Transform domain signal-to-noise ratio estimation method and device, and communication equipment

Abstract

A transform domain signal-to-noise ratio estimation method and device, and a communication device, the method comprising: obtaining a channel estimation sequence H LS (k), the channel estimation sequence H LS (k) comprises a plurality of sample points; determining the channel estimation sequence H LS (k) a rough estimated signal-to-noise ratio; if the rough estimated signal-to-noise ratio is greater than a threshold value, the channel estimation sequence H LS The left and right edges of (k) are N Win The invention provides a method for calculating the signal-to-noise ratio of a transform domain signal-to-noise ratio by performing an orthogonal transformation on the processed channel estimation sequence to obtain a transformed channel estimation sequence. The invention provides a method for calculating the signal-to-noise ratio of a transform domain signal-to-noise ratio by performing an orthogonal transformation on the processed channel estimation sequence to obtain a transformed channel estimation sequence.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a transform domain signal-to-noise ratio estimation method and device, and communication equipment. Background Art

[0002] SNR (Signal-Noise Ratio) is an important parameter of communication signals and one of the criteria for measuring communication quality. In many cases in wireless communication, it is necessary to know the value of the signal-to-noise ratio to obtain the best performance. For example, accurate SNR estimation can improve the accuracy of channel estimation filter selection, MIMO (Multi-input Multi-output) equalization, AMC (Adaptive Modulation and Coding), power control, DTX (Discontinuous Transmission) judgment, etc.

[0003] In the prior art, the commonly used signal-to-noise ratio estimation methods mainly include: frequency domain signal-to-noise ratio estimation and time domain signal-to-noise ratio estimation. Among them, the principle of transform domain signal-to-noise ratio estimation is as follows: use orthogonal transform, such as DFT (Discrete Fourier Transform) / IDFT (Inverse Discrete Fourier Transform), on the original channel estimation sequence to compress the signal energy into certain intervals, and consider the noise power outside the interval to obtain the signal-to-noise ratio. The transform domain signal-to-noise ratio estimation algorithm can be a transform from the frequency domain to the time domain, from the time domain to the frequency domain, or from the spatial domain to the beam domain. The existing transform domain signal-to-noise ratio estimation method will cause the signal energy to be dispersed in the transform domain, that is, the signal energy cannot be well compressed into certain intervals, resulting in a large noise power estimate value and a small signal estimate value, thereby making the signal-to-noise ratio estimate value small, affecting the accuracy of the signal-to-noise ratio estimation result. Summary of the invention

[0004] The embodiment of the present invention provides a transform domain signal-to-noise ratio estimation method and device, and communication equipment to improve the accuracy of the signal-to-noise ratio estimation result.

[0005] An embodiment of the present invention provides a transform domain signal-to-noise ratio estimation method, the method comprising:

[0006] Get the channel estimation sequence H LS (k), the channel estimation sequence H LS (k) contains multiple sample points;

[0007] Determine the channel estimation sequence H LS(k) Rough estimate of signal-to-noise ratio;

[0008] If the rough estimated signal-to-noise ratio is greater than the threshold, the channel estimation sequence H LS The left and right edges of (k) are N Win Sample points plus transition window are added to obtain the processed channel estimation sequence;

[0009] performing an orthogonal transformation on the processed channel estimation sequence to obtain a transformed channel estimation sequence;

[0010] A signal-to-noise ratio is calculated according to the transformed channel estimation sequence.

[0011] Optionally, the determining of the channel estimation sequence H LS The rough estimate of the signal-to-noise ratio of (k) includes any of the following:

[0012] Determine a rough estimate of the signal-to-noise ratio based on a difference in least squares channel estimation values ​​of adjacent frequency domain receiving channels;

[0013] Determine a rough estimate of the signal-to-noise ratio based on a difference in least squares channel estimation values ​​of adjacent time-domain receiving channels;

[0014] Determine the rough estimate of signal-to-noise ratio based on the path loss deduction;

[0015] Determine a rough estimate of the signal-to-noise ratio based on the scheduling modulation and coding strategy;

[0016] A rough estimate of the signal-to-noise ratio is determined based on the historical signal-to-noise ratio.

[0017] Optionally, the method further comprises:

[0018] In the channel estimation sequence H LS The left and right edges of (k) are N Win Before adding the transition window to the sample points, the channel estimation sequence H LS Add N to the left and right edges of (k) extra Values;

[0019] The channel estimation sequence H LS The left and right edges of (k) are N Win Sample points plus transition window include:

[0020] N is added to the left and right edges extra Add transition windows to the left and right of the sequence after the value.

[0021] Optionally, the channel estimation sequence H LS Add N to the left and right edges of (k) extra Values ​​include any of the following:

[0022] The channel estimation sequence H is repeated in an edge value manner.LS Add N to the left and right edges of (k) extra Values;

[0023] The channel estimation sequence H is transformed into LS Add N to the left and right edges of (k) extra Values;

[0024] The channel estimation sequence H is expanded by edge value symmetric expansion. LS Add N to the left and right edges of (k) extra value.

[0025] Optionally, the channel estimation sequence H is repeated in an edge value manner. LS Add N to the left and right edges of (k) extra The values ​​are:

[0026] According to the number of ports multiplexed by the code division multiplexing group and N extra Determine the channel estimation sequence H LS (k) The number of sample points and the number of repetitions contained in the subsequence to be repeated on the left and right edges;

[0027] The channel estimation sequence H is respectively LS The subsequences to be repeated at the left and right edges of (k) are repeatedly added.

[0028] Optionally, the channel estimation sequence H is extrapolated by edge value extrapolation. LS Add N to the left and right edges of (k) extra The values ​​include: LS The left and right edges of (k) are extrapolated N extra A sample point.

[0029] Optionally, the channel estimation sequence H is expanded symmetrically by edge value LS Add N to the left and right edges of (k) extra The values ​​include: LS (k) The left and right edges of N extra The sample points are expanded symmetrically.

[0030] Optionally, the transition window is any one of the following: a rectangular window, a raised cosine window, a triangular window, a Hamming window, a Hanning window, a Blackman window, and a Chebyshev window.

[0031] Optionally, the method further comprises: estimating the channel sequence H LS The linear phase of (k) is pre-compensated.

