A method, apparatus and device for determining noise power

Abstract

The application provides a method, device and equipment for determining noise power, relates to the technical field of communication, and is used for improving the accuracy of determining noise power. The method for determining noise power comprises the following steps: a network device receives a first signal from a terminal device; the network device determines a target feature vector according to the first signal, the target feature vector being used for representing a unit vector which is orthogonal to a signal direction in the first signal; and the network device determines noise power of a channel through which the terminal device transmits the first signal according to the target feature vector and the first signal.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, apparatus and device for determining noise power. Background Technology

[0002] In New Radio (NR) systems, base stations can send a Physical Downlink Control Channel (PDCCH) to terminal devices. This PDCCH includes downlink control information (DCI). After receiving the downlink control information, the terminal device can send a Physical Uplink Control Channel (PUCCH) to the base station. This PUCCH may include uplink control information (UCI). Before parsing the PUCCH, the base station can determine whether the signal carried by the PUCCH is a noisy signal based on its signal-to-noise ratio (SNR), thus detecting discontinuous transmission (DTX) at the terminal device. The SNR of the PUCCH is related to the noise power of the signal carried by the PUCCH.

[0003] Currently, noise power is calculated in the time domain. Specifically, after the base station receives the original signal sequence corresponding to the PUCCH, it can convert it into a time-domain signal sequence. An inverse discrete Fourier transform is then performed on the original signal sequence, and the noise power can be determined from the resulting time-domain signal sequence.

[0004] Due to factors such as the influence of fading channels, potential relative speeds between terminal devices and base stations, or clock asynchrony between them, a time offset may exist between the terminal device and the base station, thus affecting the received original signal sequence. Noise power calculation in the time domain directly utilizes the time-domain signal sequence, meaning it is directly related to it. When performing an inverse discrete Fourier transform on the original signal sequence, the time offset causes a shift in the sample points, resulting in a significant leakage of power from the original signal sequence at noise sample points. This leads to a larger error in calculating noise power using the time-domain signal sequence. Furthermore, the larger the time offset between the terminal device and the base station, the greater the impact of the time offset on the time-domain signal sequence, and the greater the error in determining the noise power based on the time-domain signal sequence. Summary of the Invention

[0005] This application provides a method, apparatus, and device for determining noise power, which improves the accuracy of noise power estimation in NR systems.

[0006] In a first aspect, a method for determining noise power is provided. The method includes: a network device receiving a first signal from a terminal device; the network device determining a target feature vector based on the first signal, the target feature vector representing a unit vector orthogonal to the signal direction of the first signal; and the network device determining the noise power of the channel through which the terminal device transmits the first signal based on the target feature vector and the first signal.

[0007] In this application, the network device determines a target feature vector based on a first signal sent by the terminal device. The target feature vector is a unit vector in the first signal that is orthogonal to the signal direction; in other words, the target feature vector is a unit vector in the noise direction. Since the target feature vector is a unit vector orthogonal to the signal direction of the first signal, it can be understood as a vector determined based on the signal space corresponding to the first signal. The signal space is more robust; that is, time offset will not cause a large shift in the direction of the first signal in the signal space. Therefore, the target feature vector determined based on the signal space is less affected by time offset. This makes the noise power determined based on the target feature vector less affected by time offset, which is beneficial to improving the accuracy of the determined noise power.

[0008] In one possible implementation, the network device determines the target feature vector corresponding to the first signal by: the network device performing eigenvalue decomposition on the first signal or the second signal to obtain the target feature value, wherein the second signal is obtained by eliminating the noise signal of the first signal; The network device determines the target feature vector based on the target feature value.

[0009] This application provides two methods for determining the target feature vector. The first method involves performing eigenvalue decomposition on the first signal sequence to obtain the target feature vector. This target feature vector is only related to the first signal, which helps to determine a more accurate target feature vector and further improves the accuracy of noise power determination. The second method involves the network device first removing noise from the first signal to obtain a second signal. The target feature vector is then determined based on the second signal. The target feature vector obtained in this method is only related to the second signal, which is the signal obtained after removing noise from the first signal. In other words, the target feature vector is related to the first signal, improving the accuracy of target feature vector determination and making the determined noise power more accurate.

[0010] In one possible implementation, the network device determines a target feature vector corresponding to a first signal, including: determining a first sequence cyclic shift corresponding to the first signal; and determining the target feature vector corresponding to the first sequence cyclic shift from a first correspondence, wherein the first correspondence includes feature vectors corresponding to each of a plurality of pre-stored sequence cyclic shifts.

[0011] In this application, feature vectors corresponding to multiple cyclic shifts of sequences can be pre-calculated and stored (i.e., the first correspondence). After determining the first cyclic shift of the first signal, the target feature vector can be directly determined based on the first correspondence. In this way, the network device does not need to determine the target feature vector every time it calculates noise power, thus reducing the computational load on the network device.

