An iterative method based on minimum mean square error joint maximal ratio combining

Abstract

The application provides an iterative method based on minimum mean square error joint maximum ratio combination, comprising the following steps: S1, performing minimum mean square error equalization processing, and then performing power normalization processing; S2, performing first amplitude clipping processing; S3, performing reordering; S4, performing interference cancellation processing according to the index order in the ordering sequence; S5, updating the sending symbol estimation value in the ordering sequence based on a maximum ratio combination algorithm, and then performing power normalization processing; S6, performing second amplitude clipping processing; S7, updating constellation points, and judging whether all the sending symbols are traversed; if yes, outputting an equalization result; if no, returning to step S4. The application uses the equalization result of MMSE as posterior information, uses layer reliability ordering to perform interference cancellation, so that each layer is approximately equalized by a single-layer MRC without interference, then the estimation result is updated, and the SER performance is improved through cyclic iteration.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to an iterative method based on minimum mean square error combined with maximum ratio merging. Background Technology

[0002] The Minimum Mean Square Error (MMSE) equalization algorithm is a widely used receiver equalization algorithm in wireless communication systems. In spatial multiplexing scenarios, it can effectively suppress inter-layer interference and obtain accurate constellation point estimates.

[0003] In wireless communication systems, for detailed explanation, let the receiving model be as follows: y = Hx + n, where y represents a dimension of N. rx ×1 represents the received signal; n indicates that the dimension is N. rx ×1 white noise, where x represents N-dimensional white noise. tx The initiation symbol is ×1, and H represents a dimension of N. rx ×N tx It is the wireless channel matrix, N rx N tx These are the receiving antenna and the transmitting antenna, respectively. Therefore, the MMSE equalization is represented as follows:

[0004]

[0005] The purpose of MMSE is to find a matrix W that makes WY closer to x; σ 2 Represents the prior noise power; I represents the identity matrix; This represents the MMSE equalization result. It can be seen that the closer F is to the identity matrix, the stronger the inter-flow suppression capability.

[0006] However, due to the complex and variable wireless channel environment, when the channel condition number is large, (H) H H+σ 2 I) -1 The larger the error in the inverse operation during actual implementation, the worse the inter-flow suppression effect.

[0007] In 4G and 5G wireless communication systems, the 3rd Generation Partnership Project (3GPP) requires 2-layer or 4-layer MMSE equalization to suppress inter-layer interference and obtain accurate constellation point estimates due to the large number of transmit layers and typically 2 or 4 receive antennas. However, MMSE equalization performance deteriorates when the channel matrix condition number is large, resulting in inaccurate constellation point estimates and a high Symbol Error Rate (SER). Summary of the Invention

[0008] To address the above technical problems, this invention provides an iterative method based on the combination of minimum mean square error and maximum ratio merging.

[0009] The technical problem solved by this invention can be achieved by the following technical solutions:

[0010] An iterative method based on minimum mean square error combined with maximum ratio merging includes:

[0011] Step S1: Perform minimum mean square error equalization on the received signal, noise power, and channel estimate to obtain the equalized transmitted symbol estimate, and perform power normalization on the transmitted symbol estimate.

[0012] Step S2: Perform a first clipping process on the power-normalized estimated value of the originating symbol to obtain the constellation point set of the originating symbol;

[0013] Step S3: Reorder the constellation point set according to a predetermined sorting strategy and output a sorted sequence;

[0014] Step S4: Perform interference cancellation processing according to the index order of the estimated values ​​of the originating symbols in the sorted sequence;

[0015] Step S5: Update the estimated value of the originating symbol in the sorted sequence based on the maximum ratio merging algorithm, and then perform power normalization processing.

[0016] Step S6: Perform a second clipping process on the estimated value of the starting symbol in the sorted sequence;

[0017] Step S7: Update the constellation points and determine whether all originating symbols have been traversed.

[0018] If so, output the balanced result;

[0019] If not, return to step S4.

[0020] Preferably, in step S7, after determining whether all originating symbols have been traversed and before outputting the equalization result, the method further includes:

[0021] Step S8: Preset a number of loops and determine whether the number of loops is satisfied.

[0022] If so, output the equilibrium result;

[0023] If not, return to step S3 until the specified number of iterations is met, then output the updated constellation point obtained in step S7 as the equilibrium result.

