US 11,153,124 B2Granted

Channel estimation method of steel penetration system

AssigneeUniversity of Science and Technology Beijing

InventorsTao Liu, Hongwei Zhang, Jiajia Liu

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Abstract

A channel estimation method of a steel penetration system is configured to reduce computational complexity and required memory. The method includes: reconstructing, at a receiving end, a channel estimation with a compressed-sensing sparse reconstruction algorithm according to a received pilot signal to obtain estimation values of a channel state response of the steel penetration system; and equalizing a received signal of the receiving end according to the estimation values of the channel state response to obtain an equalized signal.

Claims4

Dependent claims

A channel estimation method of a steel penetration system, comprising:

reconstructing, at a receiving end, a channel estimation with a compressed-sensing sparse reconstruction algorithm according to a received pilot signal to obtain estimation values of a channel state response of the steel penetration system;
and
equalizing a received signal of the receiving end according to the estimation values of the channel state response to obtain an equalized signal;
wherein before the step of reconstructing, at the receiving end, the channel estimation with the compressed-sensing sparse reconstruction algorithm according to the received pilot signal to obtain the estimation values of the channel state response of the steel penetration system, the channel estimation method further comprises:
determining, at a transmitting end, an insertion mode and an insertion interval of a pilot frequency;
determining an insertion position of the pilot frequency according to the insertion mode and the insertion interval of the pilot frequency;
and
inserting the pilot frequency into an orthogonal frequency division multiplexing signal of a transmitted signal according to the insertion position of the pilot frequency, and transmitting the orthogonal frequency division multiplexing signal with the pilot frequency;
wherein when the insertion mode and the insertion interval of the pilot frequency are determined to obtain a pilot-frequency selection matrix, the pilot-frequency selection matrix has a restricted isometry property after multiplied by a Fourier transformation matrix;
the pilot-frequency selection matrix, the transmitted signal, and the received signal have the following relationship:
PXFh=Φh=PY;
wherein P represents the pilot-frequency selection matrix;
Y represents the received signal;
X represents the transmitted signal;
PX represents a transmitted pilot signal;
PY represents the received pilot signal;
F represents the Fourier transformation matrix, wherein the Fourier transformation matrix is a sparse matrix for varying a channel state response value h from a time domain to a frequency domain H;
and
Φ represents a sensing matrix;
wherein the step of reconstructing, at the receiving end, the channel estimation with the compressed-sensing sparse reconstruction algorithm according to the received pilot signal to obtain the estimation values of the channel state response of the steel penetration system comprises:
A1, calculating inner products of the sensing matrix Φ and the received pilot signal PY, and selecting first L inner products in a descending order, wherein L has a value between 1 and a maximum sparsity K;
A2, representing the received pilot signal PY by y:
y=Φh, forming a matrix Φt by columns of the sensing matrix corresponding to the L inner products, and obtaining an estimation value of the channel state response value h using a least square principle:
ĥt=(ΦtTΦt)−1ΦtTy, wherein a superscript T represents a matrix transposition, and a subscript t represents a current iteration number;
A3, determining a residual error rt, wherein rt is represented as:
rt=y−Φt(ΦtTΦt)−1ΦtTy;
and
A4, judging whether the current iteration number t is less than the maximum sparsity K, if the current iteration number t is less than the maximum sparsity K, returning to step A1 to continue execution, and changing step A1 into a step of calculating the inner products of the sensing matrix Φ and the residual error rt if the current iteration number t is more than or equal to the maximum sparsity K, reconstructing an obtained h, wherein the obtained h is ĥt obtained by current iteration, and obtaining the estimation values of the channel state response H utilizing H=Fĥ;
wherein the step of equalizing the received signal of the receiving end according to the estimation values of the channel state response to obtain the equalized signal comprises:
equalizing the received signal of the receiving end according to the formula Y′=Y×H to obtain the equalized signal Y′, wherein Y represents the received signal, and H represents the estimation values of the channel state response;
wherein the channel state response value H(n+1) at an (n+1)th iteration is represented as:
H(n+1)=H(n)+μ×e×Y′,
wherein H(n) represents the channel state response value at an nth iteration;
μ represents a learning rate;
e represents a deviation, e=Y−Y′.

