SNR estimation method and device based on SRS in 5G small base station system

Abstract

The present invention relates to an SNR estimation method and device based on SRS in a 5G small base station system. The signal power is calculated by using an SRS signal periodically sent under the 5G NR communication protocol framework, and the noise power is estimated by using the signal of the adjacent carrier of the SRS signal. The whole process reduces the computational complexity of the noise calculation while using the full bandwidth data, reduces the calculation time, and thus improves the calculation efficiency. In addition, the present invention also performs smoothing filtering and denoising on the channel response of the SRS signal, and sequentially obtains a coarse channel estimate, an intermediate channel estimate, and a final channel estimate to reduce the impact of interference signal noise on the estimation performance, and improve the accuracy of the signal power calculation, thereby obtaining a more accurate SNR estimate, and thus improving the communication quality of the 5G small base station.

Description

[Technical field]

[0001] The present invention relates to the field of mobile communication technologies, and in particular to a method and device for estimating SNR based on SRS in a 5G small base station system. [Background technology]

[0002] In the mobile communication 5G small base station system, SRS (sounding reference signal) is generally used to measure SNR (signal noise ratio). SNR refers to the estimation and measurement of the information and noise power or energy in the received signal, and the calculation of the ratio of information to noise. Signal-to-noise ratio is an important indicator for measuring the quality of mobile communication 5G systems. On the one hand, the system uses the SNR parameter to measure the channel quality, and on the other hand, the SNR parameter can be used to optimize the performance of other algorithm modules in the system. With the rapid development of high-speed communication systems, the requirements for SNR estimation are becoming higher and higher, requiring higher accuracy, better performance, simpler calculation and easier implementation.

[0003] At present, the 5G small base station system calculates the SNR estimation method using the maximum likelihood estimation method. This method uses the received frequency domain signal y and the least squares criterion (ls) algorithm to calculate the channel estimation response H, and then uses yH×x (x is the locally generated SRS sequence signal) to calculate the signal noise Ni. Finally, the signal-to-noise ratio SNR = H×x / (yH×x) is calculated. Due to the design specification requirements and performance factors of the 5G small base station system, when configuring the SRS parameters, considering the inter-frequency interference, a 2-comb configuration will be adopted. At this time, the SRS configuration sent by the user is comb4, and the comb offset combOffset value is 0, 2 (that is, the RE at positions 0, 2, 4, 6, 8, and 10 in each RB is the SRS signal, and the RE at positions 1, 3, 5, 7, 9, and 11 is the interference signal). Therefore, in the entire bandwidth, there is only an SRS signal on the even carrier, and the data on all cardinality carriers is background noise. This configuration can stagger valid data during frequency domain mapping, thereby reducing inter-frequency interference and ensuring performance requirements. In this configuration, valid data only exists on even-numbered carriers, and noise floor data exists on cardinal-numbered carriers. However, when calculating SNR, only valid data on even-numbered carriers is used for calculation, and noise floor data on cardinal-numbered carriers is not considered, which will cause inaccurate SNR measurement and high calculation complexity.

[0004] Therefore, there is an urgent need to propose a SNR estimation method and device that is simpler to calculate and more accurate in measurement. [Summary of the invention]

[0005] The technical problem to be solved by the present invention is to provide an SRS-based SNR estimation method and device in a 5G small base station system, which has simpler calculation and more accurate measurement.

[0006] In order to solve the above technical problems, the present invention provides a SNR estimation method based on SRS in a 5G small base station system, comprising the following steps:

[0007] S1. Extract the SRS measurement signal Y configured by two combs from the received frequency domain data SRS (k,l,r); and adjacent SRS measurement signal Y SRS (2k+1,l,r), where k is the subcarrier index of the received SRS signal, l is the OFDM symbol, and r is the receiving antenna;

[0008] S2. Generate local SRS generation sequence X according to 3GPP protocol SRS (k,l,p); where p is the transmit antenna port index;

[0009] S3. According to the SRS measurement signal Y SRS (k, l, r) and the local SRS generation sequence X SRS (k,l,p), based on the least squares estimation algorithm, the rough channel estimate is calculated