[0012] In one possible implementation, the network device determines the first sequence cyclic shift corresponding to the first signal by: the network device performing an inverse discrete Fourier transform on the first signal to convert the first signal into a third signal in the time domain; the network device determining the cyclic shift of the sequence corresponding to the maximum signal power of the third signal as the first sequence cyclic shift, wherein the first signal is determined based on the first sequence cyclic shift.

[0013] In this application, the first signal is converted into a third signal in the time domain, which facilitates the determination of the maximum signal power. Based on the maximum signal power, the cyclic shift of the first sequence can be directly determined, simplifying the calculation process.

[0014] In one possible implementation, the noise power of the first signal is determined based on the product of the target feature vector and the first signal.

[0015] In this application, the noise power can be directly determined based on the product of the target feature vector and the first signal, making the noise power calculation method simpler.

[0016] In one possible implementation, there are multiple target feature vectors; the network device determines the noise power of the channel through which the terminal device transmits the first signal, including: the network device determines a candidate noise power based on the product of each of the multiple target feature vectors and the first signal, and obtains multiple candidate noise powers in total; the network device determines the average of the multiple candidate noise powers as the noise power.

[0017] In this application, multiple target feature vectors can be determined, and multiple candidate noise powers can be determined based on the multiple target feature vectors. The average value of the multiple candidate noise powers can be calculated, which can reduce the impact of the error of a single target feature vector on the noise power, thereby reducing the error of the determined noise power and improving the accuracy of the determined noise power.

[0018] In one possible implementation, the method further includes: the network device determining the signal-to-noise ratio (SNR) of the first signal as the ratio of the signal power to the noise power corresponding to the first signal sequence; if the SNR is less than a preset threshold, the first signal is determined to be a pure noise signal, and a negative response message is sent to the terminal device, the negative response message indicating that the specified information was not successfully parsed from the first signal.

[0019] In this application, because the accuracy of the determined noise power is higher, the determined signal-to-noise ratio is also more accurate. This enables the network device to accurately determine whether the first signal is a noise signal, so as to accurately send the response information for the first signal to the terminal device.

[0020] Secondly, an apparatus for determining noise power is provided, the apparatus comprising: The transceiver module is used to receive the first signal from the terminal device.

[0021] The processing module is configured to determine a target feature vector based on a first signal, the target feature vector representing a unit vector orthogonal to the signal direction in the first signal; the processing module is further configured to determine the noise power of the channel through which the terminal device transmits the first signal based on the target feature vector and the first signal sequence.

[0022] In one possible implementation, the processing module is further configured to: perform eigenvalue decomposition on the first signal to obtain target eigenvalues; and determine a target eigenvector based on the target eigenvalues.

[0023] In one possible implementation, the processing module is further configured to: eliminate noise signals in the first signal, determine the second signal; perform eigenvalue decomposition on the second signal to obtain the target feature value, and determine the target feature vector based on the target feature value.

[0024] In one possible implementation, the processing module is further configured to: determine a first sequence cyclic shift corresponding to the first signal; and determine the target feature vector corresponding to the first sequence cyclic shift from a first correspondence, wherein the first correspondence includes feature vectors corresponding to each of a plurality of pre-stored sequence cyclic shifts.

[0025] In one possible implementation, the processing module is further configured to: perform an inverse discrete Fourier transform on the first signal to convert the first signal into a third signal in the time domain; and determine the cyclic shift of the sequence corresponding to the maximum signal power of the third signal as the cyclic shift of the first sequence.

[0026] In one possible implementation, the processing module is further configured to: determine the first signal by cyclic shifting according to the first sequence.

[0027] In one possible implementation, the processing module is further configured to: determine the noise power of the first signal based on the product of the target feature vector and the first signal.

[0028] In one possible implementation, the processing module is further configured to: have multiple target feature vectors; determine a candidate noise power based on the product of each of the multiple target feature vectors and the first signal, thereby obtaining multiple candidate noise powers; and determine the average of the multiple candidate noise powers as the noise power.

[0029] In one possible implementation, the processing module is further configured to: determine the signal-to-noise ratio of the first signal as the ratio of the signal power to the noise power corresponding to the first signal; if the signal-to-noise ratio is less than a preset threshold, determine that the first signal is a pure noise signal, and send a negative response information to the terminal device, wherein the negative response information is used to indicate that the specified information was not successfully parsed from the first signal.

[0030] Thirdly, this application provides an apparatus for determining noise power, comprising: at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the method as described in any one of the first aspects by executing the instructions stored in the memory.

[0031] Fourthly, this application provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of the first aspects.

