel estimation sequence H is defined asLS(k) Respectively extrapolate the left and right edges of NextraValue of：
k＝0，...，NextraAt the time of-1, Hextra(k)＝(Nextra-k+1)*HLS(0)-(Nextra-k)*HLS (1)；
if N is presentFoccWhen the channel estimation sequence is 2, the channel estimation sequence is HLS(k) Respectively extrapolate the left and right edges of NextraValue, but extrapolation from NFoccThe difference is 1:
k＝0，2，...，Nextraat the time of-2, the reaction mixture,
k＝1，3，...，Nextrawhen the reaction temperature is 1, adding a catalyst,
for the sequence edge value adding method of the above (3):
this approach requires the channel estimation sequence H to be appliedLS(k) N of left and right edges of (1)extraAnd (3) symmetrically expanding each sampling point:
k＝0，...，Nextraat the time of-1, Hextra(k)＝HLS(Nextra-k)；
thus, the sequence H is estimated for the channel in a number of different ways as described aboveLS(k) After the addition processing of the edge value, the number of frequency domain sample points of the obtained processed channel estimation sequence is changed into
Wherein the content of the first and second substances,
representing a pre-processing channel estimation sequence HLS(k) Number of frequency domain sample points.
Then to Hextra(k) Plus length NWinK is 0,.., NWin-1, wherein NWin≤Nextra：
For example, the frequency domain response amplitude diagrams before and after the channel estimation sequence edge adding and windowing are respectively shown in fig. 3 and fig. 4, after adding 24 values repeatedly and adding a cosine window to the channel estimation sequence edge.
The data preprocessing method for transform domain channel estimation provided by the embodiment of the invention aims at the process of adopting the transform domain to carry out channel estimation, and carries out the channel estimation sequence H before orthogonal transformationLS(k) Adding N to the left and right edges ofextraValues and N added to the left and right edgesextraAll or part of the values are windowed, which greatly improves the performance of transform domain noise reduction.
Estimating the sequence H in view of the original channelLS(k) With linear phase, to avoid channel estimation sequence HLS(k) The problem that the noise reduction effect is deteriorated due to the fact that linear phase incoherence exists between the left edge adding value and the right edge adding value and the original channel estimation sequence can be solved, and the influence of the phase compensation or the phase adding can be reduced.
Fig. 5 shows another flow chart of the data preprocessing method for transform domain channel estimation according to the embodiment of the present invention, which includes the following steps:
For example, the channel estimation sequence HLS(k) Multiplying each value in (a) by exp (-j x 2 pi x k x delta theta), i.e. order
The implementation manner of step 503 is the same as that of step 102 in fig. 1, and is not described herein again.
It should be noted that, the sequence H is estimated for the channelLS(k) The pre-compensation is performed, so that when channel estimation is performed after the transform domain noise reduction processing, the channel estimation value after noise reduction needs to be subjected to corresponding linear phase compensation.
Alternative to avoid channel estimation sequence HLS(k) The scheme of the deterioration of the noise reduction effect caused by the linear phase incoherence between the left and right edge adding values and the original channel estimation sequence is shown in fig. 6, that is, in step 602, N is added to the left and right edges of the original channel estimation sequence respectivelyextraValues are added linearly in phase. For example, for k ═ 0., NextraWhen-1, let Hextra(k)＝Hextra(k)*exp(j*2π*(k-Nextra) Δ θ); for the
Order to
Where Δ θ represents the angle of increase of the linear phase.
FIG. 6 shows another non-limiting embodiment of the present invention for a data pre-processing method for transform-domain channel estimation, comprising the steps of:
step 601 is the same as step 101 in fig. 1, i.e. a channel estimation sequence before domain transformation is obtained, said channel estimation sequence comprising a plurality of sample numbers.
In step 602, the channel estimation sequence is processedAdding N to the left and right edges respectivelyextraValues and N added to the left and right edges respectivelyextraLinear phase addition of values, N added to the left and right edgesextraAll or part of the values are windowed.
In the above embodiments, the sequence H is estimated by applying to the channelLS(k) Can be made to follow the channel estimation sequence HLS(k) And when the transform domain noise reduction processing is carried out, energy reduction is carried out on noise interference. The preprocessed channel estimation sequence is subjected to Transform domain noise reduction, specifically, an orthogonal Transform such as DFT (Discrete Fourier Transform)/IDFT (Inverse Discrete Fourier Transform) is used for the preprocessed channel estimation sequence, so that the signal energy is compressed to some intervals, and noise interference outside the intervals is considered and is subjected to energy reduction.
The data preprocessing method for transform domain channel estimation provided by the embodiment of the invention can effectively improve the noise reduction performance of the transform domain, and the noise reduction performance is verified through simulation test.
