CN113595945A - Channel estimation method suitable for PDSCH of 5G system - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
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- H04L25/0202—Channel estimation
- H04L25/022—Channel estimation of frequency response
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/0224—Channel estimation using sounding signals
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- Y02D30/00—Reducing energy consumption in communication networks
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Abstract
The invention relates to a channel estimation method suitable for a PDSCH (physical downlink shared channel) of a 5G system, belonging to the technical field of communication. Combining the basic idea of DFT channel estimation algorithm, firstly, according to the frequency domain response at the position of the pilot DMRS estimated by LS algorithm, then adding Hamming window and then carrying out inverse discrete Fourier transform, then taking the average value of the median and the maximum value of the channel response amplitude module of all sample points outside the cyclic prefix as the threshold value threshold in the cyclic prefix to carry out noise reduction operation, and finally obtaining the channel estimation result after discrete Fourier transform and windowing function removal. The invention can not only inhibit the influence of energy leakage under non-integral multiple time delay channels, but also effectively filter noise in the cyclic prefix and improve the accuracy of channel estimation.
Description
Technical Field
The invention belongs to the technical field of communication, and relates to a channel estimation method suitable for a PDSCH (physical downlink shared channel) of a 5G system.
Background
The PDSCH in the 5G system is mainly used for transmitting downlink data, carrying paging information, and transmitting part of system information. The channel estimation technology plays an extremely important role in the whole link of the receiving end as the key to whether the 5G terminal can accurately and effectively recover the transmitted signal. During signal transmission, communication transmission quality is greatly affected due to randomness and time variability of channel environment. In order to ensure that the receiving end can receive the signal of the transmitting end without distortion, a channel estimation technique is generally adopted.
The most common channel estimation algorithm in engineering implementation is based on Least-squares (LS), which is low in complexity and easy to implement, does not need to acquire related prior information in a channel, but does not consider the influence of noise, and has a large mean Square error when the signal-to-noise ratio is small. Although the channel estimation algorithm based on Linear Minimum Mean Square Error (LMMSE) well suppresses the influence of noise, the channel estimation algorithm is based on the optimal criterion in the pilot channel estimation algorithm at present, but the prior information of a channel needs to be acquired, which is difficult to obtain in a burst communication system, and the algorithm complexity is high due to the fact that a large number of matrix inversion processes are involved, and the hardware is very difficult to implement. Compared with an LS algorithm and an LMMSE algorithm, the channel estimation algorithm based on Discrete Fourier Transform (DFT) has certain improvement on noise processing, is not complex in hardware implementation, and has performance between the two, but the defects that the traditional DFT algorithm only removes noise outside a Cyclic Prefix (CP) and does not filter noise inside the Cyclic Prefix, and the problem of energy leakage existing under a non-integer time delay channel is not considered, so that the performance of the algorithm is influenced. In order to suppress the influence of noise in a cyclic prefix, most of the existing improved DFT algorithms set a threshold and a decision threshold in the cyclic prefix to filter noise sample points, and the selection of the threshold is generally based on energy values and amplitude modes of internal and external sample points of the cyclic prefix, and the threshold is set in a mean value, maximum value or median manner. Chinese patent CN104468426A proposes that the threshold corresponding to the lowest error probability of each tap detection is used as the filtering threshold in noise filtering, which can reduce the influence of channel energy leakage, but cannot solve the problem that effective data is filtered when the threshold is too large and noise is not filtered when the threshold is too small under the influence of sudden large impulse noise; in chinese patent CN201910212458.6, it is proposed that a noise sample point outside a cyclic prefix and a sample point inside the cyclic prefix are smoothed to obtain a noise filtering threshold, so as to solve the problem of filtering a useful sample point inside the cyclic prefix due to an excessively large threshold caused by line impulse noise interference, but not consider the problem of energy leakage under a non-integer time delay channel.
Disclosure of Invention
In view of this, an object of the present invention is to provide a channel estimation method suitable for a PDSCH in a 5G system, in which an average value of channel response amplitude modulo median and a maximum value of all sample points outside a cyclic prefix is taken as a threshold in the cyclic prefix to perform noise reduction operation, and a window function is added to simultaneously filter noise in the cyclic prefix and an influence of energy leakage existing under a non-integer time delay channel.