[0032] The beneficial effects of the second to fourth aspects can be referred to the discussion in the first aspect above, and will not be listed here again. Attached Figure Description

[0033] Figure 1 An application scenario diagram provided for an embodiment of this application; Figure 2A flowchart illustrating a method for determining noise power provided in an embodiment of this application; Figure 3 This is a schematic diagram showing the comparison of block error rate between the embodiments of this application and the noise power calculation method in the time domain; Figure 4 This is a schematic diagram showing the comparison of block error rate between the embodiments of this application and the noise power calculation method in the time domain; Figure 5 Structure of a device for determining noise power provided in an embodiment of this application Figure 1 ; Figure 6 Structure of a device for determining noise power provided in an embodiment of this application Figure 2 ; Figure 7 This is a structural diagram of a device for determining noise power, provided in an embodiment of this application. Detailed Implementation

[0034] To better understand the technical solutions provided in this application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0035] To facilitate understanding of the technical solutions provided in the embodiments of this application, the background technology related to the embodiments of this application will be introduced first.

[0036] PUCCH has multiple formats. Taking PUCCH format 0 as an example, this paper introduces existing time-domain noise power estimation methods.

[0037] Specifically, the calculation formula for the noise power estimation method in this time domain is shown in formula (1).

[0038] (1)

[0039] in, The power sequence after performing an inverse discrete Fourier transform on the received signal, where m is the index of the inverse discrete Fourier transform points. The number of orthogonal frequency division multiplexing (OFDM) symbols occupied by PUCCH format 0. This specifies the number of users that PUCCH format 0 can support simultaneously. The number of subcarriers within a resource block (RB).

[0040] Because there is a certain time offset between the terminal device and the base station, when the received signal undergoes the inverse discrete Fourier transform... At that time, the time offset caused the sample point to shift, making There is a certain error, and therefore based on The calculated noise power is also affected by time deviation, resulting in some error. Furthermore, the larger the time deviation, the greater the error in calculating the noise power.

[0041] In view of this, embodiments of this application provide a method for determining noise power. A network device receives a first signal from a terminal device. The network device can determine a target feature vector based on the first signal. Since the target feature vector is determined based on the signal space corresponding to the first signal, and the signal space has higher robustness, that is, the first signal will not have a large directional shift due to time offset. Furthermore, the target feature vector is less affected by time offset. Therefore, by determining the noise power based on the target feature vector and the first signal, the obtained noise power also avoids the influence of time offset, thereby obtaining an accurate noise power.

[0042] Please refer to Figure 1 This is an application scenario diagram provided in an embodiment of this application, and can also be understood as a schematic diagram of a communication system architecture. The communication system can be fifth-generation (5G). th The term "generation" (abbreviated as 5G) refers to a communication system under NR, or a next-generation communication system, etc. This application does not limit the specific implementation of this concept.

[0043] like Figure 1 As shown, this application scenario includes network device 101 and terminal device 102, which can communicate with each other. The network device is, for example, an access network device, such as a base station. Terminal devices include, but are not limited to: mobile phones, personal computers (PCs), tablets, laptops, handheld computers, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminal devices in industrial control, wireless terminal devices in autonomous driving, wireless terminal devices in smart grids, wireless terminal devices in transportation safety, wireless terminal devices in smart cities, or wireless terminal devices in smart homes, etc.

[0044] For example, terminal device 102 sends a first signal to network device 101. Correspondingly, network device 101 receives the first signal from terminal device. The first signal is, for example, a UCI carried on PUCCH, specifically, a signal in PUCCH format 0, or a signal corresponding to PUCCH format 0.

[0045] Network device 101 can determine the noise power of the first signal, and then determine whether DTX has occurred in terminal device 102.

[0046] Please refer to Figure 2 The above is a flowchart of a method for determining noise power provided in an embodiment of this application. Figure 2 The illustrated embodiments are applicable Figure 1 The application scenarios shown. Figure 2 The network device in the illustrated embodiment is, for example, Figure 1 Network device 101 in the middle, Figure 2 The terminal device in the illustrated embodiment is, for example, Figure 1 Terminal device 102 in the middle.

[0047] S201, the network device receives the first signal from the terminal device.

[0048] The terminal device sends a first signal to the network device. The first signal is, for example, a UCI carried on the PUCCH. Specifically, it can be a signal in PUCCH format 0, or a signal corresponding to PUCCH format 0.

[0049] For example, taking the first signal as the signal corresponding to PUCCH format 0 as an example, the expression of the first signal will be introduced with an example.

[0050] For example, an expression for PUCCH format 0 using a low peak-to-average ratio sequence is shown in formula (2).

[0051] (2)

[0052] in, The value is related to the group hop parameter configured on the network device. The value of is shown in formula (3).

[0053] (3)

[0054] Among them, the initial cyclic shift For high-level configurations, when multiple terminal devices reuse the same value, different terminal devices will have different values. The value is related to the time slot occupied by the first signal sent by the terminal device and the OFDM symbol index, and is as shown in formula (4).

