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WO2012041047A1 - Procédé d'estimation de canal et station de base - Google Patents

Procédé d'estimation de canal et station de base Download PDF

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Publication number
WO2012041047A1
WO2012041047A1 PCT/CN2011/072467 CN2011072467W WO2012041047A1 WO 2012041047 A1 WO2012041047 A1 WO 2012041047A1 CN 2011072467 W CN2011072467 W CN 2011072467W WO 2012041047 A1 WO2012041047 A1 WO 2012041047A1
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WO
WIPO (PCT)
Prior art keywords
carrier
channel estimation
mean square
base station
signal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2011/072467
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English (en)
Chinese (zh)
Inventor
吴昊
肖辉
杨芸霞
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ZTE Corp
Original Assignee
ZTE Corp
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Filing date
Publication date
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Publication of WO2012041047A1 publication Critical patent/WO2012041047A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • 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
    • H04L25/0204—Channel estimation of multiple channels
    • 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
    • H04L25/0228—Channel estimation using sounding signals with direct estimation from sounding signals
    • H04L25/023—Channel estimation using sounding signals with direct estimation from sounding signals with extension to other symbols
    • H04L25/0232—Channel estimation using sounding signals with direct estimation from sounding signals with extension to other symbols by interpolation between sounding signals
    • 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
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/025—Channel estimation channel estimation algorithms using least-mean-square [LMS] method

