WO2018077386A1 - Estimation de canal de système mimo à porteuses multiples - Google Patents

Estimation de canal de système mimo à porteuses multiples Download PDF

Info

Publication number
WO2018077386A1
WO2018077386A1 PCT/EP2016/075664 EP2016075664W WO2018077386A1 WO 2018077386 A1 WO2018077386 A1 WO 2018077386A1 EP 2016075664 W EP2016075664 W EP 2016075664W WO 2018077386 A1 WO2018077386 A1 WO 2018077386A1
Authority
WO
WIPO (PCT)
Prior art keywords
grid
sub
mimo
decomposition
matrix
Prior art date
Application number
PCT/EP2016/075664
Other languages
English (en)
Inventor
Chaitanya TUMULA
Junshi Chen
Peter Almers
Original Assignee
Huawei Technologies Co., Ltd.
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Huawei Technologies Co., Ltd. filed Critical Huawei Technologies Co., Ltd.
Priority to PCT/EP2016/075664 priority Critical patent/WO2018077386A1/fr
Publication of WO2018077386A1 publication Critical patent/WO2018077386A1/fr

Links

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
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • 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
    • H04L25/0222—Estimation of channel variability, e.g. coherence bandwidth, coherence time, fading frequency
    • 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
    • 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
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • 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
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • H04L25/0244—Channel estimation channel estimation algorithms using matrix methods with inversion
    • 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
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • H04L25/0246—Channel estimation channel estimation algorithms using matrix methods with factorisation