[0055] (4)

[0056] in, The timeslot number within the wireless frame. For the OFDM symbol index of PUCCH transmission, The number of OFDM symbols in a time slot. It is a pseudo-random sequence.

[0057] Secondly, by combining the above formulas (2) and (3), the expression of the first signal can be obtained as shown in formula (5).

[0058] (5)

[0059] Among them, the initial cyclic shift This can be configured on the terminal device. It should be noted that in multi-user multiplexing, the initial cyclic shift value differs for different users; sequence cyclic shift... The value is related to the value of the hybrid automatic repeat request (HARQ) response (also known as the HARQ value), which is used to represent the positive acknowledgment (ACK) and negative acknowledgment (NACK) information for the PDCCH. For subcarrier index, the value is... ; This is the OFDM symbol index, with a value of ; The number of terminal devices that can be multiplexed simultaneously for PUCCH format 0; For the terminal device index, the value is... ; The channel fading coefficient; It is noise.

[0060] Please refer to Table 1 below for a type of sequence cyclic shift. An example of the relationship between the value and the HARQ value.

[0061] Table 1

[0062] As shown in Table 1, when 1 bit is used to represent the hybrid automatic repeat request acknowledgment and no scheduling request, if the value of this 1 bit is 0, then the HARQ value is 0, which is NACK, and the sequence is cyclically shifted by 0; if the value of this 1 bit is 1, then the HARQ value is 1, which is ACK, and the sequence is cyclically shifted by 6.

[0063] When 1 bit is used to represent a hybrid automatic repeat request acknowledgment and a scheduling request, if the value of this 1 bit is 0, the HARQ value is 0, which is NACK, and the sequence is cyclically shifted by 3; if the value of this 1 bit is 1, the HARQ value is 1, which is ACK, and the sequence is cyclically shifted by 9.

[0064] When using 2 bits to represent a hybrid automatic repeat request acknowledgment (HARQ) and no-scheduling request (NSQ), if the value of these 2 bits is {0, 0}, it indicates that the HARQ value is {0, 0}, which is {NACK, NACK}, and the sequence is cyclically shifted by 0; if the value of these 2 bits is {0, 1}, it indicates that the HARQ value is {0, 1}, which is {NACK, ACK}, and the sequence is cyclically shifted by 3; if the value of these 2 bits is {1, 1}, it indicates that the HARQ value is {1, 1}, which is {ACK, ACK}, and the sequence is cyclically shifted by 6; if the value of these 2 bits is {1, 0}, it indicates that the HARQ value is {1, 0}, which is {ACK, NACK}, and the sequence is cyclically shifted by 9.

[0065] When using 2 bits to represent a hybrid automatic repeat request acknowledgment (HARQ) and a scheduled request, if the value of the 2 bits is {0, 0}, it indicates that the HARQ value is {0, 0}, which is {NACK, NACK}, and the sequence is cyclically shifted by 1; if the value of the 2 bits is {0, 1}, it indicates that the HARQ value is {0, 1}, which is {NACK, ACK}, and the sequence is cyclically shifted by 4; if the value of the 2 bits is {1, 1}, it indicates that the HARQ value is {1, 1}, which is {ACK, ACK}, and the sequence is cyclically shifted by 7; if the value of the 2 bits is {1, 0}, it indicates that the HARQ value is {1, 0}, which is {ACK, NACK}, and the sequence is cyclically shifted by 10.

[0066] S202, the network device determines the target feature vector based on the first signal.

[0067] The target feature vector is used to represent a unit vector orthogonal to the signal direction of the first signal, or it can be understood as a unit vector in the direction of noise.

[0068] The network device determines the target feature vector based on the first signal, which includes two implementations: The first implementation involves the network device directly determining the target feature vector based on the first signal; the second implementation involves the network device determining the target feature vector based on a second signal obtained by processing the first signal. These will be explained below.

[0069] The first implementation: The network device directly determines the target feature vector based on the first signal.

[0070] For example, a network device determines a first sequence of cyclic shifts of a first signal, and then determines a target feature vector based on the first signal.

[0071] For example, a terminal device can send multiple signals to a network device, each signal corresponding to a different sequence cyclic shift. Therefore, after the network device receives the first signal, it needs to first determine the first sequence cyclic shift corresponding to the first signal, and then substitute the determined first sequence cyclic shift into the expression of the first signal to obtain the specific result of the first signal. Based on the specific result of the first signal, the target feature vector can then be determined.

[0072] The following describes how network devices determine the first sequence of cyclic shifts.

[0073] The network device performs an inverse discrete Fourier transform on the first signal, converting it into a third signal in the time domain. Based on the third signal, the signal power value corresponding to each cyclic shift can be determined from multiple cyclic shift sequences. The maximum value among these multiple signal power values ​​is determined as the maximum signal power, and the cyclic shift sequence corresponding to the maximum signal power is determined as the first cyclic shift sequence. Multiple cyclic shift sequences can be pre-stored in the network device, for example, as shown in Table 1 above.