[0024] Preferably, in step S1, the minimum mean square error equalization is achieved using the following formula:

[0025]

[0026] Where H represents N rx ×N tx A wireless channel matrix of dimension N rx N represents the number of receiving antennas at the receiving port. tx H represents the number of transmitting antennas at the transmitting port. H σ represents the conjugate transpose of the wireless channel matrix H; I represents the identity matrix; 2 y represents the noise power; y represents the received signal; This represents the estimated value of the originating symbol after equilibrium.

[0027] Preferably, in step S1, the power normalization process is performed using the following formula:

[0028]

[0029] Where F(i,i) represents the autocorrelation matrix of the i-th originating symbol; N tx Indicates the number of transmitting antennas at the transmitting port; This represents the estimated value of the originating symbol after power normalization.

[0030] Preferably, in steps S2 and S6, the method for tapering is implemented based on the slicer algorithm.

[0031] Preferably, in step S2, the slicer algorithm is implemented using the following formula:

[0032]

[0033] in, This represents the estimated value of the transmitted symbol corresponding to the i-th transmitted symbol after power normalization; x i Indicates the i-th initiating symbol; The set of constellation points modulated by the originating symbol i.

[0034] Preferably, in step S6, the slicer algorithm is implemented using the following formula:

[0035]

[0036] Where i represents the initiation symbol, i = 1,...,N tx N tx Indicates the number of transmitting antennas at the transmitting port; This represents the estimated value of the transmitted symbol corresponding to the i-th transmitted symbol after power normalization; x i Indicates the i-th initiating symbol; M represents the set of constellation points modulated by the originating symbol i; M represents the state space of constellation modulation. This represents the normalized set of constellation points obtained by modulating the originating symbol i.

[0037] Preferably, in step S4, the following method is used to achieve parallel stream interference cancellation:

[0038]

[0039] s(i), s(j) = 1,...,N tx

[0040] in, Indicates the Nth rx The received signal from each receiving antenna; Represents the Nth digit of the wireless channel matrix rx The element in row s(j) and column s(j); This represents the element in the s(j)th column of the constellation point set; The Nth terminator of the i-th terminator rx The received signal from each receiving antenna.

[0041] Preferably, in step S5, the maximum ratio merging is performed as follows:

[0042]

[0043] in, This represents the k-th received signal of the i-th transmitted symbol; Represents the element in the k-th row and s(i)-th column of the conjugate transpose of the wireless channel matrix; This represents the updated estimate of the originating symbol.

[0044] Preferably, in step S5, the second power normalization process is performed using the following formula:

[0045]

[0046] in, Represents the element in the k-th row and s(i)-th column of the conjugate transpose of the wireless channel matrix; This represents the updated estimate of the originating symbol.

[0047] The advantages or beneficial effects of the technical solution of this invention are as follows:

[0048] This invention, based on MMSE equalization, uses the equalization result of MMSE as posterior information and uses layer reliability ranking to cancel inter-layer interference, so that each layer has an equalization effect of approximately interference-free single-layer MRC. Then, the estimation result is updated and iterated in a loop to improve SER performance and significantly reduce algorithm complexity. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a preferred embodiment of the equalization method for multiple input multiple output channels, specifically embodiment 1.

[0050] Figure 2 This is a flowchart illustrating a preferred embodiment of the equalization method for multiple-input multiple-output channels, specifically embodiment 2. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0053] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0054] See Figure 1 In a preferred embodiment of the present invention, based on the above-mentioned problems existing in the prior art, an iterative method based on minimum mean square error (MMSE) combined with maximum ratio merging (MRC) is provided, comprising:

[0055] Step S1: Perform minimum mean square error equalization on the received signal, noise power, and channel estimate to obtain the equalized transmitted symbol estimate, and then perform power normalization on the transmitted symbol estimate.

[0056] Step S2: Perform the first clipping process on the power-normalized transmitted symbol estimate to obtain the constellation point set of the transmitted symbol;

[0057] Step S3: Reorder the constellation point set according to a predetermined sorting strategy and output a sorted sequence;

[0058] Step S4: Perform interference cancellation processing based on the index order of the estimated values ​​of the originating symbols in the sorted sequence;

[0059] Step S5: Update the estimated value of the starting symbol in the sorted sequence based on the maximum ratio merging algorithm, and then perform power normalization.

