WO2015196384A1 - Procédé, dispositif, et système d'estimation de canaux conjoints épars - Google Patents

Procédé, dispositif, et système d'estimation de canaux conjoints épars Download PDF

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Publication number
WO2015196384A1
WO2015196384A1 PCT/CN2014/080717 CN2014080717W WO2015196384A1 WO 2015196384 A1 WO2015196384 A1 WO 2015196384A1 CN 2014080717 W CN2014080717 W CN 2014080717W WO 2015196384 A1 WO2015196384 A1 WO 2015196384A1
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joint
joint sparse
channel
base station
channel estimation
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PCT/CN2014/080717
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Chinese (zh)
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戚晨皓
朱鹏程
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东南大学
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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

Definitions

  • the present invention relates to a wireless communication system, and more particularly to a joint sparse channel estimation method, apparatus and system.
  • the basic feature of the multi-antenna wireless communication system is that a certain number of antennas are configured in the base station, and the mobile phone users within the coverage of the base station are only configured with a single antenna because of the size of the mobile phone; multi-input and single output from the base station to the mobile phone (Multi- Downlink transmission of Input Single-Output, MIS0), uplink transmission of Single-Input Multi-Output (SIM0) from mobile phone to base station.
  • MIS0 Multi- Downlink transmission of Input Single-Output
  • SIM0 Single-Input Multi-Output
  • the first method is that the base station transmits a pilot, and the mobile phone uses the received pilot to perform channel estimation, acquires downlink channel information, and feeds it back to the base station, which is usually used for frequency division duplex (FDD).
  • the second method is that the mobile phone transmits a pilot, and the base station uses the received pilot to perform channel estimation to obtain uplink channel information, because in the Time-Duplex Division (TDD) system, the uplink channel and the downlink channel With reciprocity, the base station also acquires downlink channel information, which is usually used in TDD systems.
  • TDD Time-Duplex Division
  • LTE and LTE-Advanced usually use Orthogonal Frequency Division Multiplexing (OFDM) technology for downlink transmission and single carrier frequency division multiple access for uplink transmission ( Single -carrier Frequency-division Multiple Access, SC-FDMA) technology.
  • OFDM Orthogonal Frequency Division Multiplexing
  • SC-FDMA Single -carrier Frequency-division Multiple Access
  • CIR Channel Impulse Response
  • MMSE Mean Square Errors
  • the time at which signals from different antennas of the base station are simultaneously transmitted to the mobile phone are approximately the same, and the signals transmitted from the mobile phone to the different antennas of the base station are approximately the same, that is, corresponding to different base station antennas.
  • the positions of the non-zero elements of the CIR sequence of different channels can be considered to be the same, and the values of the non-zero elements are different. Therefore, the information of the same location of the non-zero elements can be fully utilized, and the joint sparse channel estimation of the multiple channels can be performed to acquire the channel information.
  • the receiver usually performs separate channel estimation for each channel by using the received pilot and the transmitted pilot, and the related art utilizes the sparsity of the channel for separate sparse channel estimation, but there is no technology yet.
  • the multi-channel joint sparse channel estimation is implemented by using the information that multiple channels have the same non-zero element position. Therefore, the pilot overhead of the prior art is still large.
  • the present invention provides an efficient channel estimation method and apparatus for a multi-antenna wireless communication system, which can perform joint sparse channel estimation on multiple channels, improve channel estimation accuracy, and reduce pilot overhead.
  • the present invention provides a joint sparse channel estimation method, which includes the following steps:
  • step S2 the following steps are further included:
  • initializing residuals are joint observation values of the joint sparse reconstruction model, normalizing each column of the joint observation matrix of the joint sparse reconstruction model, initializing the selection into an empty set, and setting a loop number to 0, wherein Normalization refers to an operation that makes the square of the modulus of all elements of the column one;
  • S22 determining whether the power of the residual is greater than a product of a noise variance and a square of the number of base station antennas, determining whether the number of cycles is less than the channel length, and if both are, performing S23; otherwise, executing S24;
  • the invention also provides a joint sparse channel estimation apparatus, comprising:
  • a joint sparse vector computing unit for solving positions of all non-zero element blocks of the joint sparse vector of the joint sparse reconstruction model
  • An information acquiring unit configured to solve a value of a non-zero element of each of the channels.