Definitions

  • the invention relates to a receiving device. Furthermore, the invention also relates to a corresponding method, a computer program, and a computer program product.
  • MIMO Multiple input multiple output
  • LTE Long Term Evolution
  • LTE-Advanced Long Term Evolution
  • Wi-Fi Wi-Fi
  • ADSL Asymmetric Digital Subscriber Line
  • MIMO transmission schemes are combined together with multi-carrier (MC) modulation schemes such as the Orthogonal Frequency Division Duplexing (OFDM) technology and such systems are referred to as MIMO-OFDM systems.
  • OFDM enables reliable broadband communications by distributing user data across a number of closely spaced, narrowband subchannels denoted as subcarriers. This arrangement makes it possible to eliminate the biggest obstacle to reliable broadband communications, i.e. IS I .
  • Each subcarrier in a MC- MIMO system is carrying a transmit signal vector comprising data corresponding to one or more transmission layers of the MC-MIMO transmission.
  • MIMO channel matrix decomposition methods Two such MIMO channel matrix decomposition methods are briefly described herein, namely: iterative decomposition method, and interpolation based decomposition method.
  • MIMO channel matrix decomposition of all the subcarriers in a given MC-MIMO symbol are carried out in an iterative manner by using a MIMO channel matrix decomposition of one subcarrier and the difference of two MIMO channel matrices corresponding to two adjacent subcarriers.
  • MIMO channel matrix decomposition is performed on a set of representative resource elements (REs), and the channel decomposition on the remaining REs is obtained by interpolating the MIMO channel matrix decompositions of the representative REs.
  • Detection methods which use decomposition of MIMO channel matrices corresponding to all subcarriers in all MC-MIMO symbols result in high computational complexity and processing delay.
  • the iterative decomposition method reduces the processing delay. However, it does not necessarily result in a complexity reduction.
  • the interpolation based decomposition method requires a lot of memory to store the results of the MIMO channel matrix decompositions of all the representative REs.
  • the memory requirement on the chip can lead to an increase in the chip area. Hence, this conventional solution is not viable for implementation.
  • An objective of embodiments of the invention is to provide a solution which mitigates or solves the drawbacks and problems of conventional solutions.
  • Another objective of embodiments of the invention is to provide a solution having reduced complexity compared to conventional solutions.
  • the above mentioned and other objectives are achieved with a receiving device for a communication system, the receiving device being configured to
  • MC-MIMO Multi Carrier-Multiple Input Multiple Output
  • the MC-MIMO signal comprises information carried on a plurality of resource elements, REs, arranged in a time-frequency grid,
  • each sub-grid comprises at least two REs, estimate a MIMO channel matrix for each RE of the plurality of REs in the time-frequency grid based on the received MC-MIMO signal,
  • the word information herein may relate to data or reference signals or control signal or any other type of signals, or to all possible combinations thereof.
  • the time-frequency resource grid is divided into one or more sub-grids. It is possible to divide all the time-frequency resources of one MC-MIMO frame into only one sub-grid if the channel between the transmitter and receiver is frequency non-selective and time-invariant.
  • the at least one RE in the sub-grid on which the MIMO detection is performed may in one case be the same RE as the at least one RE associated with the estimated MIMO channel matrix. In another case, the at least one RE in the sub-grid on which the MIMO detection is performed is a different RE compared to the mentioned at least one RE associated with the estimated MIMO channel matrix.
  • the receiving device provides a number of advantages over convention solutions. Reduced computational complexity is one of the advantages of receiving device according to the first aspect. Reducing the processing delay associated with the MIMO channel matrix decomposition for detection in MC-MIMO systems is another advantage.
  • the receiving device is further configured to
  • the first implementation form is beneficial in that it may minimize the MIMO detection performance loss relative to conventional solutions in which MIMO channel matrix decomposition is performed on each RE of the MC-MIMO frame.
  • the convex combination is the average of the estimated MIMO channel matrices of the REs in the sub-grid.
  • the second implementation form may be advantageous for minimizing the MIMO detection performance loss of using the decomposition of the single MIMO channel matrix for performing MIMO detection on all the REs in the sub-grid.
  • the receiving device is further configured to
  • the third implementation form is advantageous as it has no complexity in obtaining the single MIMO channel matrix for performing the decomposition.
  • the receiving device is further configured to
  • the decomposition of the single MIMO channel matrix for the sub-grid by decomposing the single MIMO channel matrix for the sub-grid into a first decomposition matrix and a second decomposition matrix, wherein the single MIMO channel matrix for the sub-grid is a product of the first decomposition matrix and the second decomposition matrix; compute a second decomposition matrix of the RE in the sub-grid based on a multiplication of the estimated MIMO channel matrix of the RE in the sub-grid and a function of the first decomposition matrix of the single MIMO channel matrix for the sub-grid,
  • the fourth implementation form is beneficial in obtaining the second decomposition matrix of the RE in the sub-grid using a matrix multiplication rather than the computationally expensive matrix decomposition used in conventional solutions.
  • the fourth implementation form is also beneficial to obtain a similar performance with a reduced complexity of the MIMO detection as in conventional solutions.
  • the receiving device is further configured to
  • the decomposition of the single MIMO channel matrix for the sub-grid by decomposing the single MIMO channel matrix for the sub-grid into a first decomposition matrix and a second decomposition matrix, wherein the single MIMO channel matrix for the sub-grid is a product of the first decomposition matrix and the second decomposition matrix; perform the MIMO detection on the RE in a sub-grid based on the first decomposition matrix of the single MIMO channel matrix for the sub-grid, the second decomposition matrix of the single MIMO channel matrix for the sub-grid, and the received MIMO signal of the RE in the sub-grid.
  • the fifth implementation form is advantageous in terms of complexity and processing delay reduction as it avoids computing matrix decompositions for all the REs in the sub-grid.
  • the first decomposition matrix is a semi-unitary matrix and the second decomposition matrix is a triangular matrix, such as an upper triangular matrix.
  • the sixth implementation form corresponds to QR or QL decomposition based MIMO detection methods.
