CN108737032B - Compressed superposition sequence CSI feedback method - Google Patents
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Abstract
The invention provides a compressed superposition sequence CSI feedback method, which comprises the following steps: constructing a position sequence, and recording position indexes of non-zero elements and zero elements in the CSI; carrying out single-bit compression perception quantization on the CSI to obtain bit information; forming a bit sequence by the position sequence, the bit information and the sparsity; performing spread spectrum processing on the bit sequence to obtain a spread spectrum data sequence, and then adding zeros to the tail of the spread spectrum data sequence to form a spread sequence; the spreading sequence and the data sequence are weighted and then superposed into a sending sequence; a receiver receives the sending sequence, recovers a bit sequence by utilizing a de-spreading technology, and then recovers a position sequence, bit information and sparsity; the receiver reconstructs the CSI using the position sequence, bit information and sparsity. The invention has the advantages that: improving the bit overhead required for feedback; the system bandwidth overhead is reduced, and the accuracy of CSI feedback is improved.
Description
Technical Field
The invention relates to the technical field of compression and superposition processing of a channel feedback technology at a user terminal and signal reconstruction at a base station terminal in a communication system, in particular to a CSI feedback method of a compression superposition sequence.
Background
Compared with a traditional Frequency Division Duplex (FDD) Multiple-Input Multiple-Output (MIMO) system, the FDD-based large-scale MIMO system can improve the system capacity, stability and other performances. However, many operations (e.g., multiuser scheduling, rate allocation, transmit-side precoding, etc.) that bring performance improvements to FDD-based massive MIMO systems rely on accurate CSI acquisition. Therefore, whether the base station can accurately acquire the downlink CSI directly affects the performance of the FDD massive MIMO system.
In a massive MIMO system based on FDD, the downlink CSI acquisition usually relies on channel feedback techniques. The traditional CSI feedback method based on codebook is difficult to be applied in practice due to the fact that the feedback overhead of a large number of antennas of a large-scale MIMO system is huge. Compared with a CSI feedback method based on a codebook, the CSI feedback technology based on compressed sensing (the technology is called as the traditional compressed sensing CSI feedback technology) utilizes the sparse characteristic of the CSI to compress and feed back the CSI, and the CSI feedback overhead can be improved to a certain extent. Even in this case, the conventional CSI feedback technology still has a high feedback overhead, occupies a certain bandwidth resource, and needs to be further improved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for compressing a feedback method of a superposition sequence CSI. When channel feedback is carried out, the non-zero element of the CSI and the position index information of the zero element are superposed on the data information for feedback together with the CSI quantized by a single bit. Therefore, the CSI feedback does not need extra bandwidth overhead of a system, and the accuracy of the CSI feedback can be improved.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a compressed superposition sequence CSI feedback method comprises the following steps:
the following operations are performed on sparse N × 1 Channel State Information (CSI):
a) constructing a position sequence Z of Nx 1 by using 0,1 element, and recording position indexes of non-zero elements and zero elements in the CSI;
b) carrying out single-bit quantization on the CSI by using a single-bit compressed sensing method to obtain quantized M multiplied by 1 'bit information' Y;
c) forming N by the position sequence Z, the bit information Y and the sparsity KLA "bit sequence" L of x 1; said NLSatisfies NL＝N+2M+NK；
Said NKThe number of bits required for representing the sparsity K in binary representation is set according to engineering experience and generally meets NK＜log2N；
d) Spreading the 'bit sequence' L to obtain 'spread data sequence'
Then "spread data sequence"
Tail addition of NzeroAfter zero to form ND"spreading sequence" W for x 1;
said "spread spectrum data sequence"
According to the formula
Is obtained in which
Representing spreading sequences, symbols
Represents the Kronecker product;
said NzeroSatisfy the requirement of
Said NDSelected according to engineering experience, usually ND＞＞NL(ii) a Where c represents the length of spreading, also known as spreading gain.
e) Will "spread sequences" W and ND"data sequence" D of x 1 according to the formula
T＝a×W+(1-a)×D
Performing weighting processing to form NDTransmitting by the "transmission sequence" T of x 1;
the constant a is selected according to engineering experience, and a is more than 0 and less than or equal to 1;
f) a receiver receives a sending sequence T, recovers a bit sequence L by using a despreading technology, and then recovers a position sequence Z, bit information Y and sparsity K;
g) the receiver reconstructs the CSI using the "position sequence" Z, the "bit information" Y and the sparsity K.