[0010] S4. Estimating the rough channel Perform N consecutive m Subcarrier smoothing and interference removal to obtain the intermediate channel estimate

[0011] Among them, the is the number of SRS ports, N u is the number of users, or 8;

[0012] S5. Estimating the intermediate channel according to the MMSE equalization algorithm Perform interpolation filtering to obtain the covariance Sinc function θ(k) and MMSE matrix Φ(k') between different subcarriers at different times;

[0013] S6. Calculate the weight w(k, l; k′, l′) according to the covariance Sinc function θ(k) and the MMSE matrix Φ(k′), and then estimate the intermediate channel according to the weight w(k, l; k′, l′) Perform RE-level interpolation operation to obtain the final channel estimate H SRS (k,l,r,p);

[0014] S7. Estimating H based on the final channelSRS (k, l, r, p) and the local SRS generation sequence X SRS (k, l, p) calculates the signal power Pu of the even carrier on the frequency band, and measures the signal Y according to the adjacent SRS SRS (2k+1,l,r) calculates the noise power Ni of the odd carriers in the frequency band;

[0015] S8. Based on the signal power Pu of the even carrier on the frequency band and the noise power Ni of the odd carrier on the frequency band, based on the signal-to-noise ratio calculation formula Determine the intermediate signal-to-noise ratio SNR′;

[0016] S9. According to the intermediate signal-to-noise ratio SNR′ and the covariance Sinc function θ(k), a new MMSE matrix Φ(k) is obtained. ′ ); then, return to step S6 to perform calculations again in sequence, until step S8 obtains the final signal-to-noise ratio SNRest according to the signal-to-noise ratio calculation formula.

[0017] Furthermore, in step S3, the rough channel estimation

[0018]

[0019] Furthermore, in step S4,

[0020] The intermediate channel estimate

[0021] Furthermore, in step S5,

[0022] The covariance Sinc function θ(k)=sinc(2τ max ΔF(kk ′ ));

[0023] Among them, τ max is the maximum delay of channel propagation, ΔF is the carrier space, k is the carrier index value of the entire bandwidth, and k ′ is the SRS carrier index value;

[0024] The MMSE matrix

[0025] In step S9, the MMSE matrix

[0026] Furthermore, the SNR0 is 30db.

[0027] Furthermore, in step S6,

[0028] The weight w(k,l; k′ ,l′)=θ(k)×Φ -1 (k ′ );

[0029] The final channel estimate Where T represents the matrix transpose.

[0030] Furthermore, in step S7,

[0031] The signal power of even-numbered carriers on the frequency band is Pu=mean(X e ×X e H ); wherein, the X e =∑ p X SRS (k,l,p)×H SRS (k,l,r,p), the X e H For X e The transposed conjugate of

[0032] The noise power of odd-numbered carriers on the frequency band is Ni=mean(δ×δ H ); wherein, the δ=∑ r Y SRS (2k+1,l,r), the δ H is the transposed conjugate of δ.

[0033] In order to solve the above technical problems, the present invention provides an SNR estimation device based on SRS in a 5G small base station system, comprising a first signal unit, a second signal unit, a first operation unit, a second operation unit and a signal-to-noise ratio calculation unit;

[0034] The first signal unit extracts the SRS measurement signal Y configured by two combs from the received frequency domain data. SRS (k,l,r); where k is the subcarrier index of the received SRS signal, l is the OFDM symbol, and r is the receiving antenna;

[0035] The second signal unit generates a local SRS generation sequence X according to the 3GPP protocol SRS (k,l,p); where p is the transmit antenna port index;

