Method and apparatus for calculating error vector magnitude

Abstract

The present disclosure relates to the technical field of communication, and discloses a method and device for calculating error vector magnitude. For a received symbol carried on one subcarrier, soft bit information of each bit carried by the received symbol is obtained, the received symbol is modulated by M times of 2 and carries M bits; the soft bit information of the M bits is divided into a first group corresponding to a real part and a second group corresponding to an imaginary part; the minimum value of the soft bit information is selected from the two groups respectively; and the error vector magnitude of the subcarrier is calculated according to the two minimum values. The present disclosure directly calculates the error vector magnitude according to the soft bit information, without additional hard demodulation to find the nearest constellation point, and can reduce the complexity of error vector magnitude calculation without sacrificing the calculation accuracy.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a technique for calculating the magnitude of an error vector. Background Technology

[0002] The descriptions in this section are intended to provide background information for the implementation of this disclosure and should not be construed as an admission or implication that they constitute prior art.

[0003] In orthogonal frequency division multiplexing (OFDM) receivers, calculating the error vector magnitude for each symbol and subcarrier is a common requirement. The error vector magnitude measures the degree to which the received signal deviates from the ideal constellation point, thus assessing the quality of the received signal. In related technologies, the error vector magnitude for each subcarrier is typically calculated using the received symbols after frequency domain equalization. Specifically, this method first performs hard demodulation on the received constellation point to find the nearest standard constellation point; then, it calculates the Euclidean distance between the received constellation point and this nearest standard constellation point, thereby obtaining the corresponding error vector magnitude. On the other hand, for communication protocols employing channel coding, such as WLAN protocols and LTE protocols, the receiver typically calculates soft bit information for each bit based on the received constellation point for decoding; hard demodulation itself is not a necessary step in the decoding process. Summary of the Invention

[0004] This disclosure provides a method and apparatus for calculating the magnitude of the error vector, which can solve the technical problem of how to reduce the complexity of calculating the magnitude of the error vector.

[0005] According to one aspect of this disclosure, a method for calculating the magnitude of an error vector is provided, applied to a receiver in a communication system, comprising: For a received symbol carried on a subcarrier, obtain the soft bit information of each bit carried by the received symbol; wherein, the received symbol adopts... Quadrature amplitude modulation, each of the received symbols carries bits, It is a positive even number; The The soft bit information of each bit is divided into a first group and a second group. The first group corresponds to the real part of the received symbol, and the second group corresponds to the imaginary part of the received symbol. The minimum value of soft bit information is selected from the first group and the second group respectively. The minimum value represents the distance between the received symbol and the nearest decision boundary in the corresponding real or imaginary part. The error vector magnitude of the subcarrier is calculated based on the minimum values ​​of the first group and the second group; wherein the error vector magnitude is the result of the sum of the terms corresponding to the real part and the terms corresponding to the imaginary part, weighted by a coefficient related to the noise variance, the terms corresponding to the real part being the square of the difference between the reference value and the minimum value of the first group, and the terms corresponding to the imaginary part being the square of the difference between the reference value and the minimum value of the second group.

[0006] In a preferred embodiment, the soft bit information is log-likelihood ratio information, and the soft bit information is information that the receiver has calculated during the decoding of the channel coding and has been reused to calculate the magnitude of the error vector.

[0007] In a preferred embodiment, the reference value is 1, and the coefficient related to the noise variance is half of the noise variance; The magnitude of the error vector is calculated using the following formula: In the formula, The magnitude of the error vector, Let Variance be the noise variance. This refers to the soft bit information of the first group. This refers to the soft bit information of the second group. This indicates taking the minimum value.

[0008] In a preferred embodiment, The received symbol is equal to 4, and uses hexadecimal quadrature amplitude modulation. Each received symbol carries 4 bits, and the 4 bits of soft bit information are denoted as... , , and The first group is The second group is ; The magnitude of the error vector is calculated using the following formula: .

[0009] In a preferred embodiment, the soft bit information of each bit is determined based on the value of the received symbol in the real or imaginary part corresponding to that bit, the decision boundary corresponding to that bit, and the noise variance.