[0060] Step S6: Perform a second clipping process on the estimated value of the starting symbol in the sorted sequence;

[0061] Step S7: Update the constellation points and determine whether all originating symbols have been traversed.

[0062] If so, output the balanced result;

[0063] If not, return to step S4.

[0064] Specifically, in a MIMO channel, both the transmit port and / or the receive port are equipped with multiple antennas; assuming the transmit port has N... tx There are N transmitting antennas and N receiving ports. rx Based on the receiving antenna, assume that the transmitting and receiving antennas communicate with each other; let the receiving model be:

[0065] y = Hx + n;

[0066] Where y represents a dimension of N rx A ×1 received signal matrix; n represents a dimension of N. rx A white noise matrix of dimension N × 1, where x represents a white noise matrix of dimension N. tx A matrix of originating symbols of dimension N, where H represents a matrix of dimension N. rx ×N tx It is the wireless channel matrix, N rx N tx These represent the number of antennas at the receiving port and the transmitting port, respectively.

[0067] Specifically, the receiving model is as follows:

[0068]

[0069] Where, N rx N represents the number of receiving antennas. tx Indicates the number of transmitting antennas; Represents the Nth channel in the wireless channel matrix H rx The receiving antenna receives the Nth... tx Channel estimates corresponding to the responses of each transmit antenna; The Nth terminator in the originating symbol matrix x tx Transmitted symbols sent by each transmitting antenna; Represents the Nth element in the white noise matrix n. rx Noise power corresponding to each receiving antenna; Represents the receiving model, y Nrx The Nth element in the received signal matrix y represents... rx The received signal from each receiving antenna.

[0070] In step S1, the minimum mean square error equalization is achieved using the following formula:

[0071]

[0072] Where y represents the received signal; σ 2 H represents noise power; H represents dimension N. rx ×N tx The wireless channel matrix, N rx N represents the number of receiving antennas at the receiving port. tx H represents the number of transmitting antennas at the transmitting port. H H represents the conjugate transpose of matrix H; I represents the identity matrix. This represents the estimated value of the originating sign after equilibrium.

[0073] Given the received signal y, the channel estimate H, and the noise power σ 2 The solution obtained by the above MMSE equalization algorithm is as follows. in, Indicates the Nth rx The estimated value of the transmitted symbol received by each receiving antenna; It is a matrix The transpose of the matrix can be considered as This is the optimal solution under the minimum mean square error performance for each originating symbol i. Initial suppression of interference can be achieved through MMSE equalization.

[0074] Then, the power is normalized using the following formula:

[0075]

[0076] Where F(i,i) represents the autocorrelation matrix of the i-th originating symbol, F=(H H H+σ 2 I) -1 H H H; N tx Indicates the number of transmitting antennas at the transmitting port; denoted by , represents the estimated value of the transmitted symbol after power normalization, and g represents the power gain that needs to be normalized due to the modulation method.

[0077] Then, clipping is performed based on the slicer algorithm. In step S2, the slicer algorithm is specifically implemented using the following formula:

[0078]

[0079] in, This represents the estimated value of the transmitted symbol corresponding to the i-th transmitted symbol after power normalization; x i Represents the i-th initiating symbol; Xset is the set of constellation points; This represents the set of constellation points modulated by the originating symbol i.

[0080] After slicing, to reduce the potential propagation of errors, the columns are reordered according to a predetermined sorting strategy. Preferably, this predetermined sorting strategy can be to sort the column weights of the wireless channel matrix H from weakest to strongest, i.e.

[0081] s = sort j (‖H(:,j)‖),;

[0082] Where j represents the transmitting antenna, j = 1,...,N tx N tx The number of transmit antennas at the transmit port is represented; H(:,j) represents the column weights of the wireless channel matrix H; s represents the column weight sorting sequence, and s(i) and s(j) in the following text are their sorting positions in the column weight sorting sequence.

[0083] Inter-flow interference cancellation is performed according to the index order of the sorting sequence, i.e., parallel inter-flow interference cancellation is achieved as follows:

[0084]

[0085] s(i), s(j) = 1,...,N tx

[0086] in, Indicates the Nth rx The received signal from each receiving antenna; Represents the Nth digit of the wireless channel matrix rx The element in row s(j) and column s(j); This represents the element in the s(j)th column of the constellation point set; The Nth terminator of the i-th terminator rx The received signal from each receiving antenna.

