Patent Description:
Equalization in the field of communication refers to equalization of characteristics of a channel. That is, a receiver produces characteristics opposite to those of the channel to cancel inter-symbol interference caused by time-varying and multipath effects. In multi-user detection, equalization technologies adopted include Zero Forcing (ZF), Minimum Mean Square Error (MMSE), Interference Rejection Combining (IRC), Sphere Decoding (SD) and BP algorithms. The BP algorithm uses a Factor Graph (FG) idea to update belief information between nodes iteratively. The performance is better than that of a linear equalization algorithm, and the implementation complexity is lower than that of a conventional linear equalization algorithm because no large matrix inverse operation is required. In some cases, the BP equalization algorithm is mainly implemented in two manners, namely Factor Graph based Graphical Model with Gaussian Approximation of Interference (FG-GAI) and Channel Hardening-Exploiting Message Passing (CHEMP). FG-GAI and CHEMP both have problems of high costs and limited application scenarios.

In the paper "<NPL>, with an equivalent objective function by QR decomposition and further relaxation on the constrains, the authors develop an improved semidefinite further relaxation detector (SFRD), which is proved to be convex and has solutions within polynomial complexity time. Using the detection result from the proposed SFRD as the initial vector, they propose a novel semidefinite further relaxation on the likelihood ascent search (SFRLAS) detection algorithm. It has been shown through their studies that the proposed SFRLAS scheme can effectively approach the optimum bit error rate from the maximum-likelihood detection algorithm for systems with high-order QAM and large-scale antennas, however, with a lower computational complexity. The spectral efficiency converges to the theoretical value at a much lower required average received signal-to-noise ratio. It is an effective method for high-order QAM signal detection in massive MIMO system.

According to some embodiments of the present disclosure, a BP equalization method and apparatus, a communication device and a computer-readable storage medium are provided, so as to solve at least to some extent the problems of high overhead and limited application scenarios in the BP equalization algorithm in some cases.

In view of the above, according to an embodiment of the present disclosure, a BP equalization method is provided. The method includes: splitting a received signal Yc, a channel estimation Hc and a symbol estimation Xc into real parts and imaginary parts to obtain a received signal matrix Y, a channel estimation matrix H and a symbol estimation matrix X; performing orthogonal triangular (QR) decomposition on the channel estimation matrix H to obtain an equivalent received signal Ybp, an equivalent channel R and a noise power σ<NUM>; and performing iteration based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol.

According to another embodiment of the present disclosure, a BP equalization apparatus is further provided. The apparatus includes: a splitting module, a decomposition module and an iteration module. The splitting module is configured to split a received signal Yc, a channel estimation Hc and a symbol estimation Xc into real parts and imaginary parts to obtain a received signal matrix Y, a channel estimation matrix H and a symbol estimation matrix X. The decomposition module is configured to perform QR decomposition on the channel estimation matrix H to obtain an equivalent received signal Ybp, an equivalent channel R and a noise power σ<NUM>. The iteration module is configured to perform iteration based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol.

According to yet another embodiment of the present disclosure, a communication device is further provided. The device includes a processor, a memory and a communication bus. The communication bus is configured to connect the processor to the memory. The processor is configured to execute a computer program stored in the memory to carry out the BP equalization method as described above.

According to yet another embodiment of the present disclosure, provided is a computer-readable storage medium storing one or more computer programs, where the one or more computer programs, when executed by one or more processors, cause the one or more processors to carry out the BP equalization method as described above.

Other features and corresponding beneficial effects of the present disclosure will be set forth in part in the description which follows, and it is to be understood that at least part of the beneficial effects will become apparent from the description of the present disclosure.

In order to make objects, technical schemes and advantages of the present disclosure clear, embodiments of the present disclosure are described in further detail below with reference to specific implementations in conjunction with the drawings. It is to be understood that the embodiments described herein are intended only to illustrate and not to limit the present disclosure.

In view of the problems of high overhead and limited application scenarios in a BP equalization algorithm in some cases, a BP equalization method is provided according to an embodiment. The method involves performing QR decomposition on a channel estimation matrix H, so that an update dimension is the number of streams multiplied by the number of streams, which does not increase with the number of antennas, achieving dimensionality reduction and being more suitable for Massive MIMO multi-user detection scenarios. Thus, the application scenarios of the BP equalization method are expanded. At the same time, since an R matrix is an upper triangular matrix, and only non-zero nodes need to be iterated during iteration, the computation overhead can be further reduced and the accuracy of symbol estimation in the equalization algorithm can be improved. The BP equalization algorithm according to this embodiment is applicable to a variety of communication devices, including at least one of a variety of network-side communication devices and a variety of terminal-side communication devices. Moreover, the BP equalization algorithm according to this embodiment is also applicable to QR-GAI algorithms of any-order QAM. For easy understanding, the BP equalization algorithm according to this embodiment may be called a QR-GAI algorithm.

For easy understanding, this embodiment is illustrated below by taking the schematic flowchart of the BP equalization method shown in <FIG> as an example, including steps S101 to S103.

At S101, a received signal Yc, a channel estimation Hc and a symbol estimation Xc are split into real parts and imaginary parts to obtain a received signal matrix Y, a channel estimation matrix H and a symbol estimation matrix X.