  • the joint sparse vector computing unit further includes:
  • An initialization module configured to initialize joint observations whose residuals are joint sparse reconstruction models, normalize each column of the joint observation matrix of the joint sparse reconstruction model, initialize the selection into an empty set, and set a loop number to 0;
  • a determining module configured to determine whether the power of the residual is greater than a product of a noise variance and a square of the number of base station antennas, and determine whether the number of cycles is less than a channel length, and if both are, performing an update module; otherwise, executing an output module;
  • Update module used to update the residuals and selections, and the number of cycles is increased by 1;
  • An output module that sequentially outputs all elements of the ensemble as the locations of all non-zero element blocks of the joint sparse vector.
  • the present invention also provides a joint sparse channel estimation system, comprising: setting the joint sparse channel estimation apparatus in an uplink transmission or a downlink transmission of the system.
  • the uplink transmission includes: the data of the mobile terminal is sequentially transmitted through the constellation point mapping, the fast Fourier transform, the inserted pilot, the subcarrier mapping, the inverse fast Fourier transform, the insertion guard interval, and the up-conversion, and then sent to the wireless channel to reach the base station.
  • the transmission data is extracted after down-conversion, de-protection interval, fast Fourier transform, sub-carrier demapping, joint sparse channel estimation, channel equalization, inverse fast Fourier transform, and constellation point de-mapping.
  • the downlink transmission comprises: the data of the base station is sequentially transmitted through the constellation point mapping, the insertion pilot, the subcarrier mapping, the inverse fast Fourier transform, the insertion protection interval, and the up-conversion, and then sent to the wireless channel, and after reaching the mobile phone, sequentially After down-conversion, de-protection interval, fast Fourier transform, sub-carrier demapping, joint sparse channel estimation, channel equalization, and constellation point de-mapping, the transmitted data is extracted.
  • the present invention is used for joint sparse channel estimation on multiple channels.
  • FIG. 2 is a flow chart of S2 of Figure 1 of the present invention.
  • FIG. 3 is a schematic structural diagram of a joint sparse channel estimation apparatus according to the present invention.
  • FIG. 4 is a schematic diagram of transmission of a SIMO multi-antenna system used in Embodiment 1 of the present invention.
  • FIG. 5 is a block diagram of an SC-FDMA system according to Embodiment 1 of the present invention.
  • FIG. 7 is a schematic diagram of transmission of a MISO multi-antenna system used in Embodiment 2 of the present invention.
  • FIG. 8 is a block diagram of an OFDM system according to Embodiment 2 of the present invention.
  • FIG. 9 is a comparison of the mean square error performance of the second embodiment of the present invention with the single-sparse channel estimation for each channel in the prior art.
  • S1 Establish a joint sparse reconstruction model, and combine multiple channels into one joint sparse vector;
  • FIG. 2 is a flow chart of S2 of FIG. 1 of the present invention, which includes the following steps:
  • initializing residuals are joint observation values of the joint sparse reconstruction model, normalizing each column of the joint observation matrix of the joint sparse reconstruction model, initializing the selection into an empty set, and setting a loop number to 0, wherein Normalization refers to an operation that makes the square of the modulus of all elements of the column one;
  • S22 determining whether the power of the residual is greater than a product of a noise variance and a square of the number of base station antennas, determining whether the number of cycles is less than the channel length, and if both are, performing S23; otherwise, executing S24;
  • S24 All elements in the selection set are sequentially output as positions of the all non-zero element blocks of the joint sparse vector.
  • 3 is a schematic structural diagram of a joint sparse channel estimation apparatus according to the present invention. The device consists of the following 3 units:
  • a joint sparse vector calculation unit for solving the positions of all non-zero element blocks of the joint sparse vector of the joint sparse reconstruction model.
  • An information acquisition unit configured to solve a value of a non-zero element of each of the channels.
  • the joint sparse vector computing unit further includes the following four modules:
  • an initialization module for initializing joint observations whose residuals are joint sparse reconstruction models, normalizing each column of the joint observation matrix of the joint sparse reconstruction model, initializing the selection into an empty set, and setting the number of loops to 0.