[0087] Then, the maximum ratio is combined with the MRC to obtain the updated originating symbol estimate, as implemented below:

[0088]

[0089] in, This represents the k-th received signal of the i-th transmitted symbol; Represents the element in the k-th row and s(i)-th column of the conjugate transpose of the wireless channel matrix; This represents the updated estimate of the originating symbol.

[0090] Then, power normalization of MRC is performed, specifically as follows:

[0091]

[0092] in, Represents the element in the k-th row and s(i)-th column of the conjugate transpose of the wireless channel matrix; Let s(i) = 1,...,N represent the updated origin sign estimate. tx , g represents the power gain that needs to be normalized due to the modulation method.

[0093] Update the equilibrium result again, let Next, interference cancellation and MRC are performed, and x(s(i)), i = 1, ..., N, are updated step by step. tx .

[0094] This invention improves SER performance by first performing parallel interference cancellation, then MRC, updating the single-layer constellation points, using the updated constellation points for further parallel interference cancellation, and then performing MRC again through multiple iterations. Furthermore, it only requires N iterations after MMSE equalization. rx It can be implemented with a simple multiplication and addition operation, which is easy to implement in parallel and has extremely low complexity.

[0095] In a preferred embodiment, in step S6, the slicer algorithm is implemented using the following formula:

[0096]

[0097] Where i represents the initiation symbol, i = 1,...,N tx N tx Indicates the number of transmitting antennas at the transmitting port; This represents the estimated value of the transmitted symbol corresponding to the i-th transmitted symbol after power normalization; x i Indicates the i-th initiating symbol; M represents the set of constellation points modulated by the originating symbol i; M represents the state space of constellation modulation. This represents the normalized set of constellation point estimates obtained by demodulating the transmitting symbol i at the receiving port.

[0098] Specifically, in this embodiment, a threshold is set according to the state space of the modulated constellation modulation, i.e. To The decision operation is simplified from complex number calculation and comparison under ideal conditions to rounding to the nearest whole number, which reduces the complexity by 90% compared to the traditional slicer algorithm based on minimum Euclidean distance.

[0099] In a preferred embodiment, such as Figure 2 As shown, in step S7, before outputting the equalization result after determining whether all originating symbols have been traversed, the following steps are also included:

[0100] Step S8: Preset a loop count N, and determine whether the loop count N is satisfied:

[0101] If so, output the balanced result;

[0102] If not, return to step S3 until the number of iterations N is satisfied, then output the updated constellation points obtained in step S7 as the equilibrium result.

[0103] Specifically, in this embodiment, after steps S1-S7 are completed, it is recorded as the first loop. Preferably, a number of loops can be preset. If the number of loops is not met, the process returns to step S3, and the updated constellation point set is reordered according to a predetermined sorting strategy, and then interference cancellation processing is performed again. In this embodiment, only after MMSE equalization is N*N... rx It can be implemented with a simple multiplication and addition operation, which is easy to implement in parallel and has extremely low complexity.

[0104] Furthermore, the number of iterations N can be set according to requirements. The larger the value of N, the better the performance, but the algorithm complexity will also increase. As an example rather than a limitation, the number of iterations N can be set to 3 times, which can improve performance while reducing algorithm complexity, achieving a balance between performance and complexity.

[0105] The advantages or beneficial effects of the technical solution of this invention are as follows: Based on MMSE equilibrium, this invention uses the equilibrium result of MMSE as posterior information and uses layer reliability ranking to cancel inter-layer interference, so that each layer has an equilibrium effect of approximately interference-free single-layer MRC. Then, the estimation result is updated and iterated in a loop to improve SER performance and significantly reduce algorithm complexity.

[0106] The above description is merely a preferred embodiment of the present invention and does not limit the implementation and protection scope of the present invention. Those skilled in the art should realize that any equivalent substitutions and obvious changes made using the content of this specification and illustrations should be included within the protection scope of the present invention.