The simulation test parameters are shown in table 1 below:
TABLE 1
In the simulation test, the following two schemes are respectively tested:
1. the scheme of the invention is as follows: transform domain filtering, repeating 24 times on the edge data values and adding a raised cosine window.
2. The prior art scheme is as follows: transform domain filtering, which is repeated 24 times for the edge data values, without applying a raised cosine transition window.
FIGS. 7-10 show MSE (MSE) for channel estimation simulation testing using the method of the present inventionMean Squared Error, Mean Squared Error) results. Wherein FIG. 7 corresponds to NFoccThe MSE results for 1, AWGN channel, fig. 8 corresponds to NFoccThe MSE result for 1, TDLA channel, FIG. 9 corresponds to NFoccMSE results for 1, TDLB channel, fig. 10 corresponds to N Focc1, the MSE result for the TDLC channel,
as can be seen from simulation results, the MSE of the scheme of the invention is obviously lower than that of the original scheme, no matter in a scene with high signal-to-noise ratio or low signal-to-noise ratio.
Further, the TDLB channel and the TDLC channel were selected, and MSE per RE was observed when SNR is 30dB, and the results are shown in fig. 11 and fig. 12, respectively.
It can be seen from the figure that, after the frequency domain edge RE is extended, the MSE after adding the transition window is obviously lower than the MSE without adding the transition window.
By the simulation test, the edge of the channel estimation sequence is subjected to external expansion, and a transition window is added, so that the MSE can be reduced, and the noise reduction effect is effectively improved.
Accordingly, an embodiment of the present invention further provides a data preprocessing apparatus for transform domain channel estimation, as shown in fig. 13, which is a schematic structural diagram of the apparatus.
In this embodiment, the data preprocessing apparatus 110 includes the following modules:
a data obtaining module 111 for obtaining the channel estimation sequence H before domain transformationLS(k) The channel estimation sequence comprises a plurality of sample points;
a preprocessing module 112 for generating a channel estimation sequence HLS(k) Adding N to the left and right edges ofextraValue, and N added to the left and right edgesextraAll or part of the values plus a transition window, Nextra≥1。
The preprocessing module 112 can estimate the sequence H for the channel in various waysLS(k) For example, the preprocessing module 112 may include any one or more of the following units to perform different manners of sequential edge processing:
a first processing unit, configured to repeat the channel estimation sequence H by using an edge valueLS(k) Respectively adding N to the left and right edges ofextraA value;
a second processing unit for extrapolating the channel estimation sequence H by means of an edge valueLS(k) Adding N to the left and right edges ofextraA value;
a third processing unit, configured to apply the channel estimation sequence H in an edge value symmetric extension mannerLS(k) Adding N to the left and right edges ofextraA value.
The above units estimate the sequence H to the channelLS(k) The specific manner of adding data to the left and right edges of the image frame can refer to the description in the embodiment of the method of the present invention, and is not described herein again.
The transition window may be any one of: a rectangular window, a raised cosine window, a triangular window, a hamming window, a hanning window, a brakman window, a chebyshev window, etc., which are not limited in the embodiments of the present invention.
Estimating the sequence H of the channel by the data preprocessing deviceLS(k) Can make the subsequent transform domain noise reduction module in the channel estimation sequence HLS(k) And when the transform domain noise reduction processing is carried out, energy reduction is carried out on noise interference. The transform domain noise reduction module is configured to perform transform domain noise reduction on the preprocessed channel estimation sequence, and specifically, apply orthogonal transform, such as DFT/IDFT, to the preprocessed channel estimation sequence, so that signal energy is compressed to some intervals, and noise interference outside the intervals is considered, and energy reduction is performed on the noise interference. The transform domain noise reduction module may adopt the prior art for the specific processing modes such as the orthogonal transform of the preprocessed channel estimation sequence, and the embodiment of the present invention is not limited.
Further, the sequence H is estimated in consideration of the original channelLS(k) Under the condition of linear phase, in order to avoid the influence on the noise reduction effect caused by the fact that the linear phase of the left and right edge adding values is not consistent with the original channel estimation sequence, the influence can be reduced through phase compensation.
As shown in fig. 14, in the present inventionIn another non-limiting embodiment of the present invention, the data pre-processing apparatus 110 further comprises: a phase compensation module 121 for estimating the channel estimation sequence HLS(k) Is pre-compensated.
Accordingly, in this embodiment, the data obtaining module 111 obtains the pre-compensated channel estimation sequence; the pre-processing module 112 adds N to the left and right edges of the pre-compensated sequence, respectivelyextraValue, N added to left and right edgesextraAll or part of the values are windowed.
It should be noted that, the sequence H is estimated for the channelLS(k) The pre-compensation is performed, so that when the channel estimation is performed after the transform domain noise reduction processing, the corresponding compensation module needs to perform the corresponding linear phase compensation on the noise-reduced channel estimation value.