The received signal Yc in this embodiment may be a received signal of a communication device. The communication device may be a network-side communication device, which, for example, may include, but is not limited to, a variety of base stations. Certainly, in some examples, the communication device may also include a variety of terminal-side communication devices. In this embodiment, after receiving the received signal Yc, the communication device may perform the BP equalization method shown in <FIG> in this embodiment.

In this embodiment, S101 is intended to split a complex field QAM constellation point into two real number field PAM constellation points to allow for a real number field operation in subsequent steps.

In this embodiment, a receiving model may be flexibly arranged according to a specific application scenario. In an example, for the received signal Yc, the channel estimation Hc and the symbol estimation Xc, the receiving model (it is to be understood that the receiving model is not limited to the following example model, and may be flexibly adjusted as required) may be arranged as follows: <MAT> where the received signal satisfies Yc ∈ Cjante×<NUM>; the channel estimation satisfies Hc ∈ Cante×flow; the symbol estimation satisfies Xc ∈ Cflow×<NUM>; the white Gaussian noise satisfies Nc ∈ Cante×<NUM> , with a mean of <NUM> and a variance of <NUM>·σ<NUM> ; C denotes a complex field; ante denotes the number of antennas; flow denotes the number of streams; and σ<NUM> denotes a noise power.

Correspondingly, a received signal matrix Y, a channel estimation matrix H and a symbol estimation matrix Sσ<NUM>,ONI obtained through splitting into real parts and imaginary parts satisfy: <MAT> where the received signal matrix satisfies <MAT>; the channel estimation matrix satisfies <MAT>; the symbol estimation matrix satisfies <MAT>; Re{. } denotes taking a real part, and Im{. } denotes taking an imaginary part.

At S <NUM>, QR decomposition is performed on the channel estimation matrix H to obtain an equivalent received signal Ybp, an equivalent channel R and a noise power σ<NUM>; so as to serve as input in a next step.

In an example of this embodiment, QR decomposition is performed on the channel estimation matrix H to obtain: <MAT> and according to Y = HX + N = QRX + N, the following is obtained: <MAT> where the equivalent noise QTN has a mean of <NUM>, and the noise power is σ<NUM>.

The QR decomposition performed on the channel estimate H=QR ∈ R<NUM>·ante×<NUM>·flow in this step has at least the following advantages. <NUM>) The equivalent channel R has a dimension of R ∈ R<NUM>·flow×<NUM>·flow, which under Massive MIMO, is much smaller than an original channel dimension H ∈ R<NUM>·ante×<NUM>·flow, thereby greatly reducing the complexity of subsequent iterative algorithms. <NUM>) According to characteristics of a unitary matrix, var (QTN) = var (N), that is, the noise power is constant, where var (. ) denotes a variance. <NUM>) Since R denotes an upper triangular matrix, only non-zero elements need to be updated in subsequent iteration, which can further reduce the computation overhead.

At S103, iteration is performed based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol.

For example, to be continued with the above example, the performing iteration based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol may include:.

After the equivalent received signal mean Su,ONI and the equivalent received signal variance estimate Sσ<NUM>,ONI are obtained, a per stream symbol likelihood probability llr ∈ RM×<NUM> and a per stream belief probability proT ∈ RM×<NUM> may be calculated according to Su,ONI and Sσ<NUM>,ONI.

In order to further improve the accuracy of symbol estimation in the equalization algorithm, before the per stream symbol likelihood probability llr ∈ RM×<NUM> and the per stream belief probability proT ∈ RM×<NUM> are calculated according to Su,ONI and Sσ<NUM>,ONI, a mean and a variance of a stream ONI may be removed from the equivalent received signal mean Su,ONI and the equivalent received signal variance estimate Sσ<NUM>,ONI according to a principle of Extrinsic Information to obtain µ and s. The process is as follows: <MAT> and <MAT>.

µ and s obtained above are obtained by removing a mean and a variance of the equivalent received signal of the ONI stream from Observation Node ONI.

In the above step, since R is an upper triangular matrix, only ONI = {<NUM> · flow,. ,<NUM>} and SNI = {ONI,. , <NUM> · flow} need to be traversed. In this way, iteration overhead of the QR-GAI algorithm can be further reduced. In addition, GAI white noise estimation (GAI estimation performance deteriorates significantly under high channel correlation) is not required for nodes of ONI = <NUM> · flow due to the absence of inter-stream interference. Therefore, a Log Likelihood Ratio (LLR) belief probability is high, which makes the iterative algorithm have dominant nodes in the iteration, thereby improving the estimation performance of the overall iterative algorithm.

In an example of this embodiment, a per stream symbol likelihood probability llr ∈ RM×<NUM> and a per stream belief probability proT ∈ RM×<NUM> are calculated according to the equivalent received signal mean Su,ONI and the equivalent received signal variance estimate Sσ<NUM>,ONI.