Claims

1. A signal processing method based on minimum mean square error combined with maximum ratio combining, characterized in that, include: Step S1: Perform minimum mean square error equalization on the received signal, noise power, and channel estimate to obtain the equalized transmitted symbol estimate, and perform power normalization on the transmitted symbol estimate. Step S2: Perform a first clipping process on the power-normalized estimated value of the originating symbol to obtain the constellation point set of the originating symbol; Step S3: Reorder the constellation point set according to a predetermined sorting strategy and output a sorted sequence; Step S4: Perform interference cancellation processing according to the index order of the estimated values ​​of the originating symbols in the sorted sequence; Step S5: Update the estimated value of the originating symbol in the sorted sequence based on the maximum ratio merging algorithm, and then perform power normalization processing; Step S6: Perform a second clipping process on the estimated value of the starting symbol in the sorted sequence; Step S7: Update the constellation points and determine whether all originating symbols have been traversed. If so, output the balanced result; If not, return to step S4.

2. The signal processing method based on minimum mean square error combined with maximum ratio merging according to claim 1, characterized in that, In step S7, after determining whether all originating symbols have been traversed and before outputting the equalization result, the following steps are also included: Step S8: Preset a number of loops and determine whether the number of loops is satisfied. If so, output the equilibrium result; If not, return to step S3 until the specified number of iterations is met, then output the updated constellation point obtained in step S7 as the equilibrium result.

3. The signal processing method based on minimum mean square error combined with maximum ratio combining according to claim 1, characterized in that, In step S1, the minimum mean square error balancing is achieved using the following formula: ； Where H represents A dimensional wireless channel matrix, Indicates the number of receiving antennas at the receiving port. Indicates the number of transmitting antennas at the transmitting port; I represents the conjugate transpose of the wireless channel matrix H; I represents the identity matrix. y represents the noise power; y represents the received signal; This represents the estimated value of the originating symbol after equilibrium.

4. The signal processing method based on minimum mean square error combined with maximum ratio combining according to claim 1, characterized in that, In step S1, the power normalization process is performed using the following formula: ； Where F(i,i) represents the autocorrelation matrix of the i-th originating symbol; Indicates the number of transmitting antennas at the transmitting port; This represents the estimated value of the originating symbol after power normalization.

5. The signal processing method based on minimum mean square error combined with maximum ratio combining according to claim 1, characterized in that, In steps S2 and S6, the method for clipping is implemented based on the slicer algorithm.

6. The signal processing method based on minimum mean square error combined with maximum ratio combining according to claim 5, characterized in that, In step S2, the slicer algorithm is implemented using the following formula: ； in, This represents the estimated value of the transmitting symbol corresponding to the i-th transmitting symbol after power normalization. Indicates the i-th initiating symbol; The set of constellation points modulated by the originating symbol i.

7. The signal processing method based on minimum mean square error combined with maximum ratio combining according to claim 5, characterized in that, In step S6, the slicer algorithm is implemented using the following formula: ； Where i represents the initiation symbol, , Indicates the number of transmitting antennas at the transmitting port; This represents the estimated value of the transmitting symbol corresponding to the i-th transmitting symbol after power normalization. Indicates the i-th initiating symbol; M represents the set of constellation points modulated by the originating symbol i; M represents the state space of constellation modulation. This represents the normalized set of constellation points obtained by modulating the originating symbol i.

8. The signal processing method based on minimum mean square error combined with maximum ratio combining according to claim 1, characterized in that, In step S4, the following method is used to achieve parallel inter-stream interference cancellation: ； ； in, Indicates the first The received signal from each receiving antenna; The first element of the wireless channel matrix represents the... Line number Column elements; The set of constellation points represents the first... Column elements; The i-th starting symbol represents the first... The received signal from each receiving antenna.

9. The signal processing method based on minimum mean square error combined with maximum ratio combining according to claim 1, characterized in that, In step S5, the maximum ratio is combined as follows: ； in, This represents the k-th received signal of the i-th transmitted symbol; The k-th row of the conjugate transpose of the wireless channel matrix is... Column elements; This represents the updated estimate of the originating symbol.

10. The signal processing method based on minimum mean square error combined with maximum ratio combining according to claim 1, characterized in that, In step S5, the second power normalization process is performed using the following formula: ； in, The k-th row of the conjugate transpose of the wireless channel matrix is... Column elements; This represents the updated estimate of the originating symbol.

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