In order to achieve the purpose, the invention provides the following technical scheme:
a channel estimation method suitable for a PDSCH of a 5G system comprises the following steps:
s1: according to the LS algorithm idea, dividing the received pilot signal and the locally generated pilot signal to obtain a channel frequency domain response estimation value at the pilot frequency;
s2: multiplying the frequency domain response at the pilot frequency position estimated by the LS algorithm by a frequency domain window function, and then converting the frequency domain response to a time domain through inverse discrete Fourier transform;
s3: performing time domain noise reduction operation, including setting all sample points outside the cyclic prefix to zero, and taking the average value of the channel response amplitude modulo median and the maximum value of all the sample points outside the cyclic prefix as a threshold value threshold in the cyclic prefix to perform noise reduction operation;
s4: performing discrete Fourier transform on the time domain data points with the noise removed to convert the time domain data points into a frequency domain, and removing a window function to obtain all channel estimation frequency domain response results;
further, the step S1 specifically includes: channel frequency response H of pilot frequency subcarrier obtained by LS algorithmLSExpressed as:
HLS(k)＝Y(k)/X(k)＝H(k)+W(k)/X(k)
wherein, x (k) is a pilot signal sent by the sending end, y (k) is a pilot signal received by the receiving end, and w (k) is noise added in the channel transmission process.
Further, the step S2 includes the following steps:
s21: the pilot frequency domain response obtained by the LS estimation algorithm is multiplied by the window function to obtain Hw(k) Expressed as:
Hw(k)＝HLS(k)·C(k)
wherein C (k) is a frequency domain version of a Hamming window function.
S22: after a channel frequency response windowing function of a pilot frequency subcarrier is obtained by using an LS algorithm, a channel impulse response of an nth sample point is obtained by converting a pilot frequency domain channel response to a time domain by using inverse discrete Fourier transform to obtain an hLS(n), expressed as:
wherein 0. ltoreq. n.ltoreq.N-1, w (N) ═ IDFT (W (k)/X (k)).
Further, step S3 includes the steps of:
s31: after the pilot frequency domain channel response is converted to the time domain, all the sample points except the cyclic prefix are set to zero, and the method is represented as follows:
wherein N isCPIs the length of the cyclic prefix, hDFTAnd (N) is a sample point within the cyclic prefix after the cyclic prefix external noise reduction operation, L is the channel impulse response length, and N is the total length of the sample point.
S32: taking the channel response amplitude modulus median T of all sample points except the cyclic prefix1Expressed as:
T1＝mediam||hDFT-1(NCP)|,|hDFT-1(NCP+1)|,…,|hDFT-1-(N-1)|]
wherein the median function represents taking the median, and then taking the maximum value T of the amplitude modulus of the channel response of all sample points outside the cyclic prefix2Expressed as:
T2＝max[|hDFT-1(NCP)|,|hDFT-1(NCP+1)|,…,|hDFT-1(N-1)|]
where the max function represents taking the maximum value. Then, T is obtained1And T2The average value T of (A) is:
taking T as a threshold denoising threshold in the cyclic prefix, reserving sample points larger than the threshold and setting sample points smaller than the threshold to zero to obtain a denoised channel time domain response hnew-DFT(n)。
Further, step S4 specifically includes the following steps:
s41: the time domain data points from which noise is filtered are transformed to the frequency domain by discrete fourier transform, which is expressed as:
Hnew-DFT(k)＝DFT[hnew-DFT(n)]
s42: then removing the window function to obtain the whole frequency domain response result H of the channel estimation after the noise is filteredDFT(k) It can be expressed as:
HDFT(k)＝Hnew-DFT(k)/W(k)。
the invention has the beneficial effects that: and taking the average value of the median and the maximum value of the channel response amplitude modulus of all sample points outside the cyclic prefix as a threshold value threshold in the cyclic prefix to carry out noise reduction operation, effectively filtering noise in the cyclic prefix, and adding a window function to inhibit the influence of energy leakage in the inverse discrete Fourier transform process.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
fig. 1 is a flow chart of a DMRS pilot-based channel estimation system in a 5G OFDM system according to a preferred embodiment of the present invention;
fig. 2 is a flowchart of a channel estimation method applicable to PDSCH of 5G system according to the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
Please refer to fig. 1-2.