[0010] In a preferred embodiment, for any bit, under the assumption that the noise received by the received symbol follows a Gaussian distribution with variance equal to the noise variance, the conditional probability of the value of the real or imaginary part of the received symbol corresponding to that bit being 0 and 1, respectively, is calculated, and the logarithm of the ratio of the two conditional probabilities is used as the soft bit information of that bit, i.e.: In the formula, This is the soft bit information of that bit. The received symbol, For this bit, and These are the conditional probabilities of the received symbol when the bit is 0 and when it is 1, respectively.

[0011] In a preferred embodiment, the soft bit information is proportional to the distance between the value of the received symbol in the corresponding real or imaginary part and the decision boundary corresponding to that bit, and inversely proportional to the noise variance.

[0012] In a preferred embodiment, the receiver is an orthogonal frequency division multiplexing (OFDM) receiver, and the received symbols are frequency-equalized symbols carried on the subcarriers; the error vector magnitude is calculated for each subcarrier of each OFDM symbol; or... The received symbols are symbols in signals that conform to wireless local area network protocols or long-term evolution protocols and employ channel coding.

[0013] According to another aspect of this disclosure, an apparatus for calculating the magnitude of an error vector is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for calculating the magnitude of the error vector described above.

[0014] According to another aspect of this disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the method for calculating the magnitude of the error vector described above.

[0015] In the embodiments of this disclosure, by obtaining the soft bit information of each bit carried by the received symbol on a subcarrier, the soft bit information of each bit is divided into a first group corresponding to the real part and a second group corresponding to the imaginary part. The minimum value of the soft bit information is selected from the two groups respectively, and the error vector amplitude of the subcarrier is calculated based on the minimum value of the two groups. The error vector amplitude can be obtained directly based on the soft bit information, without the need to perform hard demodulation on the received symbol to find the nearest standard constellation point to calculate the error vector amplitude, thus saving the hard demodulation step. Furthermore, the computational complexity of the calculation based on the soft bit information is comparable to the complexity of Euclidean distance calculation in related technologies. Therefore, the overall complexity of error vector amplitude calculation can be significantly reduced without sacrificing the accuracy of error vector amplitude calculation.

[0016] Furthermore, by setting the soft bit information as log-likelihood ratio information and reusing this information, which has already been calculated by the receiver during the decoding of the channel coding, to calculate the error vector magnitude, it is possible to avoid calculating the soft bit information separately for calculating the error vector magnitude, fully reuse the existing calculation results of the receiver, and thus further reduce the computational overhead.

[0017] Furthermore, by setting the baseline value to 1 and taking the coefficient related to the noise variance as half of the noise variance to calculate the error vector magnitude, a method for calculating the error vector magnitude that is easy to implement in engineering can be provided.

[0018] Furthermore, by determining the grouping method of the 4 soft bits for hexadecimal quadrature amplitude modulation and the corresponding error vector amplitude calculation formula, the above error vector amplitude calculation method can be applied to hexadecimal quadrature amplitude modulation.

[0019] Furthermore, by determining the soft bit information of each bit based on the value of the real or imaginary part of the received symbol corresponding to that bit, the decision boundary corresponding to that bit, and the noise variance, the determined soft bit information can accurately reflect the degree of deviation of the received symbol from the decision boundary, providing a reliable basis for the calculation of the error vector magnitude.

[0020] Furthermore, by assuming that the noise received by the received symbol follows a Gaussian distribution, the conditional probabilities of the received symbol taking the value of 0 and 1 respectively are calculated, and the logarithm of the ratio of the two conditional probabilities is used as the soft bit information of the bit. This provides a clear and easy-to-implement method for calculating soft bit information.

[0021] Furthermore, by making the values ​​of the soft bit information and the received symbol in the corresponding real or imaginary part proportional to the distance between the decision boundary corresponding to that bit and inversely proportional to the noise variance, the calculation process of the soft bit information can be simplified, making it easier for engineering implementation.

[0022] Furthermore, by applying the above method to an orthogonal frequency division multiplexing receiver to calculate the error vector magnitude for each subcarrier of each orthogonal frequency division multiplexing symbol, or to signals that use channel coding such as those conforming to wireless local area network protocols and long-term evolution protocols, the quality of received signals on each time-frequency resource can be evaluated without additional hard demodulation, and the existing soft bit information of the receiver can be reused.