[0032] Optionally, the method further comprises: adding N to the left and right edges respectively. extra The values ​​are linearly phase added.

[0033] Optionally, calculating the signal-to-noise ratio according to the transformed channel estimation sequence includes:

[0034] Determining a noise sample point index set and a valid signal energy interval in the transformed channel estimation sequence according to the number of orthogonal transformation points and the multipath delay spread range of the current channel model;

[0035] Adjusting the noise sample point index set and the effective signal energy interval according to the roughly estimated signal-to-noise ratio;

[0036] The signal-to-noise ratio is calculated based on the adjusted noise sample point index set and the effective signal energy interval.

[0037] Optionally, the transformation domain is any one of the following: transformation from frequency domain to time domain, transformation from time domain to frequency domain, transformation from spatial domain to beam domain, and transformation from beam domain to spatial domain.

[0038] The embodiment of the present invention further provides a transform domain signal-to-noise ratio estimation device, the device comprising:

[0039] Data acquisition module, used to obtain the channel estimation sequence H LS (k), the channel estimation sequence includes a plurality of sample points;

[0040] A rough estimation module is used to determine the channel estimation sequence H LS (k) Rough estimate of signal-to-noise ratio;

[0041] A windowing processing module is used for processing the channel estimation sequence H when the rough estimated signal-to-noise ratio is greater than a threshold value. LS The left and right edges of (k) are N Win Sample points plus transition window are added to obtain the processed channel estimation sequence;

[0042] A transformation module, used for performing an orthogonal transformation on the processed channel estimation sequence to obtain a transformed channel estimation sequence;

[0043] A calculation module is used to calculate a signal-to-noise ratio according to the transformed channel estimation sequence.

[0044] Optionally, the rough estimation module determines the channel estimation sequence H in any of the following ways: LS Rough estimate of signal-to-noise ratio of (k):

[0045] Determine a rough estimate of the signal-to-noise ratio based on a difference in least squares channel estimation values ​​of adjacent frequency domain receiving channels;

[0046] Determine a rough estimate of the signal-to-noise ratio based on a difference in least squares channel estimation values ​​of adjacent time-domain receiving channels;

[0047] Determine the rough estimate of signal-to-noise ratio based on the path loss deduction;

[0048] Determine a rough estimate of the signal-to-noise ratio based on the scheduling modulation and coding strategy;

[0049] A rough estimate of the signal-to-noise ratio is determined based on the historical signal-to-noise ratio.

[0050] Optionally, the device further comprises:

[0051] The edge data adding module is used to process the channel estimation sequence H in the windowing processing module. LS The left and right edges of (k) are N Win Before adding the transition window to the sample points, the channel estimation sequence H LS Add N to the left and right edges of (k) extra values;

[0052] The windowing processing module is specifically used to add N extra Add transition windows to the left and right of the sequence after the value.

[0053] Optionally, the edge data adding module includes any one or more of the following units:

[0054] The first processing unit is used to convert the channel estimation sequence H into LS Add N to the left and right edges of (k) extra Values;

[0055] The second processing unit is used to convert the channel estimation sequence H into LS Add N to the left and right edges of (k) extra Values;

[0056] The third processing unit is used to expand the channel estimation sequence H by edge value symmetric expansion. LS Add N to the left and right edges of (k) extra value.

[0057] Optionally, the device further comprises: a phase compensation module, configured to compensate the channel estimation sequence H LS The linear phase of (k) is pre-compensated.

[0058] Optionally, the device further comprises: a phase adding module, configured to add N phases to the left and right edges respectively. extra The values ​​are linearly phase added.

[0059] Optionally, the calculation module includes:

[0060] An interval determination unit, used to determine a noise sample point index set and a valid signal energy interval in the transformed channel estimation sequence according to the number of orthogonal transformation points and the multipath delay spread range of the current channel model;

[0061] An adjustment unit, configured to adjust the noise sample point index set and the effective signal energy interval according to the roughly estimated signal-to-noise ratio;

[0062] The calculation unit is used to calculate the signal-to-noise ratio according to the adjusted noise sample point index set and the effective signal energy interval.

[0063] Optionally, the transformation domain is any one of the following: transformation from frequency domain to time domain, transformation from time domain to frequency domain, transformation from spatial domain to beam domain, and transformation from beam domain to spatial domain.

[0064] An embodiment of the present invention further provides a communication device, wherein the communication device includes the transform domain signal-to-noise ratio estimation device as described above.

[0065] An embodiment of the present invention further provides a computer-readable storage medium, which is a non-volatile storage medium or a non-transient storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are executed.

[0066] An embodiment of the present invention further provides a communication device, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor executes the steps of the above method when running the computer program.

[0067] The transform domain signal-to-noise ratio estimation method and apparatus, and the communication device provided by the embodiment of the present invention, before performing an orthogonal transform on the channel estimation sequence, first determine the rough estimated signal-to-noise ratio of the channel estimation sequence, and then process the channel estimation sequence according to the rough estimated signal-to-noise ratio. Specifically, if the rough estimated signal-to-noise ratio is greater than a threshold value, the N left and right edges of the channel estimation sequence are processed. Win The sample points are added with a transition window, and then the processed channel estimation sequence is orthogonally transformed. The signal-to-noise ratio is calculated according to the transformed channel estimation sequence. The accuracy of the transform domain signal-to-noise ratio estimation result can be effectively improved by windowing processing.

[0068] Furthermore, edge data values ​​may be added to the channel estimation sequence before windowing, and then a transition window may be added to the added data, so that the signal-to-noise ratio estimation result may be more accurate.