The invention is suitable for the 5G system shown in figure 1, and the pilot signal is a DMRS signal in the standard of the 3GPP 5G NR protocol under the assumption of a single antenna model. The binary data stream input at the transmitting end of the 5G OFDM system is modulated, converted from serial to parallel, inserted with pilot frequency and Inverse Fast Fourier Transform (IFFT) to convert the frequency domain data X of N sub-carriersL(k) Conversion into time-domain data xl(n) then a cyclic prefix is added, the length of which is typically greater than the maximum channel delay, and the IFFT transformation equation is as follows:
after parallel-serial conversion, the signal is sent to a multipath channel, and the impulse response of the channel can be expressed as:
where l is the number of paths of the multipath channel of the channel, aiIs the amplitude, τ, of the ith pathiIs the delay of the ith path. After the signal passes through the multipath fading channel and the cyclic prefix is removed, the time domain signal obtained by performing convolution operation on the signal and the channel response can be represented as follows:
wherein w (n) is additive white gaussian noise, and the frequency domain signal obtained by performing discrete fourier transform is:
YR(k)＝XL(k)H(k)+W(k),0≤k≤N-1
in the formula XL(k) Is a transmission signal, YR(k) Is the received signal, h (k) is the frequency response of the multipath channel, and w (k) is the noise response in the frequency domain.
Based on the system, with reference to fig. 1 and fig. 2, taking an example of a transmitting antenna port to a receiving antenna, the channel estimation method applicable to the PDSCH of the 5G system according to the present invention includes the following steps:
step S1: for frequency domain data Y of received signal in 5G systemR(k) The extracted pilot signal Y (k) is divided by the locally generated pilot signal X (k) to obtain the channel frequency domain response estimated value H at the pilot frequencyLS(k) Is as follows;
HLS(k)＝Y(k)/X(k)＝H(k)+W(k)/X(k)
where N is 0. ltoreq.n.ltoreq.n-1, and w (N) ═ IDFT (w (k)/x (k)) is a noise signal. The useful CIR of the channel is mainly concentrated on the first L sample points within the cyclic prefix and includes only noise outside the cyclic prefix, which can result in:
step S2: the pilot frequency domain response obtained by the LS estimation algorithm is multiplied by the window function to obtain Hw(k) Expressed as:
Hw(k)＝HLS(k)·C(k)
and C (k) is a frequency domain form of a Hamming window function, the Hamming window is simple and practical, is suitable for unknown signals, and is also suitable for received signals interfered by noise in an actual system, in addition, the sidelobe and the fluctuation of the Hamming window are smaller, the selectivity is higher, and the Hamming window can be known to more effectively inhibit the influence of energy leakage according to the principle that the smaller the sidelobe is, the less the leakage is, so the Hamming is selected by the window function.
LS algorithm obtains channel frequency response windowing function of pilot frequency subcarrier, then utilizes discrete Fourier inversion transformation to convert pilot frequency domain channel response to time domain to obtain channel impulse response of nth sampling point to obtain hLS(n), expressed as:
where N is 0. ltoreq. n.ltoreq.n-1, w (N) ═ IDFT (w (k)/x (k)), k is 0, and 1 … N-1 indicates the pilot subcarrier number.
