WO2022094778A1 - 多输入多输出mimo系统的检测方法和装置 - Google Patents

多输入多输出mimo系统的检测方法和装置 Download PDF

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WO2022094778A1
WO2022094778A1 PCT/CN2020/126381 CN2020126381W WO2022094778A1 WO 2022094778 A1 WO2022094778 A1 WO 2022094778A1 CN 2020126381 W CN2020126381 W CN 2020126381W WO 2022094778 A1 WO2022094778 A1 WO 2022094778A1
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constellation point
matrix
tree search
layer
metric
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PCT/CN2020/126381
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English (en)
French (fr)
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樊文贵
陈莉
汪浩
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华为技术有限公司
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Priority to PCT/CN2020/126381 priority Critical patent/WO2022094778A1/zh
Priority to CN202080015215.4A priority patent/CN114731323B/zh
Publication of WO2022094778A1 publication Critical patent/WO2022094778A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/28—Systems using multi-frequency codes with simultaneous transmission of different frequencies each representing one code element

Definitions

  • the present application relates to the field of wireless communication, and more particularly, to a method and apparatus for detecting a multiple-input multiple-output MIMO system.
  • a multiple-input multiple-output (MIMO) system refers to a system in which multiple antennas are used at the transmitter and receiver of a wireless communication link to transmit and receive data simultaneously.
  • MIMO multiple-input multiple-output
  • data can be split into multiple streams that can be simultaneously sent by the transmitter and received by the receiver, increasing system capacity without requiring significant additional spectrum or power.
  • these streams are generated by dividing the data into streams, grouping bits in each stream, mapping each bit group to a constellation point, and then using these streams as multiple transmit antennas based on the constellation points mapped for each stream.
  • a modulated carrier for transmission After receiving the modulated signal using multiple antennas, the receiving end uses various signal detection techniques to obtain data from the stream received at the receiving end's antennas.
  • the receiving end can eliminate or suppress the interference between multiple transmitted symbols as much as possible through a certain MIMO equalization algorithm, so as to recover the multiple transmitted signals by the transmitting end. transmit symbols.
  • linear detection eg, minimum mean squared error (MMSE), zero forcing (ZF)
  • nonlinear detection eg, maximum likelihood (ML)
  • the implementation The purpose of the transmitted signal is obtained from the received signal.
  • MMSE minimum mean squared error
  • ZF zero forcing
  • ML maximum likelihood
  • the implementation The purpose of the transmitted signal is obtained from the received signal.
  • the detection result will be inaccurate.
  • the computational complexity increases exponentially with the scale of the constellation diagram and the number of transmitted symbols, resulting in a high implementation complexity of the nonlinear detection algorithm.
  • the present application provides a detection method and device for a multiple-input multiple-output MIMO system, which can effectively reduce the complexity of the detection method on the premise that the detection result has high accuracy.
  • a method for detecting a multiple-input multiple-output MIMO system which can be applied to a communication system including a MIMO system, and the method includes:
  • the conjugate symmetric matrix is determined according to the first channel matrix, the noise variance and the identity matrix, and the first channel matrix is obtained by processing the received signal;
  • the preprocessing matrix and the unit lower triangular matrix are obtained by LDL decomposition of the conjugate symmetric matrix;
  • the topmost layer of the tree search model is expanded down layer by layer, and a metric set is determined.
  • the metric set includes a metric set of the root node and a metric set of multiple leaf nodes.
  • the metric set of the root node is included in the first A candidate set, the first candidate set is determined from the first constellation point set according to the first rule, the first constellation point set is obtained by encoding and mapping the transmitted signal corresponding to the root node, and the metric set makes the
  • the tree search model has the smallest path metric;
  • a log-likelihood ratio for each information bit in the transmitted signal is determined.
  • the transmitted signal corresponding to the received signal is mapped to the tree search model, thus avoiding the square root operation, which can effectively reduce the computational complexity.
  • the search space of the root node ie, the first candidate set
  • the detection method of the MIMO system provided by the present application can effectively reduce the complexity of the detection method on the premise that the detection result has a high accuracy.
  • the first rule includes determining the following constellation points in the first constellation point set as the constellation points included in the first candidate set:
  • the first constellation point, the second constellation point, and the third constellation point the first constellation point is the constellation point closest to the Euclidean distance of the estimated transmitted signal of the root node
  • the second constellation point is the closest constellation point to the first constellation point
  • the Euclidean distance is the closest and respectively contains the constellation point corresponding to the inverse bit of each bit of the first constellation point
  • the third constellation point is the constellation point except the first constellation point and the second constellation point included in the first area.
  • the first region is included in the region corresponding to the first constellation point set, and the first region is determined from the region corresponding to the first constellation point set according to a preset complexity.
  • the preset complexity may be set according to a specific application scenario.
  • the determined search space of the root node (ie, the above-mentioned first candidate set) includes not only the first constellation point, but also the second constellation point and the third constellation point, thereby ensuring that the search space of the root node does not contain There is a problem of missing bits.
  • the MIMO system is an M ⁇ M-dimensional MIMO system, where M is a positive integer greater than or equal to 2, and before determining the conjugate symmetric matrix, the method further includes:
  • the M channel matrices are in one-to-one correspondence with the M tree search models, and the M tree search models correspond to the M layers of the MIMO system. one-to-one correspondence;
  • column permutation is performed on the original channel through a column transformation matrix to obtain a plurality of permuted channel matrices.
  • the transmitted signals corresponding to the received signals can be mapped to multiple tree search models according to the multiple channel matrices, so that each layer of the transmitted signals of the MIMO system has the opportunity to be at the root node of the tree search model.
  • the M channel matrices are represented by the following formula:
  • the best performing sorting method is Bell Labs vertical hierarchical space-time code V-BLAST sorting, the complexity of which increases exponentially with the number of transmission layers; simplified sorting methods, such as the sorted QR algorithm SQRD, cannot be estimated in advance because The signal-to-noise ratio of the root node has a risk of robustness, and it is not considered that the degree that the root node traverses the constellation point set is much larger than the degree that the leaf node traverses the constellation point set.
  • the original channel matrix H is subjected to column permutation through the column transformation matrix p l to obtain a plurality of different channel matrices H l after the replacement, which can be determined from the plurality of different channel matrices H l after the replacement
  • a permuted channel matrix is the first channel matrix, which avoids the process of sorting the layers to be traversed in the tree search model.
  • the MIMO system is an M ⁇ M-dimensional MIMO system
  • the tree search model includes M layers
  • the root node corresponds to the Mth layer
  • the plurality of leaf nodes Corresponding to layers M-1 to 1 respectively
  • M is a positive integer greater than or equal to 2
  • the tree-based search method extends downward from the topmost layer of the tree search model layer by layer to determine a set of metrics, including:
  • a search is performed in the first candidate set based on the first distance metric function, and a fourth constellation point is determined as a constellation point included in the metric set of the root node, and the transmitted signal corresponding to the fourth constellation point makes the first distance metric
  • the value of the function is less than or equal to the first threshold
  • the second candidate set is searched, and the fifth constellation point is determined as the constellation point included in the metric set of the ith leaf node, and the transmission signal corresponding to the fifth constellation point makes the second constellation point
  • the value of the distance metric function is less than or equal to the second threshold
  • the second candidate set is determined according to the estimated transmission signal of the ith leaf node and the second constellation point set
  • the second constellation point set is the ith leaf node.
  • the transmitted signal corresponding to the node is encoded and mapped.
  • the first candidate set is searched according to the first distance metric function, and it is determined that the transmitted signal corresponding to the obtained fourth constellation point is the optimal estimation result .
  • the first distance metric function is the same as the metric function of the transmitted signal corresponding to the Mth layer estimated according to the linear minimum mean square error LMMSE algorithm.
  • the fusion of the NML algorithm and the LMMSE algorithm process is realized. That is, by processing the metric function of the root node determined according to the present application (that is, the above-mentioned first distance metric function) through the intermediate variables, the metric function of the transmitted signal corresponding to the Mth layer estimated based on the LMMSE algorithm can be obtained, avoiding Therefore, there is a problem of high computational complexity when directly using the LMMSE algorithm to estimate the transmitted signal.
  • the tree search manner includes one of the following manners: a generalized-first tree search and a depth-first tree search.
  • the detection method provided in this application is applicable to a MIMO system in which the number of antennas at the transmitting end is greater than or equal to the number of antennas received.
  • a MIMO system in which the number of antennas at the transmitting end is greater than or equal to the number of antennas received.
  • a 4x4 dimensional MIMO system e.g., a 4x4 dimensional MIMO system.
  • a 4 ⁇ 4 dimensional multi-user-multiple-input multiple-output (MU-MIMO) system e.g., MU-MIMO
  • the present application provides a detection device for a multiple-input multiple-output MIMO system, the device comprising:
  • the processing unit is used to determine a conjugate symmetric matrix, the conjugate symmetric matrix is determined according to the first channel matrix, the noise variance and the identity matrix, and the first channel matrix is obtained by processing the received signal.
  • the processing unit 702 uses for building a training dataset;
  • the processing unit is further configured to map the transmitted signal corresponding to the received signal to a tree search model by using a preprocessing matrix and a unit lower triangular matrix for LDL decomposition of the conjugate symmetric matrix owned;
  • the processing unit is further configured to expand downward layer by layer from the topmost layer of the tree search model based on a tree search method, and determine a metric set, where the metric set includes a metric set of a root node and a metric set of multiple leaf nodes, the root node
  • the metric set is included in the first candidate set, the first candidate set is determined from the first constellation point set according to the first rule, and the first constellation point set is obtained by encoding and mapping the transmitted signal corresponding to the root node , the metric set makes the tree search model have the smallest path metric;
  • the processing unit is further configured to determine the log-likelihood ratio of each information bit in the transmitted signal according to the metric set.