  • a judging module configured to determine whether the power of the residual is greater than a product of the noise variance and the number of base station antennas, determine whether the number of loops is less than the channel length, and if both are, execute the update module; otherwise, execute the output module.
  • the invention relates to a joint sparse channel estimation system, characterized in that, in the uplink transmission or the downlink transmission of the system, a device as shown in FIG. 3 is provided, and correspondingly, the system will be in the first embodiment and the second embodiment of the present invention. Explain separately.
  • the uplink transmission refers to that, in the coverage of the base station, the mobile phone configured with a single antenna transmits a signal, and the base station receives the signal. It is assumed that the base station is configured with M antennas (M is a positive integer and M > 1), and each antenna corresponds to one uplink channel. In order to estimate the uplink channel, the mobile phone transmits a pilot, and the base station estimates the M channels by using the received pilot, and the computational complexity is proportional to M. In the TDD system, the uplink channel and the downlink channel have reciprocity, and once the base station acquires the uplink channel information, the downlink channel information is acquired.
  • Embodiment 1 of the present invention performs joint sparse channel estimation on multiple uplink channels to reduce pilot resource overhead.
  • the downlink transmission refers to the base station communicating with the mobile phone configured with a single antenna within its coverage range.
  • the signal is sent by the base station, and the mobile phone receives the signal to complete the downlink transmission.
  • M is a positive integer, and M> l
  • each antenna corresponds to one downlink channel.
  • FDD is another mainstream technology in addition to TDD.
  • the base station transmits pilots, and the mobile phone estimates the M channels by using the received pilot.
  • the M pilots transmitted by the base station In order to effectively distinguish the M pilots received by the single antenna of the mobile phone, the M pilots transmitted by the base station must be orthogonal in the time domain, the frequency domain, or the code domain.
  • Embodiment 2 of the present invention performs joint sparse channel estimation on multiple downlink channels, and reduces pilot resource overhead.
  • FIG. 4 is a schematic diagram of transmission of a snro multi-antenna system according to Embodiment 1 of the present invention.
  • the signal transmitted by the mobile phone after reflection from multiple buildings, reaches the base station, forming multipath effects and causing inter-symbol interference.
  • LTE and LTE-Advanced adopt SC-FDMA, which can effectively fight wireless. Multipath effects in propagation simplify the design of the equalizer.
  • FIG. 5 is a block diagram of an SC-FDMA system in accordance with a first embodiment of the present invention.
  • the data of the mobile terminal is processed by constellation point mapping, Fast Fourier Transform (FFT), insertion pilot, subcarrier mapping, Inverse Fast Fourier Transform (IFFT), insertion guard interval and up-conversion.
  • FFT Fast Fourier Transform
  • IFFT Inverse Fast Fourier Transform
  • the transmission data is extracted after being subjected to down-conversion, removal of guard interval, FFT, sub-carrier demapping, joint sparse channel estimation, channel equalization, IFFT, and constellation point de-mapping.
  • SC-FDMA performs FFT before the IFFT and subcarrier mapping on the transmitting end, which can effectively suppress the peak-to-average ratio of the signal and reduce the burden on the mobile power amplifier. It should be noted that the present invention employs joint sparse channel estimation instead of the individual sparse channel estimation for each channel in the prior art.
  • FIG. 1 is a flow chart of a method for acquiring channel information of a multi-antenna wireless communication system according to the present invention. Referring to Figure 1, the method includes:
  • S1 Establishing a joint sparse reconstruction model, combining all channels to be estimated into one joint sparse direction.
  • the number of SC-FDMA subcarriers is N
  • the number of pilots used is (0 ⁇ ⁇ ⁇ ⁇ )
  • the subcarrier index corresponding to each pilot subcarrier is ⁇ 2 , ... , ⁇ ⁇ ( 1 ⁇ ⁇ ⁇ 2 ⁇ ... ⁇ ⁇ N )
  • the pilot symbol transmitted by the mobile phone is represented as ⁇ ( ⁇ 1 ), ⁇ ( ⁇ 2 ), - , ⁇ ( ⁇ ) ⁇
  • the mobile phone sends a pilot symbol, and the base station will receive a different pilot. Symbols, corresponding to M different upstream channels.