  • This form of matrix decomposition is useful for performing efficient tree- search based MIMO detection as well as reducing the complexity of MIMO detection.
  • the first decomposition matrix is a unimodular matrix comprising integer elements.
  • the seventh implementation form corresponds to lattice reduction based MIMO detection methods. This form of decomposition may result in a better MIMO detection performance in correlated channel cases.
  • the receiving device is further configured to
  • the augmented form of the single MIMO channel matrix corresponds to appending a scaled identity matrix to the single MIMO channel matrix for the sub-grid.
  • the eighth implementation form may result in a better MIMO detection performance compared to the case of without augmentation.
  • the computed MIMO channel property comprises the delay spread or the coherence bandwidth
  • the receiving device is further configured to
  • the ninth implementation form provides a concept by which the time-frequency grid can be divided into smaller sub-grids along the frequency-dimension.
  • the sub-grid length can be optimized along the frequency- dimension without a degradation in MIMO detection performance.
  • the number of REs in the frequency-dimension of each sub-grid is based on the coherence bandwidth and the subcarrier spacing of the MC- MIMO signal.
  • the tenth implementation form is beneficial as the size of the sub-grid can be determined along the frequency-dimension based on the coherence bandwidth and the subcarrier spacing of the MC-MIMO signal.
  • the receiving device is further configured to divide the time-frequency grid into the sub-grids in the frequency-dimension such that for each sub-grid the overall frequency bandwidth of all subcarriers in the sub-grid is smaller than or equal to the coherence bandwidth.
  • the eleventh implementation form is advantageous as all the sub-carriers in the sub-grid are within a coherence bandwidth. Hence, performing a single MIMO channel matrix decomposition may not result in performance degradation for MIMO detection.
  • the computed MIMO channel property comprises the Doppler spread or the coherence time
  • the receiving device is further configured to
  • the twelfth implementation form provides a solution by which the time-frequency grid can be divided into smaller sub-grids along the time-dimension.
  • the grid length can be optimized along the time-dimension without degradation in MIMO detection performance.
  • the number of REs in the time-dimension of each sub-grid is based on the coherence time and the symbol duration of the symbols in the MC- MIMO signal.
  • the thirteenth implementation form is beneficial as the size of the sub-grid can be determined along the time-dimension based on the coherence time and the symbol duration of the MC-MIMO signal.
  • the receiving device is further configured to divide the time-frequency grid into the sub-grids in the time-dimension such that for each sub-grid, the overall time duration of all symbols in the sub-grid is smaller than or equal to the coherence time.
  • the fourteenth implementation form is advantageous as all the symbols in the sub-grid are within a coherence time interval, hence, performing a single MIMO channel matrix decomposition may not result in performance degradation for MIMO detection.
  • MC-MIMO Multi Carrier-Multiple Input Multiple Output
  • the MC-MIMO signal comprises information carried on a plurality of resource elements, REs, arranged in a time-frequency grid,
  • each sub-grid comprises at least two REs, estimating a MIMO channel matrix for each RE of the plurality of REs in the time- frequency grid based on the received MC-MIMO signal,
  • the convex combination is the average of the estimated MIMO channel matrices of the REs in the sub-grid.
  • computing the decomposition of the single MIMO channel matrix for the sub-grid by decomposing the single MIMO channel matrix for the sub-grid into a first decomposition matrix and a second decomposition matrix, wherein the single MIMO channel matrix for the sub-grid is a product of the first decomposition matrix and the second decomposition matrix; computing a second decomposition matrix of the RE in the sub-grid based on a multiplication of the estimated MIMO channel matrix of the RE in the sub-grid and a function of the first decomposition matrix of the single MIMO channel matrix for the sub-grid, performing the MIMO detection on the RE in the sub-grid based on the second decomposition matrix of the RE in the sub-grid, the function of the first decomposition matrix of the single MIMO channel matrix for the sub-grid, and the received MIMO signal of the RE in the sub-grid.
  • the decomposition of the single MIMO channel matrix for the sub-grid by decomposing the single MIMO channel matrix for the sub-grid into a first decomposition matrix and a second decomposition matrix, wherein the single MIMO channel matrix for the sub-grid is a product of the first decomposition matrix and the second decomposition matrix; performing the MIMO detection on the RE in a sub-grid based on the first decomposition matrix of the single MIMO channel matrix for the sub-grid, the second decomposition matrix of the single MIMO channel matrix for the sub-grid, and the received MIMO signal of the RE in the sub-grid.
  • the first decomposition matrix is a semi-unitary matrix and the second decomposition matrix is a triangular matrix, such as an upper triangular matrix.
  • the first decomposition matrix is a unimodular matrix comprising integer elements.
  • the computed MIMO channel property comprises the delay spread or the coherence bandwidth
  • the method comprising dividing the time-frequency grid into the sub-grids in the frequency-dimension based on the delay spread or the coherence bandwidth.
  • the number of REs in the frequency-dimension of each sub-grid is based on the coherence bandwidth and the subcarrier spacing of the MC-MIMO signal.
  • the computed MIMO channel property comprises the Doppler spread or the coherence time, and the method comprising
  • the number of REs in the time-dimension of each sub-grid is based on the coherence time and the symbol duration of the symbols in the MC- MIMO signal.
  • the invention also relates to a computer program, characterized in code means, which when run by processing means causes said processing means to execute any method according to the present invention. Further, the invention also relates to a computer program product comprising a computer readable medium and said mentioned computer program, wherein said computer program is included in the computer readable medium, and comprises of one or more from the group: ROM (Read-Only Memory), PROM (Programmable ROM), EPROM (Erasable PROM), Flash memory, EEPROM (Electrically EPROM) and hard disk drive.
  • ROM Read-Only Memory
  • PROM Programmable ROM
  • EPROM Erasable PROM
  • Flash memory Flash memory
  • EEPROM Electrically EPROM
  • FIG. 1 shows a receiving device according to an embodiment of the invention.
  • FIG. 2 shows a method according to an embodiment of the invention.
  • - Fig. 3 shows a communication system according to an embodiment of the invention.
  • - Fig. 4 shows an example RE sub-grid of size N x N 2 over which the single MIMO channel matrix decomposition is performed.
  • FIG. 5 shows an example RE sub-grid of size l x JV 2 over which the single MIMO channel matrix decomposition is performed.