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Dependencies are inferred from the claim text.

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Abstract

A channel estimation method of a steel penetration system, comprising:

reconstructing, at a receiving end, a channel estimation with a compressed-sensing sparse reconstruction algorithm according to a received pilot signal to obtain estimation values of a channel state response of the steel penetration system;
and
equalizing a received signal of the receiving end according to the estimation values of the channel state response to obtain an equalized signal;
wherein before the step of reconstructing, at the receiving end, the channel estimation with the compressed-sensing sparse reconstruction algorithm according to the received pilot signal to obtain the estimation values of the channel state response of the steel penetration system, the channel estimation method further comprises:
determining, at a transmitting end, an insertion mode and an insertion interval of a pilot frequency;
determining an insertion position of the pilot frequency according to the insertion mode and the insertion interval of the pilot frequency;
and
inserting the pilot frequency into an orthogonal frequency division multiplexing signal of a transmitted signal according to the insertion position of the pilot frequency, and transmitting the orthogonal frequency division multiplexing signal with the pilot frequency;
wherein when the insertion mode and the insertion interval of the pilot frequency are determined to obtain a pilot-frequency selection matrix, the pilot-frequency selection matrix has a restricted isometry property after multiplied by a Fourier transformation matrix;
the pilot-frequency selection matrix, the transmitted signal, and the received signal have the following relationship:
PXFh=Φh=PY;
wherein P represents the pilot-frequency selection matrix;
Y represents the received signal;
X represents the transmitted signal;
PX represents a transmitted pilot signal;
PY represents the received pilot signal;
F represents the Fourier transformation matrix, wherein the Fourier transformation matrix is a sparse matrix for varying a channel state response value h from a time domain to a frequency domain H;
and
Φ represents a sensing matrix;
wherein the step of reconstructing, at the receiving end, the channel estimation with the compressed-sensing sparse reconstruction algorithm according to the received pilot signal to obtain the estimation values of the channel state response of the steel penetration system comprises:
A1, calculating inner products of the sensing matrix Φ and the received pilot signal PY, and selecting first L inner products in a descending order, wherein L has a value between 1 and a maximum sparsity K;
A2, representing the received pilot signal PY by y:
y=Φh, forming a matrix Φt by columns of the sensing matrix corresponding to the L inner products, and obtaining an estimation value of the channel state response value h using a least square principle:
ĥt=(ΦtTΦt)−1ΦtTy, wherein a superscript T represents a matrix transposition, and a subscript t represents a current iteration number;
A3, determining a residual error rt, wherein rt is represented as:
rt=y−Φt(ΦtTΦt)−1ΦtTy;
and
A4, judging whether the current iteration number t is less than the maximum sparsity K, if the current iteration number t is less than the maximum sparsity K, returning to step A1 to continue execution, and changing step A1 into a step of calculating the inner products of the sensing matrix Φ and the residual error rt if the current iteration number t is more than or equal to the maximum sparsity K, reconstructing an obtained h, wherein the obtained h is ĥt obtained by current iteration, and obtaining the estimation values of the channel state response H utilizing H=Fĥ;
wherein the step of equalizing the received signal of the receiving end according to the estimation values of the channel state response to obtain the equalized signal comprises:
equalizing the received signal of the receiving end according to the formula Y′=Y×H to obtain the equalized signal Y′, wherein Y represents the received signal, and H represents the estimation values of the channel state response;
wherein the channel state response value H(n+1) at an (n+1)th iteration is represented as:
H(n+1)=H(n)+μ×e×Y′,
wherein H(n) represents the channel state response value at an nth iteration;
μ represents a learning rate;
e represents a deviation, e=Y−Y′.

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