[0036] The first operation unit is used to estimate the final channel H SRS (k, l, r, p) and the local SRS generation sequence Xsrs (k, l, p) calculate the signal power Pu of the even carrier on the frequency band; the final channel estimation H SRS (k,l,r,p) estimates the intermediate channel according to the weights w(k,l;k′,l′) The weight w(k, l; k′, l′) is calculated based on the covariance Sinc function θ(k) and the MMSE matrix Φ(k′) between different subcarriers at different times; the covariance Sinc function θ(k) and the MMSE matrix Φ(k′) between different subcarriers at different times are used to estimate the intermediate channel according to the MMSE equalization algorithm. Interpolation filtering is performed to obtain the intermediate channel estimation By estimating the coarse channel Perform N consecutive m Subcarrier smoothing and interference removal processing is performed to obtain the coarse channel estimation According to the SRS measurement signal Y SRS (k, l, r) and the local SRS generation sequence X SRS (k, l, p), calculated based on the least squares estimation algorithm; wherein, is the number of ports of SRS; N u is the number of users, or 8;

[0037] The second operation unit is used to measure the signal Y according to the adjacent SRS SRS (2k+1,l,r) calculates the noise power Ni of the odd carriers in the frequency band;

[0038] The signal-to-noise ratio calculation unit is used to calculate the signal-to-noise ratio based on the signal power Pu of the even-numbered carriers on the frequency band and the noise power Ni of the odd-numbered carriers on the frequency band. Determine an intermediate signal-to-noise ratio SNR′, and output the intermediate signal-to-noise ratio SNR′ to the first operation unit; then receive the signal power Pu of the even carrier on the new frequency band output by the first operation unit, and calculate the signal-to-noise ratio based on the signal-to-noise ratio formula Get the final signal-to-noise ratio SNRest;

[0039] The first operation unit is also used to obtain a new MMSE matrix Φ(k) according to the intermediate signal-to-noise ratio SNR-1 and the covariance Sinc function θ(k): ′ ); then calculate the new weights w(k,l; k′,l′) according to the covariance Sinc function θ(k) between different subcarriers at different times and the new MMSE matrix Φ(k'), and then estimate the intermediate channel according to the new weights w(k,l; k′,l′) Perform RE-level interpolation to obtain the new final channel estimate H SRS (k,l,r,p), and then estimate H based on the new final channel SRS (k, l, r, p) and the local SRS generation sequence XSRS (k, l, p) is calculated to obtain the signal power Pu of the even-numbered carrier on the new frequency band, and then output to the signal-to-noise ratio calculation unit.

[0040] Furthermore, the rough channel estimation

[0041] The intermediate channel estimate

[0042] The covariance Sinc function θ(k)=sinc(2τ max ΔF(kk ′ ));

[0043] The MMSE matrix

[0044] The weight w(k,l; k ′ ,l′)=θ(k)×Φ -1 (k ′ );

[0045] The final channel estimate Where T represents matrix transpose;

[0046] The signal power of even-numbered carriers on the frequency band is Pu=mean(X e ×X e H ); wherein, the X e =∑ p X SRS (k,l,p)×H SRS (k,l,r,p), the X e H For X e The transposed conjugate of

[0047] The noise power of odd-numbered carriers on the frequency band is Ni=mean(δ×δ H ); wherein, the δ=∑ r Y SRS (2k+1,l,r), the δ H is the transposed conjugate of δ.

[0048] Furthermore, the SNR0 is 30db.

[0049] Compared with the prior art, the present invention has the following beneficial effects: the present invention reduces the computational complexity of computing noise and reduces the computation time while utilizing full bandwidth data. In addition, the present invention also performs smoothing filtering and denoising on the channel response of the SRS signal, and sequentially obtains a coarse channel estimate, an intermediate channel estimate, and a final channel estimate to reduce the impact of interference signal noise on the estimation performance and improve the accuracy of signal power calculation, thereby obtaining a more accurate SNR estimate, thereby improving the communication quality of the 5G small base station system.

Brief Description of the Drawings

[0050] Figure 1 It is a step diagram of an SNR estimation method based on SRS in a 5G small base station system in an embodiment of the present invention;

[0051] Figure 2 It is a structural block diagram of an SRS-based SNR estimation device in a 5G small base station system in an embodiment of the present invention. [Specific implementation method]

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0053] It should be noted that the terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances so as to be implemented in other orders than the embodiments of the present invention.