  • FIG. 6 shows an example RE sub-grid of size N x 1 over which the single MIMO channel matrix decomposition is performed.
  • Fig. 7 shows a flow chart of a first method according to one aspect of the invention.
  • FIG. 8 shows a flow chart of a second method according to another aspect of the invention.
  • FIG. 9 shows the performance of a tree-search based MIMO detection scheme incorporating different channel matrix decomposition methods.
  • - Fig. 10 shows an example OFDM time-frequency grid.
  • a Multi Carrier-MIMO (MC-MIMO) system such as a MIMO OFDM system.
  • Fig. 10 shows for illustration purposes an example of OFDM time-frequency resource grid which can be used for such MC-MIMO system.
  • the MC-MIMO system has N sub number of subcarriers and T number of symbols per frame.
  • BW Sub denote the subcarrier spacing (e.g. of an OFDM system)
  • T s denote the symbol duration of one MC-MIMO symbol.
  • a resource element (RE) denotes one subcarrier in one MC-MIMO symbol.
  • N Sub T resource elements The received signal on an ith subcarrier of a j ' th MC-MIMO symbol can be written as given by Equation 1 .
  • yt Hi xt + Wi , 1 ⁇ i ⁇ N sub , 1 ⁇ j ⁇ T Equation 1
  • x t is the vector of transmitted symbols from the ith subcarrier of a / th MC-MIMO symbol with size N T x 1 , with N T denoting the number of transmit antennas.
  • Each element in Xi belongs to the finite-alphabet set ⁇ , e.g., M-QAM constellation ; y i:j is the vector of received signals of size N R l , with N R denoting the number of receive antennas; H t denotes the channel matrix between the transmitter and receiver corresponding to the ith subcarrier of a /th MC-M IMO symbol with size N R x N T .
  • H corresponds to the effective channel matrix seen by the receiver of size N R x v, where v denotes the number of transmission layers.
  • the knowledge of the channel matrix on a given subcarrier is obtained using channel estimation based on reference signals; n is the vector of noise samples on the ith subcarrier of a y ' th MC-MIMO symbol.
  • the noise usually refers to Additive White Gaussian Noise (AWGN).
  • AWGN Additive White Gaussian Noise
  • the M IMO detection in the following disclosure is based on the assumption that noise is AWGN. The invention is however not limited thereto.
  • Any MIMO detection method involves evaluating the ML decision metric for any candidate transmit symbol vector x, where
  • many of the MIMO detection methods according to conventional solutions employ a decomposition of the channel matrix to reduce the complexity of MIMO detection or employ tree-search based MIMO detection methods like the Sphere Decoding (SD), K-best algorithm or QR-M detection.
  • decomposition method is the QR decomposition method.
  • the channel matrix H t may be decomposed as i,j— Qi Ri Equation 3
  • MMSE QR decomposition the decomposition is performed on an augumented channel matrix as
  • a is a scale factor and / denotes an identity matrix.
  • the channel matrix H t is decomposed as
  • the channel matrices on a given subcarrier are highly correlated across different MC-MIMO symbols.
  • Embodiments of the present invention exploit these facts to reduce the complexity associated with the channel decomposition required to perform MIMO detection in MC-MIMO systems.
  • FIG. 1 shows a receiving device 100 according to an exemplary embodiment of the invention.
  • the receiving device 100 comprises a processor 102 coupled to a transceiver 104.
  • the processor 102 and the transceiver 104 are coupled to each other by means of communication means 108 known in the art.
  • the receiving device 100 further comprises an antenna 106 and/or a wired interface 1 10 coupled to the transceiver 104, which means that the receiver device 100 is configured for wireless and/or wired communications in a communication system, such as the one shown in Fig. 3.
  • the receiving device 100 according to the invention is configured to receive a Multi Carrier- Multiple Input Multiple Output, MC-MIMO, signal over a MIMO channel.
  • the MC-MIMO signal comprises information carried on a plurality of resource elements arranged in a time- frequency grid.
  • the receiving device 100 is further configured to compute at least one MIMO channel property of the MIMO channel based on the received MC-MIMO signal, and divide the time-frequency grid into one or more (non-overlapping) sub-grids based on the computed MIMO channel property.
  • Each sub-grid comprises at least two REs.
  • the receiving device 100 is further configured to estimate a MIMO channel matrix for each RE of the plurality of REs in the time-frequency grid based on the received MC-MIMO signal, and compute a decomposition of a single (exactly one and not more) MIMO channel matrix for a sub-grid based on the estimated MIMO channel matrix of at least one RE in the sub-grid so as to obtain a decomposition of the single MIMO channel matrix for the sub-grid.
  • the receiving device 100 is further configured to perform MIMO detection on at least one RE in the sub- grid based on the decomposition of the single MIMO channel matrix for the sub-grid.
  • the method 200 comprises receiving 202 a MC-MIMO signal over a MIMO channel.
  • the MC-MIMO signal comprises information carried on a plurality of REs arranged in a time-frequency grid.
  • one RE can be understood as one subcarrier in one time slot (e.g. OFDM or MC- MIMO symbol).
  • the method 200 further comprises computing 204 at least one MIMO channel property of the MIMO channel based on the received MC-MIMO signal, and dividing 206 the time-frequency grid into one or more (non-overlapping) sub-grids based on the computed MIMO channel property.
  • Each sub-grid comprises at least two REs but typically a plurality of REs.
  • a single sub-grid can be spread over a plurality of time slots (e.g. OFDM or MC-MIMO symbols) and/or over a plurality of subcarriers.
  • the method 200 further comprises estimating 208 a MIMO channel matrix for each RE of the plurality of REs in the time- frequency grid based on the received MC-MIMO signal, and computing 210 a decomposition of a single (exactly one and not more) MIMO channel matrix for a sub-grid based on the estimated MIMO channel matrix of at least one RE in the sub-grid so as to obtain a decomposition of the single MIMO channel matrix for the sub-grid.
  • the method 200 further comprises performing 212 MIMO detection on at least one RE in the sub-grid based on the decomposition of the single MIMO channel matrix for the sub-grid.
  • Fig. 3 illustrates a communication system 300 according to an embodiment of the invention in which the receiving device 100 is comprised in a client device 400.
  • the communication system 300 comprises at least one transmitting device 500 and at least one client device 400.
  • the transmitting device 500 is in one example as illustrated in Fig. 3 comprised in a network node which is configured to transmit the previously described MC-MIMO signal to the client device 400.
  • the transmitting device 500 is in another example as also illustrated in Fig. 3 part of a wired communication system.
  • the MC-MIMO signal is received by the receiving device 100 and processed according to the invention.
  • the receiving device 100 may be comprised in a client device 400.