[0023] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Various aspects and embodiments of this disclosure can be combined in different ways. This summary section is not intended to identify key or essential features of the claimed subject matter, nor is it intended to aid in determining the scope of the claimed subject matter. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a method for calculating the magnitude of an error vector according to an embodiment of the present disclosure; Figure 2 It is a constellation diagram of hexadecimal quadrature amplitude modulation and a schematic diagram of the mapping relationship between each bit and constellation point; Figure 3 This is a schematic diagram illustrating the calculation of the distance between the real and imaginary parts of the received symbol and the corresponding decision boundary when calculating each bit of soft bit information, according to an embodiment of the present disclosure. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this disclosure clearer, the disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] Example 1 This embodiment provides a method for calculating the magnitude of the error vector, applied to a receiver in a communication system. For example... Figure 1 As shown, the overall process of this method mainly includes four steps: obtaining soft bit information, dividing soft bits into groups according to their real and imaginary parts, selecting the minimum value of each group, and calculating the magnitude of the error vector. These are steps 101 to 104 below. Each step will be explained in the order of execution.

[0027] Step 101: For a received symbol carried on a subcarrier, obtain the soft bit information of each bit carried by that received symbol. The purpose of this step is to provide input for subsequent error vector magnitude calculation. In this embodiment, the received symbol uses... Quadrature amplitude modulation (QAM), each received symbol carries bits, It is a positive even number. For example, when using hexadecimal quadrature amplitude modulation. The value equals 4 when using base-64 quadrature amplitude modulation. It equals 6 when using base-256 quadrature amplitude modulation. The value equals 8. When processing the received symbols, the receiver uses soft demodulation to output the information carried by each received symbol. Each bit contains its own soft-bit information, reflecting the confidence level of whether that bit is 0 or 1. The input to this step is the received symbol on a subcarrier, and the output is the soft-bit information corresponding to that received symbol. One soft bit of information.

[0028] Step 102, will The soft bit information of each bit is divided into a first group and a second group, where the first group corresponds to the real part of the received symbol and the second group corresponds to the imaginary part of the received symbol. Since each constellation point of quadrature amplitude modulation is determined by both real and imaginary coordinates, the information carried on a single received symbol... Of the bits, a portion is used to determine the value of the symbol in the real part direction, and another portion is used to determine the value of the symbol in the imaginary part direction. Accordingly, the soft bit information corresponding to the bits used to determine the real part value is grouped into the first group, and the soft bit information corresponding to the bits used to determine the imaginary part value is grouped into the second group, thus... Each soft bit information is divided into two groups according to its real part and its imaginary part, each group containing... One soft bit information. The input to this step is the information obtained in step 101. Each soft bit information is output as a first group and a second group corresponding to the real part and the imaginary part, respectively.

[0029] Step 103: Select the minimum value of the soft bit information from the first group and the second group respectively. Specifically, compare the values ​​of each soft bit information in the first group and take the minimum value; similarly, compare the values ​​of each soft bit information in the second group and take the minimum value, thus obtaining the minimum value of the first group and the minimum value of the second group. The minimum value of each group represents the distance between the received symbol and the nearest decision boundary in the real or imaginary part corresponding to that group. That is, in the real or imaginary part direction, the minimum soft bit information in the group corresponds to the distance of the received symbol from the nearest boundary in the decision region boundary. The input of this step is the soft bit information of the first group and the second group, and the output is the minimum value of the first group and the minimum value of the second group.

[0030] Step 104: Calculate the error vector magnitude of the subcarrier based on the minimum values ​​of the first and second groups. This error vector magnitude is the sum of the terms corresponding to the real and imaginary parts, weighted by a coefficient related to the noise variance. The terms corresponding to the real part are the square of the difference between the reference value and the minimum value of the first group, and the terms corresponding to the imaginary part are the square of the difference between the reference value and the minimum value of the second group. In other words, first calculate the difference between the reference value and the minimum value of the first group, and then calculate the difference between the reference value and the minimum value of the second group. Then, square these two differences to obtain the terms corresponding to the real and imaginary parts. Sum these two terms and multiply by the coefficient related to the noise variance to obtain the error vector magnitude of the subcarrier. The input to this step is the minimum values ​​of the first and second groups, and the output is the error vector magnitude of the subcarrier.