Further, the "position sequence" Z in step a) is to mark a position where an element in H is zero as 0 and mark a position where the element is non-zero as 1 after reading the pre-stored CSI sequence H.
Further, step a) comprises:
reading pre-stored channel state information H with the length of N and the sparsity of K, and a random distribution measurement matrix phi with the size of M multiplied by N.
Further, the sparse level K in step a) is obtained by channel estimation of a receiver at a user end; the channel state information H is obtained by channel estimation of a receiver at a user terminal.
Further, said random distribution measurement may be taken as: gaussian distributed random matrices, bernoulli distributed random matrices, sub-gaussian random matrices, and very sparse projection matrices, among others.
In particular, step a) is different from the existing channel feedback process in that position index information of non-zero and zero elements of CSI elements is considered when CSI feedback is performed.
Further, step b) comprises:
the real part H of the CSI sequence HrealAnd an imaginary part HimagCompressing by using a single-bit compression perception principle to obtain 'bit information' Y, wherein a real part and an imaginary part of the 'bit information' Y are respectively marked as Yreal，Yimag。
The single bit is compressed to obtain the real part Y of the bit information YrealAnd imaginary part YimagObtained according to the following formula:
the operation sign () represents a sign operation, that is:
further, step c) comprises:
the real part and imaginary part of the ' position sequence ' Z with the length of N and the ' bit information ' Y with the length of M and the sparsity K are obtained to form the position sequence ' Z with the length of N and the real part and the imaginary part of the ' bit information ' Y with the length of MLI.e.:
L＝(ZT,(Yreal)T,(Yimag)T，(Kbin)T)T
wherein, the superscript "T" represents the operation of finding the transposition;
said KbinRepresenting the length of the structure N when the sparsity K is represented as binaryKA column vector of (a);
further, step d) comprises:
the above-mentioned
Represents a spreading sequence having a spreading length c of
Wherein, the symbol
Denotes a downward integer, said NzeroSatisfy the requirement of
Said NDSelected according to engineering experience, usually ND＞＞NL(ii) a The symbol
Indicating a downward integer. Where c represents the length of spreading, also known as spreading gain.
Further, step 0 comprises:
after recovering the ' bit sequence ' L by using a despreading technology, recovering a ' position sequence ' Z, bit information ' Y and sparsity K from the ' bit sequence ' L according to the position relation in c) and the length of each sequence, and then taking a real part and an imaginary part of the ' bit information ' Y. The position relation in c) is as follows:
L＝(ZT,(Yreal)T,(Yimag)T，(Kbin)T)T
wherein, the superscript "T" represents the operation of finding the transposition;
said KbinRepresenting the length of the structure N when the sparsity K is represented as binaryKA column vector of (a);
further, step g) further comprises: the CSI reconstruction refers to adding a position sequence Z into a reconstruction algorithm to form a constraint condition, and combining the constraint condition with the existing reconstruction method based on single-bit compressed sensing. The existing reconstruction method based on single-Bit compressed sensing can adopt Binary Iterative Hard Threshold (BIHT), Iterative Hard Threshold (IHT) and Adaptive 1-Bit compressed sensing (Adaptive 1-Bit CS).
Compared with the prior art, the invention has the advantages that:
a single-bit compressed sensing technology is introduced on the basis of the traditional compressed sensing CSI feedback, so that the bit overhead required by feedback is improved; meanwhile, a 'superposition feedback' mode is also introduced: and superposing the CSI information subjected to single-bit compression on the uplink data together with position index information formed by the CSI nonzero elements and the zero elements for feedback. The system bandwidth overhead is reduced, and the accuracy of CSI feedback is improved.
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FIG. 1 is a flow chart of a CSI feedback method according to the present invention;
fig. 2 is a flowchart of an algorithm for reconstructing a channel signal according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings by way of examples.
As shown in fig. 1, the following operations are performed on the sparse N × 1 Channel State Information (CSI):
a) constructing a position sequence Z of Nx 1 by using 0,1 element, and recording position indexes of non-zero elements and zero elements in the CSI;
a1) reading pre-stored channel state information H with the length of N and the sparsity of K, and a random distribution measurement matrix phi with the size of M multiplied by N.
H is a sparse signal with the sparsity of K, namely H only has K nonzero elements and satisfies K < N.
In order to ensure the reconstruction performance of single-bit compressed sensing, the number M of the compressed sampling points generally meets the condition that M is more than or equal to N.