  • the client device 400 herein may be denoted as a user device, a User Equipment (UE), a mobile station, an internet of things (loT) device, a sensor device, a wireless terminal and/or a mobile terminal, is enabled to communicate wirelessly in a wireless communication system, sometimes also referred to as a cellular radio system.
  • the UEs may further be referred to as mobile telephones, cellular telephones, computer tablets or laptops with wireless capability.
  • the UEs in the present context may be, for example, portable, pocket-storable, hand-held, computer-comprised, or vehicle-mounted mobile devices, enabled to communicate voice and/or data, via the radio access network, with another entity, such as another receiver or a server.
  • the UE can be a Station (STA), which is any device that contains an IEEE 802.11 - conformant Media Access Control (MAC) and Physical Layer (PHY) interface to the Wireless Medium (WM).
  • STA Station
  • the receiving device 100 may also be configured for communication in 3GPP related LTE and LTE-Advanced, in WiMAX and its evolution, and in fifth generation wireless technologies, such as New Radio.
  • the invention is not limited to the receiving device 100 being comprised in the client device 400.
  • the receiving device 100 can also be comprised in a network node or any other communication device configured to receive MC-MIMO signals from a single client device or equivalently receive MC single-input multiple-output (SIMO) signals from multiple client devices sharing the same time-frequency resources.
  • SIMO single-input multiple-output
  • the network node herein may also be denoted as a radio network node, an access network node, an access point, or a base station, e.g. a Radio Base Station (RBS), which in some networks may be referred to as transmitter, "eNB”, “eNodeB”, “NodeB” or “B node”, depending on the technology and terminology used.
  • the radio network nodes may be of different classes such as e.g. macro eNodeB, home eNodeB or pico base station, based on transmission power and thereby also cell size.
  • the radio network node can be a Station (STA), which is any device that contains an IEEE 802.11 -conformant Media Access Control (MAC) and Physical Layer (PHY) interface to the Wireless Medium (WM).
  • STA Station
  • MAC Media Access Control
  • PHY Physical Layer
  • the network node may also be a base station corresponding to the fifth generation (5G) wireless systems.
  • 5G fifth generation
  • the decompositions given in Equation 3 and Equation 4 are used for explaining the advantages of embodiments of the invention.
  • the invention is equally applicable for any other channel matrix decomposition method used for performing detection in an MC-MIMO system.
  • Two main methods and corresponding implementations in a receiving device 100 for performing MIMO detection within the framework of the invention may be employed and are denoted as the first method and the second method, respectively.
  • the flow chart for the first method is shown in Fig. 7 and the flow chart for the second method is shown in Fig. 8.
  • the two methods have common steps I) to III) which are described in the following section of the present disclosure.
  • At least one MIMO channel property of the MIMO channel based on the received MC-MIMO signal is computed.
  • the computed MIMO channel property comprises in one example the delay spread or the coherence bandwidth.
  • the computed MIMO channel property comprises the Doppler spread or the coherence time.
  • the delay spread or equivalently coherence bandwidth denoted as BW C and the Doppler spread or equivalently coherence time denoted as T c of the channel between the transmitting device 500 and the receiving device 100 are computed e.g. based on measurements related to transmissions of pilots, beacons or other reference signals between the transmitting device 500 and the receiving device 100.
  • N CorrFreq max (
  • ⁇ J , l) and N CorrTime max (j ⁇ J , l) are computed using the coherence bandwidth BW C and the coherence time T c .
  • BW Sub is the subcarrier spacing and T s is the MC-MIMO symbol duration.
  • the values N CorrFreq and N CorrTime characterize the number of subcarriers along the frequency-dimension and the number of MC-MIMO symbols along the time-dimension over which the channel is highly correlated, respectively.
  • N and N 2 design parameters, defining the size of the sub-grid(s) into which the time-frequency grid is divided are chosen, such that 1 ⁇ N ⁇ N CorrFreq and 1 ⁇ N 2 ⁇ N CorrTime .
  • N 1 and N 2 NcorrTime -
  • the total TN sub REs in one MC-MIMO frame are divided into sub-grids of size N x N 2 as illustrated in Fig. 4.
  • the x-axis in Fig. 4 represents the time dimension and the y-axis the frequency dimension.
  • the sub-grid consists of N 2 MC-MIMO symbols with symbol indices from j to ; + N 2 - 1 and N subcarriers with indices from i to i + N - 1.
  • the number of REs in the time-dimension of each sub-grid may be based on the coherence time and the symbol duration of the symbols in the MC-MIMO signal.
  • the time frequency grid is divided into the sub-grids in the time dimension such that for each sub-grid the overall time duration of all symbols in the sub-grid is smaller than or equal to the coherence time.
  • NLOS non-line-of-sight
  • the sub-grid consists of only one subcarrier with index i for all of the N 2 MC-MIMO symbols with indices from j to j + N 2 - 1.
  • the number of REs in the frequency-dimension of each sub-grid may be based on the coherence bandwidth and the subcarrier spacing of the MC-MIMO signal.
  • the time frequency grid may be divided into sub-grids in the frequency dimension such that for each sub-grid the overall frequency bandwidth of all subcarriers in the sub-grid is smaller than or equal to the coherence bandwidth.
  • the sub- grid consists of only one MC-MIMO symbol with index j and A? ! subcarriers with indices from i to i + N ! - 1.
  • the aforementioned first method further comprises steps IV) to VI).
  • IV) One of the RE of each sub-grid of size N x N 2 is selected as a representative RE for that sub-grid.
  • the RE with subcarrier index i is selected as the representative tone of the sub-grid spanning over MC-MIMO symbol and adjacent subcarriers i and i + 1.
  • the single MIMO channel matrix for this case is given by H t .
  • channel matrix decomposition is performed by computing the decomposition of the single MIMO channel matrix for the sub-grid by decomposing the single M IMO channel matrix for the sub-grid into a first decomposition matrix and a second decomposition matrix.
  • the single MIMO channel matrix for the sub-grid is a product of the first decomposition matrix and the second decomposition matrix.
  • computing a second decomposition matrix of the RE in the sub-grid based on a multiplication of the estimated MIMO channel matrix of the RE in the sub-grid and a function of the first decomposition matrix of the single MIMO channel matrix for the sub-grid.
  • the function of the first decomposition matrix can correspond to obtaining the Hermitian (or self-adjoint) matrix of the first decomposition matrix.
  • the function of the first decomposition matrix can correspond to obtaining an inverse matrix of the first decomposition matrix. For the example case related to a sub-grid with two REs spanning over two adjacent subcarriers i and i + 1 but in one and the same MC-MIMO symbol ; ' , the single MIMO channel matrix H j is decomposed into a product of two matrices.