[0054] like Figure 1 As shown, the SNR estimation method based on SRS in the 5G small base station system of an embodiment of the present invention includes the following steps:

[0055] S1. Extract the SRS measurement signal Y configured by two combs from the received frequency domain data SRS (k,l,r); and adjacent SRS measurement signal Y SRS (2k+1,l,r). k is the subcarrier index of the received SRS signal. According to the protocol, k is an even number, which can be 0, 2, 4, 8, etc., or 0, 4, 8, 12, etc. l is an OFDM (Orthogonal Frequency Division Multiplexing) symbol, and r is a receiving antenna.

[0056] S2. Generate local SRS generation sequence X according to 3GPP protocol SRS (k,l,p); where p is the transmit antenna port index.

[0057] 3GPP TS38.211 protocol specifies the generation of antenna port p i The SRS sequence X SRS The generation formula of (k,l,p) is:

[0058]

[0059] in:

[0060] consecutive OFDM symbols;

[0061] is the SRS sequence length,

[0062] i is the index value of the antenna port.

[0063] m SRS,b Indicates the number of RBs occupied by SRS in the frequency domain. For settings, refer to Table 6.4.1.4.3-1 of the 3GPP TS38.211 protocol. Let b = B SRS , B SRS ∈{0,1,2,3},C SRS ∈{0,1,...,63} is the SRS bandwidth configuration index. It is set by the upper layer parameter freqHopping to determine m SRS,b The value of K TC is the number of transmission combs, which can be 2 or 4 and is included in the high-level parameter transmissionComb. TC ).

[0064] Antenna port p i The cyclic shift α i Obtained according to the following formula:

[0065]

[0066] in, Contained in the high-level parameter transmissionComb, the protocol stipulates that if K TC =4, then If K TC =2, then

[0067] is a low peak-to-average ratio series, generated by the following formula:

[0068]

[0069] Among them, r u,v (k) is the base sequence, is the sequence length, is the number of carriers in each RB, j is a complex number, and α is the cyclic shift. By using different α and δ, multiple sequences can be generated from a single base sequence.

[0070] Base sequence r u,v (k) is divided into multiple groups, where u∈{0,1,...,29} is the group number, v is the base sequence number in the sequence, and when a group contains only one base sequence (v=0), the length of each base sequence is Where 1 / 2≤m / 2 δ ≤ 5. With this configuration, a group contains only one base sequence.

[0071] Base sequence r u,v (0),...,r u,v (M ZC -1) depends on the length M ZC When the base sequence length is greater than or equal to 36, that is Base sequence r u,v (0),...,r u,v (M ZC -1) is defined by the following formula:

[0072] r u,v (k) = x q (k mod N ZS )

[0073]

[0074] Where: π = 3.14,

[0075] Length N ZC is to satisfy N ZC <M ZC The largest prime number.

[0076] When the base sequence length is less than 36, it is divided into the following two cases:

[0077] For M ZC =30,

[0078] For M ZC ∈{6,12,18,24},

[0079] Defined by the four tables in Section 5.2.2 of the 3GPP TS38.211 protocol, corresponding to M ZC There are 4 cases, which are equal to 6 / 12 / 18 and 24, which will not be described here.

[0080] S3. Measure signal Y based on SRS SRS (k,l,r) and local SRS generation sequence X SRS (k,l,p), based on the least squares estimation algorithm, the rough channel estimate is calculated Right now

[0081] S4. Rough channel estimation Perform N consecutive m Subcarrier smoothing and interference removal to obtain the intermediate channel estimate

[0082] Right now,

[0083] in, is the number of SRS ports, N u is the number of users, or 8.

[0084] S5. Estimation of the intermediate channel based on the MMSE equalization algorithm Interpolation filtering is performed to obtain the covariance Sinc function θ(k) and the MMSE matrix Φ(k') between different subcarriers at different times.

[0085] For SRS channel estimation, since only a single symbol needs to be configured, only frequency domain interpolation needs to be considered, and time domain interpolation can be ignored. Therefore, the covariance Sinc function θ(k) = sinc(2τ max ΔF(kk ′ ));MMSE matrix

[0086] Among them, τ max is the maximum delay of channel propagation, ΔF is the carrier space, k is the carrier index value of the entire bandwidth, and k ′ is the SRS carrier index value; SNR0 can be set to the default value of 30db.