The process of calculating the per stream symbol likelihood probability llr ∈ RM×<NUM> includes: defining a symbol index vector <MAT>, and for an index i = <NUM>, <NUM>,. , M , calculating the per stream symbol likelihood probability llr ∈ RM×<NUM> according to the following formulas: <MAT> where llr(i) denotes a likelihood ratio of the symbol X(ONI) being si(<NUM>)/β to being si(i)/β at an ith symbol position, i = <NUM>,<NUM>,.

Then, the per stream belief probability proT ∈ RM×<NUM> is calculated according to the following formulas: <MAT> where proT(i) denotes a probability that an ONIth stream symbol X(ONI) is si(i)/β.

In some examples of this embodiment, in order to prevent probability jumps in adjacent iteration cycles, the performing iteration based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol may further include updating the ONI th stream symbol with the following damping algorithm: <MAT> where pro(:,ONI) denotes all rows and ONI columns, and df denotes a damping factor. The value of df in this embodiment may be flexibly determined. In an example, the value may be any value from <NUM> to <NUM>.

In this embodiment, after the per stream symbol position probability is obtained through S103, the obtained per stream symbol position probability may be applied flexibly as required.

In an example of this embodiment, a complex QAM constellation point of per stream symbol SNI = {<NUM>,. , flow} may be calculated by utilizing the obtained per stream symbol position probability, so as to output a per stream symbol bit-level LLR probability.

The calculating a complex QAM constellation point of per stream symbol SNI = {<NUM>,. , flow} includes: <MAT> and <MAT> where pro(rp,SNI) and pro(ip, SNI + flow) denote probabilities of real-value symbol positions of a real part and an imaginary part of an SNI th stream.

In some examples, pro(rp, SNI) and pro(ip, SNI + flow) may be directly used as decoding module input. In some examples, the BP equalization method in this embodiment further includes calculating a symbol hard decision, including: <MAT> where X̂(SNI) denotes a hard decision value of a per stream <NUM>M QAM complex symbol, and j denotes the imaginary part.

The QR-GAI algorithm according to this embodiment has at least the following advantages over the FG-GAI algorithm in some cases. <NUM>) Dimensionality reduction is realized, and computation overhead in Massive MIMO scenarios is greatly reduced. <NUM>) A high belief probability of a last stream is guaranteed by a Successive Interference Cancellation (SIC) principle, and the accuracy of multi-user detection is improved. <NUM>) Only non-zero elements of an R matrix are updated during the iteration, which further reduces the computation overhead. In addition, the QR-GAI algorithm according to this embodiment has at least the following advantages over the CHEMP algorithm. <NUM>) The dimensionality reduction operation does not increase the noise power, and improves the accuracy of symbol estimation. <NUM>) A high belief probability of a last stream is guaranteed by the SIC principle. <NUM>) The equivalent channel R matrix is sparser than a covariance matrix of H, which makes the algorithm still applicable under high channel correlation. In addition, the QR-GAI algorithm according to this embodiment may be extended to QAM of any order. The application scenarios can be further expanded.

For easy understanding, the QR-GAI algorithm shown in Embodiment one of this embodiment is illustrated in combination with two application scenarios based on 4QAM and 16QAM.

Example one: Refer to <FIG>, a 4QAM-based QR-GAI equalization process includes steps S201 to S204.

At S201, a two-dimensional QAM complex field constellation point is split into two PAM constellation points. A received signal Yc = HcXc + Nc is split into a real part and an imaginary part to obtain: <MAT> and <MAT> where Y = HX + N is satisfied.

At S202, QR decomposition is performed on a channel estimation matrix H=QR ∈ R<NUM>·ante×<NUM>·flow to obtain <MAT>. In this example, equivalent noise QTN has a mean of <NUM> and a variance of σ<NUM>. Input Ybp ∈ R<NUM>·flow×<NUM>, R ∈ R<NUM>·flow×<NUM>·flow and σ<NUM> at <NUM> is obtained.

At S203, a main iteration process of a QR-GAI algorithm is performed. In this example, a fully connected BP network is constructed, with an Observation Node of Ybp ∈ R<NUM>·flow<NUM> and an Information Node of X ∈ R<NUM>·flow×<NUM>. Per stream position probability <MAT> is initialized, where M denotes an order of QAM, and for 4QAM, Pro = <NUM> · I<NUM>×<NUM>·flow. In this example, referring to <FIG>, the process includes steps S2031 to S2033.

At S2031, GAI interference estimation is performed, and for each Observation Node ONI = {<NUM> · flow,. ,<NUM>}, the following processing is performed: <MAT> and <MAT> where SNI ={ONI ,. ,<NUM>·flow} denotes a Symbol Node index, <MAT> denotes per stream symbol estimate, Su,ONI denotes an equivalent received signal mean on Observation Node ONI, <NUM>·Pro (<NUM>, SNI)·(<NUM> - Pro(<NUM>, SNI)) denotes per stream variance estimate, Sσ<NUM>,ONI denotes an equivalent received signal variance estimate on Observation Node ONI, and σ<NUM> denotes a noise power value.

According to a principle of Extrinsic Information, a mean and a variance of a stream ONI are removed to obtain: <MAT> and <MAT> where µ and s obtained above are obtained by removing a mean and a variance of the equivalent received signal of the stream ONI from Observation Node ONI.