[0069] Furthermore, there are many ways to add edge data values ​​of the channel estimation sequence: repetition of sequence edge data values, extrapolation of sequence edge data values, symmetrical extension of sequence edge data values, etc., so that it can be conveniently and flexibly selected. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 is a flow chart of a transform domain signal-to-noise ratio estimation method according to an embodiment of the present invention;

[0071] Figure 2 is another flow chart of the transform domain signal-to-noise ratio estimation method according to an embodiment of the present invention;

[0072] Figure 3 This is a schematic diagram of the pilot architecture of 5G NR PUSCH DMRS configuration type Type 1;

[0073] Figure 4 1 is a schematic diagram of the frequency domain response amplitude of a channel estimation sequence under one port in an embodiment of the present invention before edge value addition and windowing processing;

[0074] Figure 5 1 is a schematic diagram of the frequency domain response amplitude after the edge value of the channel estimation sequence under two ports is added and the windowing is performed in an embodiment of the present invention;

[0075] Figure 6 and Figure 7 Schematic diagram of power spectrum in the transform domain obtained by simulating and testing the method of the present invention and the prior art respectively;

[0076] Figure 8 It is a schematic diagram of simulation results of several different SNR estimation methods;

[0077] Fig. 9 It is a structural schematic diagram of a transform domain signal-to-noise ratio estimation device according to an embodiment of the present invention;

[0078] Fig.10 It is another structural schematic diagram of the transform domain signal-to-noise ratio estimation device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0079] In order to make the above-mentioned objects, features and beneficial effects of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0080] In view of the problem that the existing transform domain signal-to-noise ratio estimation method causes energy dispersion of the signal energy in the transform domain, thereby affecting the accuracy of the signal-to-noise ratio estimation result, the embodiment of the present invention provides a transform domain signal-to-noise ratio estimation method and device. Before performing an orthogonal transform on a channel estimation sequence, a rough estimated signal-to-noise ratio of the channel estimation sequence is first determined, and then the channel estimation sequence is processed according to the size of the rough estimated signal-to-noise ratio. Specifically, if the rough estimated signal-to-noise ratio is greater than a threshold value, the N left and right edges of the channel estimation sequence are respectively processed. Win The sample points are added with a transition window, and then the processed channel estimation sequence is orthogonally transformed, and the signal-to-noise ratio is calculated according to the transformed channel estimation sequence.

[0081] It should be noted that the transform domain signal-to-noise ratio estimation method and device provided in the embodiments of the present invention can be used in a variety of different transform domains, such as: transforming from the frequency domain to the time domain, from the time domain to the frequency domain, from the spatial domain to the beam domain, from the beam domain to the spatial domain, etc. For ease of description, the following embodiments are all described by taking the frequency domain to the time domain transform as an example.

[0082] like Figure 1 FIG. 1 is a flow chart of a transform domain signal-to-noise ratio estimation method according to an embodiment of the present invention, comprising the following steps:

[0083] Step 101: Obtain channel estimation sequence H LS (k), the channel estimation sequence includes a plurality of sample points.

[0084] The channel estimation sequence H LS (k) refers to the channel estimation sequence after removing the phase influence brought by the pilot. Taking frequency domain channel estimation as an example, the number of frequency domain samples is That is

[0085] Step 102: Determine the channel estimation sequence H LS (k) Rough estimate of the signal-to-noise ratio.

[0086] It should be noted that the channel estimation sequence H LS The rough estimate of the signal-to-noise ratio of (k) can be calculated using existing technologies, for example, any of the following methods:

[0087] Determine a roughly estimated signal-to-noise ratio according to a difference in LS (Least Square) channel estimation values ​​of adjacent frequency domain receiving channels, such as determining the roughly estimated signal-to-noise ratio by using the power of the difference as the noise power;

[0088] Determine a roughly estimated signal-to-noise ratio according to a difference in LS channel estimation values ​​of adjacent time-domain receiving channels, such as determining the roughly estimated signal-to-noise ratio by using the power of the difference as the noise power;

[0089] Determine the rough estimate of signal-to-noise ratio based on the path loss deduction;

[0090] Determine a rough estimate of the signal-to-noise ratio according to the scheduling MCS (Modulation and Coding Scheme);

[0091] A rough estimate of the signal-to-noise ratio is determined based on the historical signal-to-noise ratio.

[0092] The above-mentioned channel estimation methods can adopt existing technologies and will not be described in detail here.

[0093] Of course, there may be other calculation methods, which are not limited in the embodiment of the present invention.

[0094] Step 103: If the rough estimated signal-to-noise ratio is greater than the threshold, the channel estimation sequence H LS The left and right edges of (k) are N Win The sample points are added with a transition window to obtain the processed channel estimation sequence.

[0095] The transition window may be, but is not limited to, any one of the following: a rectangular window, a raised cosine window, a triangular window, a Hamming window, a Hanning window, a Blackman window, a Chebyshev window, etc., which is not limited in this embodiment of the present invention.

[0096] For example, for the channel estimation sequence H LS The left and right edges of (k) are N Win The sample points plus the transition window are as follows:

[0097]

[0098] The number of frequency domain sample points of the processed channel estimation sequence is:

[0099] Step 104: perform an orthogonal transform on the processed channel estimation sequence to obtain a transformed channel estimation sequence.

[0100] Step 105: Calculate the signal-to-noise ratio according to the transformed channel estimation sequence.

[0101] Furthermore, the channel estimation sequence H LS The left and right edges of (k) are N Win Before adding the transition window to the sample points, the channel estimation sequence H LS Add N to the left and right edges of (k) extra values, and then added N to the left and right edges extra Add transition windows to the left and right of the sequence after the value.

[0102] It should be noted that in practical applications, N Win The value can be greater than, less than, or equal to N extra The value of is not limited in this embodiment of the present invention. Figure 2 In the embodiment shown, N Win =N extra Take this as an example to illustrate.

[0103] like Figure 2 FIG. 2 is another flow chart of a transform domain signal-to-noise ratio estimation method according to an embodiment of the present invention, comprising the following steps:

[0104] Step 201: Obtain channel estimation sequence H LS (k), the channel estimation sequence includes a plurality of sample points.

[0105] Step 202: Determine the channel estimation sequence H LS (k) Rough estimate of the signal-to-noise ratio.

[0106] Step 203: If the rough estimated signal-to-noise ratio is greater than a threshold, the channel estimation sequence H LS Add N to the left and right edges of (k) extra values, for each of the left and right edges extra The sample points are added with a transition window to obtain the processed channel estimation sequence.

[0107] Step 204: perform an orthogonal transform on the processed channel estimation sequence to obtain a transformed channel estimation sequence.

[0108] Step 205: Calculate the signal-to-noise ratio according to the transformed channel estimation sequence.

[0109] The above steps 204 and 205 are Figure 1 The implementation methods of step 104 and step 105 are the same and will not be repeated here.