Definitions

  • the present invention relates to the field of communications, and in particular, to a channel estimation method and a base station.
  • Orthogonal Frequency Division Multiplexing Orthogonal Frequency Division Multiplexing (Orthogonal Frequency Division)
  • OFDM Multiplexing
  • DAB Digital Audio Broadcasting
  • DVD Digital Video Broadcasting
  • HDTV High Definition Television
  • WLAN Wireless Local Area Network
  • WMAN Wireless Metropolitan Area Network
  • Beamforming can be applied to OFDM systems to improve system performance. The use of this technique requires knowledge of the channel information.
  • a mobile station (MS for short) can transmit a Sounding signal to a base station (BS), which enables the BS to know the channel response of the BS to the MS by using the nature of channel reciprocity.
  • BS base station
  • the algorithms for channel estimation of Sounding signals mainly include Least Squares (LS) channel estimation algorithm and Minimum Mean Square Error (MMSE) channel estimation algorithm.
  • the LS channel estimation algorithm is simple to implement, but the estimation accuracy is not high, and it is susceptible to Gaussian noise, especially in the case of low signal-to-noise ratio.
  • the MMSE channel estimation algorithm can achieve better performance.
  • the inventors have found that the MMSE channel estimation method in the related art has a high computational complexity.
  • the present invention provides a channel estimation scheme to solve the problem of high computational complexity of MMSE channel estimation in the related art.
  • a channel estimation method comprising: receiving, by a base station, a sounding signal transmitted by a terminal on each carrier, according to the received probe The measured signal and the preset detection signal corresponding to the terminal perform least square channel estimation on each carrier; the base station acquires a correlation coefficient between each carrier according to the result of the least square channel estimation; the base station obtains a mean square delay according to the correlation coefficient, and The mean square delay and the signal-to-noise ratio of the carrier are quantized, and the filter matrix corresponding to the quantized result is searched for in the correspondence between the preset quantized value and the filter matrix; the base station obtains the result of the filter matrix and the least square channel estimation Minimum mean square error channel estimation for each carrier; base station performs linear interpolation using minimum mean square error channel estimation to obtain channel estimates for each carrier on all frequency bands.
  • the base station obtains the mean square delay according to the correlation coefficient.
  • R ⁇ (a /) [Delta] represents the actual correlation coefficients spaced carriers / bearers of a detection signal, averaging £ m represents for each carrier, ⁇ ( ⁇ /) denotes estimated to be ⁇ apart correlation / bearers of a carrier detection signal, ⁇ ⁇ 2 is the noise carrier.
  • a fa O is the vector of the element.
  • the base station performs linear interpolation using the minimum mean square error channel estimation, and obtaining channel estimates for each carrier on all frequency bands includes: calculating channel estimates A fe of each carrier on all frequency bands according to the following formula:
  • H' e (w) ( ⁇ --)H' m (bP + ) +—H' m ((b + ⁇ )P +
  • H lm (bP + ) is the minimum mean square error channel estimate
  • the base station performs linear interpolation using the minimum mean square error channel estimation, and obtaining the channel estimation of each carrier on all frequency bands includes: calculating the channel estimation A fe 0 of each carrier in all frequency bands according to the following formula:
  • H le (w) (1 - ⁇ -) H lm (dD + g) + -H l '"((d + ⁇ )D + g)
  • a base station includes: a receiving module, configured to receive a sounding signal sent by a terminal on each carrier; and a first estimating module configured to receive according to the received The detection signal and the preset detection signal corresponding to the terminal perform least square channel estimation on each carrier; the first obtaining module is configured to acquire correlation coefficients between the respective carriers according to the result of the least square channel estimation; the second acquiring module, setting To obtain a mean square delay according to the correlation coefficient, and quantize the mean square delay and the signal-to-noise ratio of the carrier, and search for a filter matrix corresponding to the quantized result in a correspondence between the preset quantized value and the filter matrix; An estimation module, configured to obtain a minimum mean square error channel estimate
  • FIG. 1 is a flowchart of a channel estimation method according to an embodiment of the present invention
  • FIG. 2 is a preferred flow of an MMSE channel estimation method according to an embodiment of the present invention
  • Figure 3 is a block diagram showing the structure of a base station according to an embodiment of the present invention
  • Figure 4 is a block diagram showing the structure of an MMSE channel estimation apparatus based on feature parameter quantization according to an embodiment of the present invention.
  • Step S102 The base station receives the sounding signal sent by the terminal on each carrier, and performs least square channel estimation on each carrier according to the received sounding signal and the preset sounding signal corresponding to the terminal;
  • Step S104 The base station acquires correlation coefficients between the respective carriers according to the result of the least square channel estimation.
  • Step S106 The base station obtains a mean square delay according to the correlation coefficient, and quantizes the mean square delay and the signal to noise ratio of the carrier.
  • a filter matrix corresponding to the quantized result in a corresponding relationship between the preset quantized value and the filter matrix; for example, a table corresponding to the filter matrix and the quantized value is stored in the base station in advance, and the filter corresponding to the quantized result is obtained by looking up the table a matrix; step S108, the base station obtains a minimum mean square error channel estimation of each carrier according to a result of the filter matrix and the least square channel estimation; and step S110, the base station performs linear interpolation using a minimum mean square error channel estimation to obtain each carrier in all frequency bands. Channel estimation.
  • the corresponding relationship between the parameter quantization value and the filter matrix is stored in advance, and the filter matrix is obtained by finding the correspondence relationship.
  • Step 4 S S 106 can also use the following implementation:
  • R pp is the correlation coefficient obtained above
  • ⁇ is the R transpose conjugate of the matrix
  • RR ⁇ -1 is The inverse matrix of RR ff .
  • the mean square delay can be calculated by the following method:
  • the interval between the number of carriers between each carrier, R ⁇ (A/;> is the correlation coefficient, and ⁇ ⁇ is the mean square delay.
  • jt ⁇ in step 4 S S 106, the signal-to-noise ratio of the mean square delay and the carrier can be quantized by: Using the following formula to obtain a vector quantization neighbor condition:
  • step 4 S102 can be implemented in the following manner:
  • the base station uses the formula ⁇ O CB ⁇ B ⁇ R to perform least square channel estimation, where A fa (m) is the result of the least square channel estimation, and is the carrier carrying the sounding signal.
  • R pp ⁇ M R pp ⁇ M), where R ⁇ (A/) represents the actual correlation coefficient of the carrier separated by ⁇ /bearing detection signals, and £ m represents the average of each carrier A, ⁇ ( ⁇ /) indicates the estimated correlation coefficient of the carrier separated by ⁇ /bearing detection signals, ⁇ 2 is the noise of each carrier.
  • channel estimation is performed using R pp ( ⁇ /).
  • each carrier on all frequency bands can be calculated according to the following formula
  • H le (w) (1 -—) H lm (bP + ) +—H' m ((b + ⁇ )P + )
  • H' m (bP + ) is the minimum mean square error channel estimate.
  • Step 201 Perform LS channel estimation on each carrier that carries the Sounding signal according to the Sounding signal and the received signal.
  • the signal is a signal sent by the terminal to the base station, and the sounding signal corresponding to the terminal is pre-stored in the base station, and the received signal is a Sounding signal transmitted through the channel.
  • the Sounding signal may change, and the received signal may be sent by the terminal.
  • the sent Sounding sequence Decimation method is orthogonal by Frequency Division Multiplexing (FDM), and the sent Sounding sequence 'J Cyclic mode is orthogonal to the Code Division Multiplexing (CDM) method.
  • FDM Frequency Division Multiplexing
  • CDM Code Division Multiplexing