[0087] S6. Calculate the weight w(k,l; k′,l′) based on the covariance Sinc function θ(k) and the MMSE matrix Φ(k'), and then estimate the intermediate channel based on the weight w(k,l; k′,l′) Perform RE-level interpolation operation to obtain the final channel estimate H SRS (k,l,r,p).

[0088] Weight w(k,l;k ′ ,l′)=θ(k)×Φ -1 (k ′ ).

[0089] Final channel estimate Where T represents the matrix transpose.

[0090] S7. Based on the final channel estimate H SRS (k,l,r,p) and local SRS generation sequence X SRS (k,l,p) calculates the signal power Pu of the even carrier on the frequency band, based on the adjacent SRS measurement signal Y SRS (2k+1,l,r) calculates the noise power Ni of the odd carriers in the frequency band.

[0091] The signal power of even-numbered carriers on the frequency band is Pu = mean (X e ×X e H ), where X e =∑ p X SRS (k,l,p)×H SRS (k,l,r,p),X e H For X e The transposed conjugate of .

[0092] X e Calculated as a complex number, X e ×X e H Represents X e Multiplying by its own transposed conjugate converts it to a real number, the signal power.

[0093] The noise power of odd-numbered carriers on the frequency band is Ni = mean (δ × δ H ), where δ = ∑ r Y SRS (2k+1,l,r),δ H is the transposed conjugate of δ.

[0094] δ represents the noise value (i.e., background noise) on all odd carriers in the OFDM symbol of SRS. This noise value is a complex number, δ×δ H Denotes δ multiplied by its own transposed conjugate, which can be converted to a real number, namely the noise power.

[0095] Mean means to calculate the average value.

[0096] S8. Based on the signal power Pu of the even carrier on the frequency band and the noise power Ni of the odd carrier on the frequency band, based on the signal-to-noise ratio calculation formula Determine the intermediate signal-to-noise ratio SNR′;

[0097] S9. According to the intermediate signal-to-noise ratio SNR′ and the covariance Sinc function θ(k), a new MMSE matrix Φ(k) is obtained. ′ ); then, return to step S6 to perform another step-by-step calculation in sequence until step S8 obtains the final signal-to-noise ratio SNRest according to the signal-to-noise ratio calculation formula.

[0098] Specifically, the MMSE matrix

[0099] Then, return to step S6 to calculate the weight w(k, l; k ′ ,l ′ ), the new MMSE matrix Φ(k ′ )Enter the calculation formula w(k,l;k ′ ,l ′ )=θ(k)×Φ -1 (k ′ ), and get the new weight w(k,l;k ′ ,l ′ ), and then the new weight w(k,l;k ′ ,l ′ ) Input the calculation formula of the final channel estimate Get the new final channel estimate H SRS (k, l, r, p); then execute step S7, and the new final channel estimate H SRS The calculation formula for the signal power of even-numbered carriers on the (k, l, r, p) input frequency band is Pu = mean (X e ×X e H ), X e =∑ p X SRS (k,l,p)×H SRS (k, l, r, p), and obtain the signal power Pu of the even-numbered carrier on the new frequency band; then execute step S8, and input the signal power Pu of the even-numbered carrier on the new frequency band into the signal-to-noise ratio calculation formula Get the final signal-to-noise ratio SNRest.

[0100] like Figure 2 As shown, the SRS-based SNR estimation device in the 5G small base station system of an embodiment of the present invention includes a first signal unit, a second signal unit, a first operation unit, a second operation unit and a signal-to-noise ratio calculation unit.

[0101] The first signal unit extracts the SRS measurement signal Y after two-comb configuration from the received frequency domain data. SRS(k,l,r); where k is the subcarrier index of the received SRS signal, l is the OFDM symbol, and r is the receiving antenna.

[0102] The second signal unit generates a local SRS generation sequence X according to the 3GPP protocol SRS (k,l,p); where p is the transmit antenna port index.