[0110] It should be noted that in the above step 203, the channel estimation sequence H LS (k) There are many ways to add sequence edge values, such as:

[0111] (1) Repeating the edge data values ​​of the sequence, that is, repeating the edge values ​​to obtain the channel estimation sequence H LS Add N to the left and right edges of (k) extra value.

[0112] (2) Extrapolation of sequence edge data values, that is, the channel estimation sequence H is extrapolated by edge value extrapolation. LS Add N to the left and right edges of (k) extra value.

[0113] (3) Symmetric expansion of the edge data values ​​of the sequence, that is, the channel estimation sequence H is symmetrically expanded by edge value expansion. LS Add N to the left and right edges of (k) extra value.

[0114] Of course, in practical applications, there may be other adding methods, which can be selected according to application needs, and the embodiment of the present invention does not limit this.

[0115] In 5G (5th Generation Mobile Communication Technology) NR (New Radio), DMRS (Demodulation Reference Signal) is introduced for downlink and uplink channels, which is used for channel estimation at the receiving end (base station side or UE (User Equipment) side) to demodulate the relevant physical channels. The UE determines the mapping of DMRS to physical resources according to the high-level configuration parameter dmrs-Type. If the high-level parameter DMRS-DownlinkConfig=>maxLength, that is, the maximum number of symbols of the pre-DMRS, is not configured, then a single-symbol DMRS is used; if the high-level parameter maxLength is configured as "len2', the UE determines whether it is a single symbol or a double symbol according to the received DCI (Downlink Control Information). When the configuration type is type1, a single symbol supports a maximum of 4 ports, and a double symbol supports a maximum of 8 ports; when the configuration type is type2, a single symbol supports a maximum of 6 ports, and a double symbol supports a maximum of 12 ports.

[0116] Taking 5G NR PUSCH (Physical Uplink Shared Channel) DMRS (Demodulation Reference Signal) configuration type Type 1 as an example, its pilot architecture is as follows: Figure 3 shown.

[0117] The DMRS corresponding to the ports in a CDM group (Code Division Multiplexing group) share the same RE (Resource Element) resources. Figure 3 As shown in , for single-symbol DMRS, Port0 and Port1 share the same RE resources, both are ports in CDM group0; Port2 and Port3 share the same RE resources, both are ports in CDM group1. For dual-symbol DMRS, Port0, Port1, Port4 and Port5 share the same RE resources, both are ports in CDM group0; Port2, Port3, Port6 and Port7 share the same RE resources, both are ports in CDMgroup1.

[0118] For the convenience of description, for CDM group 0 single-symbol DMRS, if only Port 0 or only Port 1 data is sent, it is called sending 1 port or N Focc =1; For CDM group 0 single-symbol DMRS, if data of Port 0 and Port 1 are sent at the same time, it is called sending 2 ports or N Focc =2.

[0119] The same is true for CDM group1.

[0120] Accordingly, for the above three different methods of adding sequence edge values, the sample point values ​​to be added need to consider the number of ports sharing the same RE resources. That is to say, the sample point values ​​added will also be different according to the number of ports sharing the same RE resources. Focc =1 and N Focc =2 are used as examples to illustrate the above three methods of adding sequence edge values ​​and adding transition windows. Similarly, if there are more than two ports sharing the same RE resource, a similar method is also adopted.

[0121] For the above-mentioned method (1) of adding sequence edge values:

[0122] First, we need to calculate the number of ports reused by the CDM group and N extra Determine the channel estimation sequence H LS The number of samples and the number of repetitions of the subsequence to be repeated at the left and right edges of (k) are then calculated, and the channel estimation sequence H is calculated according to the number of repetitions. LS The subsequences to be repeated on the left and right edges of (k) are added repeatedly. For example:

[0123] If N Focc =1, then firstly transform the channel estimation sequence H LS The subsequence of a value on the left and right edges of (k) is repeated N times extra Second-rate:

[0124] k=0,…,N extra -1, H extra (k) = H LS (0);

[0125] When H extra (k) = H LS (kN extra );

[0126] hour,

[0127] If N Focc =2, then the subsequences of the left and right edge values ​​of the channel estimation sequence are repeated N times respectively. extra / 2 times:

[0128] k=0,2,…,N extra -2, H extra (k) = H LS (0);

[0129] k=1,3,…,N extra -1, H extra (k) = H LS (1);

[0130] When H extra (k) = H LS (kN extra );

[0131] hour,

[0132] hour,

[0133] For the above-mentioned method (2) of adding sequence edge values:

[0134] This method requires that the channel estimation sequence H LS The left and right edges of (k) are extrapolated N extra For example:

[0135] If N Focc =1, then the channel estimation sequence H LS The left and right edges of (k) are extrapolated N extra Values:

[0136] k=0,…,N extra -1, H extra (k)=(N extra -k+1)*H LS (0)-(N extra -k)*H LS (1);

[0137] When H extra (k) = H LS (kN extra );

[0138] hour,

[0139] If N Focc =2, then the channel estimation sequence H LS The left and right edges of (k) are extrapolated N extra values, but the extrapolation method is the same as N Focc =1 is different:

[0140] k=0,2,…,N extra -2 hours,

[0141] k=1,3,…,N extra -1 hour,

[0142] When H extra (k) = H LS (kN extra );

[0143] hour,

[0144] hour,

[0145] For the above-mentioned method (3) of adding sequence edge values:

[0146] This method requires the current sequence H LS (k) The left and right edges of N extra The sample points are expanded symmetrically:

[0147] k=0,…,N extra -1, H extra (k) = H LS (N extra -k);

[0148] When H extra (k) = H LS (kN extra );

[0149] hour,

[0150] In this way, the channel estimation sequence H is calculated by the above-mentioned various methods. LS (k) After the edge value is added, the number of frequency domain sample points of the processed channel estimation sequence becomes in, Denotes the channel estimation sequence H before processing LS (k) is the number of frequency domain sample points.

[0151] Then for H extra (k) The added length is N Win The transition window W(k), k=0,…,N Win -1:

[0152]

[0153] For example, 24 values ​​are repeatedly added to the edge of the channel estimation sequence, and a raised cosine window is added. The frequency domain response amplitude diagrams before and after the edge value addition and windowing of the channel estimation sequence are shown as follows: Figure 4 and Figure 5 shown.