  • Step 203 Calculate a correlation coefficient between carriers carrying the So Ding signal by using the LS channel estimation obtained in the previous step.
  • H k (m) H(m) + N(m) (3)
  • HO the true channel response
  • ⁇ ( ⁇ noise.
  • R (M) R (M) + R Z (M)
  • ⁇ ( ⁇ /) the correlation coefficient between the estimated carriers carrying the ding signal, which is the correlation coefficient between the true bearer signal carriers.
  • R Z (A/) the correlation coefficient between noises. If the noise on the different carriers carrying the Sounding signal is assumed to be uncorrelated, then it can be known that 3 ⁇ 4( /) is an i to function. Therefore, the formula (4) can be expressed as follows:
  • Step 205 Estimate the parameters of the channel at this time based on the correlation coefficient of the carrier carrying the Sounding signal: Mean Square Delay. The two characteristic parameters of delay and signal-to-noise are quantized, and the filter matrix is obtained by looking up the table. Specifically, in the following derivation process, a main assumption is made: Multipath power attenuation obeys a negative exponential distribution. It can be obtained by simulation and actual measurement. Assuming that the multipath power attenuation is distributed from a negative exponent, the power delay distribution can be approximated by equation (6):
  • the filter matrix can be calculated in advance.
  • the matrix can be obtained by looking up the table to avoid real-time matrix inversion and matrix.
  • the multiplication operation greatly reduces the operation.
  • the following describes the process of parameter quantization.
  • the distortion of the overall quantization can be expressed as follows: F Use the Frobenius norm to measure the quantization distortion of the matrix . Let /) for ⁇ , the partial guide of 0, can get the following formula:
  • a new vector quantization proximity condition can be obtained by solving the nonlinear equations (11) and (12). According to this new vector quantization proximity condition and centroid condition (select a point in the quantization interval [x ⁇ xJUD,, ⁇ .), the average distortion of the vector falling within the above quantization interval to be the smallest),
  • the LBG ( Linde-Buzo-Gray ) algorithm can obtain the optimal parameter quantization of mean square delay and signal to noise ratio.
  • Unconstrained optimal parameter quantization has no limit on the quantization order of each parameter. In the case where the storage space is limited, it is necessary to limit the quantization order of each parameter to meet the limitation of the storage space. At the same time, it is necessary to minimize the quantization distortion to improve system performance.
  • Step 207 Calculate the LS
  • the channel estimation and filtering matrix calculates the MMSE channel estimation of each carrier carrying the Sounding signal.
  • the MMSE channel estimation based on the sounding signal can be applied as follows:
  • the channel estimation of the carrier on all frequency bands is obtained by linear interpolation by the following formula: For the Sounding sequence
  • H' e (w) ( ⁇ --)H' m (bP + ) +—H' m ((b + ⁇ )P + (17)
  • P-l b is an integer satisfying the condition bP ⁇ w ⁇ (b + l)P
  • H lm (bP + ) is the MMSE channel estimate determined in step 20 ⁇ .
  • H ls (w) (1 - ⁇ -) H lm (dD + g) + ⁇ -H ⁇ d + ⁇ )D + g)
  • w is the position of the carrier
  • g is the actual decimal offset
  • the A' m (6E» + g) is the MMSE channel estimation determined in step 207.
  • the embodiment of the present invention further provides a base station, which is used to implement the foregoing method.
  • Figure 3 is a base station according to an embodiment of the present invention.
  • the base station includes: a receiving module 302, configured to receive a sounding signal sent by the terminal on each carrier; a first estimating module 304 coupled to the receiving module 302, configured to receive the detected signal according to the received signal The least square channel estimation is performed on each carrier with the preset detection signal corresponding to the terminal.
  • the first obtaining module 306 is coupled to the first estimation module 304, and is configured to acquire each carrier according to the result of the least square channel estimation and the noise of each carrier.
  • the second obtaining module 308 is configured to use the calculation of the mean square p p 1 + 2 ⁇ / ⁇ ⁇ ⁇ delay, where J is the frequency interval of the adjacent carriers, and ⁇ / is the number of carriers between the carriers.
  • the interval, R ⁇ (A/) is the correlation coefficient, and ⁇ ⁇ is the mean square delay.
  • an MMSE channel estimation apparatus based on a sounding signal is further provided.
  • FIG. 4 is a structural block diagram of an MMSE channel estimation apparatus based on feature parameter quantization according to an embodiment of the present invention, as shown in FIG.
  • the LS channel estimation module 401 corresponds to the first estimation module.
  • the respective carriers of the Sounding signal perform LS channel estimation;
  • the correlation coefficient calculation module 403, corresponding to the first obtaining module 306, calculates the correlation coefficient between the carriers carrying the Sounding signal by using the LS channel estimation of the carrier carrying the Sounding signal;
  • the delay estimation and feature parameter quantization module 405, corresponding to the second acquisition module 308, estimates the parameters that can characterize the channel characteristics according to the correlation coefficient of the carrier carrying the Sounding signal: a mean square delay.
  • the MMSE channel estimation module 407 corresponding to the second estimation module 310, is configured to calculate the MMSE channel estimation of each carrier carrying the Sounding signal according to the LS channel estimation and the filtering matrix; linear interpolation Module 409, corresponding to the third estimation module 312, is set to root According to the MMSE channel estimation of each carrier carrying the Sounding signal, linear interpolation is performed to obtain channel estimation of carriers on all frequency bands.
  • the MMSE channel estimation method based on the sounding signal provided by the present invention has the following advantages: 1. When only the LS channel estimation and the linear interpolation channel estimation method are used, the channel response of the carrier position carrying the Sounding signal is susceptible to The effect of Gaussian noise.
  • the channel response on the other carriers is calculated according to the channel response at the carrier position of the sounding signal obtained by the LS channel estimation, and the performance of the system is significantly reduced.
  • part of the noise can be removed by the filter matrix, so that the channel estimation of other carriers is closer to the real channel response, and the performance of the system is also improved.
  • the derivation can be obtained by the following two parameters: Mean Square Delay and Signal Power Ratio Noise Power.
  • the MMSE channel estimation method based on the sounding signal provided by the invention quantizes the two parameters and calculates the filter matrix in advance. In this way, when performing MMSE channel estimation, the filter interpolation matrix can be obtained by looking up the table, thereby avoiding matrix inversion and matrix multiplication in real time, which greatly reduces the operation. Moreover, the channel signal response can be obtained more accurately by using the sounding signal-based MMSE channel estimation method according to the present invention. Compared with the LS channel estimation algorithm and the linear interpolation channel estimation algorithm, the embodiment of the present invention can obtain a performance improvement of about 1.0 dB without significantly increasing the operation.
  • modules or steps of the present invention can be implemented by a general-purpose computing device, which can be concentrated on a single computing device or distributed over a network composed of multiple computing devices. Alternatively, they may be implemented by program code executable by the computing device, such that they may be stored in the storage device by the computing device and, in some cases, may be different from the order herein.
  • the steps shown or described are performed, or they are separately fabricated into individual integrated circuit modules, or a plurality of modules or steps are fabricated as a single integrated circuit module.
  • the invention is not limited to any specific combination of hardware and software.
  • the above is only the preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes can be made to the present invention. Where in the invention ⁇ " God and Within the principles, any modifications, equivalent substitutions, improvements, etc., are intended to be included within the scope of the present invention.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