  • the first decomposition matrix is a semi-unitary matrix and the second decomposition matrix is a triangular matrix, such as an upper triangular matrix.
  • the first decomposition matrix may be a unimodular matrix comprising integer elements.
  • the flow chart of the second method of the invention is shown in Fig. 8.
  • the second method of the invention is described using two exemplary matrix decomposition methods. It is however to be noted that the second method may be applied with any other matrix decomposition method required for performing MIMO detection.
  • the second method further comprises steps IV) to VI ' ).
  • the second method also has two different alternatives denoted as the first alternative of the second method and the second alternative of the second method.
  • the first alternative and the second alternative differ from each other in how MIMO detection is performed using the decomposition of the single MIMO channel matrix for the sub-grid.
  • the single MIMO channel matrix for the sub-grid is computed based on a convex combination of the estimated MIMO channel matrices of the REs in the sub-grid.
  • the single MIMO channel matrix for the sub-grid is computed as the average of the estimated MIMO channel matrices of all REs in each sub-grid.
  • the decomposition of the single MIMO channel matrix for the sub-grid is computed based on any convex combination of the estimated MIMO channel matrices of the REs in the sub-grid.
  • V Decomposition of the computed single MIMO channel matrix (average of the estimated MIMO channel matrices of all the REs in the sub-grid) is performed in this step.
  • the decomposition is performed by computing the decomposition of the single MIMO channel matrix for the sub-grid by decomposing the single MIMO channel matrix for the sub- grid into a first decomposition matrix and a second decomposition matrix.
  • the single MIMO channel matrix for the sub-grid is a product of the first decomposition matrix and the second decomposition matrix.
  • the first decomposition matrix is a semi-unitary matrix and the second decomposition matrix is a triangular matrix, such as an upper triangular matrix.
  • the first decomposition matrix may be a unimodular matrix comprising integer elements.
  • the decomposition of the single MIMO channel matrix for the sub-grid H avg may be computed based on an augmented form of the single MIMO channel matrix for the sub-grid. That is, we could perform decomposition of the augmented single MIMO channel matrix, which can be denoted as [H aV g aI ] , where a is a scale factor and / denotes an identity matrix.
  • the MIMO detection is performed on the RE in a sub-grid based on the first decomposition matrix of the single MIMO channel matrix for the sub-grid, the second decomposition matrix of the single MIMO channel matrix for the sub- grid, and the received MIMO signal of the RE in the sub-grid.
  • the MIMO detection on the ith subcarrier is performed using H a vg > T avg > ancl Vi -
  • MIMO detection on the (i + l)th subcarrier is performed using H a vg > T avg > ancl Vi+i -
  • a second decomposition matrix of the RE in the sub-grid is computed based on a multiplication of the estimated MIMO channel matrix of the RE in the sub-grid and a function of the first decomposition matrix of the single MIMO channel matrix for the sub-grid.
  • the MIMO detection is performed on the RE in the sub-grid based on the second decomposition matrix of the RE in the sub-grid, the function of the first decomposition matrix of the single MIMO channel matrix for the sub- grid, and the received MIMO signal of the RE in the sub-grid.
  • embodiments of the invention introduce an improved method and corresponding receiving device 100 for performing matrix decomposition required for detection in MC-MIMO systems.
  • One such improvement is reduced complexity.
  • the first method of the invention results in savings of up to 24 % multiplications (Mul) and 39 % additions (Add) compared to the conventional method.
  • the first alternative of the second method of the invention results in computational savings of 87.5% multiplications and 68% additions compared to the conventional method.
  • N t 4
  • N 2 2 Mul: 30016 Mul: 238336 Mul: 1899520
  • N x Mul: 4928 Mul: 37632 Mul: 293888
  • Fig. 9 The performance of a tree-search based MIMO detection scheme for a conventional method as well as for the first method and the second method (including the first and second alternatives) according to the invention is illustrated Fig. 9.
  • the x-axis in Fig. 9 shows the signal-to-noise ratio (SNR) and the y-axis in Fig. 9 shows block error rate (BLER).
  • SNR signal-to-noise ratio
  • BLER block error rate
  • Fig. 9 illustrates that even by reducing the complexity associated with the channel matrix decomposition, the first method and the second method according to the invention do not result in any performance loss compared to the conventional method. It is therefore concluded that embodiments of the invention result in reduced complexity with no performance loss compared to conventional solutions.
  • any method according to embodiments of the invention may be implemented in a computer program, having code means, which when run by processing means causes the processing means to execute the steps of the method.
  • the computer program is included in a computer readable medium of a computer program product.
  • the computer readable medium may comprises essentially any memory, such as a ROM (Read-Only Memory), a PROM (Programmable Read-Only Memory), an EPROM (Erasable PROM), a Flash memory, an EEPROM (Electrically Erasable PROM), or a hard disk drive.
  • embodiments of the receiving device 100 comprises the necessary communication capabilities in the form of e.g., functions, means, units, elements, etc., for performing the present solution.
  • means, units, elements and functions are: processors, memory, buffers, control logic, encoders, decoders, rate matchers, de-rate matchers, mapping units, multipliers, decision units, selecting units, switches, interleavers, de-interleavers, modulators, demodulators, inputs, outputs, antennas, amplifiers, receiver units, transmitter units, DSPs, MSDs, TCM encoder, TCM decoder, power supply units, power feeders, communication interfaces, communication protocols, etc. which are suitably arranged together for performing the present solution.
  • the processor 102 of the receiving device 100 may comprise, e.g., one or more instances of a Central Processing Unit (CPU), a processing unit, a processing circuit, a processor, an Application Specific Integrated Circuit (ASIC), a microprocessor, or other processing logic that may interpret and execute instructions.
  • CPU Central Processing Unit
  • ASIC Application Specific Integrated Circuit
  • the expression "processor” may thus represent a processing circuitry comprising a plurality of processing circuits, such as, e.g., any, some or all of the ones mentioned above.
  • the processing circuitry may further perform data processing functions for inputting, outputting, and processing of data comprising data buffering and device control functions, such as call processing control, user interface control, or the like.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Radio Transmission System (AREA)