[0154] The following describes in detail the process of transforming the channel estimation sequence into other domains to perform signal-to-noise ratio estimation through orthogonal transformation in the embodiment of the present invention.

[0155] Assume that IDFT is used for transform domain processing, let N IDFT is the number of points of IDFT transformation.

[0156] Add the above edge RE to the windowed channel estimation sequence H Win (k) Supplement 0, get N IDFT Point vector H Win It should be noted that the additional 0 can be located in the channel estimation sequence H Win (k) Any position of the sequence, i.e. the head, middle or tail, is acceptable, but continuity is required.

[0157] Vector H Win Perform N IDFT IDFT transformation of the point is used to obtain the channel estimation sequence h in the time domain LS (n),n=0,…,N IDFT -1.

[0158] According to the number of points N of orthogonal transformation IDFT The noise sample point index set and the effective signal energy interval in the transformed channel estimation sequence are determined based on the multipath delay spread range of the current channel model.

[0159] Assume that the noise sample point index set is ψ noise , the number of elements is N noise , then the noise estimate for:

[0160]

[0161] Assume that the effective signal energy interval is ψ signal , the number of elements is N signal , then the effective signal energy estimate is:

[0162]

[0163] It should be noted that in Figure 2 In the illustrated embodiment, since the windowed edge value is added to the channel estimation sequence, it is necessary to subtract the energy of the added windowed edge value from the effective signal energy estimate SignalPower.

[0164] The estimated SNR value (dB) is:

[0165]

[0166] In particular, when the rough estimated SNR is small, the noise sample point index set ψ noise Appropriately increase the effective signal energy interval ψ signal Appropriately reduce; when the rough estimated signal-to-noise ratio is large, the noise sample point index set ψ noise Appropriately reduce the effective signal energy interval ψ signal This can make the transform domain SNR estimation have good accuracy under both low SNR and high SNR.

[0167] Taking 5G NR PUSCH DMRS configuration type Type 1 as an example, for the common CP (Cyclic Prefix) scenario, there are the following situations:

[0168] When N Focc = 1, if the rough estimate but:

[0169]

[0170] If a rough estimate but:

[0171]

[0172] When N Focc =2, if the rough estimate but:

[0173]

[0174] If a rough estimate but:

[0175]

[0176] Among them, NIDFT is the number of points of IDFT transformation.

[0177] The transform domain signal-to-noise ratio estimation method provided by the embodiment of the present invention determines the rough estimated signal-to-noise ratio of the channel estimation sequence before performing orthogonal transformation on the channel estimation sequence, and then processes the channel estimation sequence according to the rough estimated signal-to-noise ratio. Specifically, if the rough estimated signal-to-noise ratio is greater than a threshold value, the N edges of the left and right edges of the channel estimation sequence are respectively Win The sample points are added with transition windows, and then the processed channel estimation sequence is orthogonally transformed, and the signal-to-noise ratio is calculated according to the transformed channel estimation sequence. The accuracy of the signal-to-noise ratio estimation result in the transform domain can be effectively improved by windowing. Furthermore, edge data values ​​can be added to the channel estimation sequence before windowing, and then a transition window is added to the added data, so that the signal-to-noise ratio estimation result can be more accurate.

[0178] It should be noted that in the above Figure 1 and Figure 2 In the embodiment shown, if the channel estimation sequence H LS If the rough estimated signal-to-noise ratio of (k) is less than the threshold value, the channel estimation sequence H LS (k) is orthogonally transformed. That is, there is no need to perform an orthogonal transformation on the channel estimation sequence H LS (k) Add edge values ​​and perform windowing.

[0179] If the channel estimation sequence H LS If the rough estimated signal-to-noise ratio of (k) is equal to the threshold value, the channel estimation sequence H can be directly LS (k) Perform an orthogonal transformation, or Figure 1 or Figure 2 The channel estimation sequence H LS The processing method when the rough estimated signal-to-noise ratio of (k) is greater than the threshold value is that the channel estimation sequence H LS (k) Adding edge values ​​may further include adding transition windows, which is not limited in this embodiment of the present invention.

[0180] Considering the original channel estimation sequence H LS (k) Situations with linear phase, such as: time offset leading to linear phase of frequency domain sequence, frequency offset leading to linear phase of time domain sequence, incident angle offset leading to linear phase of antenna domain sequence, etc. If there is a linear phase, the signal after the transform domain will be offset, resulting in inaccurate selection of signal interval or noise interval, and the original channel estimation sequence and the added edge sequence will have phase discontinuity, which will affect the accuracy of signal-to-noise ratio estimation. Therefore, in order to avoid the influence of linear phase on signal-to-noise ratio estimation, its influence can also be reduced by phase compensation or phase addition. Specifically, the following two methods can be adopted:

[0181] 1) First estimate the channel sequence H LS (k) is pre-compensated by the linear phase, for example, the channel estimation sequence H LS Multiply each value in (k) by exp(-j*2π*k*Δθ), that is, let H LS (k) = H LS (k)*exp(-j*2π*k*Δθ), Then, the above sequence edge values ​​are added and windowed, wherein j represents an imaginary unit, and Δθ represents an increasing angle of the linear phase.

[0182] 2) N added to the left and right edges respectively extra For example, for k = 0, ..., N extra -1, let H extra (k) = H extra (k)*exp(j*2π*(kN extra )*Δθ); for make Where Δθ represents the incremental angle of the linear phase.

[0183] The transform domain signal-to-noise ratio estimation method provided by the embodiment of the present invention can effectively improve the accuracy of the signal-to-noise ratio estimation result, which has also been verified through simulation tests.

[0184] The simulation test parameters are shown in Table 1 below:

[0185]

[0186]

[0187] In the simulation test, the following three solutions were tested respectively:

[0188] 1. TDF_SNR_wFwin: transform domain SNR estimation method of the present invention (the edge data value of the channel estimation sequence is repeated 24 times and a raised cosine window is added);

[0189] 2. TDF_SNR_woFwin: Existing transform domain SNR estimation method (the edge data values ​​of the channel estimation sequence are repeated 24 times without adding a raised cosine transition window);

[0190] 3. EstiSNR_Freq: frequency domain SNR estimation method.