  • the base station Since the base station knows the pilot symbols transmitted by the mobile phone, after receiving the M different pilot symbols, the base station performs channel estimation on the M channels, and uses the channel estimation result for subsequent channel equalization.
  • the pilot symbol received by the ith antenna of the base station is represented as a column vector
  • the joint sparse reconstruction model can be expressed as
  • the present invention first uses the joint observation value Z and the joint observation matrix ⁇ to solve the positions of all non-zero element blocks of the joint sparse vector IV, and then separately solves the values of the non-zero elements of each channel.
  • the base station uses the joint sparse reconstruction model formula (3) to solve the positions of all the non-zero element blocks of the joint sparse vector ,.
  • the flow refers to FIG. 2, and the method includes:
  • anthology ⁇ to store the locations of non-zero element blocks that are obtained in turn. Since 17 and IV exhibit the same block-like sparse structure, the position of the non-zero element can be represented by the index of the non-zero element block, thus,
  • the index of a non-zero element block in V directly corresponds to the index of the non-zero element in Central.
  • Set the number of loops ⁇ 0.
  • S22 Determine whether the power of the residual is greater than a product of the noise variance and the square of the number of base station antennas, and determine whether the number of cycles is less than the channel length. If both are, perform S23; otherwise, execute S24.
  • the element that satisfies the above condition is denoted by /, adding / adding to the selection and updating the selection ⁇ ⁇ U ⁇ / ⁇ , where the superscript - 1 indicates the matrix inversion and the superscript indicates the conjugate transpose.
  • the definition ⁇ ⁇ is a matrix composed of blocks of ⁇ corresponding to the elements in the selection ,, and the new residual is simultaneously, and the number of cycles is increased by 1, that is, ⁇ + 1.
  • S24 sequentially output all elements in the ensemble as all non-zero element blocks of the joint sparse vector
  • the definition ⁇ is a matrix composed of 4 columns corresponding to the elements in the selection , and the column vector composed of the non-zero elements of the i-th upstream channel is
  • FIG. 3 is a schematic structural diagram of a joint sparse channel estimation apparatus according to the present invention.
  • the device consists of the following 3 units:
  • a joint sparse vector calculation unit for solving the positions of all non-zero element blocks of the joint sparse vector of the joint sparse reconstruction model.
  • An information acquisition unit configured to solve a value of a non-zero element of each of the channels.
  • the joint sparse vector computing unit further includes the following four modules:
  • an initialization module for initializing joint observations whose residuals are joint sparse reconstruction models, normalizing each column of the joint observation matrix of the joint sparse reconstruction model, initializing the selection into an empty set, and setting the number of loops to 0.
  • a judging module configured to determine whether the power of the residual is greater than a product of the noise variance and the number of base station antennas, determine whether the number of loops is less than the channel length, and if both are, execute the update module; otherwise, execute the output module.
  • the number of pilot subcarriers 16
  • the pilot subcarrier index 5 ⁇ , ., . is [8, 40, 48, 52, 72, 82, 99, 142, 145, 154, 158, 161, 183, 209, 212, 230].
  • QPSK modulation is used.
  • Mobile phone send 1 For the pilot symbols, the base station receives 8 pilot symbols at the same time, and the base station needs to estimate the values of the non-zero elements of the 8 channels and the values of the non-zero elements.
  • Table 1 also shows the performance comparison when using the present invention to perform joint sparse channel estimation for 2 out of 8 channels, 4 out of 8 channels, and 6 out of 8 channels, it is not difficult to find that The more the number of channels estimated by the joint sparse channel, the easier it is to accurately estimate the position of the channel non-zero element, indicating that the larger the size of the antenna array system, the more obvious the beneficial effect of the present invention, because it utilizes multiple sparse channels non-zero. The a priori information of the same element position, so that the position of the non-zero element can be obtained more accurately.
  • FIG. 6 is a comparison of mean square error performance of a single sparse channel estimation for each channel of the present invention and the prior art. According to the position of the non-zero element of the channel CIR sequence obtained according to Table 1, the value of the non-zero element is obtained. Define Mean Square Errors (MSE) as
  • FIG. 6 shows the performance comparison of the joint sparse channel estimation for two of the eight channels, four of the eight channels, and six of the eight channels using the present invention. It can be seen that the more channels that perform joint sparse channel estimation, the better the MSE performance.