[0103] The first operation unit is used to estimate the final channel H SRS (k,l,r,p) and local SRS generation sequence X SRS (k, l, p) calculates the signal power Pu of the even carrier on the frequency band; the final channel estimate H SRS (k,l,r,p) estimates the intermediate channel according to the weights w(k,l;k′,l′) The weight w(k, l; k′, l′) is calculated based on the covariance Sinc function θ(k) and the MMSE matrix Φ(k') between different subcarriers at different times; the covariance Sinc function θ(k) and the MMSE matrix Φ(k') between different subcarriers at different times are used to estimate the intermediate channel according to the MMSE equalization algorithm. Interpolation filtering is performed to obtain the intermediate channel estimation By estimating the coarse channel Perform N consecutive m Subcarrier smoothing and interference removal processing; coarse channel estimation Based on SRS measurement signal Y SRS (k,l,r) and local SRS generation sequence X SRS (k,l,p) is calculated based on the least squares estimation algorithm.

[0104] Among them, the coarse channel estimation

[0105] Intermediate Channel Estimation in, is the number of ports of SRS; N u is the number of users, or 8. According to the 3GPP TS38.211 protocol, if K TC =4, then If K TC =2, then

[0106] For SRS channel estimation, since only a single symbol needs to be configured, only frequency domain interpolation needs to be considered, and time domain interpolation can be ignored. Therefore, the covariance Sinc function θ(k) = sinc(2τ maxΔF(kk ′ ));MMSE matrix

[0107] Among them, τ max is the maximum delay of channel propagation, ΔF is the carrier space, and SNR0 can be set to the default value of 30db.

[0108] Weight w(k,l;k ′ ,l′)=θ(k)×Φ -1 (k ' ).

[0109] Final channel estimate Where T represents the matrix transpose.

[0110] The signal power of even-numbered carriers on the frequency band is Pu = mean (X e ×X e H ), where X e =∑ p X SRS (k,l,p)×H SRS (k,l,r,p),X e H For X e The transposed conjugate of .

[0111] X e Calculated as a complex number, X e ×X e H Represents X e Multiplying by its own transposed conjugate converts it to a real number, the signal power.

[0112] The second operation unit is used to measure the signal Y according to the adjacent SRS SRS (2k+1,l,r) calculates the noise power Ni of the odd carriers in the frequency band.

[0113] Specifically, the noise power of odd-numbered carriers on the frequency band is Ni = mean (δ × δ H ), where δ = ∑ r Y SRS (2k+1,l,r),δ H is the transposed conjugate of δ.

[0114] δ represents the noise value (i.e., background noise) on all odd carriers in the OFDM symbol of SRS. This noise value is a complex number, δ×δ H It means δ multiplied by its own transposed conjugate, which can be converted into a real number, that is, the noise power. mean means to calculate the average value.

[0115] The signal-to-noise ratio calculation unit is used to calculate the signal-to-noise ratio based on the signal power Pu of the even-numbered carriers on the frequency band and the noise power Ni of the odd-numbered carriers on the frequency band. Determine the intermediate signal-to-noise ratio SNR′, and output the intermediate signal-to-noise ratio SNR′ to the first operation unit; then receive the signal power Pu of the even-numbered carrier on the new frequency band output by the first operation unit, and calculate the signal-to-noise ratio based on the signal-to-noise ratio formula Get the final signal-to-noise ratio SNRest;

[0116] The first operation unit is also used to obtain a new MMSE matrix Φ(k) according to the intermediate signal-to-noise ratio SNR′ and the covariance Sinc function θ(k) ′ ); then calculate the new weights w(k,l; k′,l′) based on the covariance Sinc function θ(k) between different subcarriers at different times and the new MMSE matrix Φ(k'), and then estimate the intermediate channel based on the new weights w(k,l; k′,l′) Perform RE-level interpolation to obtain the new final channel estimate H SRS (k,l,r,p), and then estimate H based on the new final channel SRS (k, l, r, p) and the local SRS generation sequence Xsrs(k, l, p) are used to calculate the signal power Pu of the even-numbered carrier on the new frequency band, and then output to the signal-to-noise ratio calculation unit.