[0031] Through steps 101 to 104 described above, the error vector magnitude of a subcarrier can be directly calculated from the soft bit information of each bit of the received symbol on that subcarrier. Those skilled in the art can fully implement the calculation of the error vector magnitude by following these four steps. The entire calculation process does not require additional hard demodulation of the received symbol to find the constellation point closest to it, thus reducing the overall computational complexity without compromising the accuracy of the error vector magnitude calculation. Several optional improvements to this scheme are described below.

[0032] In some optional implementations, the soft bit information for each bit can be the log-likelihood ratio. This log-likelihood ratio information can be information already calculated by the receiver during the decoding process of channel coding and multiplexed to calculate the error vector magnitude. For communication systems using channel coding, the receiver itself needs to calculate the log-likelihood ratio information for each bit as input during decoding. Therefore, the log-likelihood ratio information already calculated by the receiver during decoding can be directly multiplexed to calculate the error vector magnitude, without needing to obtain the soft bit information separately for calculating the error vector magnitude. It should be noted that multiplexing the log-likelihood ratio information from the decoding process is not a necessary condition for implementing this method. Even without channel decoding or multiplexing the decoding results, the soft bit information for each bit can be obtained specifically for calculating the error vector magnitude, and this method can still operate normally.

[0033] Optionally, the aforementioned baseline value can be set to 1, and the coefficient related to the noise variance can be set to half of the noise variance. In this case, the soft bit information of the first group is denoted as... The soft bit information of the second group is The error vector magnitude of the subcarrier is calculated using the following formula: In the formula, This represents the magnitude of the error vector for that subcarrier; The noise variance represents the variance of the noise received by the received symbol when it follows a normal distribution. Its value can be obtained from the receiver's channel estimation or noise power estimation. This is the soft bit information of the first group. This is the soft bit information for the second group; This represents the operation of finding the minimum value within the corresponding group. In the above formula, the base value 1 and the coefficient... This is only one specific value. Those skilled in the art can also select other reference values ​​or other coefficients related to noise variance according to the specific definition and normalization method of soft bit information, so as to ensure the accuracy of the error vector amplitude calculation results.

[0034] Example 2 This embodiment, based on Embodiment 1, further details the method for obtaining each soft bit information in step 101, and provides a specific implementation of error vector amplitude calculation using hexadecimal quadrature amplitude modulation as an example, while also combining... Figure 2 and Figure 3 Explain the physical meaning of soft bit information.

[0035] The soft bit information of each bit can be determined based on the value of the received symbol in the real or imaginary part corresponding to that bit, the decision boundary corresponding to that bit, and the noise variance. In other words, for any bit carried on the received symbol, that bit is either used to determine the real part or the imaginary part. By examining the value of the received symbol in the real or imaginary part direction accordingly, and combining this with the decision boundary and noise variance corresponding to that bit on the constellation diagram, the soft bit information of that bit can be determined.

[0036] Specifically, for any given bit, under the assumption that the noise received by the received symbol follows a Gaussian distribution with variance equal to the noise variance, the conditional probabilities of the value of the real or imaginary part of the received symbol corresponding to that bit when the bit is 0 and 1 are calculated respectively. The logarithm of the ratio of these two conditional probabilities is taken as the soft bit information of that bit, i.e.: In the formula, This is the soft bit information for that bit; For receiving symbols; For this bit; and These represent the received symbols when the bit is 0 and 1, respectively. The conditional probability; This represents the natural logarithm operation. The above formula is derived using the assumption that the probability of sending each bit as 0 is equal to the probability of sending it as 1.