  • the pilot subcarrier index 5 is [4] , 8, 12, 16, 24, 27, 34, 39, 49, 74, 76, 81, 88, 101, 104, 109, 125, 129, 133, 146, 171, 189, 202, 205, 214, 222 , 234, 244, 252, 256]
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • FIG. 7 is a schematic diagram of transmission of a MIS0 multi-antenna system according to Embodiment 2 of the present invention.
  • the signals transmitted by the antennas of the base station are reflected by multiple buildings and reach the mobile phone, forming multipath effects and causing intersymbol interference. Therefore, LTE and LTE-Advanced adopt OFDM, which can effectively combat wireless. Multipath effects in propagation simplify the design of the equalizer.
  • Figure 8 is a block diagram of an OFDM system according to a second embodiment of the present invention.
  • the data of the base station is processed by the constellation point mapping, the insertion pilot, the subcarrier mapping, the IFFT, the insertion protection interval, and the up-conversion, and then sent to the wireless downlink channel.
  • the system After reaching the mobile phone, the system performs the down-conversion, the guard interval, the FFT, and the FFT.
  • the transmitted data is extracted.
  • the transmitted pilots must be orthogonal in the time domain, frequency domain, or code domain. It should be noted that the present invention uses channel joint sparse channel estimation instead of each channel individual channel estimation in the prior art.
  • FIG. 1 is a flow chart of a joint sparse channel estimation method of the present invention. Referring to Figure 1, the method includes:
  • S1 Establish a joint sparse reconstruction model, and combine all the channels to be estimated into one joint sparse direction.
  • the number of OFDM subcarriers is N
  • the number of pilots used is AT (KM ⁇ N
  • the M different antennas of the base station use M pilot sequences that are mutually orthogonal in the frequency domain, and the pilot sequence of the ith antenna is ⁇ ( ⁇ ), corresponding to the index of different OFDM pilot subcarriers, and
  • ⁇ ( ⁇ ) n 0, i ⁇ j, where r represents the intersection of the two sets.
  • Each antenna of the base station corresponds to one downlink channel
  • the relationship between the ith downlink channel transmission pilot and the reception pilot can be established as follows
  • the L elements Due to the sparsity of the radio channel, most of the L elements are zero, and only a few are non-zero, where The number of zero elements is the number of multipaths of the wireless channel.
  • the existing related literature indicates that for the same transmitted signal, the ToA of the received signals of different antennas of the base station are similar, and it can be considered that the lengths of the CIR sequences of different channels are the same, and the CIR sequence is the same. The position of the zero element in the middle is the same, and the value of the non-zero element is different.
  • the values of the elements are different, either the whole block is zero, or the whole block is non-zero, and M is presented as a block sparse structure, so the position of the non-zero element in M can be used to represent the position of the non-zero element in M.
  • the joint observations defining the M channels are as follows
  • n the Zth element block of the column vector n
  • 1 1, 2, ⁇ , !
  • the joint sparse reconstruction model can be expressed as
  • the present invention first uses the joint observation value Z and the joint observation matrix ⁇ to solve the positions of all non-zero element blocks of the joint sparse vector IV, and then separately solves the values of the non-zero elements of each channel.
  • the base station uses the joint sparse reconstruction model formula (8) to solve the positions of all the non-zero element blocks of the joint sparse vector ,.
  • the flow refers to FIG. 4, and the method includes:
  • anthology ⁇ to store the locations of non-zero element blocks that are obtained in turn. Since 1? and IV exhibit the same block sparse structure, the position of the non-zero element can be represented by the index of the non-zero element block, so that
  • the index of a non-zero element block in V directly corresponds to the index of the non-zero element in Central.
  • Set the number of loops ⁇ 0.
  • S22 Determine whether the power of the residual is greater than a product of the noise variance and the square of the number of base station antennas, and determine whether the number of cycles is less than the channel length. If both are, perform S23; otherwise, execute S24.
  • the element that satisfies the above condition is denoted by /, adding / adding to the selection and updating the selection ⁇ ⁇ U ⁇ / ⁇ , where the superscript - 1 indicates the matrix inversion and the superscript indicates the conjugate transpose.