[0074] For example, a formula for converting a first signal into a third signal in the time domain is shown in (6).

[0075] (6) in, As the third signal, The time-domain channel fading coefficient is... For time-domain noise, Represents convolution. Let be the impulse function.

[0076] According to the above formula (6), the network device can set the noise to zero, and thus determine the signal power of each cyclic shift, denoted as . Network devices can determine the signal power corresponding to each cyclic shift in a series of cyclic shifts. They can also determine the maximum signal power, denoted as... The sequence cyclic shift corresponding to the maximum signal power is determined as the first sequence cyclic shift.

[0077] Furthermore, the network device can determine the HARQ value based on the value of the first sequence cyclic shift and Table 1 above. For example, if the network device determines that the first sequence cyclic shift is 3, it will then determine that the HARQ returned by the terminal device is NACK.

[0078] Example 1: The network device can perform eigenvalue decomposition on the first signal to obtain the target feature vector.

[0079] After determining the first signal, the network device can perform eigenvalue decomposition on the first signal to obtain multiple eigenvalues. Based on these multiple eigenvalues, the target feature vector is determined.

[0080] For example, the network device converts the first signal into a covariance matrix, performs eigenvalue decomposition on the covariance matrix of the first signal, calculates the characteristic polynomial of the covariance matrix, and obtains multiple eigenvalues. The network device can determine the eigenvalues ​​less than a preset threshold as target eigenvalues. Alternatively, the network device can sort these multiple eigenvalues ​​in ascending order, and determine at least one eigenvalue whose order satisfies a preset order as the target eigenvalue.

[0081] For example, a formula for eigenvalue decomposition of the covariance matrix of the first signal is shown in formula (7).

[0082] (7)

[0083] in, Let be the covariance matrix of the first signal. This is the expected value of the first signal.

[0084] For example, one formula for determining the target feature value is shown in formula (8).

[0085] (8)

[0086] in, For the target feature value, It is an identity matrix.

[0087] Based on the above formula (8), the covariance matrix of the first signal, and the identity matrix, the target eigenvalues ​​are determined.

[0088] A network device can determine a target feature vector based on a single target feature value. If there are multiple target feature values, the network device can determine multiple target feature vectors accordingly. For example, the network device determines the target feature vector based on the target feature value and a first relational expression.

[0089] For example, the first relation can be shown as Equation (9), which represents the relationship between the target eigenvalues, the covariance matrix, and the target eigenvectors.

[0090] (9)

[0091] in, The target feature vector.

[0092] Specifically, according to the above formula (9), the network device can determine the target feature vector based on the covariance matrix of the first signal and the target feature value.

[0093] Example 2: The network device can determine the target feature vector based on the first correspondence.

[0094] The first correspondence is the target feature vector corresponding to each cyclic shift of a sequence in a plurality of cyclic shift sequences pre-stored by the network device. After determining the first cyclic shift of the first signal, the network device can directly determine the feature vector corresponding to the first cyclic shift as the target feature vector based on the first cyclic shift and the first correspondence.

[0095] Specifically, the network device can perform eigenvalue decomposition on the fourth signal at each of the multiple cyclic shifts to determine the feature vector corresponding to each cyclic shift, thus obtaining the first correspondence. The fourth signal is the signal received by the network device from the terminal device before receiving the first signal. For example, the fourth signal might be the signal corresponding to PUCCH format 0. The method for performing eigenvalue decomposition on the fourth signal to determine the feature vector can be referenced from the previous section on eigenvalue decomposition of the first signal, and will not be listed here again.

[0096] As an example, the eigenvector corresponding to each cyclic shift of multiple sequences in the first correspondence can be one or more.

[0097] For example, if each of the multiple cyclic shifts in the first correspondence corresponds to multiple feature vectors, then the network device can also determine multiple target feature vectors from the first correspondence based on the first cyclic shift.

[0098] In one possible implementation, the first signal in Examples 1 and 2 above can be the first signal itself, or it can be the first signal after the reference sequence in the first signal has been removed. For example, the network device can obtain the first signal after the reference sequence has been removed by multiplying the first signal with the conjugate sequence. The expression of the first signal is shown in Equation (10) as an example.

[0099] (10)

[0100] A calculation formula for eliminating the reference sequence in the first signal of the above formula (10) is shown in formula (11).

[0101] (11) (12) in, The first signal received by the network device. The first signal after conversion. This is a preset baseline sequence.

[0102] The second implementation: The network device determines the target feature vector based on the second signal obtained from the first signal processing.

[0103] Network devices can determine a second signal based on a first signal. For example, the first device can simplify the first signal to obtain the second signal.

[0104] For example, with the first signal as shown in formula (5) above, a second signal expression is shown in formula (14).

[0105] (13) (14) in, This indicates the second signal.