  • the second rule includes determining the following constellation points in the second constellation point set as the constellation points included in the first candidate set:
  • the first constellation point, the second constellation point, and the third constellation point the first constellation point is the constellation point closest to the Euclidean distance of the estimated transmitted signal of the root node
  • the second constellation point is the closest constellation point to the first constellation point
  • the Euclidean distance is the closest and respectively contains the constellation point corresponding to the inverse bit of each bit of the first constellation point
  • the third constellation point is the constellation point except the first constellation point and the second constellation point included in the first area.
  • the first region is included in the region corresponding to the first constellation point set, and the first region is determined from the region corresponding to the first constellation point set according to a preset complexity.
  • the acquisition unit is also used to acquire the received signal
  • the processing unit is further configured to process the received signal to obtain an original channel matrix
  • the processing unit is also used to:
  • the M channel matrices correspond to the M tree search models one-to-one, and the M tree search models correspond to the MIMO system.
  • M layers One-to-one correspondence of M layers;
  • the M channel matrices can be represented by the following formula:
  • the processing unit is further configured to:
  • a search is performed in the first candidate set based on the first distance metric function, and a fourth constellation point is determined as a constellation point included in the metric set of the root node, and the transmitted signal corresponding to the fourth constellation point makes the first distance metric
  • the value of the function is less than or equal to the first threshold
  • the second candidate set is searched, and the fifth constellation point is determined as the constellation point included in the metric set of the ith leaf node, and the transmission signal corresponding to the fifth constellation point makes the second constellation point
  • the value of the distance metric function is less than or equal to the second threshold
  • the second candidate set is determined according to the estimated transmission signal of the ith leaf node and the second constellation point set
  • the second constellation point set is the ith leaf node.
  • the transmitted signal corresponding to the node is encoded and mapped.
  • the first distance metric function is the same as the metric function of the transmitted signal corresponding to the Mth layer estimated according to the linear minimum mean square error LMMSE algorithm.
  • the tree search manner includes one of the following manners: a generalized-first tree search and a depth-first tree search.
  • the present application provides a detection device for a MIMO system, the detection device includes a memory and a processor, where the memory is used for storing instructions, and the processor is used for reading the instructions stored in the memory, so that the device
  • the detection device includes a memory and a processor, where the memory is used for storing instructions, and the processor is used for reading the instructions stored in the memory, so that the device
  • the present application provides a processor, including: an input circuit, an output circuit, and a processing circuit.
  • the processing circuit is configured to receive a signal through the input circuit and transmit a signal through the output circuit, so that any aspect of the first aspect and the method of any possible implementation of the first aspect are accomplish.
  • the above-mentioned processor may be a chip
  • the input circuit may be an input pin
  • the output circuit may be an output pin
  • the processing circuit may be a transistor, a gate circuit, a flip-flop, and various logic circuits.
  • the input signal received by the input circuit may be received and input by, for example, but not limited to, a receiver
  • the signal output by the output circuit may be, for example, but not limited to, output to and transmitted by a transmitter
  • the circuit can be the same circuit that acts as an input circuit and an output circuit at different times.
  • the embodiments of the present application do not limit the specific implementation manners of the processor and various circuits.
  • the present application provides a processing apparatus including a processor and a memory.
  • the processor is configured to read the instructions stored in the memory, and can receive signals through the receiver and transmit signals through the transmitter, so as to execute the first aspect and the method in any possible implementation manner of the first aspect.
  • processors there are one or more processors and one or more memories.
  • the memory may be integrated with the processor, or the memory may be provided separately from the processor.
  • the memory can be a non-transitory memory, such as a read only memory (ROM), which can be integrated with the processor on the same chip, or can be separately set in different On the chip, the embodiment of the present application does not limit the type of the memory and the setting manner of the memory and the processor.
  • ROM read only memory
  • the relevant data interaction process such as sending indication information, may be a process of outputting indication information from the processor, and receiving capability information may be a process of receiving input capability information by the processor.
  • the data output by the processing can be output to the transmitter, and the input data received by the processor can be from the receiver.
  • the transmitter and the receiver may be collectively referred to as a transceiver.
  • the present application provides a computer-readable storage medium for storing a computer program, the computer program comprising a method for executing the above-mentioned first aspect and any possible implementation manner of the above-mentioned first aspect. instruction.
  • the present application provides a computer program product comprising instructions, which, when run on a computer, cause the computer to execute the method in the above-mentioned first aspect and any possible implementation manner of the above-mentioned first aspect.
  • the present application provides a chip, including at least one processor and an interface; the at least one processor is used to call and run a computer program, so that the chip executes the above-mentioned first aspect and The method in any possible implementation manner of the above-mentioned first aspect.
  • the present application provides and provides a communication system, including the MIMO system detection apparatus according to the second aspect and/or the MIMO system detection device according to the third aspect.
  • FIG. 1 is a schematic diagram of a wireless multiple access communication system provided by the present application.
  • FIG. 2 is a schematic diagram of a MIMO system 200 that can be applied to the methods provided herein.
  • FIG. 3 is a schematic flowchart of a detection method 100 of a MIMO system provided by the present application.
  • FIG. 4 is a schematic diagram of determining a root node candidate set provided by the present application.
  • FIG. 5 is a schematic flowchart of a detection method 200 of a MIMO system provided by the present application.
  • FIG. 6 is an architectural diagram of a detection method of a MIMO system provided by the present application.
  • FIG. 7 is a schematic block diagram of an apparatus 700 for detecting a MIMO system provided by the present application.
  • FIG. 8 is a schematic block diagram of a detection device 800 of a MIMO system provided by the present application.
  • words such as “first”, “second” and “third” are used to distinguish the same or similar items with basically the same function and function. It should be understood that “first”, “second” and “second” ” and “third” have no logical or temporal dependency, nor do they limit the quantity and execution order.
  • the "protocol” involved in the embodiments of this application may refer to standard protocols in the communication field, for example, may include LTE protocols, NR protocols, and related protocols applied in future communication systems, which are not limited in this application.
  • At least one means one or more, and “plurality” means two or more.
  • And/or which describes the association relationship of the associated objects, indicates that there can be three kinds of relationships, for example, A and/or B, which can indicate: the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A, B can be singular or plural.
  • the character “/” generally indicates that the associated objects are an “or” relationship.
  • At least one item(s) below” or similar expressions thereof refer to any combination of these items, including any combination of single item(s) or plural items(s).
  • At least one (a) of a, b and c can represent: a, or, b, or, c, or, a and b, or, a and c, or, b and c, or, a , b and c.
  • a, b and c can be single or multiple respectively.
  • LTE Long Term Evolution
  • FDD frequency division duplex
  • TDD time division duplex
  • UMTS universal mobile telecommunication system
  • WiMAX worldwide interoperability for microwave access
  • 5G mobile communication system may include a non-standalone (NSA, NSA) and/or an independent network (standalone, SA).
  • NSA non-standalone
  • SA independent network
  • the technical solutions provided in this application can also be applied to machine type communication (MTC), Long Term Evolution-machine (LTE-M), device-to-device (D2D) Network, machine to machine (M2M) network, internet of things (IoT) network or other network.
  • the IoT network may include, for example, the Internet of Vehicles.
  • vehicle to X, V2X, X can represent anything
  • the V2X may include: vehicle to vehicle (vehicle to vehicle, V2V) communication, vehicle and vehicle Infrastructure (V2I) communication, vehicle to pedestrian (V2P) or vehicle to network (V2N) communication, etc.
  • the network device may be any device with a wireless transceiver function.
  • the device includes but is not limited to: evolved Node B (evolved Node B, eNB), radio network controller (radio network controller, RNC), Node B (Node B, NB), base station controller (base station controller, BSC) , base transceiver station (base transceiver station, BTS), home base station (for example, home evolved NodeB, or home Node B, HNB), baseband unit (baseband unit, BBU), wireless fidelity (wireless fidelity, WiFi) system Access point (AP), wireless relay node, wireless backhaul node, transmission point (TP) or transmission and reception point (TRP), etc.
  • evolved Node B evolved Node B
  • RNC radio network controller
  • Node B Node B
  • BSC base station controller
  • base transceiver station base transceiver station
  • BTS home base station
  • home base station for example, home evolved NodeB, or home Node B, HNB
  • It can also be 5G, such as NR , a gNB in the system, or, a transmission point (TRP or TP), one or a group of (including multiple antenna panels) antenna panels of a base station in a 5G system, or, it can also be a network node that constitutes a gNB or a transmission point, Such as baseband unit (BBU), or distributed unit (distributed unit, DU) and so on.
  • BBU baseband unit
  • DU distributed unit
  • a gNB may include a centralized unit (CU) and a DU.
  • the gNB may also include an active antenna unit (AAU).
  • CU implements some functions of gNB
  • DU implements some functions of gNB.
  • CU is responsible for processing non-real-time protocols and services, implementing radio resource control (RRC), and packet data convergence protocol (PDCP) layer function.
  • RRC radio resource control
  • PDCP packet data convergence protocol
  • the DU is responsible for processing physical layer protocols and real-time services, and implementing the functions of the radio link control (RLC) layer, medium access control (MAC) layer, and physical (PHY) layer.
  • RLC radio link control
  • MAC medium access control
  • PHY physical layer.
  • AAU implements some physical layer processing functions, radio frequency processing and related functions of active antennas.
  • the higher-layer signaling such as the RRC layer signaling
  • the network device may be a device including one or more of a CU node, a DU node, and an AAU node.
  • the CU can be divided into network devices in an access network (radio access network, RAN), and the CU can also be divided into network devices in a core network (core network, CN), which is not limited in this application.
  • the network equipment provides services for the cell, and the terminal equipment communicates with the cell through the transmission resources (for example, frequency domain resources, or spectrum resources) allocated by the network equipment, and the cell may belong to a macro base station (for example, a macro eNB or a macro gNB, etc.) , can also belong to the base station corresponding to the small cell, where the small cell can include: urban cell (metro cell), micro cell (micro cell), pico cell (pico cell), femto cell (femto cell), etc. , these small cells have the characteristics of small coverage and low transmission power, and are suitable for providing high-speed data transmission services.