[0191] The simulation test results are as follows Figure 6 and Figure 7 As shown, Figure 6 Corresponding 24RB, N Focc=1, Transformation domain of TDLB channel |h LS (n)| 2 That is, the simulation result of PDP (Power delay profile) in the figure, Figure 7 Corresponding 24RB, N Focc =2, Transformation domain of TDLB channel |h LS (n)| 2 The simulation results are shown in Figure 2.

[0192] The simulation results of several different SNR estimation methods are shown in Figure 2. Figure 8 shown.

[0193] It can be seen that after the edge of the frequency domain channel estimation sequence is windowed using the solution of the present invention, the signal energy in the transform domain can be well compressed, while the existing transform domain SNR estimation method does not window the edge of the frequency domain channel estimation sequence, and the signal energy dispersion in the transform domain is more serious.

[0194] Further, Figure 8 The simulation results of several different SNR estimation methods are shown.

[0195] Ideal SNR corresponds to the ideal SNR estimation curve;

[0196] EstiSNR corresponds to the SNR estimation curve obtained by the frequency domain SNR estimation method;

[0197] TDF_SNR_woFwin corresponds to the SNR estimation curve obtained using the existing transform domain SNR estimation method;

[0198] TDF_SNR_woFwin corresponds to the SNR estimation curve obtained by using the transform domain SNR estimation method of the present invention.

[0199] from Figure 8 The simulation results show that the SNR obtained by the solution of the present invention has a high accuracy, which is almost the same as the ideal SNR estimation curve ( Figure 8 The SNR estimation curves obtained by the second and third methods are obviously more accurate than those obtained by the second and third methods.

[0200] Through the above simulation test, the edge of the channel estimation sequence is expanded and a transition window is added, so that the signal energy in the transform domain can be well compressed and the accuracy of the SNR estimation result can be improved.

[0201] Accordingly, an embodiment of the present invention further provides a transform domain signal-to-noise ratio estimation device, such as Fig. 9 Shown is a structural schematic diagram of the device.

[0202] In this embodiment, the transform domain signal-to-noise ratio estimation device 900 includes the following modules:

[0203] Data acquisition module 901, used to obtain the channel estimation sequence H LS (k), the channel estimation sequence includes a plurality of sample points;

[0204] The rough estimation module 902 is used to determine the channel estimation sequence H LS (k) Rough estimate of signal-to-noise ratio;

[0205] The windowing processing module 903 is used to process the channel estimation sequence H when the rough estimated signal-to-noise ratio is greater than the threshold value. LS The left and right edges of (k) are N Win The sample points are added with a transition window to obtain a processed channel estimation sequence; the transition window may be any one of the following: a rectangular window, a raised cosine window, a triangular window, a Hamming window, a Hanning window, a Blackman window, a Chebyshev window, etc., which is not limited in the embodiment of the present invention;

[0206] A transformation module 904, configured to perform an orthogonal transformation on the processed channel estimation sequence to obtain a transformed channel estimation sequence;

[0207] The calculation module 905 is used to calculate the signal-to-noise ratio according to the transformed channel estimation sequence.

[0208] The rough estimation module 902 may determine the channel estimation sequence H in any of the following ways: LS (k) A rough estimated signal-to-noise ratio: determining a rough estimated signal-to-noise ratio according to a difference in LS channel estimation values ​​of adjacent frequency domain receiving channels, determining a rough estimated signal-to-noise ratio according to a difference in LS channel estimation values ​​of adjacent time domain receiving channels, determining a rough estimated signal-to-noise ratio according to path loss derivation, determining a rough estimated signal-to-noise ratio according to a scheduling modulation and coding strategy, and determining a rough estimated signal-to-noise ratio according to a historical signal-to-noise ratio, which is not limited in the embodiments of the present invention.

[0209] The calculation module 905 may specifically include the following units:

[0210] An interval determination unit, used to determine a noise sample point index set and a valid signal energy interval in the transformed channel estimation sequence according to the number of orthogonal transformation points and the multipath delay spread range of the current channel model;

[0211] An adjustment unit, used for adjusting the noise sample point index set and the effective signal energy interval according to the roughly estimated signal-to-noise ratio. The specific adjustment method can refer to the description in the above embodiment of the method of the present invention;

[0212] The calculation unit is used to calculate the signal-to-noise ratio according to the adjusted noise sample point index set and the effective signal energy interval.

[0213] The transform domain signal-to-noise ratio estimation device provided by the embodiment of the present invention can effectively improve the accuracy of the transform domain signal-to-noise ratio estimation result by performing windowing processing on the channel estimation sequence through the windowing processing module.

[0214] like Fig.10 As shown, in another non-limiting embodiment of the transform domain signal-to-noise ratio estimation device of the present invention, it can further include: an edge data adding module 906, which is used to process the channel estimation sequence H in the windowing processing module 903 LS The left and right edges of (k) are N Win Before adding the transition window to the sample points, the channel estimation sequence H LS Add N to the left and right edges of (k) extra value.

[0215] Accordingly, in this embodiment, the windowing processing module 903 may perform windowing on the channel estimation sequence H LS (k) N is added to the left and right edges extra Add transition windows to the left and right of the sequence after the value.

[0216] In a specific application, the edge data adding module 906 can use multiple methods to add the channel estimation sequence H LS (k) is used to add data to the left and right edges of the sequence. For example, the edge data adding module 906 may include any one or more of the following units to perform sequence edge data adding processing in different ways:

[0217] The first processing unit is used to convert the channel estimation sequence H into LS Add N to the left and right edges of (k) extra Values;

[0218] The second processing unit is used to convert the channel estimation sequence H into LS Add N to the left and right edges of (k) extra Values;

[0219] The third processing unit is used to expand the channel estimation sequence H by edge value symmetric expansion. LS Add N to the left and right edges of (k) extra value.

[0220] Each of the above units estimates the channel sequence H LS The specific manner of adding data to the left and right edges of (k) can be referred to the description in the above-mentioned embodiment of the method of the present invention, which will not be repeated here.

[0221] Considering the original channel estimation sequence H LS(k) In the case of linear phase, in order to avoid the influence of linear phase on the signal-to-noise ratio estimation result, its influence can be reduced by phase compensation.

[0222] For example, in another non-limiting embodiment of the transform domain signal-to-noise ratio estimation device of the present invention, it may further include: a phase compensation module (not shown) for compensating the channel estimation sequence H LS (k) is pre-compensated. Accordingly, in this embodiment, the rough estimation module 902 and the windowing processing module 903 need to perform corresponding processing on the pre-compensated channel estimation sequence.