[0037] The following is combined with Figure 2 The calculation of the soft bit information mentioned above will be further explained. Figure 2 The diagram illustrates a hexadecimal quadrature amplitude modulation (QAM) constellation and the mapping relationship between each bit and constellation point. The horizontal axis represents the real part, and the vertical axis represents the imaginary part. Each constellation point corresponds to 4 bits, and the real and imaginary coordinates are taken from -3, -1, 1, and 3, respectively. Taking a single bit used to determine the real part value as an example, suppose it is necessary to determine the value based on the received symbol... The position of the bit distinguishes between two cases: the bit being 0 and the bit being 1. The 0 and 1 bits correspond to constellation points with real part absolute values ​​of 3 and 1, respectively. Under the Gaussian assumption, the conditional probabilities of receiving the symbol when the bit is 0 and 1 are respectively: In the formula, For receiving symbols The real part, The absolute value of the real part; For noise variance; is a natural constant; the meanings of the other symbols are the same as before. Substituting the above two equations into the formula for calculating soft bit information, we get: As can be seen from the above formula, the soft bit information of this bit is proportional to... ,and Just a received symbol The distance between the real part of the bit and the decision boundary (i.e., the boundary where the absolute value of the real part equals 2) that distinguishes the two constellation points. From this, it can be seen that the value of the soft bit information and the received symbol in the corresponding real or imaginary part is directly proportional to the distance between the decision boundary corresponding to that bit and inversely proportional to the noise variance: the larger the distance, the larger the absolute value of the soft bit information, indicating a more reliable decision for that bit; the larger the noise variance, the smaller the absolute value of the soft bit information, indicating a less reliable decision for that bit.

[0038] like Figure 3 As shown, when calculating the soft bit information of each bit of the received symbol, it is necessary to calculate the received symbol separately. The distance between the real part, the imaginary part, and the decision boundary corresponding to each bit. Figure 3 China and Israel , , , This schematically represents the soft bit information of each bit determined by the corresponding distance.

[0039] After obtaining the soft bit information of each bit in the above manner, the specific calculation of the error vector magnitude can be given for hexadecimal quadrature amplitude modulation. At this point... The value equals 4, and each received symbol carries 4 bits. This 4-bit soft-bit information is denoted as... , , and And divide it into two groups according to the real part and the imaginary part, with the first group corresponding to the real part being... The second group corresponding to the imaginary part is Take the minimum value from each of the two sets, and calculate the error vector magnitude of the subcarrier using the following formula: In the formula, This represents the magnitude of the error vector for that subcarrier; For noise variance; , This corresponds to the first set of soft bits of the real part. , This is the second set of soft bit information corresponding to the imaginary part; Indicates taking and The smaller one, Indicates taking and The smaller one.

[0040] The above example, using hexadecimal quadrature amplitude modulation, illustrates the specific process of calculating the error vector amplitude based on soft bit information. In a variation of this embodiment, this can be further extended to general... Quadrature amplitude modulation (QAM): Each constellation point can be demodulated. 1 bit, which Each soft bit is separated into real and imaginary parts, and a group corresponding to the real part is denoted as . The group corresponding to the imaginary part is Then, find the minimum value for each of the two sets, and calculate the magnitude of the error vector using the following formula: In the formula, This is a set of soft bits corresponding to the real part. For each set of soft bits corresponding to the imaginary part, the number of elements in both is [number missing]. The remaining symbols have the same meaning as before. Therefore, for orthogonal amplitude modulation of different orders such as hexadecimal, 64-ary, and 256-ary, the error vector amplitude of the corresponding subcarrier can be calculated by grouping the soft bit information into its real and imaginary parts and taking the minimum value for each. It should be noted that the above method of calculating soft bit information based on the Gaussian assumption and the logarithm of the conditional probability ratio is only one specific implementation. When this method is not used, other soft demodulation algorithms or their approximate algorithms can be used to obtain the soft bit information of each bit, and then substituted into the above formula for calculating the error vector amplitude to achieve the same technical objective.

[0041] Example 3 This embodiment further illustrates the application scenarios of the above method, focusing on the specific application of the above error vector amplitude calculation method in orthogonal frequency division multiplexing receivers and under specific communication protocols.

[0042] The receiver can be an orthogonal frequency division multiplexing (OFDM) receiver, and the received symbols can be symbols carried on subcarriers after frequency domain equalization. In an OFDM system, the transmitter modulates data onto multiple mutually orthogonal subcarriers and performs an inverse transform to form a time-domain signal for transmission. The receiver transforms the signal back to the frequency domain and performs frequency domain equalization on each subcarrier to compensate for amplitude and phase distortions introduced by the channel, thereby obtaining the received symbols on each subcarrier. For each subcarrier of each OFDM symbol, the error vector amplitude is calculated according to steps 101 to 104 above. That is, for each subcarrier, the soft bit information of each bit of its received symbol is obtained, grouped by real and imaginary parts, and the minimum value is taken respectively to calculate the error vector amplitude of that subcarrier. In this way, the error vector amplitude of each OFDM symbol and each subcarrier can be obtained to evaluate the quality of the received signal on each time-frequency resource. It should be noted that the orthogonal frequency division multiplexing receiver is only one applicable scenario for this method. This method can also be applied to other single-carrier or multi-carrier receivers that use orthogonal amplitude modulation.