Abstract

L'invention concerne un dispositif de réception et un procédé correspondant. Le dispositif de réception (100) est configuré pour : recevoir un signal sortie multiple entrée multiple à porteuses multiples (MC-MIMO) sur un canal MIMO, le signal MC-MIMO comprenant des informations transportées sur une pluralité d'éléments de ressources (RE) agencés dans une grille temps-fréquence ; calculer au moins une propriété de canal MIMO du canal MIMO sur la base du signal MC-MIMO reçu ; diviser la grille temps-fréquence en une ou plusieurs sous-grilles (non chevauchantes) sur la base de la propriété de canal MIMO calculée, chaque sous-grille comprenant au moins deux RE ; estimer une matrice de canal MIMO pour chaque RE de la pluralité de RE dans la grille temps-fréquence sur la base du signal MC-MIMO reçu ; calculer une décomposition d'une seule matrice de canal MIMO par sous-grille sur la base d'une matrice de canal MIMO estimée pour au moins un RE dans la sous-grille, de façon à obtenir une décomposition d'une unique matrice de canal MIMO pour la sous-grille ; et effectuer une détection MIMO sur au moins un RE dans la sous-grille sur la base de la décomposition de la matrice de canal MIMO unique dans la sous-grille. La présente invention concerne également un programme informatique et un produit programme informatique.
PCT/EP2016/075664 2016-10-25 2016-10-25 Estimation de canal de système mimo à porteuses multiples WO2018077386A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/EP2016/075664 WO2018077386A1 (fr) 2016-10-25 2016-10-25 Estimation de canal de système mimo à porteuses multiples

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP2016/075664 WO2018077386A1 (fr) 2016-10-25 2016-10-25 Estimation de canal de système mimo à porteuses multiples

Publications (1)

Publication Number Publication Date
WO2018077386A1 true WO2018077386A1 (fr) 2018-05-03