[0223] For another example, in another non-limiting embodiment of the transform domain signal-to-noise ratio estimation device of the present invention, it may further include: a phase adding module (not shown) for adding N phases to the left and right edges respectively. extra The values ​​are linearly phase added.

[0224] The transform domain signal-to-noise ratio estimation device provided by the embodiment of the present invention determines the rough estimated signal-to-noise ratio of the channel estimation sequence before performing an orthogonal transform on the channel estimation sequence, and then performs windowing processing on the channel estimation sequence according to the size of the rough estimated signal-to-noise ratio. Furthermore, edge data values ​​are added to the channel estimation sequence before the windowing processing, and the processed channel estimation sequence is orthogonally transformed. The signal-to-noise ratio is calculated according to the transformed channel estimation sequence. The windowing processing can effectively improve the accuracy of the transform domain signal-to-noise ratio estimation result.

[0225] Correspondingly, an embodiment of the present invention further provides a communication device including the above-mentioned transform domain signal-to-noise ratio estimation device, and the communication device can be a network side device or a terminal side device.

[0226] The network side device may be, for example, a base station, for example: the device providing the base station function in the 2G network includes a base transceiver station (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 (evolved NodeB, eNB), in the wireless local area network (Wireless Local Area Networks, referred to as WLAN), the device providing the base station function is an access point (Access Point, AP), the device providing the base station function in the 5G new radio (New Radio, referred to as NR) is a gNB, and the evolving node B (ng-eNB), wherein the gNB and the terminal communicate using NR technology, the ng-eNB and the terminal communicate using E-UTRA (Evolved Universal Terrestrial Radio Access) technology, and both the gNB and the ng-eNB can be connected to the 5G core network. The base station in the embodiment of the present invention also includes a device that provides a base station function in a future new communication system, etc.

[0227] The terminal side device may be various forms of user equipment (UE), access terminal, user unit, user station, mobile station, mobile station (MS), remote station, remote terminal, mobile device, user terminal, terminal equipment (Terminal Equipment), wireless communication equipment, user agent or user device. 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), etc., and the embodiments of the present invention are not limited to this.

[0228] In a specific implementation, the above-mentioned transform domain signal-to-noise ratio estimation device can correspond to a chip in a network device, such as a SoC (System-On-a-Chip), a baseband chip, a chip module, etc.

[0229] In a specific implementation, each module / unit included in each device or product described in the above embodiments may be a software module / unit or a hardware module / unit, or may be partly a software module / unit and partly a hardware module / unit.

[0230] For example, for each device or product applied to or integrated in a chip, each module / unit contained therein may be implemented in the form of hardware such as circuits, or at least some of the modules / units may be implemented in the form of software programs, which run on a processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for each device or product applied to or integrated in a chip module, each module / unit contained therein may be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or different components of the chip module, or at least some of the modules / units may be implemented in the form of software programs. The element can be implemented in the form of a software program, which runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the terminal, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or in different components in the terminal, or, at least some modules / units can be implemented in the form of a software program, which runs on a processor integrated inside the terminal, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits.

[0231] The embodiment of the present invention further provides a computer-readable storage medium, which is a non-volatile storage medium or a non-transient storage medium, on which a computer program is stored, and the computer program is executed by a processor when it is executed. Figure 1 or Figure 2 or Figure 6 The steps of the method provided in the corresponding embodiment.

[0232] The embodiment of the present invention further provides another noise reduction device for transform domain channel estimation, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor executes the above-mentioned Figure 1 or Figure 2 The steps of the method provided in the corresponding embodiment.

[0233] Furthermore, an embodiment of the present invention also discloses a terminal, including a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, and the processor executes the above-mentioned Figure 1 or Figure 2 The method and technical solution described in the illustrated embodiment. Preferably, the terminal may be a multi-card multi-mode terminal supporting 4G and 5G access technologies.

[0234] Furthermore, an embodiment of the present invention also discloses a network device, including a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, and the processor executes the above-mentioned Figure 1 or Figure 2 The method and technical solution described in the illustrated embodiment. Preferably, the network device may be a core network, such as a 4G core network EPC and a 5G core network 5GC.

[0235] In the several embodiments provided by the present invention, it should be understood that the disclosed methods, devices and systems can be implemented in other ways. For example, the device embodiments described above are only schematic; for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0236] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0237] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0238] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A transform domain signal-to-noise ratio estimation method, characterized in that: The method comprises: Get the channel estimation sequence H LS (k), the channel estimation sequence H LS (k) contains multiple sample points; Determine the channel estimation sequence H LS (k) Rough estimate of signal-to-noise ratio; If the rough estimated signal-to-noise ratio is greater than the threshold, the channel estimation sequence H LS The left and right edges of (k) are N Win Sample points plus transition window are added to obtain the processed channel estimation sequence; performing an orthogonal transformation on the processed channel estimation sequence to obtain a transformed channel estimation sequence; A signal-to-noise ratio is calculated according to the transformed channel estimation sequence.

2. The method according to claim 1, characterized in that The determining of the channel estimation sequence H LS The rough estimate of the signal-to-noise ratio of (k) includes any of the following: Determine a rough estimate of the signal-to-noise ratio based on a difference in least squares channel estimation values ​​of adjacent frequency domain receiving channels; Determine a rough estimate of the signal-to-noise ratio based on a difference in least squares channel estimation values ​​of adjacent time-domain receiving channels; Determine the rough estimate of signal-to-noise ratio based on the path loss deduction; Determine a rough estimate of the signal-to-noise ratio based on the scheduling modulation and coding strategy; A rough estimate of the signal-to-noise ratio is determined based on the historical signal-to-noise ratio.

3. The method according to claim 1, characterized in that The method further comprises: In the channel estimation sequence H LS The left and right edges of (k) are N Win Before adding the transition window to the sample points, the channel estimation sequence H LS Add N to the left and right edges of (k) extra values; The channel estimation sequence H LS The left and right edges of (k) are N Win Sample points plus transition window include: N is added to the left and right edges extra Add transition windows to the left and right of the sequence after the value.