At S2032, a likelihood probability llr ∈ R<NUM>×<NUM> is calculated first, and then a belief probability pro ∈ R<NUM>×<NUM>·flow is calculated. Criteria for calculating likelihood probability llr ∈ R<NUM>×<NUM> are: <MAT> and <MAT> where llr (<NUM>) denotes a likelihood ratio of an ONIth stream symbol being <MAT> to being <MAT>, with the value being <NUM>. llr (<NUM>) denotes a likelihood ratio of an ONIth stream symbol being <MAT> to being <MAT>.

The belief probability pro ∈ R<NUM>×<NUM>·flow is calculated, and update criteria of a temporary belief probability proT ∈ R<NUM>×<NUM> are defined as: <MAT> and <MAT> where proT(<NUM>) and prot(<NUM>) denote probabilities of the ONIth stream symbol being <MAT> and <MAT>, respectively.

At S2033, the ONIth stream is updated with a damping algorithm to obtain: <MAT> where df denotes a damping factor, and the value of df in this example may be any value from <NUM> to <NUM>.

At S204, per stream symbol position decision is outputted. A symbol vector si = {<NUM>,- <NUM>} is defined, and for a complex QAM constellation point of each stream SNI = {<NUM>,. , flow} , <MAT> and <MAT> where pro(rp, SNI) and pro(ip, SNI + flow) denote probabilities of real value symbol positions of a real part and an imaginary part of an SNI stream, which may be directly used as decoding module input.

In this example, for a symbol hard decision, <MAT> where X̂(SNI) denotes a hard decision value of a 4QAM symbol of an SNIth stream.

Example two: Refer to <FIG>, a 16QAM-based QR-GAI equalization process include steps S401 to S404.

At S401, a two-dimensional 16QAM complex field constellation point is split into two 4QAM constellation points. A received signal Yc = HcXc + Nc is split into a real part and an imaginary part to obtain: <MAT> and <MAT> where Y = HX + N is satisfied.

At S402, QR decomposition is performed on a channel estimation matrix H=QR ∈ R<NUM>·ante×<NUM>·flow to obtain <MAT>. In this example, equivalent noise QTN has a mean of <NUM> and a variance of σ<NUM>. Input Ybp ∈ R<NUM>·flow×<NUM>, R ∈ R<NUM>·flow×<NUM>·flow and σ<NUM> at <NUM> is obtained.

At S403, a main iteration process of a QR-GAI algorithm is performed. A fully connected BP network is constructed, with an Observation Node of Ybp ∈ R2f·low×<NUM> and an Information Node of X ∈ R<NUM>·flow×<NUM>. A belief probability Pro = <NUM>/<NUM>·I<NUM>×<NUM>·flow is initialized. The process includes sub-steps a) to c).

At sub-step a), GAI interference estimation is performed. For each Observation Node ONI = {<NUM> · flow,. ,<NUM>} , a symbol vector si = {<NUM>,<NUM>, -<NUM>, -<NUM>} is defined to obtain: <MAT> and <MAT> where Su,ONI denotes an equivalent received signal mean on Observation Node ONI, Sσ<NUM>,ONI denotes an equivalent received signal variance estimate on Observation Node ONI, and σ<NUM> denotes a noise power value.

A mean and a variance of the stream ONI are removed to obtain: <MAT> and <MAT> where µ and s are obtained by removing a mean and a variance of the equivalent received signal of the stream ONI from Observation Node ONI.

At sub-step b), a likelihood probability llr ∈ R<NUM>×<NUM> and a belief probability Pro ∈ R<NUM>×<NUM>·flow are calculated. For a symbol position index i = {<NUM>, <NUM>, <NUM>, <NUM>}, <MAT> where llr (i) denotes a likelihood ratio of an ONIth stream symbol being <MAT> to being <MAT>.

A temporary belief probability is then calculated. For a symbol position index i = {<NUM>,<NUM>,<NUM>,<NUM>}, <MAT> where proT(i) denote a probability of the ONIth stream symbol being <MAT>.

At sub-step c), the ONIth stream is updated with a damping algorithm to obtain: <MAT> where df denotes a damping factor, and df may be set to <NUM> to <NUM>.

At S404, a bit-level LLR decision or symbol hard decision is outputted. For a symbol index vector si = {<NUM>,<NUM>,-<NUM>,-<NUM>}, for a complex constellation point of each stream SNI = {<NUM>,. , flow}, <MAT> and <MAT> where pro(rp,SNI) and pro(ip,SNI + flow) denote LLR probabilities of the SNIth stream real value symbol, which may be used as decoding module input or used for other purposes as required.

For a symbol hard decision, <MAT> where X̂(SNI) denotes a hard decision value of per stream 16QAM symbol.

It is to be understood that, in this embodiment, the BP equalization method according to this embodiment is illustrated with only two application scenarios based on 4QAM and 16QAM. Application scenarios based on 64QAM and 256QAM may be deduced by analogy with reference to the above embodiments, which are not described in detail herein. Besides, it is to be understood that the BP equalization method according to this embodiment is applicable to QAM of any order.

This embodiment provides a BP equalization apparatus. The BP equalization apparatus may be arranged in a communication device. The communication device may include at least one of a user-side communication device and a network-side communication device. Refer to <FIG>, the BP equalization apparatus according to this embodiment may include a splitting module <NUM>, a decomposition module <NUM> and an iteration module <NUM>.