  • a macro base station for example, a macro eNB or a macro gNB, etc.
  • the small cell can include: urban cell (metro cell), micro cell (micro cell), pico cell (pico cell), femto cell (femto cell), etc.
  • these small cells have the characteristics of small coverage and low transmission power, and are suitable for providing high-speed data
  • a terminal device may also be referred to as user equipment (user equipment, UE), an access terminal, a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, Terminal, wireless communication device, user agent or user equipment.
  • user equipment user equipment
  • UE user equipment
  • an access terminal a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, Terminal, wireless communication device, user agent or user equipment.
  • the terminal device may be a device that provides voice/data connectivity to the user, such as a handheld device with a wireless connection function, a vehicle-mounted device, and the like.
  • some examples of terminals can be: mobile phone (mobile phone), tablet computer (pad), computer with wireless transceiver function (such as notebook computer, palmtop computer, etc.), mobile internet device (mobile internet device, MID), virtual reality (virtual reality, VR) equipment, augmented reality (augmented reality, AR) equipment, wireless terminals in industrial control (industrial control), wireless terminals in unmanned driving (self driving), wireless terminals in remote medical (remote medical) Terminal, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, cellular phone, cordless Telephone, session initiation protocol (SIP) telephone, wireless local loop (WLL) station, personal digital assistant (PDA), handheld device, computing device or connection with wireless communication capabilities
  • wearable devices can also be called wearable smart devices, which is a general term for the intelligent design of daily wear and the development of wearable devices using wearable technology, such as glasses, gloves, watches, clothing and shoes.
  • a wearable device is a portable device that is worn directly on the body or integrated into the user's clothing or accessories.
  • Wearable device is not only a hardware device, but also realizes powerful functions through software support, data interaction, and cloud interaction.
  • wearable smart devices include full-featured, large-scale, complete or partial functions without relying on smart phones, such as smart watches or smart glasses, and only focus on a certain type of application function, which needs to cooperate with other devices such as smart phones. Use, such as all kinds of smart bracelets, smart jewelry, etc. for physical sign monitoring.
  • the terminal device may also be a terminal device in an internet of things (Internet of things, IoT) system.
  • IoT Internet of things
  • IoT is an important part of the development of information technology in the future. Its main technical feature is to connect items to the network through communication technology, so as to realize the intelligent network of human-machine interconnection and interconnection of things.
  • IoT technology can achieve massive connections, deep coverage, and terminal power saving through, for example, narrow band (NB) technology.
  • NB narrow band
  • terminal equipment can also include sensors such as smart printers, train detectors, and gas stations.
  • the main functions include collecting data (part of terminal equipment), receiving control information and downlink data of network equipment, and sending electromagnetic waves to transmit uplink data to network equipment. .
  • the ML algorithm can make the MIMO system obtain the best detection performance, and is the optimal detection algorithm of the MIMO system.
  • the goal of the ML algorithm is to find the optimal transmitted signal vector Make Minimum, y represents the received signal of the MIMO system, and H represents the channel matrix of the MIMO system.
  • the computational complexity of the ML algorithm increases exponentially with the size of the constellation diagram and the number of transmitted symbols, so the computational complexity of the ML algorithm is high.
  • A is a conjugate symmetric matrix and any k-order principal subform is not zero, then A has a unique decomposition form:
  • L is the unit lower triangular matrix
  • D is the diagonal matrix
  • L H is the conjugate transpose matrix of L.
  • the tree search detection algorithm is a kind of signal detection algorithm based on the tree search strategy. This algorithm can reduce the complexity of the detection algorithm by reducing the spatial range of the candidate signal under the condition close to the maximum likelihood detection. In the design of the MIMO detector Has broad application prospects.
  • a wireless multiple-access communication system suitable for the detection method of the MIMO system provided by the present application is now introduced with reference to FIG. 1 .
  • FIG. 1 is a schematic diagram of a wireless multiple access communication system provided by the present application.
  • an access point (AP) 100 includes multiple antenna groups. As shown in FIG. 1 , one antenna group may include antenna 104 and antenna 106 , another may include antenna 108 and antenna 110 , and yet another may include antenna 112 and antenna 114 . It should be understood that FIG. 1 is only for illustration and does not constitute any limitation, for example, in another example, each antenna group may also utilize more or less antennas.
  • access terminal 116 may be in direct communication with antenna 112 and antenna 114 that communicate information to access terminal 116 on forward link 120 and in reverse Information is received from access terminal 116 over link 118 .
  • access terminal 122 may be in direct communication with antenna 104 and antenna 106 that communicate information to access terminal 122 on forward link 126 and on reverse link 124 information is received from the access terminal 122 on the network.
  • access terminal 116 and access terminal 122 may have multiple antennas with which access terminal 116 and MIMO communication is established between the access terminal 122 and the access point 100 .
  • the communication links 118, 120, 124 and 126 may communicate using different frequencies. For example, forward link 120 may use a different frequency than that used by reverse link 118 .
  • Each group of antennas and/or the area in which they are designed to communicate may be referred to as a sector of the access point.
  • an antenna cluster may be designed to communicate with access terminals that fall within a sector of the area covered by access point 100.
  • the transmit antenna of access point 100 may utilize beamforming to improve the signal-to-noise ratio of the forward links for different access terminals 116 and 122.
  • the use of beamforming by an access point to transmit to access terminals randomly scattered throughout its coverage can cause significant damage to access terminals in neighboring cells, as opposed to an access point transmitting to all of its access terminals through a single antenna. Less distraction.
  • An access point such as access point 100, may be a fixed station used to communicate with terminals, and may also be referred to by base station, Node B, and/or other suitable terms. Additionally, an access terminal, such as access terminal 116 or access terminal 122, may also be referred to in terms of mobile terminal, UE, wireless communication device, terminal, wireless terminal, and/or other terminology.
  • the following describes a MIMO system that can be applied to the wireless multiple access communication system shown in FIG. 1 with reference to FIG. 2 .
  • FIG. 2 is a schematic diagram of a MIMO system 200 that can be applied to the methods provided herein.
  • the system 200 can include an access point AP 210 and an access terminal AT 220, wherein the access terminal AT 220 and the access point AP 210 can communicate (eg, wirelessly or wiredly, etc.).
  • AP 210 includes a data source 212 that can generate or otherwise obtain data to be communicated to one or more ATs 220.
  • Data from data source 212 may be sent to encoding component 214 to process the data for communication to AT 220 via MIMO transmission.
  • encoding component 214 a series of bits comprising data to be transmitted to AT 220 can be grouped into a spatial stream for simultaneous transmission by transmitter 216 via antenna 218.
  • the encoding component may modulate each spatial stream using one or more digital modulation techniques such as phase shift keying (PSK), binary phase shift keying (BPSK) ), quadrature phase shift keying (QPSK), 16 quadrature amplitude modulation (16 quadrature amplitude modulation, 16-QAM), 64 quadrature amplitude modulation (64 quadrature amplitude modulation, 64-QAM), and /or another suitable modulation technique under which the data bits comprising each stream may be mapped to a series of modulation symbols based on a set of constellation points.
  • digital modulation techniques such as phase shift keying (PSK), binary phase shift keying (BPSK) ), quadrature phase shift keying (QPSK), 16 quadrature amplitude modulation (16 quadrature amplitude modulation, 16-QAM), 64 quadrature amplitude modulation (64 quadrature amplitude modulation, 64-QAM), and /or another suitable modulation technique under which the data bits comprising each stream may be mapped to a
  • orthogonal frequency division multiplexing can be used to divide the spatial stream among multiple orthogonal sub-carriers, so that each sub-carrier can individually use one or more modulation techniques.
  • the mapped modulation symbols corresponding to each stream may then be provided to the respective transmitter 216 for communication to the AT 220 via a series of M antennas 218 as a modulated analog signal.
  • spatial streams corresponding to signals transmitted by AP 210 may be received by a series of M receivers 224 via respective antennas 222.
  • the M-dimensional received signal vector y corresponding to the stream received at AT 220 may be expressed as follows:
  • y is the received signal with dimension M ⁇ 1
  • H is the channel matrix with dimension M ⁇ M
  • x is the transmitted signal with dimension M ⁇ 1
  • N is the independent and identically distributed statistics
  • the dimension of N is M ⁇ 1
  • ⁇ 2 is the noise variance
  • I is the identity matrix.
  • the spatial stream received by the receiver 224 can be communicated to a signal detection component 226, which can utilize the stream received by the receiver 224 and knowledge about the effective MIMO channel to obtain information from the AP 210 transmitted stream.
  • signal detection component 226 can determine the hard-decision output for each bit in the spatial stream received from AP 210 by determining the expected sign for those bits. For example, a bit with a value of 1 may be represented by a hard decision output +1, and a bit with a value of 0 may be represented by a hard decision output of -1.
  • the signal detection component 226 can determine the expected sign of each bit in the spatial stream received from the AP 210 plus the likelihood that the corresponding expected sign of each bit has been correctly detected—e.g., a bit as +1 or The likelihood of -1 being sent - to determine the soft decision output for these bits.
  • the signal detection component 226 can provide low complexity soft output detection by employing one or more near-soft output maximum likelihood detection algorithms as described below. After successful detection, the detected transport stream may be provided to data sink 228 for use by AT 220.
  • FIG. 2 is only for illustration and does not constitute any limitation to the MIMO system to which the detection method of the MIMO system provided by the present application is applicable.
  • the system 200 may also include more data APs 210 and/or a greater number of ATs 220.
  • similar components and techniques may be used by AP 210 and/or AT 220 for communications from AT 220 to AP 120 (eg, communications on reverse link 118 and reverse link 124).