[0117] In summary, the present invention reduces the computational complexity of computing noise and reduces the computation time while utilizing full bandwidth data. In addition, the present invention also performs smoothing filtering and denoising on the channel response of the SRS signal, and sequentially obtains a coarse channel estimate, an intermediate channel estimate, and a final channel estimate to reduce the impact of interference signal noise on the estimation performance and improve the accuracy of signal power calculation, thereby obtaining a more accurate SNR estimate, thereby improving the communication quality of the 5G small base station system.

[0118] The above embodiments only express the preferred implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, such as combining different features in various embodiments, etc., which all belong to the protection scope of the present invention.

Claims

1. A SNR estimation method based on SRS in a 5G small base station system, characterized in that: The following steps are involved: S1. Extract the SRS measurement signal Y from the received frequency domain data SRS (k,l,r) and adjacent SRS measurement signal Y SRS (2k+1,l,r); where k is the subcarrier index of the received SRS signal, l is the OFDM symbol, and r is the receiving antenna; S2. Generate local SRS generation sequence X SRS (k,l,p); where p is the transmit antenna port index; S3. According to the SRS measurement signal Y SRS (k, l, r) and the local SRS generation sequence X SRS (k,l,p), based on the least squares estimation algorithm, the rough channel estimate is calculated S4. Estimating the rough channel Perform N consecutive m Subcarrier smoothing and interference removal to obtain the intermediate channel estimate Among them, the is the number of SRS ports, N u is the number of users, or 8; S5. Estimating the intermediate channel according to the MMSE equalization algorithm Perform interpolation filtering to obtain the covariance Sinc function θ(k) and MMSE matrix Φ(k′) between different subcarriers at different times; S6. Calculate the weights w(k, l; k', l') according to the covariance Sinc function θ(k) and the MMSE matrix Φ(k') between different subcarriers at different times in step S5, and then estimate the intermediate channel according to the weights w(k, l; k', l') Perform RE-level interpolation operation to obtain the final channel estimate H SRS (k,l,r,p); S7. Estimating H based on the final channel SRS (k, l, r, p) and the local SRS generation sequence X SRS (k, l, p) calculates the signal power Pu of the even carrier on the frequency band, and measures the signal Y according to the adjacent SRS SRS (2k+1,l,r) calculates the noise power Ni of the odd carriers in the frequency band; S8. According to the signal power Pu of the even carrier on the frequency band and the noise power Ni of the odd carrier on the frequency band, based on the signal-to-noise ratio calculation formula, determine the intermediate signal-to-noise ratio S9. According to the intermediate signal-to-noise ratio SNR′ and the covariance Sinc function θ(k), a new MMSE matrix Φ(k) is obtained. ′ ); then, return to step S6 to perform calculations again in sequence, until step S8 obtains the final signal-to-noise ratio SNRest according to the signal-to-noise ratio calculation formula.

2. The SNR estimation method based on SRS in the 5G small base station system according to claim 1, characterized in that: In step S3, the rough channel estimation 3. The SNR estimation method based on SRS in the 5G small base station system according to claim 1, characterized in that: In step S4, The intermediate channel estimate 4. The SNR estimation method based on SRS in the 5G small base station system according to claim 1, characterized in that: In step S5, The covariance Sinc function θ(k)=sinc(2τ max ΔF(kk ′ )); Among them, τ max is the maximum delay of channel propagation, ΔF is the carrier space; The MMSE matrix In step S9, the new MMSE matrix The SNR0 is 30db.

5. The SNR estimation method based on SRS in the 5G small base station system according to claim 1, characterized in that: In step S6, The weight w(k,l; k ′ ,l′)=θ(k)×Φ -1 (k ′ ); The final channel estimate Where T represents the matrix transpose.

6. The SNR estimation method based on SRS in the 5G small base station system according to claim 1, characterized in that: In step S7, The signal power of even-numbered carriers on the frequency band is Pu=mean(X e ×X e H ); wherein, the X e =∑ p X SRS (k,l,p)×H SRS (k,l,r,p), the X e H For X e The transposed conjugate of The noise power of odd-numbered carriers on the frequency band is Ni=mean(δ×δ H ); wherein, the δ=∑ r Y SRS (2k+1,l,r), the δ H is the transposed conjugate of δ.