The splitting module <NUM> is configured to split a received signal Yc, a channel estimation Hc and a symbol estimation Xc into real parts and imaginary parts to obtain a received signal matrix Y, a channel estimation matrix H and a symbol estimation matrix X.

In this embodiment, the splitting module <NUM> splits a complex field QAM constellation point into two real number field PAM constellation points to allow for a real number field operation in subsequent steps.

In this embodiment, the splitting module <NUM> may flexibly arrange a receiving model according to a specific application scenario. In an example, for the received signal Yc, the channel estimation Hc and the symbol estimation Xc, the receiving model may be arranged as follows: <MAT> where the received signal satisfies Yc ∈ C'ante×<NUM>; the channel estimation satisfies Hc ∈ Cante×flow; the symbol estimation satisfies Xc ∈ Cflow×<NUM>; the white Gaussian noise satisfies Nc ∈ Cante×<NUM>, with a mean of <NUM> and a variance of <NUM>·σ<NUM>; C denotes a complex field; ante denotes the number of antennas; flow denotes the number of streams; and σ<NUM> denotes a noise power.

Correspondingly, a received signal matrix Y, a channel estimation matrix H and a symbol estimation matrix X obtained through splitting into real parts and imaginary parts by the splitting module <NUM> satisfy: <MAT> the received signal matrix is <MAT>; the channel estimation matrix is <MAT>; the symbol estimation matrix is <MAT>; Re{. } denotes taking a real part, and Im{. } denotes taking an imaginary part.

The decomposition module <NUM> is configured to perform QR decomposition on the channel estimation matrix H to obtain an equivalent received signal Ybp, an equivalent channel R and a noise power σ<NUM>.

In an example of this embodiment, the decomposition module <NUM> performs QR decomposition on the channel estimation matrix H to obtain: <MAT> and according to Y = HX + N = QRX + N, the decomposition module <NUM> obtains: <MAT> where the equivalent noise QTN has a mean of <NUM>, and the noise power is σ<NUM>.

The QR decomposition performed by the decomposition module <NUM> on the channel estimate H=QR ∈ R<NUM>·ante×<NUM>·flow has at least the following advantages. <NUM>) The equivalent channel R has a dimension of R ∈ R<NUM>·flow×<NUM>·flow, which under Massive MIMO, is much smaller than an original channel dimension H ∈ R<NUM>·ante×<NUM>·flow, thereby greatly reducing the complexity of subsequent iterative algorithms. <NUM>) According to characteristics of a unitary matrix, var(QTN) = var(N), that is, the noise power is constant, where var(. ) denotes a variance. <NUM>) Since R denotes an upper triangular matrix, only non-zero elements need to be updated in subsequent iteration, which can further reduce the computation overhead.

The iteration module <NUM> is configured to perform iteration based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol.

For example, to be continued with the above example, the performing, by the iteration module <NUM>, iteration based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol may include: constructing, by the iteration module <NUM>, a fully connected BP network by taking the equivalent received signal Ybp as an Observation Node and the symbol estimation matrix X as a Symbol Node, and initializing a per stream symbol position probability as <MAT>, M being an order of QAM, that is, <NUM>M QAM; and then performing the following calculation: calculating, by the iteration module <NUM>, an equivalent received signal mean Su,ONI and an equivalent received signal variance estimate Sσ<NUM>,ONI on each observation node index ONI = {<NUM> · flow,. To be continued with the above example, the process may include: <MAT> and <MAT> where <MAT> denotes a per stream estimate, <MAT> denotes a per symbol variance estimate, SNI denotes a stream index, and β denotes a symbol energy normalization factor. For example, for 4QAM, 16QAM, 64QAM and 256QAM, values of β are <MAT>, respectively. QAM of other orders may be deduced by analogy, which are not described in detail herein.

After the equivalent received signal mean Su,ONI and the equivalent received signal variance estimate Sσ<NUM>,ONI are obtained, the iteration module <NUM> may calculate a per stream symbol likelihood probability llr ∈ RM×<NUM> and a per stream belief probability proT ∈ RM×<NUM> according to Su,ONI and Sσ<NUM>,ONI.

In this embodiment, in order to further improve the accuracy of symbol estimation in the equalization algorithm, before the iteration module <NUM> calculates the per stream symbol likelihood probability llr ∈ RM×<NUM> and the per stream belief probability proT ∈ RM×<NUM> according to Su,ONI and Sσ<NUM>,ONI, a mean and a variance of a stream ONI may be removed from the equivalent received signal mean Su,ONI and the equivalent received signal variance estimate Sσ<NUM>,ONI according to a principle of Extrinsic Information to obtain µ and s. The process is as follows: <MAT> and <MAT> where µ and s obtained above are obtained by removing a mean and a variance of the equivalent received signal of the ONI stream from Observation Node ONI.