L'invention porte sur un procédé d'estimation de canal et une station de base, le procédé comprenant les opérations suivantes : la station de base reçoit des signaux de sondage émis sur chaque porteuse par un terminal, et effectue l'estimation de canal par moindres carrés pour chaque porteuse conformément aux signaux de sondage reçus et aux signaux de sondage préréglés qui correspondent au terminal; la station de base obtient des coefficients de corrélation entre les porteuses conformément au résultat de l'estimation de canal par moindres carrés; la station de base obtient des temps de propagation carrés moyens conformément aux coefficients de corrélation, quantifie les temps de propagation carrés moyens et les rapports signal sur bruit des porteuses, et recherche une matrice de filtrage correspondant au résultat de quantification dans la relation de correspondance entre des valeurs de quantification prédéfinies et des matrices de filtrage; la station de base obtient l'estimation de canal à erreur quadratique moyenne minimale de chaque porteuse conformément à la matrice de filtrage et au résultat de l'estimation de canal par moindres carrés; et la station de base effectue une interpolation linéaire avec l'estimation de canal à erreur quadratique moyenne minimale et obtient l'estimation de canal de chaque porteuse sur toutes les bandes de fréquence. La complexité de calcul est réduite par l'invention.
PCT/CN2011/072467 2010-09-29 2011-04-06 Procédé d'estimation de canal et station de base Ceased WO2012041047A1 (fr)

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CN201010505976.6 2010-09-29
CN201010505976.6A CN102437976B (zh) 2010-09-29 2010-09-29 信道估计方法及基站

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CN104486266B (zh) * 2014-12-12 2018-07-03 北京思朗科技有限责任公司 一种基于mimo-ofdm系统的信道估计方法及装置
CN106161290B (zh) * 2015-03-23 2020-07-07 中兴通讯股份有限公司 一种流间干扰计算方法、装置及通信系统
CN106713188B (zh) * 2015-11-13 2020-05-29 中兴通讯股份有限公司 信道响应的获取方法和装置
US10708002B2 (en) * 2017-08-02 2020-07-07 Apple Inc. Adaptive channel estimation for power optimization for narrow band systems
CN120185970B (zh) * 2025-05-19 2025-09-05 杰创智能科技股份有限公司 信道估计方法、装置、设备、介质及产品

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