7. A SNR estimation device based on SRS in a 5G small base station system, characterized in that: The method comprises a first signal unit, a second signal unit, a first operation unit, a second operation unit and a signal-to-noise ratio calculation unit; the first signal unit extracts an SRS measurement signal Y from the received frequency domain data. SRS (k,l,r) and adjacent SRS measurement signal Y SRS (2k+1,l,r); where k is the subcarrier index of the received SRS signal, l is the OFDM symbol, and r is the receiving antenna; The second signal unit is used to generate a local SRS generation sequence X SRS (k,l,p); where p is the transmit antenna port index; The first operation unit is used to estimate the final channel H SRS (k, l, r, p) and the local SRS generation sequence X SRS (k, l, p) calculates the signal power Pu of the even carrier on the frequency band; the final channel estimate H SRS (k,l,r,p) estimates the intermediate channel according to the weights w(k,l;k′,l′) The weight w(k, l; k′, l′) is calculated based on the covariance Sinc function θ(k) and the MMSE matrix Φ(k′) between different subcarriers at different times; the covariance Sinc function θ(k) and the MMSE matrix Φ(k′) between different subcarriers at different times are used to estimate the intermediate channel according to the MMSE equalization algorithm. Interpolation filtering is performed to obtain the intermediate channel estimation By estimating the coarse channel Perform N consecutive m Subcarrier smoothing and interference removal processing is performed to obtain the coarse channel estimation According to the SRS measurement signal Y SRS (k, l, r) and the local SRS generation sequence X SRS (k, l, p), calculated based on the least squares estimation algorithm; wherein, is the number of ports of SRS; N u is the number of users, or 8; The second operation unit is used to measure the signal Y according to the adjacent SRS SRS (2k+1,l,r) calculates the noise power Ni of the odd carriers in the frequency band; The signal-to-noise ratio calculation unit is used to determine the intermediate signal-to-noise ratio based on the signal power Pu of the even carrier on the frequency band and the noise power Ni of the odd carrier on the frequency band based on the signal-to-noise ratio calculation formula. Outputting the intermediate signal-to-noise ratio SNR′ to the first operation unit; then receiving the signal power Pu of the even-numbered carrier on the new frequency band output by the first operation unit, and obtaining the final signal-to-noise ratio SNRest based on the signal-to-noise ratio calculation formula; The first operation unit is further configured to obtain a new MMSE matrix Φ(k) according to the intermediate signal-to-noise ratio SNR′ and the covariance Sinc function θ(k): ′ ); then calculate the new weights w(k,l; k',l') according to the covariance Sinc function θ(k) between different subcarriers at different times and the new MMSE matrix Φ(k'), and then estimate the intermediate channel according to the new weights w(k,l; k',l') Perform RE-level interpolation to obtain the new final channel estimate H SRS (k,l,r,p), and then estimate H based on the new final channel SRS (k, l, r, p) and the local SRS generation sequence X SRS (k, l, p) is calculated to obtain the signal power Pu of the even-numbered carrier on the new frequency band, and then output to the signal-to-noise ratio calculation unit.

8. The SNR estimation device based on SRS in the 5G small base station system according to claim 7, characterized in that: The coarse channel estimation The intermediate channel estimate The covariance Sinc function θ(k)=sinc(2τ max ΔF(kk ′ )); The MMSE matrix The SNR0 is 30db; The weight w(k,l; k ′ ,l′)=θ(k)×Φ -1 (k ′ ); The final channel estimate Where T represents matrix transpose; The signal power of even-numbered carriers on the frequency band is Pu=mean(X e ×X e H ); wherein, the X e =∑ p X SRS (k,l,p)×H SRS (k,l,r,p), the X e H For X e The transposed conjugate of The noise power of odd-numbered carriers on the frequency band is Ni=mean(δ×δ H ); wherein, the δ=∑ r Y SRS (2k+1,l,r), the δ H is the transposed conjugate of δ.

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