In the above step, since R is an upper triangular matrix, only ONI ={<NUM>·flow,. ,<NUM>} and SNI = {ONI,. ,<NUM> · flow} need to be traversed. In this way, iteration overhead of the QR-GAI algorithm can be further reduced. In addition, GAI white noise estimation (GAI estimation performance deteriorates significantly under high channel correlation) is not required for nodes of ONI = <NUM>·flow due to the absence of inter-stream interference. Therefore, a Log Likelihood Ratio (LLR) belief probability is high, which makes the iterative algorithm have dominant nodes in the iteration, thereby improving the estimation performance of the overall iterative algorithm.

In an example of this embodiment, the iteration module <NUM> calculates a per stream symbol likelihood probability and a per stream belief probability proT ∈ RM×<NUM> according to the equivalent received signal mean Su,ONI and the equivalent received signal variance estimate Sσ<NUM>,ONI.

The process of calculating, by the iteration module <NUM>, the per stream symbol likelihood probability llr ∈ RM×<NUM> includes: defining a symbol index vector <MAT>, and for an index i = <NUM>, <NUM>,. , M , calculating the per stream symbol likelihood probability llr ∈ RM×<NUM> according to the following formulas: <MAT> where llr (i) denotes a likelihood ratio of the symbol X(ONI) being si(<NUM>)/β to being si(i)/β at an ith symbol position, i = <NUM>,<NUM>,.

Then, the iteration module <NUM> calculates the per stream belief probability proT ∈ RM×<NUM> according to the following formulas: <MAT> where proT(i) denotes a probability that an ONIth stream symbol X(ONI) is si(i)β.

In some examples of this embodiment, in order to prevent probability jumps in adjacent iteration cycles, the performing, by the iteration module <NUM>, iteration based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol may further include updating the ONI th stream symbol with the following damping algorithm: <MAT> where pro(:,ONI) denotes all rows and ONI columns, and df denotes a damping factor. The value of df in this embodiment may be flexibly determined. In an example, the value may be any value from <NUM> to <NUM>.

In this embodiment, after the iteration module <NUM> obtains the per stream symbol position probability, the obtained per stream symbol position probability may be applied flexibly as required. For example, referring to <FIG>, the BP equalization apparatus further includes an application processing module <NUM>.

In an example of this embodiment, the application processing module <NUM> may calculate a complex QAM constellation point of per stream symbol SNI = {<NUM>,. , flow} by utilizing the obtained per stream symbol position probability, so as to output a per stream symbol bit-level LLR probability.

The calculating, by the application processing module <NUM>, a complex QAM constellation point of per stream symbol SNI = {<NUM>,. , flow} includes: <MAT> and <MAT> where pro(rp, SNI) and pro(ip, SNI + flow) denote probabilities of real-value symbol positions of a real part and an imaginary part of an SNI th stream.

In some examples, pro(rp,SNI) and pro(ip, SNI + flow) may be directly used as decoding module input. In some examples, the application processing module <NUM> in this embodiment may be further configured to calculate a symbol hard decision, including: <MAT> where X̂(SNI) denotes a hard decision value of a per stream <NUM>M QAM complex symbol, and j denotes the imaginary part.

This embodiment further provides a communication device. The communication device may be a user-side device, for example, a variety of user-side user equipment (e.g., user terminals), or a network-side communication device, such as various AAUs (e.g., base station devices). Referring to <FIG>, the communication device includes a processor <NUM>, a memory <NUM> and a communication bus <NUM>.

The communication bus <NUM> is configured to realize communication connection between the processor <NUM> and the memory <NUM>.

In an example, the processor <NUM> may be configured to execute one or more computer programs stored in the memory <NUM>, so as to carry out the BP equalization method in the above embodiment.

For easy understanding, a description is given in an example of this embodiment by taking a base station as the communication device. Moreover, it is to be understood that the base station of this embodiment may be a cabinet macro base station, a distributed base station or a multimode base station. Referring to <FIG>, the base station of this example includes a building base band unit (BBU) <NUM>, a radio remote unit (RRU) <NUM> and an antenna <NUM>.

The BBU <NUM> is configured to perform centralized control and management of the entire base station system and uplink and downlink baseband processing and provide physical interfaces for information interaction with the radio remote unit and a transport network. According to different logic functions, as shown in <FIG>, the BBU <NUM> may include a baseband processing unit <NUM>, a main control unit <NUM> and a transmission interface unit <NUM>. The main control unit <NUM> is configured mainly to provide functions such as control and management of the BBU, signaling processing, data transmission, interaction control and system clock provision. The baseband processing unit <NUM> is configured to perform baseband protocol processing such as signal coding and modulation, resource scheduling and data encapsulation and provide an interface between the BBU and the RRU. The transmission interface unit <NUM> is configured to provide a transmission interface connected to a core network. In the example, the above logical function units may be distributed on different physical boards or may be integrated on the same board. Optionally, the baseband unit <NUM> may be of a structure in which the baseband processing unit is integrated with the main control unit or a structure in which the baseband processing unit is separated from the main control unit. In the structure in which the baseband processing unit is integrated with the main control unit, main control, transmission, and baseband are integrally designed. That is, the baseband processing unit, the main control unit and the transmission interface unit are integrated on the same physical board. This structure has a higher reliability, a lower delay, a higher resource sharing and scheduling efficiency, and a lower power consumption. In the structure in which the baseband processing unit is separated from the main control unit, the baseband processing unit and the main control unit are distributed on different boards, which correspond to a baseband board and a main control board, respectively. This structure allows for free combination between boards, facilitating flexible expansion of the baseband. In practical application, the arrangement flexibly depends on requirements.