4. The method according to claim 3, characterized in that The channel estimation sequence H LS Add N to the left and right edges of (k) extra Values ​​include any of the following: The channel estimation sequence H is repeated in an edge value manner. LS Add N to the left and right edges of (k) extra values; The channel estimation sequence H is transformed into LS Add N to the left and right edges of (k) extra values; The channel estimation sequence H is expanded by edge value symmetric expansion. LS Add N to the left and right edges of (k) extra value.

5. The method according to claim 4, characterized in that The channel estimation sequence H is repeated in an edge value manner. LS Add N to the left and right edges of (k) extra The values ​​are: According to the number of ports multiplexed by the code division multiplexing group and N extra Determine the channel estimation sequence H LS (k) The number of sample points and the number of repetitions contained in the subsequence to be repeated on the left and right edges; The channel estimation sequence H is respectively LS The subsequences to be repeated at the left and right edges of (k) are repeatedly added.

6. The method according to claim 4, characterized in that The channel estimation sequence H is extrapolated by edge value LS Add N to the left and right edges of (k) extra The values ​​are: The channel estimation sequence H LS The left and right edges of (k) are extrapolated N extra A sample point.

7. The method according to claim 4, characterized in that The channel estimation sequence H is expanded symmetrically by edge value LS Add N to the left and right edges of (k) extra The values ​​are: The channel estimation sequence H LS (k) The left and right edges of N extra The sample points are expanded symmetrically.

8. The method according to claim 1, characterized in that The transition window is any one of the following: a rectangular window, a raised cosine window, a triangular window, a Hamming window, a Hanning window, a Blackman window, and a Chebyshev window.

9. The method according to claim 1, characterized in that: The method further comprises: The channel estimation sequence H LS The linear phase of (k) is pre-compensated.

10. The method according to claim 1, characterized in that The method further comprises: N is added to the left and right edges respectively. extra The values ​​are linearly phase added.

11. The method according to claim 1, characterized in that: Calculating the signal-to-noise ratio according to the transformed channel estimation sequence comprises: Determining a noise sample point index set and a valid signal energy interval in the transformed channel estimation sequence according to the number of orthogonal transformation points and the multipath delay spread range of the current channel model; Adjusting the noise sample point index set and the effective signal energy interval according to the roughly estimated signal-to-noise ratio; The signal-to-noise ratio is calculated based on the adjusted noise sample point index set and the effective signal energy interval.

12. The method according to any one of claims 1 to 11, characterized in that: The transformation domain is any one of the following: transformation from frequency domain to time domain, transformation from time domain to frequency domain, transformation from spatial domain to beam domain, and transformation from beam domain to spatial domain.

13. A transform domain signal-to-noise ratio estimation device, characterized in that: The device comprises: Data acquisition module, used to obtain the channel estimation sequence H LS (k), the channel estimation sequence includes a plurality of sample points; A rough estimation module is used to determine the channel estimation sequence H LS (k) Rough estimate of signal-to-noise ratio; A windowing processing module is used for processing the channel estimation sequence H when the rough estimated signal-to-noise ratio is greater than a threshold value. LS The left and right edges of (k) are N Win Sample points plus transition window are added to obtain the processed channel estimation sequence; A transformation module, used for performing an orthogonal transformation on the processed channel estimation sequence to obtain a transformed channel estimation sequence; A calculation module is used to calculate a signal-to-noise ratio according to the transformed channel estimation sequence.

14. The device according to claim 13, characterized in that The rough estimation module determines the channel estimation sequence H in any of the following ways: LS Rough estimate of signal-to-noise ratio of (k): Determine a rough estimate of the signal-to-noise ratio based on a difference in least squares channel estimation values ​​of adjacent frequency domain receiving channels; Determine a rough estimate of the signal-to-noise ratio based on a difference in least squares channel estimation values ​​of adjacent time-domain receiving channels; Determine the rough estimate of signal-to-noise ratio based on the path loss deduction; Determine a rough estimate of the signal-to-noise ratio based on the scheduling modulation and coding strategy; A rough estimate of the signal-to-noise ratio is determined based on the historical signal-to-noise ratio.

15. The device according to claim 13, characterized in that The device also includes: The edge data adding module is used to process the channel estimation sequence H in the windowing processing module. LS The left and right edges of (k) are N Win Before adding the transition window to the sample points, the channel estimation sequence H LS Add N to the left and right edges of (k) extra values; The windowing processing module is specifically used to add N extra Add transition windows to the left and right of the sequence after the value.

16. The device according to claim 14, characterized in that The edge data adding module includes any one or more of the following units: The first processing unit is used to convert the channel estimation sequence H into LS Add N to the left and right edges of (k) extra values; The second processing unit is used to convert the channel estimation sequence H into LS Add N to the left and right edges of (k) extra values; The third processing unit is used to expand the channel estimation sequence H by edge value symmetric expansion. LS Add N to the left and right edges of (k) extra value.

17. The device according to claim 13, characterized in that The device also includes: A phase compensation module is used to estimate the channel sequence H LS The linear phase of (k) is pre-compensated.

18. The device according to claim 13, characterized in that The device also includes: A phase adding module is used to add N phases to the left and right edges respectively. extra The values ​​are linearly phase added.

19. The device according to claim 13, characterized in that The calculation module comprises: An interval determination unit, used to determine a noise sample point index set and a valid signal energy interval in the transformed channel estimation sequence according to the number of orthogonal transformation points and the multipath delay spread range of the current channel model; An adjustment unit, configured to adjust the noise sample point index set and the effective signal energy interval according to the roughly estimated signal-to-noise ratio; The calculation unit is used to calculate the signal-to-noise ratio according to the adjusted noise sample point index set and the effective signal energy interval.

20. The device according to any one of claims 13 to 19, characterized in that The transformation domain is any one of the following: transformation from frequency domain to time domain, transformation from time domain to frequency domain, transformation from spatial domain to beam domain, and transformation from beam domain to spatial domain.

21. A communication device, characterized in that: It comprises the transform domain signal-to-noise ratio estimation device as described in any one of claims 13 to 20.

22. A computer-readable storage medium, wherein the computer-readable storage medium is a non-volatile storage medium or a non-transient storage medium, and a computer program is stored thereon, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are performed.

23. A communication device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor runs the computer program, the steps of the method according to any one of claims 1 to 12 are performed.

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