Family

ID=57209450

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2016/075664 WO2018077386A1 (fr) 2016-10-25 2016-10-25 Estimation de canal de système mimo à porteuses multiples

Country Status (1)

Country Link
WO (1) WO2018077386A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080069261A1 (en) * 2006-09-18 2008-03-20 Nec Laboratories America, Inc. Interpolation Based QR Decomposition for MIMO-OFDM Systems Using D-SMC Demodulator with Per Chunk Ordering
EP2445150A1 (fr) * 2010-10-19 2012-04-25 ST-Ericsson SA Procédé pour exécuter une décomposition QR de matrice de canal dans un système de communication MIMO sans fil, et récepteur permettant de l'obtenir
WO2015174666A1 (fr) * 2014-05-13 2015-11-19 엘지전자 주식회사 Procédé d'attribution de ressource à un utilisateur par un émetteur mimo et procédé de programmation d'un utilisateur auquel des données doivent être envoyées en utilisant la ressource

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080069261A1 (en) * 2006-09-18 2008-03-20 Nec Laboratories America, Inc. Interpolation Based QR Decomposition for MIMO-OFDM Systems Using D-SMC Demodulator with Per Chunk Ordering
EP2445150A1 (fr) * 2010-10-19 2012-04-25 ST-Ericsson SA Procédé pour exécuter une décomposition QR de matrice de canal dans un système de communication MIMO sans fil, et récepteur permettant de l'obtenir
WO2015174666A1 (fr) * 2014-05-13 2015-11-19 엘지전자 주식회사 Procédé d'attribution de ressource à un utilisateur par un émetteur mimo et procédé de programmation d'un utilisateur auquel des données doivent être envoyées en utilisant la ressource
EP3145107A1 (fr) * 2014-05-13 2017-03-22 LG Electronics Inc. Procédé d'attribution de ressource à un utilisateur par un émetteur mimo et procédé de programmation d'un utilisateur auquel des données doivent être envoyées en utilisant la ressource

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
CESCATO D ET AL: "Interpolation-based QR decomposition in MIMO-OFDM systems", SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS, 2005 IEEE 6TH W ORKSHOP ON NEW YORK, NY, USA JUNE 2-8, 2005, PISCATAWAY, NJ, USA,IEEE, 2 June 2005 (2005-06-02), pages 945 - 949, XP010834656, ISBN: 978-0-7803-8867-3, DOI: 10.1109/SPAWC.2005.1506279 *

Similar Documents

Publication Publication Date Title
EP3556041B1 (fr) Système de communication sans fil qui effectue une sélection basée sur une mesure de ports de signal de référence de suivi de phase (psh)
CN108289018B (zh) 一种传输参考信号的方法以及设备
US8548101B2 (en) Demodulation method and demodulator for orthogonal frequency multiplexing—multiple input multiple output system
KR102058231B1 (ko) 미모-오에프디엠 통신 시스템에서 채널 추정을 위한 방법들 및 장치
Danneberg et al. Implementation of a 2 by 2 MIMO-GFDM Transceiver for Robust 5G Networks
KR102318817B1 (ko) 무선통신 시스템에서 필터 뱅크 다중 반송파 기법을 위한 전처리 방법 및 장치
CN102118350B (zh) 基于奇异值分解频域滤波移动WiMAX下行系统信道估计方法
WO2012046252A2 (fr) Estimation et interpolation robustes de canal
WO2022068684A1 (fr) Procédé et appareil de compensation de bruit de phase
WO2007111198A1 (fr) Procede et dispositif de transmission
CN102546483A (zh) 一种宽带同频干扰噪声估计和干扰抑制的方法及相应系统
WO2014047163A9 (fr) Procédés et appareils d'estimation de canal dans des réseaux sans fil
WO2021047759A1 (fr) Nœud d'accès au réseau et dispositif client pour les modèles adaptatifs de dmrs
WO2017167386A1 (fr) Émetteur et récepteur destinés à émettre et recevoir une pluralité de signaux de modulation à porteuses multiples
WO2018077386A1 (fr) Estimation de canal de système mimo à porteuses multiples
del Amo et al. Suppression of cyclic prefix in down-link LTE like systems to increase capacity
Ganji et al. A block-based non-orthogonal multicarrier scheme
CN102480440B (zh) 一种宽带同频干扰环境下的信道估计方法及系统
CN108243124B (zh) 一种信道估计方法和装置
Dayi Improving 5g nr uplink channel estimation with artificial neural networks: a practical study on NR PUSCH receiver
WO2014110872A1 (fr) Procédé d'estimation de canal permettant de remédier à une discontinuité de canal entre des sous-bandes d'un système de multiplexage par répartition orthogonale de fréquence (ofdm)
CN114500184B (zh) 一种信道估计方法和装置及设备
Qin et al. N-continuous subcarrier index modulation OFDM
CN110089082B (zh) 滤波的cp-ofdm波形的构造
Gangarajaiah et al. Low complexity adaptive channel estimation and QR decomposition for an LTE-A downlink

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16788071

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 16788071

Country of ref document: EP

Kind code of ref document: A1