The RRU <NUM> is configured to communicate with the BBU through a baseband radio frequency (RF) interface to complete conversion between a baseband signal and an RF signal. Referring to <FIG>, an example RRU <NUM> mainly includes an interface unit <NUM>, a uplink signal processing unit <NUM>, an downlink signal processing unit <NUM>, a power amplifier unit <NUM>, a low-noise amplifier unit <NUM> and a duplexer unit <NUM>. These units constitute a downlink signal processing link and an uplink signal processing link. The interface unit <NUM> is configured to provide a forward interface with the BBU and receive and send a baseband IQ signal. The downlink signal processing unit <NUM> is configured to perform signal processing functions such as signal up-conversion, digital-to-analog conversion and RF modulation. The uplink signal processing unit <NUM> is mainly configured to perform functions such as signal filtering, mixing, analog-to-digital conversion and down-conversion. The power amplifier unit <NUM> is configured to amplify a downlink signal and then send the amplified downlink signal through the antenna <NUM> to, for example, a terminal device. The low-noise amplifier unit <NUM> is configured to amplify an uplink signal received by the antenna <NUM> and then send the amplified uplink signal to the uplink signal processing unit <NUM> for processing. The duplexer unit <NUM> supports multiplexing and filtering of received and sent signals.

Additionally, it is to be understood that the base station of this embodiment may also use a Central Unit-Distributed Unit (CU-DU) architecture. The DU is a distributed access point responsible for underlying baseband protocol and RF processing. The CU is responsible for higher-layer protocol processing and centralized management of DUs. The CU and the DU jointly perform baseband and RF processing of the base station.

In this embodiment, the base station may further include a storage unit for storing various data. For example, the storage unit may store the one or more computer programs. The main control unit or the CU may be used as a processor to execute the one or more computer programs stored in the storage unit to carry out the BP equalization method of the above embodiments.

In this example, when the BP equalization apparatus is disposed in the base station, the functions of at least one module of the BP equalization apparatus may also be implemented by the main control unit or the central unit.

For easy understanding, a description is given in another example of this embodiment by taking a communication terminal as the communication device. Referring to <FIG>, the communication terminal may be a mobile terminal having a communication function, for example, a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a personal digital assistant (PDA), a navigation device, a wearable device or a smart band. The communication terminal may include an RF unit <NUM>, a sensor <NUM>, a display unit <NUM>, a user input unit <NUM>, an interface unit <NUM>, a memory <NUM>, a processor <NUM> and a power supply <NUM>. It is to be understood by those having skills in the art that the communication terminal is not limited to the structure of the communication terminal shown in <FIG>. The communication terminal may include more or fewer components than the components illustrated, or a combination of some of the components illustrated, or may include components arranged in a different manner than the components illustrated.

The RF unit <NUM> may be configured for communication, that is, receiving and sending signals. For example, the RF unit <NUM> receives downlink information from the base station and sends the received downlink information to the processor <NUM> for processing. Moreover, the RF unit <NUM> sends uplink data to the base station. Generally, the RF unit <NUM> includes, but not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier and a duplexer. Moreover, the RF unit <NUM> may also communicate with a network and other devices by way of wireless communication. The sensor <NUM> may be, for example, a light sensor, a motion sensor or other sensors. In an embodiment, the light sensor includes an ambient light sensor or a proximity sensor. The ambient light sensor may adjust the brightness of a display panel <NUM> according to the brightness of the ambient light.

The display unit <NUM> is configured to display information inputted by or provided for a user. The display unit <NUM> may include a display panel <NUM>, for example, an organic light-emitting diode (OLED) display panel or an active-matrix organic light-emitting diode (AMOLED) display panel.

The user input unit <NUM> may be configured to receive input digital or character information and generate key signal input related to user settings and function control of the mobile terminal. The user input unit <NUM> may include a touch panel <NUM> and another input device <NUM>.

The interface unit <NUM> serves as an interface through which at least one external device can be connected to the communication terminal. For example, the external device may include an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting an apparatus having an identification module, an audio input/output (I/O) port and the like.

The memory <NUM> may be configured to store software programs and various data. The memory <NUM> may include a high-speed random-access memory and may also include a non-volatile memory such as at least one disk memory, a flash memory device or another volatile solid-state memory.

As the control center of the communication terminal, the processor <NUM> is configured to connect various parts of the communication terminal by various interfaces and lines and perform various functions and data processing of the communication terminal by executing software programs and/or modules stored in the memory <NUM> and invoking data stored in the memory <NUM>. For example, the processor <NUM> may be configured to invoke the one or more computer programs stored in the memory <NUM> to carry out the BP equalization method as described above.

The processor <NUM> may include one or more processing units. In an embodiment, an application processor and a modem processor may be integrated into the processor <NUM>. The application processor is configured to process an operating system, a user interface and an application program. The modem processor is configured to process wireless communication. It is to be understood that the modem processor may not be integrated into the processor <NUM>.