  • the baseband equivalent model of the MIMO system can be converted into a tree search model first, and then the tree search model is processed to reduce the complexity of the algorithm.
  • the QR algorithm is usually used to convert the baseband equivalent model of the MIMO system into a tree search model, and then the tree model is processed. Specifically, the baseband equivalent model of the MIMO system is first transformed into an enumeration process of a metric function, and then the QR algorithm is used to convert the enumeration process of the metric function into a tree search process.
  • the supported M-QAM constellation scale and antenna scale vary widely.
  • the 802.11ac Wi-Fi protocol supports constellation mapping from BPSK to 256QAM
  • the antenna configuration of MIMO systems also supports from 1 ⁇ 1 to 8 ⁇ 8. Therefore, an M ⁇ M-dimensional MIMO system model is used as an example for introduction in the following.
  • the value of M may be 2, 4, or 8, etc., which is not limited.
  • the M ⁇ M dimensional MIMO system shown in FIG. 2 can be expressed as:
  • y is the received signal with dimension M ⁇ 1
  • H is the channel matrix with dimension M ⁇ M
  • x is the transmitted signal with dimension M ⁇ 1
  • N is Gaussian noise vector with independent and identically distributed statistical properties, namely N ⁇ N(0, ⁇ 2 I), and the dimension of N is M ⁇ 1
  • ⁇ 2 is the noise variance
  • I is the identity matrix.
  • the m-th (m is a positive integer, and 1 ⁇ m ⁇ M) dimension of the above-mentioned M ⁇ M-dimensional MIMO system transmits the i-th (i is a positive integer, and 1 ⁇ i ⁇ Q m , and Q m is the m-th dimension.
  • the log-likelihood ratio (LLR) of the number of transmitted bits of the transmitted signal vector can be expressed by the following formula:
  • X + 1 is The set of transmitted symbols, that is, X +1 is the set of bits included in the mth-dimension transmitted signal with +1
  • X ⁇ 1 is The set of transmitted symbols, that is, X ⁇ 1 is the set of bits included in the mth-dimension transmitted signal that is -1.
  • the computational complexity increases exponentially with the dimension of the transmitted signal x and the scale of the transmitted signal constellation points.
  • the enumeration process of the metric function expressed by the above formula (2-4) is usually converted into a tree search process, that is, the near maximum likelihood detection algorithm (NML).
  • the baseband equivalent model of the M ⁇ M-dimensional MIMO system shown in the above formula (2-1) is extended, and the extended model can be expressed by the following formula:
  • the metric function can be further expressed as:
  • () -1 represents the inversion operation, is the received signal vector of the linear minimum mean square error (LMMSE) algorithm.
  • the present application provides a detection method for a MIMO system, which can effectively reduce the complexity of the detection method on the premise that the detection result has high accuracy.
  • FIG. 3 is a schematic flowchart of a detection method 100 of a MIMO system provided by the present application. It can be understood that the detection method for a MIMO system provided by the present application is applicable to a MIMO system in which the number of antennas at the receiving end is less than or equal to the number of antennas at the transmitting end. As shown in FIG. 3 , the method 100 may include steps 110 to 140 , and the steps 110 to 140 will be described below with reference to the accompanying drawings.
  • steps 110 to 140 in the method 100 are described below by taking an M ⁇ M (M is a positive integer greater than or equal to 2) dimensional MIMO system as an example.
  • Step 110 Determine a conjugate symmetric matrix, the conjugate symmetric matrix is determined according to the first channel matrix, the noise variance and the identity matrix, and the first channel matrix is obtained by processing the received signal.
  • the above-mentioned conjugate symmetric matrix is determined according to the first channel matrix, the noise variance and the identity matrix, which can be understood as:
  • the conjugate symmetric matrix is the conjugate transpose matrix of the first channel matrix and the first channel matrix
  • the product of , and the sum of the products of the noise variance and the identity matrix, the above conjugate symmetric matrix G can be expressed by the following formula:
  • H is the first channel matrix
  • H H is the conjugate transpose matrix of the first channel matrix
  • ⁇ 2 is the noise variance
  • I is the identity matrix
  • step 110 it also includes:
  • the M channel matrices are in one-to-one correspondence with the M tree search models, and the M tree search models have a one-to-one correspondence with the M layers of the MIMO system. ;
  • One of the M channel matrices is determined as the first channel matrix, and the minimum path metric of the tree search model determined according to the first channel matrix is smaller than the tree search model determined by the remaining M-1 channel matrices in the M channel matrices. Minimum path metric.
  • the first channel matrix is determined from one of the M channel matrices above. It can be understood that the above-mentioned first channel matrix may be a channel matrix among the M channel matrices, and the tree search model determined according to the first channel matrix The minimum path metric of is less than the minimum path metric of the tree search model determined by the remaining M-1 channel matrices in the M channel matrices.
  • M channel matrices are in one-to-one correspondence with M tree search models. It can be understood that, using M different channel matrices to map the transmitted signal corresponding to the same received signal to the tree search model, M different tree search models can be obtained. .
  • the above-mentioned M tree search models are in one-to-one correspondence with the M layers of the MIMO system. It can be understood that the root node of each tree search model in the M tree search models corresponds to one layer of the MIMO system. That is to say, when each layer of the MIMO system is used as the root node of the tree search model, the M tree search models can be obtained.
  • Step 120 use a preprocessing matrix and a unit lower triangular matrix to map the transmitted signal corresponding to the received signal to a tree search model, and the preprocessing matrix and the unit lower triangular matrix are obtained by LDL decomposition of a conjugate symmetric matrix.
  • L is the unit lower triangular matrix
  • L H is the transpose of L, that is, L H is the unit upper triangular matrix
  • D is the preprocessing matrix, and is a diagonal matrix.
  • the distance metric function of the transmitted signal of the M ⁇ M dimensional MIMO system can be expressed by the following formula:
  • the distance metric function of the approximate maximum likelihood detection of the transmitted signal of the M ⁇ M dimensional MIMO system can be expressed by the following formula, that is, the above tree
  • the search model can be represented by the following formula:
  • Step 130 based on the tree search method, expand from the top layer of the tree search model to the bottom layer by layer, and determine the metric set, the metric set includes the metric set of the root node and the metric set of multiple leaf nodes, and the metric set of the root node is included in the first.
  • the candidate set, the first candidate set is determined from the first constellation point set according to the first rule, the first constellation point set is obtained by encoding and mapping the transmitted signal corresponding to the root node, and the metric set makes the tree search model have the smallest value. path metrics.
  • the above tree search model includes M layers, the root node corresponds to the Mth layer, and multiple leaf nodes correspond to the M-1th to the 1st layer respectively, M is a positive integer greater than or equal to 2, based on the tree search method from the tree search model.
  • the topmost layer expands down layer by layer to determine a set of metrics, including:
  • the first distance metric function is determined according to the preprocessing matrix, the lower triangular matrix, the received signal, the first channel matrix, the conjugate transpose matrix of the first channel matrix and the noise variance;
  • the first candidate set is searched based on the first distance metric function, the fourth constellation point is determined as the constellation point included in the metric set of the root node, and the transmitted signal corresponding to the fourth constellation point makes the value of the first distance metric function less than or equal to first threshold;
  • the second distance metric function is the distance metric function of any layer corresponding to the leaf node in the M-1 to the first layer;
  • a search is performed in the second candidate set based on the second distance metric function, and the fifth constellation point is determined as the constellation point included in the metric set of the ith leaf node, and the transmission signal corresponding to the fifth constellation point makes the second distance metric function
  • the value is less than or equal to the second threshold.
  • the second candidate set is determined according to the estimated transmission signal of the i-th leaf node and the second constellation point set.
  • the second constellation point set is obtained by encoding the transmission signal corresponding to the i-th leaf node. mapped.
  • the first distance metric function of the above root node can be represented by the following formula:
  • D M is the M-th diagonal element of the preprocessing matrix D obtained by LDL decomposition of the conjugate symmetric matrix
  • x M ⁇ S M S M is the root node candidate set.
  • the above-mentioned first rule includes determining the following constellation points in the first constellation point set as the constellation points included in the first candidate set: the first constellation point, the second constellation point and the third constellation point,
  • the first constellation point is the constellation point with the closest Euclidean distance to the estimated transmitted signal of the root node
  • the second constellation point is the closest Euclidean distance with the first constellation point and contains the inverse bit corresponding to each bit of the first constellation point.
  • the third constellation point is the constellation point included in the first area except the first constellation point and the second constellation point
  • the first area is included in the area corresponding to the first constellation point set
  • the first area is based on the pre- It is assumed that the complexity is determined from the region corresponding to the first constellation point set, and the estimated transmission signal of the root node is determined according to the preprocessing matrix, the received signal, the first channel matrix, and the noise variance.
  • the estimated transmission signal of the root node can be expressed by the following formula:
  • the above-mentioned second distance metric function does not include the interference to the i-th layer by the transmitted signals corresponding to the i+1-th layer to the M-th layer included in the tree search model.
  • the interference of the signals that have traversed the layer to the signal sent to the i-th layer is pruned.
  • the Mth layer of the tree search model may be searched first, and then the Mth layer of the tree search model may be searched first.
  • the M-1 layer is searched, and then the i-th layer of the tree search model is searched, where i is a positive integer less than or equal to M-1 and greater than or equal to 1.
  • the second distance metric function of the i-th leaf node can be represented by the following formula:
  • D i is the ith diagonal element of the preprocessing matrix D obtained after LDL decomposition of the conjugate symmetric matrix, is the equivalent received signal of the leaf node of the ith layer, x i ⁇ S i , and S i is the candidate set of the leaf node of the ith layer.