Preferably, the method for generating the random distribution measurement matrix includes existing methods for generating the random distribution measurement matrix, such as a gaussian distribution random matrix, a bernoulli distribution random matrix, a sub-gaussian random matrix, and a very sparse projection matrix.
a2) The position indexes of non-zero elements and zero elements in H are recorded by 0,1 elements, the position where the element is zero in CSI is marked as 0, and the position where the element is non-zero in CSI is marked as 1, thereby forming a position sequence Z with the length of N.
Example 1: the construction process of the position sequence Z is as follows:
let N be 16 and K be 5. Then H may be represented as H ═ H (H)1,h2,…,h16)T. In particular, in a large-scale MIMO system, the number of elements in the channel state information H is hundreds or thousands, and N is 16 for convenience of writing. Suppose that the element of H is divided by the element "H1,h3,h5,h10,h16"5 elements, the other elements are 0. That is, a "position sequence" Z can be obtained as Z ═ 1010100001000001TWherein, the superscript T represents the transpose operation of the matrix.
b) Carrying out single-bit quantization on the CSI by using a single-bit compressed sensing method to obtain quantized M multiplied by 1 'bit information' Y;
taking Channel State Information (CSI) H as a real part HrealAnd an imaginary part HimagUsing a single ratioCompressing by using special compression sensing principle to obtain 'bit information' Y, and respectively recording the real part and imaginary part of the 'bit information' Y as Yreal，Yimag。
Taking a real part and an imaginary part of Channel State Information (CSI) H according to the following formula:
Hreal＝real(H)
Himag＝imag(H)
the process of compressing by using the compressed sensing principle comprises the following steps:
wherein, the operation sign () represents a sign operation, that is:
y obtained at this timerealAnd YimagAre M × 1 vectors, and the vector elements are 0 and 1.
c) Forming N by the position sequence Z, the bit information Y and the sparsity KLA "bit sequence" L of x 1;
said NLSatisfies NL＝N+2M+NK(ii) a Said NKRepresenting the number of bits required when the sparsity K is represented by a binary system;
c1) the real part Y of the position sequence Z with the length of N and the bit information Y with the length of M are obtainedrealAnd imaginary part YimagWith a degree of sparsity K, constituting a length NLI.e. constructed according to the following formula:
L＝(ZT,(Yreal)T,(Yimag)T，(Kbin)T)T
wherein, the superscript "T" represents the operation of finding the transposition; said KbinRepresenting the length of the structure N when the sparsity K is represented as binaryKA column vector of (a); according to engineering experience setting, N is usually satisfiedK＜log2N；
d) Spreading the 'bit sequence' L to obtain 'spread data sequence'
Then "spread data sequence"
Tail addition of NzeroAfter zero to form ND"spreading sequence" W for x 1;
d1) to obtain a length of NLCalculating the spread spectrum gain c to obtain the spread spectrum sequence
Wherein, the spread spectrum gain c is obtained according to the following formula:
length N of "data sequence" D to be transmittedDFar greater than the length N formed by the "position sequence" Z and the "bit information" YLThe "bit sequence" L of (1). Wherein, the symbol
Indicating a downward integer.
d2) Spreading the 'bit sequence' L to obtain 'spread data sequence'
The spread spectrum processing is carried out according to the following formula:
wherein, the symbol
Representing the Kronecker product.
d3) Will "spread spectrum data sequence"
Tail addition of NzeroAfter zero to form ND"spreading sequence" W for x 1; said Nzero satisfies
Said NDSelected according to engineering experience, usually ND＞＞NL(ii) a The symbol
Indicating a downward integer. Where c represents the length of spreading, also known as spreading gain.
The available "spreading sequence" W:
wherein the superscript "T" denotes the operation of transpose calculation
e) Will "spread sequences" W and ND"data sequence" D for x 1 according to the formula:
T＝a×W+(1-a)×D
performing weighting processing to form NDTransmitting by the "transmission sequence" T of x 1; the constant a is selected according to engineering experience, and a is more than 0 and less than or equal to 1;
f) the receiver receives the sending sequence T, recovers the bit sequence L by using a despreading technology, and then recovers the position sequence Z, the bit information Y and the sparsity K.