The power supply <NUM> (for example, a battery) may be logically connected to the processor <NUM> through a power management system to perform functions such as charging management, discharging management and power consumption management through the power management system.

In this example, when the BP equalization apparatus is disposed in the communication terminal, the functions of at least one module of the BP equalization apparatus may also be implemented by the processor <NUM>.

This embodiment further provides a computer-readable storage medium. The computer-readable storage medium includes a volatile or non-volatile medium, removable or non-removable medium implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules or other data). The computer-readable storage medium includes, but is not limited to, a random-access memory (RAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory or another memory technology, a compact disc read-only memory (CD-ROM), a digital versatile disc (DVD) or another optical storage, a magnetic cassette, a magnetic tape, a magnetic disk or another magnetic storage device, or any other medium that can be used for storing desired information and accessed by a computer.

In an example, the computer-readable storage medium of the embodiment may be configured to store one or more computer programs which, when executed by one or more processors, cause the one or more processors to carry out the BP equalization method of the above embodiments.

This embodiment further provides a computer program (or computer software) that may be distributed in a computer-readable medium and executed by a computing device to cause the computing device to perform at least one step of the BP equalization method of the above embodiments. Moreover, in some cases, the illustrated or described at least one step may be performed in a sequence different from the sequence described in the above embodiments.

This embodiment further provides a computer program product. The computer program product includes a computer-readable apparatus having stored thereon the computer programs as illustrated above. In this embodiment, the computer-readable apparatus may include the computer-readable storage medium as illustrated above.

In the BP equalization method and apparatus, the communication device and the storage medium according to the embodiments of the present disclosure, firstly, a received signal Y , a channel estimation Hc and a symbol estimation Xc are split into real parts and imaginary parts to obtain a received signal matrix Y , a channel estimation matrix H and a symbol estimation matrix X; then, QR decomposition is performed on the channel estimation matrix H to obtain an equivalent received signal Ybp, an equivalent channel R and a noise power σ<NUM>, and iteration is performed based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol. Due to the QR decomposition, an update dimension is the number of streams multiplied by the number of streams, which does not increase with the number of antennas, achieving dimensionality reduction and being more suitable for Massive MIMO multi-user detection scenarios of large-scale array antennas. That is, application scenarios of the BP equalization method are improved. At the same time, an R matrix is an upper triangular matrix, and only non-zero nodes need to be iterated during iteration, so the computation overhead can be further reduced and the accuracy of symbol estimation in the equalization algorithm can be improved.

In the embodiments of the present disclosure, the noise power can also be ensured to be constant during dimensionality reduction by utilizing characteristics of a unitary matrix.

During information iterative update, according to the SIC principle, the use of the principle of canceling inter-stream interference of a last stream without a GAI algorithm and the optimal use of a damping filtering method can ensure the high belief probability of nodes, thereby improving the accuracy of the overall multi-user detection and accelerating the convergence speed.

As can be seen, it is to be understood by those having ordinary skills in the art that some or all steps of the method and function modules/units in the system or apparatus may be implemented as software (which may be implemented by computer program codes executable by a computing device), firmware, hardware and suitable combinations thereof. In the hardware implementation, the division of the function modules/units mentioned in the above description may not correspond to the division of physical components. For example, one physical component may have multiple functions, or one function or step may be performed jointly by several physical components. Some or all physical components may be implemented as software executed by a processor such as a central processing unit, a digital signal processor or a microprocessor, may be implemented as hardware, or may be implemented as integrated circuits such as application-specific integrated circuits.

Additionally, as is known to those having ordinary skills in the art, communication media generally include computer-readable instructions, data structures, computer program modules, or other data in carriers or in modulated data signals transported in other transport mechanisms and may include any information delivery medium. Therefore, the present disclosure is not limited to any particular combination of hardware and software.

Claim 1:
A Belief Propagation, BP, equalization method, comprising:
splitting a received signal Yc, a channel estimation Hc and a symbol estimation Xc into real parts and imaginary parts to obtain a received signal matrix Y, a channel estimation matrix H and a symbol estimation matrix X (S101);
performing orthogonal triangular, QR, decomposition on the channel estimation matrix H to obtain an equivalent received signal Ybp, an equivalent channel R and a noise power σ<NUM> (S102); and
performing iteration based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol (S103);
characterized in that:
performing iteration based on the equivalent received signal Ybp, the equivalent channel R and the noise power σ<NUM> to obtain a position probability of per stream symbol comprises:
constructing a fully connected BP network by taking the equivalent received signal Ybp as an observation node and the symbol estimation matrix X as an information node, and initializing a per stream symbol position probability as <MAT>, M being an order of quadrature amplitude modulation, QAM;
calculating an equivalent received signal mean Su,ONI and an equivalent received signal variance estimate Sσ<NUM>,ONI on each observation node index ONI = {<NUM>· flow,...,<NUM>}; and
calculating a per stream symbol likelihood probability llr ∈ RM×<NUM> and a per stream belief probability proT ∈ RM×<NUM> according to the equivalent received signal mean Su,ONI and the equivalent received signal variance estimate Sσ<NUM>,ONI.