  • the above-mentioned second candidate set is determined according to the estimated transmission signal of the ith leaf node and the second set of constellation points, including:
  • the i-th layer leaf node candidate set S i is determined. Based on this, the distance transmission symbol can be estimated The nearest constellation point, and the distance to send the symbol estimate The constellation points that are closest to each other and respectively contain the inverse bit corresponding to each bit, and the constellation points that are included in the degree of the leaf node candidate set Si and are not selected are determined as the constellation points included in the leaf node candidate set Si.
  • the above-mentioned method for determining the fourth constellation point in the first candidate set and the method for determining the fifth constellation point in the second candidate set may be existing methods, which will not be described in detail here.
  • the above-mentioned first threshold and the above-mentioned second threshold may be set according to specific application scenarios, which are not limited.
  • the first distance metric function determined by the above method is the same as the metric function of the transmitted signal corresponding to the Mth layer estimated according to the linear minimum mean square error LMMSE algorithm.
  • the above tree search methods include one of the following methods: generalized first tree search and depth first tree search.
  • Step 140 Determine the log-likelihood ratio of each information bit in the transmitted signal according to the metric set.
  • the log-likelihood ratio of the i-th (i is a positive integer greater than or equal to 1) bits of the transmission signal of the mth (m is a positive integer, and 1 ⁇ m ⁇ M) layer of the above-mentioned M ⁇ M-dimensional MIMO system It can be expressed by the following formula:
  • X +1 is The set of transmitted symbols
  • X -1 is the set of transmitted symbols
  • the i-th bit of the signal is transmitted for the m-th layer.
  • the M ⁇ M dimensional MIMO system model can be expressed as:
  • each symbol in the above formula (3-1) is as follows: y is the received signal, H is the channel matrix, x is the transmitted signal, x corresponds to Q quadrature amplitude modulation (QAM) constellation points, and the symbol N is the Gaussian noise with independent and identically distributed statistical properties, namely N ⁇ N(0, ⁇ 2 I), where ⁇ 2 is the noise variance, and I is the identity matrix.
  • QAM quadrature amplitude modulation
  • the log-likelihood ratio of the i-th (i is a positive integer greater than or equal to 1) bits of the transmitted signal of the mth (m is a positive integer, and 1 ⁇ m ⁇ M) layer of the above-mentioned M ⁇ M-dimensional MIMO system can be obtained by The following formula expresses:
  • the log-likelihood ratio of the transmitted signal of the M ⁇ M-dimensional MIMO system can be converted into an enumeration of the metric function for the transmitted signal, and the M ⁇ M-dimensional MIMO system can be determined.
  • the distance metric function of the transmitted signal can be expressed by the following formula:
  • NML near maximum likelihood
  • L is the unit lower triangular matrix
  • L H is the transpose of L, that is, L H is the unit upper triangular matrix
  • D is the diagonal matrix
  • the tree search model is from top to bottom, the order is the Mth layer, the M-1th layer, and so on, the first layer.
  • the Mth layer is recorded as the layer where the root node of the tree search model is located, and the other layers are the layers where the leaf nodes of the tree search model are located.
  • the above formula (3-8) can be solved through a tree search model by using the unit upper triangular property of the matrix L H. That is to say, the global optimal path can be approximated by calculating the local optimal node layer by layer and pruning the interference of the previous layer in the next layer.
  • z M [Wy] M , that is, z M is the M-th symbol of the MMSE estimation vector, x M ⁇ S M , and S M is the root node candidate set.
  • the set S M is the estimated symbol with the normalized LMMSE on the transmitted symbol constellation Q M (that is, an example of the above-mentioned first constellation) (That is, an example of the estimated transmission signal of the above-mentioned root node)
  • the nearest constellation point The nearest constellation point.
  • the solution to this problem is usually to replace the metric value of the inverse bit with the maximum value, the median value, the mean value or the modified value of the mean value of the selected path metric set of this bit. Not only the implementation complexity is high, but also the performance is poor.
  • the present application provides a method for determining a root node candidate set (ie, an example of the above-mentioned first candidate set), which can reduce computational complexity on the premise that the problem of missing inverse bits can be effectively avoided.
  • FIG. 4 is a schematic diagram of determining a root node candidate set provided by the present application. It should be understood that FIG. 4 is only for illustration and does not constitute any limitation to the method for determining the root node candidate set SM provided by the present application. For example, the method for determining the root node candidate set SM provided in this application is also applicable to constellations of smaller scale (eg, 16QAM constellation) or larger scale (eg, 128QAM constellation or 256QAM constellation).
  • the constellation diagram includes 64 symbols, and each symbol can be Represented by 6 bits.
  • the symbol in the first row and first column in the 64QAM constellation diagram can be represented as 101111.
  • the root node candidate set S M provided by this application can be determined according to the following steps:
  • the degree of the root node candidate set SM can be understood as the big circle in FIG. 4 .
  • the above operational complexity constraints may be predefined.
  • the computational complexity may be determined according to the computational performance of the device and the like. For example, when the performance of the device is poor, the above-mentioned constraints on the computational complexity can be set to be stricter.
  • the computational complexity constraint of the root node candidate set S M can be expressed as: log 2
  • the nearest constellation point is the constellation point #1 in FIG. 4 (ie, an example of the above-mentioned first constellation point).
  • constellation points that are closest to the constellation point selected in step (2) and respectively include the inverse bit corresponding to each bit can be selected.
  • the above distance is the closest, which can be understood as the estimation of the constellation point and the transmitted symbol on the constellation map QM .
  • the Euclidean distance is the closest.
  • the root node candidate set S M provided by the embodiment of the present application includes: the estimated transmission symbol distance from the root node The nearest constellation point (eg, constellation point #1 in Figure 4), and the distance estimate transmit symbol The constellation points that are closest to each other and respectively contain the inverse bit of each bit (e.g., constellation point #2 in Figure 4), and the constellation points that are included in the degree of the root node candidate set SM and are not selected (e.g., Figure 4) Constellation point in 4 #3)
  • the above selection rules can ensure that the selected root node candidate set SM contains the metrics of all bit symbols, and there are no missing bits. Since the determined root node candidate set SM does not have the problem of lack of bits, using the method provided by the present application can save the calculation process of the missing bit metric function (for example, using the maximum value of the selected path metric set of this bit, The median, mean or mean correction value), which can effectively reduce the computational complexity.
  • the root node candidate set of the tree search model After the root node candidate set of the tree search model is determined, it is necessary to determine the metric function of the leaf nodes included in the tree search model (that is, an example of the second distance metric function) and the corresponding leaf node candidate set (that is, the second candidate an example of a collection). In practical applications, in order to eliminate the interference between signals, when calculating the metric function of the signal sent by the mth layer, it is necessary to delete the interference of the signal of the traversed layer to the signal sent by the mth layer.
  • the Mth layer of the tree search model may be searched first, and then the Mth layer of the tree search model may be searched first.
  • the M-1 layer is searched, and then the i-th layer of the tree search model is searched, where i is a positive integer less than or equal to M-1 and greater than or equal to 1.
  • the equivalent received signal of the leaf node included in the i-th layer of the tree search model corresponding to the M ⁇ M dimensional MIMO system It can be expressed by the following formula:
  • i is a positive integer, and 1 ⁇ i ⁇ M-1; is the interference from the j-th layer to the i-th layer of the M ⁇ M-dimensional MIMO model, i+1 ⁇ j ⁇ M.
  • the distance metric function of the leaf node of the i-th layer of the above tree search model can be obtained (that is, the second distance metric function of the i-th leaf node above An example of ) can be expressed by the following formula:
  • x i ⁇ S i , and S i is a candidate set of leaf nodes at the i-th layer (ie, an example of the above-mentioned second candidate set).
  • S i may include the distance transmission symbol estimate on the constellation Q i corresponding to the leaf node of the i-th layer (that is, an example of the above-mentioned second constellation) (ie, an example of the estimated transmission signal of the i-th leaf node above) the nearest constellation point, where
  • the candidate set S i of leaf nodes at the i-th layer may also be determined according to the above-mentioned method for determining the candidate set S M of the root node.
  • the leaf node candidate set S i may include: the estimated transmission symbol from the i-th layer leaf node The nearest constellation point (eg, constellation point #1 in Figure 4), and the distance estimate transmit symbol The constellation points that are closest to each other and respectively contain the inverse bit for each bit (e.g., constellation point #2 in Figure 4), and the constellation points that are included within the degree of the leaf node candidate set Si and are not selected (e.g., Figure 4 constellation point in #3).
  • the metric function of the root node of the tree search model can also be used to calculate the log-likelihood ratio of the LMMSE estimate of the transmitted signal of the layer where the root node is located, and the specific derivation process can be as follows:
  • the metric function estimated by LMMSE at the root node level can be expressed by the following formula:
  • the root node metric function when the mth layer transmitted symbol is used as the root node in the NML algorithm can be used to calculate the log-likelihood ratio of the LMMSE estimate of the mth layer transmitted symbol. Therefore, the deep integration of the NML algorithm and the LMMSE algorithm is realized, that is, the log-likelihood ratio of the root node estimated based on the LMMSE algorithm can be obtained only by using the NML algorithm and through the transformation of intermediate variables, avoiding the high complexity of directly using the LMMSE algorithm. The problem.
  • the best performing sorting method is Bell Labs vertical hierarchical space-time code V-BLAST sorting, the complexity of which increases exponentially with the number of transmission layers; simplified sorting methods, such as the sorted QR algorithm SQRD, cannot be estimated in advance because The signal-to-noise ratio of the root node has a risk of robustness, and it is not considered that the degree that the root node traverses the constellation point set is much larger than the degree that the leaf node traverses the constellation point set.
  • the tree search model does not need to sort the layers corresponding to the tree search model according to the size of the signal-to-noise ratio.
  • the column transformation matrix p l , l 0, 1, 2, .
  • the channel matrix H is subjected to M column transformations to obtain M different channel matrices H l after column permutation.
  • the method provided above is used to detect the M ⁇ M dimensional MIMO system.