As shown in fig. 15, in another non-limiting embodiment of the data preprocessing apparatus for transform-domain channel estimation according to the present invention, the data preprocessing apparatus 110 may further include: a phase adding module 131 for adding N to the left and right edges respectivelyextraValues are added linearly in phase.
The data preprocessing device for transform domain channel estimation provided in the embodiments of the present invention performs edge processing on a channel estimation sequence, specifically, performs edge processing on the channel estimation sequence H before orthogonal transform for a process of performing channel estimation using a transform domain, and specifically, performs edge processing on the channel estimation sequence HLS(k) Adding N to the left and right edges ofextraValues and N added to the left and right edgesextraAll or part of the values are windowed, which greatly improves the performance of transform domain noise reduction.
Correspondingly, the embodiment of the present invention further provides a communication device including the data preprocessing apparatus for transform domain channel estimation, where the communication device may be a network side device or a terminal side device.
The network side device may be, for example, a base station, such as: the device providing the Base Station function in the 2G network includes a Base Transceiver Station (BTS), the device providing the Base Station function in the 3G network includes a node B (NodeB), the device providing the Base Station function in the 4G network includes an evolved node B (eNB), and in a Wireless Local Area Network (WLAN), the device providing the Base Station function is an Access Point (AP), a device gNB providing the Base Station function in a 5G New Radio (NR), and a node B (ng-eNB) continuing to evolve, where the gNB and the terminal communicate with each other by using an NR technique, the ng-eNB and the terminal communicate with each other by using an E-Universal Terrestrial Radio Access technique, and both the gNB and the ng-eNB may be connected to the 5G core network. The base station in the embodiment of the present invention further includes a device and the like that provide a function of the base station in a future new communication system.
The Terminal-side device may be various types of User Equipment (UE), an access Terminal, a subscriber unit, a subscriber Station, a Mobile Station (MS), a remote Station, a remote Terminal, a Mobile device, a User Terminal, a Terminal device (Terminal Equipment), a wireless communication device, a User agent, or a User Equipment. The terminal device may also be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), a handheld device with Wireless communication function, a computing device or other processing device connected to a Wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a future 5G Network or a terminal device in a future evolved Public Land Mobile Network (PLMN), and the like, which are not limited in this embodiment of the present invention.
In a specific implementation, the data preprocessing device for transform domain channel estimation may correspond to a Chip in a network device, such as a System-On-a-Chip (SoC), a baseband Chip, a Chip module, and the like.
In a specific implementation, each module/unit included in each apparatus and product described in the foregoing embodiments may be a software module/unit, may also be a hardware module/unit, or may also be a part of a software module/unit and a part of a hardware module/unit.
For example, for each apparatus and product applied to or integrated into a chip, each module/unit included in the apparatus and product may all be implemented by hardware such as a circuit, or at least a part of the modules/units may be implemented by a software program running on a processor integrated within the chip, and the remaining (if any) part of the modules/units may be implemented by hardware such as a circuit; for each device or product applied to or integrated with the chip module, each module/unit included in the device or product may be implemented by using hardware such as a circuit, and different modules/units may be located in the same component (e.g., a chip, a circuit module, etc.) or different components of the chip module, or at least some of the modules/units may be implemented by using a software program running on a processor integrated within the chip module, and the rest (if any) of the modules/units may be implemented by using hardware such as a circuit; for each device and product applied to or integrated in the terminal, each module/unit included in the device and product may be implemented by using hardware such as a circuit, and different modules/units may be located in the same component (e.g., a chip, a circuit module, etc.) or different components in the terminal, or at least part of the modules/units may be implemented by using a software program running on a processor integrated in the terminal, and the rest (if any) part of the modules/units may be implemented by using hardware such as a circuit.
An embodiment of the present invention further provides a computer-readable storage medium, which is a non-volatile storage medium or a non-transitory storage medium, and a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method provided in the embodiment corresponding to fig. 1, fig. 5, or fig. 6.
Another noise reduction apparatus for transform-domain channel estimation is provided in an embodiment of the present invention, and includes a memory and a processor, where the memory stores a computer program that is executable on the processor, and the processor executes the computer program to perform the steps of the method provided in the embodiment corresponding to fig. 1, fig. 5, or fig. 6.
Further, an embodiment of the present invention further discloses a terminal, which includes a memory and a processor, where the memory stores computer instructions capable of being executed on the processor, and the processor executes the computer instructions to execute the technical solution of the method in the embodiment shown in fig. 1, 5, or 6. Preferably, the terminal may be a multi-card multi-mode terminal supporting 4G and 5G access technologies.