f1) Receiving end receiving length NDAfter the 'transmission sequence' T, the length N formed by the 'bit information' Y and the 'position sequence' Z is recovered by using a despreading technologyLThe "bit sequence" L of (1). Wherein, the despreading process is as follows:
f1-1 in general, the receiving end receives a length NDThe "transmission sequence" T of (a),
and then, carrying out block processing on the matrix, and dividing the matrix into m c multiplied by 1 matrixes, wherein m values are carried out according to the following formula:
wherein the symbol
Denotes a downward integer, c is a spreading matrix
The spread spectrum amplification of (1). Thus obtaining a "transmit sequence"
The partitions of (a) are represented as:
wherein the content of the first and second substances,
f1-2 block-taking sequence "
Element (1) of
Despreading with the same spreading matrix yields a "bit sequence" L, (L ═ L1,…,lm)TDespreading is performed according to the following equation:
wherein the superscript "H" indicates the conjugate transpose operation.
f2) According to L ═ ZT,(Yreal)T,(Yimag)T，(Kbin)T)TThe "position sequence" Z has a length N, and the real part Y of the "bit information" YrealAnd imaginary part YimagThe lengths are M and KbinLength NKBy a length of NLThe "bit sequence" L of (a) may recover a "position sequence" Z, "bit information" Y and sparsity K. That is, the real part Y of the "position sequence" Z and the "bit information" Y is solved according to the following relationrealAnd imaginary part YimagSparsity K:
where the superscript T denotes the operation of transposing the matrix.
g) The receiver reconstructs the CSI using the "position sequence" Z, the "bit information" Y and the sparsity K.
The reconstructed CSI in the step g) is reconstructed by using a position sequence Z. The invention forms auxiliary information by using a 'position sequence' Z formed by non-zero elements of CSI and position index information of zero elements and an existing reconstruction method based on single-bit compressed sensing.
The "existing reconstruction method based on single-Bit compressed sensing" may adopt Binary Iterative Hard Threshold (BIHT), Iterative Hard Threshold (IHT), Adaptive 1-Bit compressed sensing (Adaptive 1-Bit CS), and the like.
Example 1
The BIHT reconstruction algorithm is taken as an example below, as shown in fig. 2.
Initializing data: reading the reconstructed position information ' position sequence ' Z and reading the restored ' bit information"Y real part YrealAnd the imaginary part YimagSparsity K, measurement matrix Φ, maximum Iteration number Iteration, step β. Reconstruction signal initial value Xreal＝0，XimagThe initial value n of the number of iterations is 0. The maximum Iteration number Iteration is set according to engineering experience. The step length beta is a step length parameter for adjusting gradient descent, is set by engineering experience, and is usually greater than 0 and less than or equal to 1.
g 1) Performing an iterative update Xreal＝Xreal+βΦT(Yreal-sign(ΦXreal))，Ximag＝Ximag+βΦT(Yimag-sign(ΦXimag)). Wherein phiTIndicating the operation of transposing the matrix phi.
g2) According to the "position sequence" Z, XlaerAnd XimagThe element is zeroed at the position index corresponding to the "position sequence" Z marker 0.
g3) Updating the Iteration number, wherein n is n +1, and if n is less than Iteration, returning to g 2); otherwise, X is addedrealAnd XimagCarrying out normalization processing, namely:
wherein "| · | purple sweet2"indicates that Frobenius norm operation is taken. Then, according to the formula:
merging
And
obtaining a CSI signal
Wherein i represents an imaginary numberSatisfy i2＝-1。
According to the specific example, compared with the traditional channel feedback method, the method of the invention uses the single-bit compressed sensing method, and reduces the bit overhead of the system. Meanwhile, the position sequence formed by the sequence zero element and the non-zero element is overlapped and fed back with the data sequence, so that the signal reconstruction with single bit is assisted by position information, and the accuracy of the reconstructed signal is greatly improved. Therefore, the bandwidth overhead of the system is reduced, the frequency spectrum utilization rate is improved, and the accuracy of CSI feedback is improved.