  • the estimation results corresponding to each channel matrix H l are synthesized to determine the optimal transmission signal of the M ⁇ M dimensional MIMO system.
  • each layer of the transmitted signal has the opportunity to be at the root node position, and the path search order of the tree search model corresponding to each layer included in the MIMO system is symmetrical, Thereby, the robust risk caused by simplified sorting can be avoided.
  • the tree search branch order set by the above formula (3-22) or the above formula (3-23) can guarantee At the same time, the NML estimated log-likelihood ratio and the LMMSE estimated log-likelihood ratio of the transmitted signal x are obtained, so as to realize the deep fusion of the NML algorithm and the LMMSE algorithm.
  • FIG. 3 is only for illustration and does not constitute any limitation to the detection method provided in the present application.
  • the above-mentioned method 100 can also be used to detect the transmission signal and estimate the modulation order ML of the transmission channel of the interference layer for the MU-MIMO system.
  • the method 200 for MIMO detection provided by the present application will be introduced below with reference to FIG. 5 and FIG. 6 .
  • FIG. 5 is a schematic flowchart of a detection method 200 of a MIMO system provided by the present application. As shown in FIG. 5 , the method 200 includes steps 210 to 270 , and the steps 210 to 270 are described below.
  • a 4 ⁇ 4-dimensional MIMO system is taken as an example to introduce the MIMO detection method provided by the present application.
  • the structure of the 4 ⁇ 4 dimensional MIMO system may be as shown in FIG. 6 .
  • 6 includes a selection module 610 for selecting whether the detection result output by the detection result 620 is based on the NML detection result or the LMMSE detection result; the detection result 620 is used for outputting the estimated logarithm of each information bit in the transmitted signal Likelihood ratio; among them, LDD -1 and ZDL are used to represent the intermediate variables for detecting the 4 ⁇ 4 dimensional MIMO system.
  • LDD -1 can be used to represent the matrix obtained after LDL decomposition of the channel matrix, which can include the preprocessing matrix obtained by the above method 100, the inverse matrix of the preprocessing matrix, and the unit upper triangular matrix, etc.; ZDL is used to represent the above The product of the unit upper triangular matrix, the received signal, and L- H D -1 L -1 H Hy in the method 100, LLR Calc is used to represent the calculated log-likelihood ratio.
  • MIMO detection method shown in FIG. 6 will be described in detail in conjunction with steps 210 to 270 . It should be understood that the detection method in the following is also applicable to MIMO systems of other scales. For example, MIMO systems of 8x8 dimensions or 16x16 dimensions.
  • Step 210 Determine the channel matrix according to the received signal of the MIMO system.
  • the above step 210 can be understood as determining a channel matrix according to a received signal of the 4 ⁇ 4-dimensional MIMO system.
  • the channel matrix H can be represented by the following formula:
  • the conjugate symmetric matrix is a matrix obtained from the channel matrix, the conjugate transpose of the channel matrix and noise, and the conjugate symmetric matrix can be expressed by the following formula:
  • H is the channel matrix
  • H H is the conjugate transpose of the channel matrix
  • ⁇ 2 is the noise variance
  • I is the identity matrix
  • the matched filter matrix is a matrix obtained from the conjugate transpose of the channel matrix and the received signal.
  • the matched filter matrix can be expressed by the following formula:
  • H H is the conjugate transpose of the channel matrix
  • y is the received signal.
  • Step 230 Perform LDL decomposition on the conjugate symmetric matrix to obtain a preprocessing matrix and a unit lower triangular matrix.
  • the LDL decomposition of the conjugate symmetric matrix can be expressed as:
  • Step 240 Determine the equivalent received signal of the MIMO system according to the preprocessing matrix, the unit lower triangular matrix and the matched filtering matrix.
  • the equivalent received signal can be represented by the following formula:
  • Step 250 according to the equivalent received signal, determine the root node of the corresponding tree model of the MIMO system, perform search, and determine the root node metric set.
  • the method for determining the root node metric set in the above step 250 is the same as the method for determining the root node metric set in the method 100, and details are not described herein again.
  • Step 260 Determine the leaf nodes of the corresponding tree model of the MIMO system to search, and determine the leaf node metric set.
  • the method for determining the leaf node metric set in the above step 260 is the same as the method for determining the leaf node metric set in the method 100, and details are not described herein again.
  • Step 270 Determine the log-likelihood ratio of the transmitted signal of the MIMO system according to the root node metric set and the leaf node metric set.
  • the method for determining the log-likelihood ratio of the transmitted signal according to the metric set of the leaf node and the root node in the above step 270 is the same as that in the method 100, and details are not repeated here.
  • FIG. 5 is only for illustration and does not constitute any limitation to the MIMO detection method provided by the present application.
  • the above-mentioned method 200 can also be used to perform transmission signal detection and interference layer transmission channel modulation order estimation and detection for a MU-MIMO system.
  • the above method 200 to detect the MU-MIMO system, when the modulation order of the interference layer is known, the leaf node constellation points of the layer are traversed and the metric functions are accumulated, otherwise, the traversal is not performed.
  • NML detection is performed on the traversal layer
  • LMMSE detection is performed on the non-traversal layer
  • the NML and LMMSE detection algorithms can be flexibly switched according to the modulation order estimation result in the MU-MIMO scenario.
  • the above-mentioned method 200 can also be used to detect an 8 ⁇ 8-dimensional MIMO system.
  • the above-mentioned column transformation matrix can be replaced by the above-mentioned formula (3-23). Column transformation matrix.
  • the detection method of the MIMO system provided by the present application is described in detail with reference to FIG. 1 to FIG. 6 .
  • the MIMO system detection apparatus and the MIMO system detection device provided by the present application will be described in detail with reference to FIG. 7 and FIG. 8 .
  • FIG. 7 is a schematic block diagram of an apparatus 700 for detecting a MIMO system provided by the present application.
  • the detection apparatus 700 of the MIMO system shown in FIG. 7 includes an acquisition unit 701 and a processing unit 702,
  • the processing unit 702 is configured to determine a conjugate symmetric matrix, the conjugate symmetric matrix is determined according to the first channel matrix, the noise variance and the identity matrix, and the first channel matrix is obtained by processing the received signal.
  • the processing unit 702 used to construct the training dataset;
  • the processing unit 702 is further configured to use a preprocessing matrix and a unit lower triangular matrix to map the transmitted signal corresponding to the received signal to a tree search model, where the preprocessing matrix and the unit lower triangular matrix are the LDL of the conjugate symmetric matrix decomposed;
  • the processing unit 702 is further configured to expand downward layer by layer from the topmost layer of the tree search model based on a tree search method, and determine a metric set, where the metric set includes a metric set of a root node and a metric set of multiple leaf nodes.
  • the metric set of the node is included in the first candidate set, the first candidate set is determined from the first constellation point set according to the first rule, and the first constellation point set is encoded and mapped to the transmitted signal corresponding to the root node Obtained, the metric set makes the tree search model have the smallest path metric;
  • the processing unit 702 is further configured to determine the log-likelihood ratio of each information bit in the transmitted signal according to the metric set.
  • the first rule includes determining the following constellation points in the first constellation point set as constellation points included in the first candidate set:
  • the first constellation point, the second constellation point, and the third constellation point the first constellation point is the constellation point closest to the Euclidean distance of the estimated transmitted signal of the root node
  • the second constellation point is the closest constellation point to the first constellation point
  • the Euclidean distance is the closest and respectively contains the constellation point corresponding to the inverse bit of each bit of the first constellation point
  • the third constellation point is the constellation point except the first constellation point and the second constellation point included in the first area.
  • the first region is included in the region corresponding to the first constellation point set, and the first region is determined from the region corresponding to the first constellation point set according to a preset complexity.
  • the obtaining unit 701 is further configured to obtain the received signal
  • the processing unit 702 is further configured to process the received signal to obtain an original channel matrix
  • the processing unit 702 is further configured to:
  • the M channel matrices correspond to the M tree search models one-to-one, and the M tree search models correspond to the MIMO system.
  • M layers One-to-one correspondence of M layers;
  • the M channel matrices are represented by the following formula:
  • processing unit 702 is further configured to:
  • a search is performed in the first candidate set based on the first distance metric function, and a fourth constellation point is determined as a constellation point included in the metric set of the root node, and the transmitted signal corresponding to the fourth constellation point makes the first distance metric
  • the value of the function is less than or equal to the first threshold
  • the second candidate set is searched, and the fifth constellation point is determined as the constellation point included in the metric set of the ith leaf node, and the transmission signal corresponding to the fifth constellation point makes the second constellation point
  • the value of the distance metric function is less than or equal to the second threshold
  • the second candidate set is determined according to the estimated transmission signal of the ith leaf node and the second constellation point set
  • the second constellation point set is the ith leaf node.
  • the transmitted signal corresponding to the node is encoded and mapped.
  • the first distance metric function is the same as the metric function of the transmitted signal corresponding to the Mth layer estimated according to the linear minimum mean square error LMMSE algorithm.
  • the tree search manner includes one of the following manners: generalized first tree search and depth first tree search.
  • FIG. 8 is a schematic block diagram of a detection device 800 of a MIMO system provided by the present application.
  • the detection device 800 of the MIMO system includes: a transceiver 810 , a processor 820 and a memory 830 .
  • the transceiver 810, the processor 820 and the memory 830 communicate with each other through an internal connection path to transmit control and/or data signals.
  • the memory 830 is used to store computer programs, and the processor 820 is used to call from the memory 830. And run the computer program to control the transceiver 810 to send and receive signals.
  • the function of the transceiver 810 corresponds to the specific function of the obtaining unit 701 shown in FIG. 7 , and details are not described herein again.
  • the function of the processor 820 corresponds to the specific function of the processing unit 702 shown in FIG. 7 , and details are not described herein again.