  • the definition ⁇ ⁇ is a matrix composed of blocks of ⁇ corresponding to the elements in the selection ,, and the new residual is simultaneously, and the number of cycles is increased by 1, that is, ⁇ + 1.
  • S3 Solve the value of the non-zero element of each channel. Defined as a matrix consisting of columns corresponding to the elements in the selection, then the i-th downlink channel
  • FIG. 3 is a schematic structural diagram of a joint sparse channel estimation apparatus according to the present invention.
  • the device consists of the following 3 units:
  • a joint sparse vector calculation unit for solving the positions of all non-zero element blocks of the joint sparse vector of the joint sparse reconstruction model.
  • An information acquisition unit configured to solve a value of a non-zero element of each of the channels.
  • the joint sparse vector computing unit further includes the following four modules:
  • an initialization module for initializing joint observations whose residuals are joint sparse reconstruction models, normalizing each column of the joint observation matrix of the joint sparse reconstruction model, initializing the selection into an empty set, and setting the number of loops to 0.
  • a judging module configured to determine whether the power of the residual is greater than a product of the noise variance and the number of base station antennas, determine whether the number of loops is less than the channel length, and if both are, execute the update module; otherwise, execute the output module.
  • M 8 frequency domain orthogonal pilot sequences used in this simulation test are shown in Table 2.
  • Embodiment 2 Joint Sparse Channel Estimation and Comparison of Individual Sparse Channel Estimation for Each Channel
  • the present invention utilizes two channels 8, 9, 10, 12, 13, 15, 21, 25, 36, 43, 44 of 8 channels for joint sparse channel estimation 50, 56, 60
  • the present invention performs joint sparse channel estimation using four of the eight channels 2, 10, 12, 13, 19, 21, 24, 41, 47, 50, 53, 54, 57, 60
  • the present invention utilizes six channels of three channels 3, 6, 7, 13, 14, 23, 29, 33, 40, 41, 42, for joint sparse channel estimation, 43, 51, 53, 60
  • the present invention utilizes 8 channels to combine sparse letters 2, 13, 21, 24, 29, 33, 41, 42, 43, 53, 54.
  • the mobile phone After receiving the pilot sequence sent by the base station, the mobile phone needs to estimate the values of the non-zero elements of the eight downlink channels and the values of the non-zero elements.
  • Table 3 compares the multiple channel joint sparse channel estimates of the present invention with individual channel sparse channel estimates for each channel. Set the signal to noise ratio to 27dB. It can be seen that when the joint sparse channel estimation is performed on eight channels by the present invention, the position of the obtained non-zero element is consistent with the position of the non-zero element of the real channel. However, using the prior art to implement separate sparse channel estimation for 8 channels, it is impossible to accurately estimate the position of non-zero elements.
  • FIG. 9 is a comparison of the mean square error performance of the single sparse channel estimation for each channel of the second embodiment of the present invention. According to the position of the non-zero element of the channel CIR sequence obtained according to Table 3, the value of the non-zero element is obtained.
  • Mean Square Errors MSE
  • the MSE of each channel in Figure 9 for performing sparse channel estimation alone represents the average of the MSEs of the 8 channels separately performing sparse channel estimation. It is not difficult to see that the performance of joint sparse channel estimation for eight channels is much better than that of single sparse channel estimation. Similar to Table 3, the performance comparison of the joint sparse channel estimation for two of the eight channels, four of the eight channels, and six of the eight channels using the present invention is also shown in FIG. It can be seen that the more channels that perform joint sparse channel estimation, the better the MSE performance.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (Random Access Memory).

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Abstract

L'invention concerne un procédé, un dispositif, et un système d'estimation de canaux conjoints épars. Le procédé consiste à : créer un modèle de reconstruction conjoint épars, et fusionner tous les canaux devant être estimés dans un vecteur conjoint épars ; résoudre les positions de tous les blocs d'éléments non nuls du vecteur conjoint épars au moyen du modèle de reconstruction conjoint épars ; et résoudre des valeurs d'éléments non nuls des canaux. La présente invention permet d'améliorer la précision d'estimation de canaux, et de réduire le surdébit pilote.
PCT/CN2014/080717 2014-06-23 2014-06-25 Procédé, dispositif, et système d'estimation de canaux conjoints épars WO2015196384A1 (fr)

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