Those skilled in the art will appreciate that, in the embodiments of the methods of the present invention, the sequence numbers of the steps are not used to limit the sequence of the steps, and it is within the scope of the present invention for those skilled in the art to change the sequence of the steps without inventive work. The examples described herein are intended to aid the reader in understanding the practice of the invention and it is to be understood that the scope of the invention is not limited to such specific statements and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (9)
1. A compressed superposition sequence CSI feedback method is characterized by comprising the following steps:
the following operations are performed on the sparse N × 1 CSI sequence H:
a) constructing a position sequence Z of Nx 1 by using 0,1 element, and recording position indexes of non-zero elements and zero elements in the CSI;
b) carrying out single-bit quantization on the CSI by using a single-bit compressed sensing method to obtain quantized M multiplied by 1 'bit information' Y;
c) forming N by the position sequence Z, the bit information Y and the sparsity KLA "bit sequence" L of x 1; said NLSatisfies NL＝N+2M+NK；
Said NKExpressing the sparsity K by twoThe number of required bits is set according to engineering experience when in binary expression, and N is satisfiedK＜log2N；
d) Spreading the 'bit sequence' L to obtain 'spread data sequence'
Then "spread data sequence"
Tail addition of NzeroAfter zero to form ND"spreading sequence" W for x 1;
said "spread spectrum data sequence"
According to the formula
Is obtained in which
Representing spreading sequences, symbols
Represents the Kronecker product;
said NzeroSatisfy the requirement of
Said NDSelecting N according to engineering experienceD＞＞NL(ii) a Wherein, c represents the length of spread spectrum, also called spread spectrum gain; the symbol
Represents a downward integer;
e) will "spread sequences" W and ND"data sequence" D of x 1 according to the formula
T＝a×W+(1-a)×D
Performing weighting processing to form NDTransmitting by the "transmission sequence" T of x 1;
the constant a is selected according to engineering experience, and a is more than 0 and less than or equal to 1;
f) a receiver receives a sending sequence T, recovers a bit sequence L by using a despreading technology, and then recovers a position sequence Z, bit information Y and sparsity K;
g) the receiver reconstructs the CSI using the "position sequence" Z, the "bit information" Y and the sparsity K.
2. The method of claim 1, wherein: the 'position sequence' Z in the step a) is marked as 0 at the position where the element in the H is zero and marked as 1 at the position where the element is non-zero after reading the pre-stored CSI sequence H; the step a) considers the position index information of the nonzero and zero elements of the CSI elements when the CSI feedback is carried out.
3. The method of claim 2, wherein: the step a) comprises the following steps:
reading a pre-stored CSI sequence H with the length of N and the sparsity of K and a random distribution measurement matrix phi with the size of M multiplied by N.
4. The method of claim 3, wherein: the sparsity K in the step a) is obtained by channel estimation of a receiver at a user end; and the CSI sequence H is obtained by channel estimation of a user terminal receiver.
5. The method of claim 4, wherein: taking a random distribution measurement matrix phi: one of a gaussian distributed random matrix, a bernoulli distributed random matrix, a sub-gaussian random matrix, and a very sparse projection matrix.
6. The method of claim 5, wherein: the step b) comprises the following steps:
the real part H of the CSI sequence HrealAnd an imaginary part HimagCompressing by using a single-bit compression perception principle to obtain 'bit information' Y, wherein a real part and an imaginary part of the 'bit information' Y are respectively marked as Yreal，Yimag(ii) a The formula is as follows:
the operation sign () represents a sign operation, that is:
7. the method of claim 6, wherein: step c) comprises:
the real part and imaginary part of the ' position sequence ' Z with the length of N and the ' bit information ' Y with the length of M and the sparsity K are obtained to form the position sequence ' Z with the length of N and the real part and the imaginary part of the ' bit information ' Y with the length of MLI.e.:
L＝(ZT,(Yreal)T,(Yimag)T，(Kbin)T)T
wherein, the superscript "T" represents the operation of finding the transposition;
said KbinRepresenting the length of the structure N when the sparsity K is represented as binaryKThe column vector of (2).
8. The method of claim 7, wherein: step f) comprises:
after recovering the ' bit sequence ' L by using a despreading technology, recovering a ' position sequence ' Z, bit information ' Y and sparsity K from the ' bit sequence ' L according to the position relation in c) and the length of each sequence, and then taking a real part and an imaginary part of the ' bit information ' Y; the position relation in c) is as follows:
L＝(ZT,(Yreal)T,(Yimag)T，(Kbin)T)T
wherein, the superscript "T" represents the operation of finding the transposition;
said KbinRepresenting the length of the structure N when the sparsity K is represented as binaryKThe column vector of (2).
9. The method of claim 8, wherein: step g) comprises: the CSI is reconstructed by adding a position sequence Z into a reconstruction algorithm to form a constraint condition and combining the existing reconstruction method based on single-bit compressed sensing; the reconstruction method of the single-bit compressed sensing adopts the following steps: binary iteration hard threshold, and adaptive 1-Bit compressed sensing.
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