Further, an embodiment of the present invention further discloses a network device, which includes a memory and a processor, where the memory stores computer instructions capable of being executed on the processor, and the processor executes the computer instructions to execute the technical solution of the method in the embodiment shown in fig. 1, 5, or 6. Preferably, the network device may be a core network, such as core network EPC of 4G, core network 5GC of 5G.
In the embodiments provided in the present invention, it should be understood that the disclosed method, apparatus and system can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative; for example, the division of the unit is only a logic function division, and there may be another division manner in actual implementation; for example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be physically included alone, or two or more units may be integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (16)
1. A method of data pre-processing for transform domain channel estimation, the method comprising:
obtaining a channel estimation sequence H prior to domain transformationLS(k) The channel estimation sequence HLS(k) Comprises a plurality of sample points;
the channel estimation sequence HLS(k) Adding N to the left and right edges ofextraValue, and N added to the left and right edgesextraAll or part of the values plus a transition window, Nextra≥1。
2. The method of claim 1, wherein the channel estimation sequence H is a sequence of channel estimatesLS(k) Adding N to the edge ofextraThe values include any of:
the channel estimation sequence H is repeated by an edge valueLS(k) Adding N to the left and right edges ofextraA value;
the channel estimation sequence H is obtained by means of edge value extrapolationLS(k) Adding N to the left and right edges ofextraA value;
the channel estimation sequence H is processed by an edge value symmetric extension modeLS(k) Adding N to the left and right edges ofextraA value.
3. The method of claim 1, wherein the transition window is any one of: rectangular window, raised cosine window, triangular window, Hamming window, Hanning window, Blackman window, Chebyshev window.
4. The method of claim 3, wherein the channel estimation sequence H is repeated by an edge valueLS(k) Adding N to the left and right edges ofextraThe values include:
number of ports and N multiplexed according to code division multiplexing groupextraDetermining the channel estimation sequence HLS(k) The number of sampling points and the number of repetition times contained in the subsequence to be repeated at the left edge and the right edge of the frame;
respectively estimating the channel estimation sequence H according to the repetition timesLS(k) The left and right edges of the sub-sequence to be repeated are repeatedly added.
5. The method of claim 3, wherein the channel estimation sequence H is extrapolated by an edge valueLS(k) Adding N to the left and right edges ofextraThe values include:
the channel estimation sequence HLS(k) Respectively extrapolate the left and right edges of NextraAnd (5) sampling points.
6. The method of claim 3, wherein the channel estimation sequence H is symmetrically spread by edge valuesLS(k) Adding N to the left and right edges ofextraThe values include:
the channel estimation sequence HLS(k) N of left and right edges ofextraThe sampling points are symmetrically expanded respectively.
7. The method of claim 1, further comprising:
for the channel estimation sequence HLS(k) Is pre-compensated.
8. The method of claim 1, further comprising:
n added to the left and right edges respectivelyextraValues are added linearly in phase.
9. The method according to any of claims 1 to 8, wherein the domain transform is any of: transform from frequency domain to time domain, transform from time domain to frequency domain, transform from spatial domain to beam domain, and transform from beam domain to spatial domain.
10. A data pre-processing apparatus for transform domain channel estimation, the apparatus comprising:
a data acquisition module for acquiring the channel estimation sequence H before domain transformationLS(k) The channel estimation sequence HLS(k) Comprises a plurality of sample points;
a pre-processing module for converting the channel estimation sequence HLS(k) Adding N to the left and right edges ofextraValue, and N added to the left and right edgesextraAll or part of the values plus a transition window, Nextra≥1。
11. The apparatus of claim 10, wherein the preprocessing module comprises any one or more of:
a first processing unit for repeating the channel estimation sequence H by an edge valueLS(k) Adding N to the left and right edges ofextraA value;
a second processing unit for extrapolating the channel estimation sequence H by means of an edge valueLS(k) Adding N to the left and right edges ofextraA value;
a third processing unit, configured to apply the channel estimation sequence H in an edge value symmetric spreading mannerLS(k) Adding N to the left and right edges ofextraA value.
12. The apparatus of claim 10, further comprising:
a phase compensation module for estimating the channel estimation sequence HLS(k) Is pre-compensated.
13. The apparatus of claim 10, further comprising:
a phase adding module for adding N to the left and right edges respectivelyextraValues are added linearly in phase.
14. A communication device comprising data preprocessing means for transform domain channel estimation according to any of claims 10 to 13.
15. A computer-readable storage medium, being a non-volatile storage medium or a non-transitory storage medium, having a computer program stored thereon, which, when being executed by a processor, performs the steps of the method according to any of the claims 1 to 9.
16. A communication device comprising a memory and a processor, the memory having stored thereon a computer program being executable on the processor, wherein the processor, when executing the computer program, performs the steps of the method of any of claims 1 to 9.
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