[0106] For example, the network device determines a first sequence cyclic shift of the first signal and determines a second signal based on the first sequence cyclic shift. Then, a target feature vector can be determined based on the second signal. The method by which the network device determines the first sequence cyclic shift can be referred to the preceding text.

[0107] Example 3: The network device performs eigenvalue decomposition on the second signal to obtain the target eigenvector. There can be one or more target eigenvectors. The details of eigenvalue decomposition on the second signal are similar to those of eigenvalue decomposition on the first signal described earlier, and will not be repeated here.

[0108] Example 4: The network device can determine the target feature vector corresponding to the first sequence cyclic shift based on the first correspondence. The first correspondence is the feature vector corresponding to each cyclic shift in a plurality of sequence cyclic shifts pre-stored by the network device.

[0109] In this context, the feature vector corresponding to each cyclic shift in multiple sequence shifts can be determined by the network device in the manner described in Example 3, which will not be elaborated here.

[0110] S203, the network device determines the noise power of the channel through which the terminal device transmits the first signal based on the target feature vector and the first signal.

[0111] For example, the network device determines the noise power based on the product of the target feature vector and the first signal.

[0112] If there is only one target feature vector, the network device can take the square of the product of the target feature vector and the first signal as the noise power.

[0113] For example, the network device, based on the target feature vector and the first signal, divides the target feature vector... The product of the first signal and the first signal is used as the noise power of the channel through which the terminal device transmits the first signal. The specific calculation formula is shown in formula (15).

[0114] (15)

[0115] in, For noise power, Let Y be the target feature vector, and Y be the first signal.

[0116] If there are multiple target feature vectors, the network device can determine the noise power corresponding to each target feature vector, i.e., the candidate noise power. The calculation method for the candidate noise power can refer to the previous method for calculating the noise power for only a single target feature vector. The network device calculates the average of these multiple candidate noise powers and uses the result of the average calculation as the noise power.

[0117] Furthermore, in one possible implementation, the network device can also determine the signal-to-noise ratio (SNR) based on the noise power and signal power calculated by the above method, and perform DTX detection. The SNR of the first signal is compared with a preset threshold T. When the SNR is less than the preset threshold T, the network device determines that the first signal is a noise signal and sends a negative response message to the second device. The negative response message indicates that the specified information was not successfully parsed from the first signal.

[0118] For example, the ratio of signal power to noise power is determined as the signal-to-noise ratio of the first signal, and the specific calculation formula is shown in formula (16).

[0119] (16)

[0120] in, For signal-to-noise ratio, This represents the signal power.

[0121] Among these, network devices can determine the maximum value among the multiple signal powers mentioned above as the maximum signal power.

[0122] For example, the formula for calculating signal power is shown in formula (17).

[0123] (17)

[0124] in, For maximum signal power, This represents the signal power.

[0125] To compare the block error rate of the scheme in this application embodiment with the time-domain noise power calculation method, taking PUCCH format0 as the first signal as an example, the following test parameters, shown in Table 2, are used under different time offset test conditions, and the block error rate (BLER) is determined through MATLAB simulation. The block error rate is the ratio of a first quantity to a second quantity. The first quantity refers to the number of UCI messages indicating NACK among multiple UCI messages received by the network device from the terminal device, and the second quantity refers to the ratio of the total number of UCI messages indicating ACK sent by the terminal device to the network device. This ratio allows for the determination of the error probability in calculating the noise power.

[0126] Table 2

[0127] Based on the above test parameters and test methods, the following results were obtained: Figure 3 and Figure 4 The comparison simulation results are shown in the figure. Figure 3 The figure shows a comparison of the block error rate of the embodiments of this application and the noise power calculation method in the time domain without considering time offset. Figure 3 The horizontal axis represents the signal-to-noise ratio, and the vertical axis represents the block error rate. Figure 3 Line A in the graph represents the relationship between block error rate and signal-to-noise ratio (SNR) under the scheme of this application embodiment. Line B represents the relationship between block error rate and SNR using the time-domain noise power calculation method. Figure 3 It can be seen that, under any signal-to-noise ratio, the scheme of this application embodiment has a lower block error rate than the method of calculating noise power in the time domain without considering time offset.

[0128] Please refer to Figure 4 , Figure 4 This indicates that the time bias is... microseconds ( The simulation results comparing the block error rate of the embodiments of this application with the noise power calculation method in the time domain are shown in the figure. Figure 4 The horizontal axis represents the signal-to-noise ratio, and the vertical axis represents the block error rate. Figure 4 Line A in the graph represents the relationship between block error rate and signal-to-noise ratio (SNR) under the scheme of this application embodiment. Line B represents the relationship between block error rate and SNR using the time-domain noise power calculation method. Figure 4 It can be seen that, at any signal-to-noise ratio, the scheme of this application embodiment is superior to the time-domain noise power calculation method in terms of time offset. In this case, the block error rate is lower.