[0043] The received symbols can also be symbols from channel-coded signals conforming to WLAN protocols or LTE protocols. For such channel-coded communication protocols, the receiver typically needs to calculate soft-bit information for each bit during reception for channel decoding. Therefore, this soft-bit information can be multiplexed for the calculation of the error vector amplitude, thereby obtaining the error vector amplitude of each subcarrier without adding additional processing such as hard demodulation. Besides WLAN protocols and LTE protocols, this method is also applicable to other communication protocols that use quadrature amplitude modulation and require soft-bit information calculation during reception. Those skilled in the art can apply this method flexibly according to the actual protocol used.

[0044] Example 4 This embodiment provides an error vector amplitude calculation device for implementing the error vector amplitude calculation method given in any of Embodiments 1 to 3 above. The device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned error vector amplitude calculation method. Specifically, for a received symbol carried on a subcarrier, the processor acquires the soft bit information of each bit, divides the soft bit information into a first group and a second group according to its real and imaginary parts, selects the minimum value of each group, and calculates the error vector amplitude of the subcarrier accordingly. The processor can be a general-purpose central processing unit, or a digital signal processor, field-programmable gate array, or application-specific integrated circuit (ASIC) with data processing capabilities. The memory can be a read-only memory, random access memory, or flash memory. In practical deployment, this calculation device can be integrated into the receiver, serving as part of the receiver to calculate the error vector amplitude for each received symbol on a subcarrier in real time.

[0045] Accordingly, one embodiment of this disclosure also provides a computer-readable storage medium storing computer-executable instructions (or computer programs) that, when executed by a processor, implement the steps in the above-described method embodiments. The computer-readable storage medium may include any type of volatile or non-volatile memory, or any combination thereof, storing computer program code. Specifically, the computer-readable storage medium may include, but is not limited to: magnetic storage devices (e.g., magnetic tape, hard disk, floppy disk), optical storage devices (e.g., optical disc (CD), digital versatile optical disc (DVD)), magneto-optical storage devices (e.g., magneto-optical disc), semiconductor memory (e.g., read-only memory (ROM), random access memory (RAM), electrically erasable programmable read-only memory (EEPROM), flash memory, etc.) or other storage technologies. The computer-readable storage medium in this disclosure refers to a non-transitory readable storage medium, excluding the transient propagation signal itself (e.g., modulated data signals, carrier waves, etc.).

[0046] Furthermore, one embodiment of this disclosure provides a computer program product including a computer program or computer-executable instructions carried on a non-transitory computer-readable medium, wherein the computer program or computer-executable instructions, when executed by a processor, cause the processor to perform the steps in the above-described method embodiments.

[0047] It should be noted that the terms "first," "second," etc., used in this specification are only used to distinguish similar objects, and the elements defined by them do not necessarily require or imply any actual relationship or order between these elements, nor are they used to describe a specific order or sequence.

[0048] The terms “comprising,” “including,” or any other variations thereof, as used herein, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0049] The phrase "execute according to a certain element" as used in this specification means at least according to that element, including both "execute only according to that element" and "execute according to that element and other elements".

[0050] Those skilled in the art will understand that variations of the method steps described above can be obtained based on the embodiments of this disclosure without any inventive effort. For example, the sequence numbers of the steps described in the method embodiments of this specification do not themselves constitute a limitation on the execution order of these steps. Unless there is an explicit specific limitation in the context, the steps may be executed in a different order than that shown in the embodiments (e.g., executing the steps with larger sequence numbers first, then the steps with smaller sequence numbers), or they may be executed in parallel. Furthermore, other steps may be inserted between multiple steps with consecutively numbered sequence numbers.