Step S3: the method specifically comprises the following steps:
step 1: according to the traditional DFT algorithm principle, when n is larger than or equal to L, hLS(n) is 0. Therefore, the last N-L sampling points can be regarded as noise, and L is the channel impulse response length, so that after the pilot frequency domain channel response is converted into the time domain, all the sampling points except the cyclic prefix can be set to zero, which is expressed as:
step 2: taking the channel response amplitude modulus median T of all sample points except the cyclic prefix1Expressed as:
T1＝mediam[|hDFT-1(NCP)|,|hDFT-1(NCP+1)|,…,|hDFT-1(N-1)|]
then, the maximum value T of the amplitude modulus of the channel response of all the sample points outside the cyclic prefix is taken2Expressed as:
T2＝max[|hDFT-1(NCP)|,|hDFT-1(NCP+1)|,…,|hDFT-1(N-1)|]
finding T1And T2The average value T of (A) is:
taking T as cyclic prefixThe time domain response h of the channel after noise reduction can be obtained by reserving the sample points which are larger than the threshold value and setting the sample points which are smaller than the threshold value to zeronew-DFT(n), the noise reduction operation is:
step S4: h is to benew-DFT(n) converting to the frequency domain and removing the window function. In the algorithm process, after the pilot frequency domain response is obtained by LS estimation, the influence of signal energy leakage under the non-integer time delay channel on the threshold selection value is inhibited by adding a Hamming window, and the energy leakage of useful signals under the non-integer time delay channel can be inhibited by the frequency domain windowing effect, so that the estimation performance of the DFT improved algorithm is further improved. After the time domain noise reduction, the frequency domain response of all channels is obtained by discrete Fourier transform and division by a window function as follows:
HDFT(k)＝DFT[hnew-DFT]/C(k)
based on a basic module and a flow of a 5G OFDM system, DMRS is used as pilot signals, noise reduction operation outside a cyclic prefix and noise reduction operation inside the cyclic prefix are respectively carried out on the basis of a traditional DFT channel estimation algorithm, all the noise reduction operation outside the cyclic prefix is carried out by setting zero and noise reduction outside the cyclic prefix, then the average value of the median and the maximum value of channel response amplitude modes of all sample points outside the cyclic prefix is taken as a threshold value in the cyclic prefix for carrying out noise reduction operation, and noise components in the cyclic prefix are effectively filtered; meanwhile, a Hamming window is added before the inverse discrete Fourier transform is carried out, so that the influence of energy leakage on the channel estimation precision in the inverse discrete Fourier transform process is restrained.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (6)
1. A channel estimation method suitable for a PDSCH of a 5G system is characterized in that: the method comprises the following steps:
s1: according to the LS algorithm idea, dividing the received pilot signal and the locally generated pilot signal to obtain a channel frequency domain response estimation value at the pilot frequency;
s2: multiplying the frequency domain response at the pilot frequency position estimated by the LS algorithm by a frequency domain window function, and then converting the frequency domain response to a time domain through inverse discrete Fourier transform;
s3: performing time domain noise reduction operation, including setting all sample points outside a cyclic prefix to zero, and filtering noise sample points in the cyclic prefix based on a threshold;
s4: and converting the data points after noise reduction into all channel frequency domain response results.
2. The channel estimation method for the PDSCH of the 5G system according to claim 1, wherein: in S1, the pilot signal selects a DMRS reference signal in a 3GPP protocol R15 release 5G system.
3. The channel estimation method for the PDSCH of the 5G system according to claim 2, wherein: in S2, the window function selects a hamming window to be expressed as:
4. the method of claim 3, wherein the method comprises: the S2 specifically includes the following steps:
s21: firstly, an LS algorithm is used for obtaining channel frequency response of a pilot frequency subcarrier, and then discrete Fourier inversion is used for converting the channel response of a pilot frequency domain into a time domain to obtain channel impulse response of an nth sampling point;
s22: and multiplying the pilot frequency domain response obtained by the LS estimation algorithm by a window function.
5. The method of claim 4, wherein the method comprises: the S3 specifically includes the following steps:
s31: setting all sample points outside the time domain cyclic prefix to zero to eliminate noise components outside the cyclic prefix;
s32: and taking the average value of the channel response amplitude modulo median and the maximum value of all sample points outside the cyclic prefix as a threshold in the cyclic prefix to carry out noise reduction operation and filter noise components in the cyclic prefix.
6. The method of claim 5, wherein the method comprises: the S4 specifically includes the following steps:
s41: performing discrete Fourier transform on the time domain data points with the noise removed to convert the time domain data points into a frequency domain;
s42: in the windowing operation, the frequency domain data obtained in S41 is divided by the window function of the frequency domain to obtain the entire channel frequency domain response.
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