  • the chip in this embodiment of the present application may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), or an application specific integrated circuit (ASIC). It is a central processing unit (CPU), a network processor (NP), a digital signal processing circuit (DSP), or a microcontroller (microcontroller unit). , MCU), it can also be a programmable logic device (PLD), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or other integrated chips.
  • FPGA field programmable gate array
  • ASIC application specific integrated circuit
  • SoC system on chip
  • ASIC application specific integrated circuit
  • CPU central processing unit
  • NP network processor
  • DSP digital signal processing circuit
  • microcontroller unit microcontroller unit
  • MCU programmable logic device
  • PLD programmable logic device
  • each step of the above-mentioned method can be completed by a hardware integrated logic circuit in a processor or an instruction in the form of software.
  • the steps of the methods disclosed in conjunction with the embodiments of the present application may be directly embodied as executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.
  • the software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art.
  • the storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware. To avoid repetition, detailed description is omitted here.
  • the processor in this embodiment of the present application may be an integrated circuit chip, which has a signal processing capability.
  • each step of the above method embodiments may be completed by a hardware integrated logic circuit in a processor or an instruction in the form of software.
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the steps of the method disclosed in conjunction with the embodiments of the present application may be directly embodied as executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor.
  • the software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art.
  • the storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware.
  • the memory in this embodiment of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically programmable Erase programmable read-only memory (electrically EPROM, EEPROM) or flash memory.
  • Volatile memory may be random access memory (RAM), which acts as an external cache.
  • RAM random access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous DRAM
  • SDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous link dynamic random access memory
  • direct rambus RAM direct rambus RAM
  • the present application also provides a computer program product, the computer program product includes: computer program code, when the computer program code is run on a computer, the computer is made to execute the steps shown in FIG. 3 and FIG. 5 .
  • the present application also provides a computer-readable medium, where the computer-readable medium stores program codes, when the program codes are run on a computer, the computer is made to execute the programs shown in FIG. 3 and FIG. 5 .
  • the present application further provides a system, which includes the aforementioned one or more first models and one or more second models.
  • the disclosed system, apparatus and method may be implemented in other manners.
  • the apparatus embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium.
  • the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution.
  • the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes .

Abstract

本申请提供了一种多输入多输出MIMO系统的检测方法和装置。该方法包括：确定共轭对称矩阵；使用预处理矩阵和单位下三角矩阵将发送信号映射至树搜索模型，预处理矩阵和单位下三角矩阵是对共轭对称矩阵进行LDL分解得到的；从树搜索模型的最顶层逐层向下扩展，确定度量集合，度量集合包括根节点的度量集合和多个叶节点的度量集合，根节点的度量集合包含于第一候选集合，第一候选集合是根据第一规则从第一星座点集合中确定的，度量集合使得树搜索模型具有最小的路径度量；根据度量集合，确定发送信号中每个信息比特的对数似然比。该方法在保证检测结果具有较高准确度的前提下，能够有效降低检测方法的复杂度。

Description

多输入多输出MIMO系统的检测方法和装置 技术领域
本申请涉及无线通信领域，并且更具体地，涉及一种多输入多输出MIMO系统的检测方法和装置。
背景技术
多输入多输出(multiple-input multiple-output，MIMO)系统，是指在无线通信链路的发射端和接收端使用多根天线来同时发射数据和接收数据的系统。通过使用MIMO系统，数据可被分成多个流，这些流可被同时由发射端发送和由接收端接收，从而在无需显著附加频谱或功率的情况下提升系统容量。在MIMO系统中，通过将数据分成流、在每个流中编组比特、将每个比特组映射至星座点、以及在随后基于为每个流映射的星座点经由多根发射天线将这些流作为已调制载波来传送。接收端使用多根天线接收经过调制的信号后，通过使用各种信号检测技术来从在接收端的天线处接收到的流获得数据。
实际应用中，当MIMO系统的接收天线数不少于发射符号数时，接收端能够通过一定的MIMO均衡算法尽可能消除或抑制多个发射符号之间的干扰，从而恢复出发射端发射的多个发射符号。通常使用线性检测(例如，最小均方误差(minimum mean squared error，MMSE)、迫零检测(zero forcing，ZF))或非线性检测(例如，最大似然检测(maximum likelihood，ML))，实现根据接收信号获得发送信号的目的。在上述线性检测过程中，由于不能获得全部信号的分集度，会导致检测结果不准确。在上述非线性检测过程中，虽然获得了全部信号的分集度，但由于计算复杂度随星座图规模及发射符号数呈指数增长，导致非线性检测算法实现复杂度高。
发明内容
本申请提供了一种多输入多输出MIMO系统的检测方法和装置，在保证检测结果具有较高准确度的前提下，能够有效降低检测方法的复杂度。
第一方面，提供了一种多输入多输出MIMO系统的检测方法，可以应用于包括MIMO系统的通信系统中，该方法包括：
确定共轭对称矩阵，该共轭对称矩阵是根据第一信道矩阵、噪声方差和单位矩阵确定的，该第一信道矩阵是对接收信号进行处理得到的；
使用预处理矩阵和单位下三角矩阵将该接收信号对应的发送信号映射至树搜索模型，该预处理矩阵和该单位下三角矩阵是对该共轭对称矩阵进行LDL分解得到的；
基于树搜索方式从该树搜索模型的最顶层逐层向下扩展，确定度量集合，该度量集合包括根节点的度量集合和多个叶节点的度量集合，该根节点的度量集合包含于第一候选集合，该第一候选集合是根据第一规则从第一星座点集合中确定的，该第一星座点集合是对该根节点对应的发送信号进行编码后映射得到的，该度量集合使得该树搜索模型具有最小的路径度量；
根据该度量集合，确定该发送信号中每个信息比特的对数似然比。
在上述技术方案中，通过对共轭对称矩阵使用LDL分解，将接收信号对应的发送信号映射至树搜索模型，避免了开平方操作，能够有效降低运算复杂度。根据第一规则从第一星座点集合中确定根节点的搜索空间(即，上述第一候选集合)，可以保证根节点的搜索空间中不存在比特缺失的问题。本申请提供的MIMO系统的检测方法，在保证检测结果具有较高准确度的前提下，能够有效降低检测方法的复杂度。
结合第一方面，在第一方面的某些实现方式中，该第一规则包括将该第一星座点集合中的如下星座点确定为该第一候选集合包括的星座点：