[0051] The specific embodiments described in this specification are merely illustrative examples of this disclosure. For instance, specific numerical values, parameters, protocols, network architectures, component models, drawing scales, and specific shapes shown in the drawings are only examples and do not constitute a limitation of this disclosure. Features of the various embodiments of this disclosure can be arbitrarily combined without contradicting each other, and the solutions formed by such combinations also fall within the protection scope of this disclosure.

[0052] Furthermore, it should be understood that after reading the contents of this disclosure, those skilled in the art can make various alterations or modifications to this disclosure, and these equivalent forms also fall within the scope of protection claimed by this disclosure.

Claims

1. A method for calculating the magnitude of an error vector, applied to a receiver in a communication system, characterized in that, include: For a received symbol carried on a subcarrier, obtain the soft bit information of each bit carried by the received symbol; wherein, the received symbol adopts... Quadrature amplitude modulation, each of the received symbols carries bits, It is a positive even number; The The soft bit information of each bit is divided into a first group and a second group. The first group corresponds to the real part of the received symbol, and the second group corresponds to the imaginary part of the received symbol. The minimum value of soft bit information is selected from the first group and the second group respectively. The minimum value represents the distance between the received symbol and the nearest decision boundary in the corresponding real or imaginary part. The error vector magnitude of the subcarrier is calculated based on the minimum values ​​of the first group and the second group; wherein the error vector magnitude is the result of the sum of the terms corresponding to the real part and the terms corresponding to the imaginary part, weighted by a coefficient related to the noise variance, the terms corresponding to the real part being the square of the difference between the reference value and the minimum value of the first group, and the terms corresponding to the imaginary part being the square of the difference between the reference value and the minimum value of the second group.

2. The method for calculating the magnitude of the error vector according to claim 1, characterized in that, The soft bit information is log-likelihood ratio information, and the soft bit information is information that the receiver has calculated during the decoding of the channel coding and has reused to calculate the magnitude of the error vector.

3. The method for calculating the magnitude of the error vector according to claim 1, characterized in that, The baseline value is 1, and the coefficient related to the noise variance is half of the noise variance; The magnitude of the error vector is calculated using the following formula: In the formula, The magnitude of the error vector, Let Variance be the noise variance. This refers to the soft bit information of the first group. This refers to the soft bit information of the second group. This indicates taking the minimum value.

4. The method for calculating the magnitude of the error vector according to claim 3, characterized in that, The received symbol is equal to 4, and uses hexadecimal quadrature amplitude modulation. Each received symbol carries 4 bits, and the 4 bits of soft bit information are denoted as... , , and The first group is The second group is ; The magnitude of the error vector is calculated using the following formula: 。 5. The method for calculating the magnitude of the error vector according to claim 1, characterized in that, The soft bit information of each bit is determined based on the value of the received symbol in the real or imaginary part corresponding to that bit, the decision boundary corresponding to that bit, and the noise variance.

6. The method for calculating the magnitude of the error vector according to claim 5, characterized in that, For any given bit, assuming that the noise received by the received symbol follows a Gaussian distribution with variance equal to the noise variance, calculate the conditional probability of the value of the real or imaginary part of the received symbol corresponding to that bit when the bit is 0 and 1, respectively. The logarithm of the ratio of the two conditional probabilities is then used as the soft bit information for that bit. In the formula, This is the soft bit information of that bit. The received symbol, For this bit, and These are the conditional probabilities of the received symbol when the bit is 0 and when it is 1, respectively.

7. The method for calculating the magnitude of the error vector according to claim 5, characterized in that, The soft bit information is proportional to the distance between the value of the received symbol in the corresponding real or imaginary part and the decision boundary corresponding to that bit, and inversely proportional to the noise variance.

8. The method for calculating the magnitude of the error vector according to claim 1, characterized in that, The receiver is an orthogonal frequency division multiplexing (OFDM) receiver, and the received symbols are frequency-equalized symbols carried on the subcarriers; the error vector magnitude is calculated for each subcarrier of each OFDM symbol; or... The received symbols are symbols in signals that conform to wireless local area network protocols or long-term evolution protocols and employ channel coding.

9. A device for calculating the magnitude of an error vector, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method for calculating the error vector magnitude as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for calculating the magnitude of the error vector according to any one of claims 1 to 8.

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