[0129] Therefore, it can be seen that the block error rate of the technical solution in this application embodiment is significantly lower than that calculated using the time-domain noise power method. Even with a large time offset, the block error rate of the technical solution in this application embodiment remains low.

[0130] This application also provides an apparatus for determining noise power, which can achieve... Figure 2 The functions of network devices. Please refer to [link / reference]. Figure 5 This is a structural diagram of a device for determining noise power according to an embodiment of this application. The device 501 for determining noise power includes a transceiver module 502 and a processing module 503.

[0131] For example, transceiver module 502 is used to receive a first signal from a terminal device.

[0132] The processing module 503 is used to determine a target feature vector based on the first signal, wherein the target feature vector is used to represent a unit vector orthogonal to the signal direction in the first signal; the processing module 503 is also used to determine the noise power of the channel through which the terminal device transmits the first signal based on the target feature vector and the first signal.

[0133] In one possible implementation, the processing module 503 is further configured to: perform eigenvalue decomposition on the first signal or the second signal to obtain target eigenvalues, wherein the second signal is obtained by eliminating noise from the first signal; and determine a target eigenvector based on the target eigenvalues.

[0134] In one possible implementation, the processing module 503 is further configured to: determine the first sequence cyclic shift corresponding to the first signal; and determine the target feature vector corresponding to the first sequence cyclic shift from the first correspondence, wherein the first correspondence includes the feature vector corresponding to each sequence cyclic shift in a plurality of pre-stored sequence cyclic shifts.

[0135] In one possible implementation, the processing module 503 is further configured to: perform an inverse discrete Fourier transform on the first signal to convert the first signal into a third signal in the time domain; and determine the cyclic shift of the sequence corresponding to the maximum signal power of the third signal as the first cyclic shift.

[0136] In one possible implementation, the processing module 503 is further configured to: determine the first signal by cyclic shifting according to the first sequence.

[0137] In one possible implementation, the processing module 503 is further configured to: determine the noise power of the first signal based on the product of the target feature vector and the first signal.

[0138] In one possible implementation, the processing module 503 is further configured to: determine a candidate noise power based on the product of each of the multiple target feature vectors and the first signal, thereby obtaining multiple candidate noise powers; and determine the average of the multiple candidate noise powers as the noise power.

[0139] In one possible implementation, the processing module 503 is further configured to: determine the ratio of the signal power to the noise power corresponding to the first signal as the signal-to-noise ratio of the first signal; if the signal-to-noise ratio is less than a preset threshold, determine that the first signal is a pure noise signal, and send a negative response information to the terminal device, wherein the negative response information is used to indicate that the specified information was not successfully parsed from the first signal.

[0140] As an example, the noise power determination device 501 in this application embodiment can also implement any of the noise power determination methods discussed above. Alternatively, the noise power determination device 501 can also implement the function of the first device mentioned above.

[0141] Please refer to Figure 6 The structure of a device for determining noise power provided in an embodiment of this application. Figure 2 .like Figure 6 As shown, the noise power determination device 601 includes a noise power module 603. Optionally, the device may also include a UCI decoding module 602, a noise power module 603, and a DTX detection module 604.

[0142] For example, the noise power module 603 is used to determine a target feature vector based on the first signal, the target feature vector being used to represent a unit vector orthogonal to the signal direction in the first signal; the noise power module 603 is also used to determine the noise power of the channel through which the terminal device transmits the first signal based on the target feature vector and the first signal.

[0143] In one possible implementation, the noise power module 603 is further configured to: perform eigenvalue decomposition on the first signal or the second signal to obtain target eigenvalues, wherein the second signal is obtained by eliminating the noise signal of the first signal; and determine the target eigenvector based on the target eigenvalues.

[0144] In one possible implementation, the noise power module 603 is further configured to: determine a first sequence cyclic shift corresponding to a first signal; and determine a target feature vector corresponding to the first sequence cyclic shift from a first correspondence, wherein the first correspondence includes feature vectors corresponding to each of a plurality of pre-stored sequence cyclic shifts.

[0145] In one possible implementation, the UCI decoding module 602 is further configured to: perform an inverse discrete Fourier transform on the first signal to convert the first signal into a third signal in the time domain; and determine the cyclic shift of the sequence corresponding to the maximum signal power of the third signal as the first cyclic shift.

[0146] In one possible implementation, the UCI decoding module 602 is further configured to: determine the first signal by cyclic shifting according to the first sequence.

[0147] In one possible implementation, the UCI decoding module 602 is further configured to: determine the product of the first signal and the conjugate sequence as the converted first signal, wherein the conjugate sequence is the conjugate result of a preset reference sequence; and perform a discrete Fourier inverse transform on the converted first signal to convert the converted first signal into a third signal in the time domain.

[0148] In one possible implementation, the UCI decoding module 602 is further configured to: determine the maximum signal power of the third signal as the signal power.

[0149] In one possible implementation, the noise power module 603 is further configured to: determine the noise power of the first signal based on the product of the target feature vector and the first signal.

[0150] In one possible implementation, the noise power module 603 is further configured to: determine a candidate noise power based on the product of each of the multiple target feature vectors and the first signal, thereby obtaining multiple candidate noise powers; and determine the average of the multiple candidate noise powers as the noise power.

[0151] In one possible implementation, the DTX detection module 604 is further configured to: determine the signal-to-noise ratio of the first signal as the ratio of the signal power to the noise power corresponding to the first signal; if the signal-to-noise ratio is less than a preset threshold, determine that the first signal is a pure noise signal, and send a negative response information to the terminal device, wherein the negative response information is used to indicate that the specified information has not been successfully parsed from the first signal.

[0152] As an example, the noise power determination device 601 in this application embodiment can also implement any of the noise power determination methods discussed above. Additionally, the noise power determination device 601 can also implement the functions of the network device described above.

[0153] As an example, Figure 5 The processing module 503 in the middle can realize Figure 6 The functions of the noise power module 603, UCI decoding module 602 and DTX detection module 604 are described.

[0154] This application provides a device for determining noise power. Please refer to... Figure 7 , Figure 7 This is a structural diagram of a noise power determination device provided in an embodiment of this application. The noise power determination device 701 includes: at least one processor 702, and a memory 703 communicatively connected to the at least one processor.

[0155] The memory 703 stores instructions that can be executed by at least one processor 702, which implements the method for determining noise power as described in any of the preceding descriptions by executing the instructions stored in the memory 703.

[0156] As an example, Figure 7 The processor 702 in the middle can achieve the above. Figure 5 The function of the processing module 503 in the middle.

[0157] As an example, Figure 7 The processor 702 in the middle can achieve the above. Figure 6 The function of the device 601 for determining noise power.

[0158] This application provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the method for determining noise power as described above.

[0159] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0160] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0161] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0162] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0163] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for determining noise power, characterized in that, include: The network device receives the first signal from the terminal device; The network device determines a target feature vector based on the first signal, the target feature vector being used to represent a unit vector orthogonal to the signal direction of the first signal; The network device determines the noise power of the channel through which the terminal device transmits the first signal based on the product of the target feature vector and the first signal.

2. The method according to claim 1, characterized in that, The network device determines the target feature vector based on the first signal, including: The network device performs feature value decomposition on the first signal or the second signal to obtain the target feature value, wherein the second signal is obtained by eliminating the noise signal of the first signal; The network device determines the target feature vector based on the target feature value.

3. The method according to claim 1, characterized in that, The network device determines the target feature vector based on the first signal, including: The network device determines the first sequence corresponding to the first signal to be cyclically shifted; The network device determines the target feature vector corresponding to the first sequence cyclic shift from the first correspondence relationship, wherein the first correspondence relationship includes the feature vector corresponding to each of the pre-stored sequence cyclic shifts.

4. The method according to claim 3, characterized in that, The network device determines the first sequence cyclic shift corresponding to the first signal, including: The network device performs an inverse discrete Fourier transform on the first signal to convert the first signal into a third signal in the time domain; The network device determines the cyclic shift of the sequence corresponding to the maximum signal power of the third signal as the first cyclic shift.

5. The method according to claim 1, characterized in that, The target feature vectors are multiple; the network device determines the noise power of the channel through which the terminal device transmits the first signal, including: The network device obtains multiple candidate noise powers based on the product of each of the multiple target feature vectors and the first signal; The network device determines the average of the multiple candidate noise powers as the noise power.

6. The method according to any one of claims 1-4, characterized in that, The method further includes: The network device determines the signal-to-noise ratio of the first signal as the ratio of the signal power to the noise power corresponding to the first signal. If the signal-to-noise ratio is less than a preset threshold, the first signal is determined to be a pure noise signal, and a negative response message is sent to the terminal device. The negative response message is used to indicate that the specified information was not successfully parsed from the first signal.

7. A device for determining noise power, characterized in that, It includes a transceiver module and a processing module, wherein: The transceiver module is used to receive a first signal from the terminal device; The processing module is configured to determine a target feature vector based on the first signal, wherein the target feature vector represents a unit vector orthogonal to the signal direction in the first signal; The processing module is further configured to determine the noise power of the channel through which the terminal device transmits the first signal based on the product of the target feature vector and the first signal.

8. The apparatus according to claim 7, characterized in that, The processing module is also used for: Based on the product of each of the multiple target feature vectors and the first signal, multiple candidate noise powers are determined; The average of the multiple candidate noise powers is determined as the noise power of the channel through which the terminal device transmits the first signal.

9. A device for determining noise power, characterized in that, include: At least one processor, and A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the method as described in any one of claims 1-6 by executing the instructions stored in the memory.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-6.

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