第一星座点，第二星座点和第三星座点，该第一星座点是与该根节点的估计发送信号的欧式距离最近的星座点，该第二星座点是与该第一星座点的欧式距离最近且分别包含该第一星座点每个比特对应反比特的星座点，该第三星座点是第一区域内包括的除该第一星座点和该第二星座点之外的星座点，该第一区域包含于该第一星座点集合对应的区域，该第一区域是根据预设复杂度从该第一星座点集合对应的区域中确定的。
其中，预设复杂度可以根据具体的应用场景进行设置。
在上述技术方案中，确定的根节点的搜索空间(即，上述第一候选集合)不仅包括第一星座点，还包括第二星座点和第三星座点，从而保证根节点的搜索空间中不存在比特缺失的问题。
结合第一方面，在第一方面的某些实现方式中，该MIMO系统是M×M维的MIMO系统，M为大于等于2的正整数，在确定共轭对称矩阵之前，该方法还包括：
获取该接收信号，并对该接收信号进行处理得到原始信道矩阵；
使用列变换矩阵对该原始信道矩阵列变换，得到列变化后的M个信道矩阵，该M个信道矩阵与M个树搜索模型一一对应，该M个树搜索模型与该MIMO系统的M层一一对应；
将该M个信道矩阵中的一个信道矩阵确定该第一信道矩阵，根据该第一信道矩阵确定的树搜索模型的最小路径度量小于该M个信道矩阵中的其余M-1个信道矩阵确定的树搜索模型的最小路径度量。
在上述技术方案中，通过列变换矩阵对原始信道进行列置换，得到置换后的多个信道矩阵。根据该多个信道矩阵将接收信号对应的发送信号可以映射至多个树搜索模型，使得MIMO系统的发送信号的每一层都有机会处于树搜索模型的根节点。
结合第一方面，在第一方面的某些实现方式中，该M个信道矩阵通过下列公式表示：
H l＝Hp l
其中，H l是该M个信道矩阵中的第l个信道矩阵，且l＝0,2,3,...,M-1，H是该原始信道矩阵，p l是该列变换矩阵。
现有技术中，为了提高估计发送信号的准确度，对MIMO系统对应的树搜索模型进行搜索之前，需要先对该树搜索模型的待遍历层进行排序，以使搜索的层的最小信噪比最大。目前，性能最佳的排序方法是贝尔实验室垂直分层空时码V-BLAST排序，该方法复杂度随发送层数指数增长；简化的排序方法，例如排序的QR算法SQRD，由于不能预先估计根节点信噪比，存在鲁棒性风险，且没有考虑根节点遍历星座点集合的度远大于叶节点遍历星座点集合的度。
在上述技术方案中，通过列变换矩阵p l对原始信道矩阵H进行列置换，得到置换后的多个不同的信道矩阵H l，可以从该置换后的多个不同的信道矩阵H l中确定一个置换后的信道矩阵为第一信道矩阵，避免了对树搜索模型的待遍历层进行排序的过程。
结合第一方面，在第一方面的某些实现方式中，该MIMO系统是M×M维的MIMO系统，该树搜索模型包括M层，该根节点与第M层对应，该多个叶节点分别与第M-1至第1层对应，M为大于等于2的正整数，该基于树搜索方式从该树搜索模型的最顶层逐层向下扩展，确定度量集合，包括：
确定该根节点的第一距离度量函数；
基于该第一距离度量函数在该第一候选集合中进行搜索，将第四星座点确定为该根节点的度量集合包括的星座点，该第四星座点对应的发送信号使得该第一距离度量函数的值小于等于第一阈值；
确定第i个叶节点的第二距离度量函数，该第二距离度量函数不包括该树搜索模型包括的第i+1层至第M层对应的发送信号分别对该第i层的干扰，i＝1,2,3,...,M-1；
基于该第二距离度量函数在第二候选集合中进行搜索，将第五星座点确定为该第i个叶节点的度量集合包括的星座点，该第五星座点对应的发送信号使得该第二距离度量函数的值小于等于第二阈值，该第二候选集合是根据该第i个叶节点的估计发送信号和第二星座点集合确定的，该第二星座点集合是对该第i个叶节点对应的发送信号进行编码后映射得到的。
在上述技术方案中，由于第一候选集合中不存在缺失比特，故根据第一距离度量函数在第一候选集合中进行搜索，确定得到的第四星座点对应的发送信号为最优的估计结果。
结合第一方面，在第一方面的某些实现方式中，该第一距离度量函数与根据线性最小均方误差LMMSE算法估计的该第M层对应的发送信号的度量函数相同。
在上述技术方案中，实现了NML算法与LMMSE算法过程的融合。也就是说，通过中间变量对根据本申请确定的根节点的度量函数(即，上述第一距离度量函数)进行处理，可以得到基于LMMSE算法估计的第M层对应的发送信号的度量函数，避免了直接采用LMMSE算法估计发送信号时存在运算复杂度高的问题。
结合第一方面，在第一方面的某些实现方式中，该树搜索方式包括以下方式中的一种：广义优先树搜索和深度优先树搜索。
在上述技术方案中，可以根据需求灵活使用不同的搜索方式，对上述确定的树搜索模型在候选集合中进行搜索。
可以理解的是，本申请提供的检测方法适用于发送端天线数目大于等于接收到天线数目的MIMO系统。例如，4×4维的MIMO系统。或者，4×4维的多用户-多输入多输出(multi-user multiple-input multiple-output，MU-MIMO)系统。
第二方面，本申请提供了一种多输入多输出MIMO系统的检测装置，该装置包括：
处理单元，用于确定共轭对称矩阵，该共轭对称矩阵是根据第一信道矩阵、噪声方差和单位矩阵确定的，该第一信道矩阵是对接收信号进行处理得到的该处理单元702，用于构建训练数据集；
该处理单元，还用于使用预处理矩阵和单位下三角矩阵将该接收信号对应的发送信号映射至树搜索模型，该预处理矩阵和该单位下三角矩阵是对该共轭对称矩阵进行LDL分解得到的；
该处理单元，还用于基于树搜索方式从该树搜索模型的最顶层逐层向下扩展，确定度量集合，该度量集合包括根节点的度量集合和多个叶节点的度量集合，该根节点的度量集合包含于第一候选集合，该第一候选集合是根据第一规则从第一星座点集合中确定的，该第一星座点集合是对该根节点对应的发送信号进行编码后映射得到的，该度量集合使得该树搜索模型具有最小的路径度量；
该处理单元，还用于根据该度量集合，确定该发送信号中每个信息比特的对数似然比。
结合第二方面，在第二方面的某些实现方式中，该第二规则包括将该第二星座点集合中的如下星座点确定为该第一候选集合包括的星座点：
第一星座点，第二星座点和第三星座点，该第一星座点是与该根节点的估计发送信号的欧式距离最近的星座点，该第二星座点是与该第一星座点的欧式距离最近且分别包含该第一星座点每个比特对应反比特的星座点，该第三星座点是第一区域内包括的除该第一星座点和该第二星座点之外的星座点，该第一区域包含于该第一星座点集合对应的区域，该第一区域是根据预设复杂度从该第一星座点集合对应的区域中确定的。
结合第二方面，在第二方面的某些实现方式中，
该获取单元，还用于获取该接收信号；
该处理单元，还用于对该接收信号进行处理得到原始信道矩阵；
该处理单元，还用于：
使用列变换矩阵对该原始信道矩阵按列进行列变换，得到列变换后的M个信道矩阵，该M个信道矩阵与M个树搜索模型一一对应，该M个树搜索模型与该MIMO系统的M层一一对应；
将该M个信道矩阵中的一个信道矩阵确定该第一信道矩阵，根据该第一信道矩阵确定的树搜索模型的最小路径度量小于该M个信道矩阵中的其余M-1个信道矩阵确定的树搜索模型的最小路径度量。
结合第二方面，在第二方面的某些实现方式中，该M个信道矩阵可以通过下列公式表示：
H l＝Hp l
其中，H l是该M个信道矩阵中的第l个信道矩阵，且l＝0,2,3,...,M-1，H是该原始信道矩阵，p l是该列变换矩阵。
结合第二方面，在第二方面的某些实现方式中，该处理单元还用于：
确定该根节点的第一距离度量函数；
基于该第一距离度量函数在该第一候选集合中进行搜索，将第四星座点确定为该根节点的度量集合包括的星座点，该第四星座点对应的发送信号使得该第一距离度量函数的值小于等于第一阈值；
确定第i个叶节点的第二距离度量函数，该第二距离度量函数不包括该树搜索模型包括的第i+1层至第M层对应的发送信号分别对该第i层的干扰，i＝1,2,3,...,M-1；
基于该第二距离度量函数在第二候选集合中进行搜索，将第五星座点确定为该第i个叶节点的度量集合包括的星座点，该第五星座点对应的发送信号使得该第二距离度量函数的值小于等于第二阈值，该第二候选集合是根据该第i个叶节点的估计发送信号和第二星座点集合确定的，该第二星座点集合是对该第i个叶节点对应的发送信号进行编码后映射得到的。
结合第二方面，在第二方面的某些实现方式中，该第一距离度量函数与根据线性最小均方误差LMMSE算法估计的该第M层对应的发送信号的度量函数相同。
结合第二方面，在第二方面的某些实现方式中，该树搜索方式包括以下方式中的一种：广义优先树搜索和深度优先树搜索。
第三方面，本申请提供了提供了一种MIMO系统的检测设备，该检测设备包括存储器和处理器，该存储器用于存储指令，该处理器用于读取该存储器中存储的指令，使得该装置执行上述第一方面及第一方面的任意可能的实现方式中的方法。
第四方面，本申请提供了提供了一种处理器，包括：输入电路、输出电路和处理电路。所述处理电路用于通过所述输入电路接收信号，并通过所述输出电路发射信号，使得所述第一方面中的任一方面，以及第一方面中任一种可能实现方式中的方法被实现。
在具体实现过程中，上述处理器可以为芯片，输入电路可以为输入管脚，输出电路可以为输出管脚，处理电路可以为晶体管、门电路、触发器和各种逻辑电路等。输入电路所接收的输入的信号可以是由例如但不限于接收器接收并输入的，输出电路所输出的信号可以是例如但不限于输出给发射器并由发射器发射的，且输入电路和输出电路可以是同一电路，该电路在不同的时刻分别用作输入电路和输出电路。本申请实施例对处理器及各种电路的具体实现方式不做限定。
第五方面，本申请提供了提供了一种处理装置，包括处理器和存储器。该处理器用于读取存储器中存储的指令，并可通过接收器接收信号，通过发射器发射信号，以执行第一方面以及第一方面任一种可能实现方式中的方法。
可选地，所述处理器为一个或多个，所述存储器为一个或多个。
可选地，所述存储器可以与所述处理器集成在一起，或者所述存储器与处理器分离设置。
在具体实现过程中，存储器可以为非瞬时性(non-transitory)存储器，例如只读存储器(read only memory，ROM)，其可以与处理器集成在同一块芯片上，也可以分别设置在不同的芯片上，本申请实施例对存储器的类型以及存储器与处理器的设置方式不做限定。
应理解，相关的数据交互过程例如发送指示信息可以为从处理器输出指示信息的过程，接收能力信息可以为处理器接收输入能力信息的过程。具体地，处理输出的数据可以输出给发射器，处理器接收的输入数据可以来自接收器。其中，发射器和接收器可以统称为收发器。
第六方面，本申请提供了提供了一种计算机可读存储介质，用于存储计算机程序，该计算机程序包括用于执行上述第一方面及上述第一方面的任意可能的实现方式中的方法的指令。
第七方面，本申请提供了提供了一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机执行上述第一方面及上述第一方面的任意可能的实现方式中的方法。
第八方面，本申请提供了提供了一种芯片，包括至少一个处理器和接口；所述至少一个所述处理器，用于调用并运行计算机程序，以使所述芯片执行上述第一方面及上述第一方面的任意可能的实现方式中的方法。
第九方面，本申请提供了提供了一种通信系统，包括如第二方面所述的MIMO系统的检测装置和/或如第三方面所述的MIMO系统的检测设备。
附图说明
图1是本申请提供的一种无线多址通信系统的示意图。
图2是能够应用于本申请提供的方法的MIMO系统200的示意图。
图3是本申请提供的MIMO系统的检测方法100的示意性流程图。
图4是本申请提供的确定根节点候选集合的示意图。
图5是本申请提供的MIMO系统的检测方法200的示意性流程图。
图6是本申请提供的MIMO系统的检测方法的架构图。
图7是本申请提供的MIMO系统的检测装置700的示意性框图。
图8是本申请提供的MIMO系统的检测设备800的示意性框图。
具体实施方式
下面将结合附图，对本申请中的技术方案进行描述。
为了更好地理解本申请实施例，在介绍本申请实施例之前，做出如下几点说明。
在下文示出的实施例中“第一”“第二”“第三”等字样用于对作用和功能基本相同的相同项或相似项进行区分，应理解，“第一”、“第二”和“第三”之间不具有逻辑或时序上的依赖关系，也不对数量和执行顺序进行限定。
本申请实施例中涉及的“协议”可以是指通信领域的标准协议，例如可以包括LTE协议、NR协议以及应用于未来的通信系统中的相关协议，本申请对此不做限定。
“至少一个”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B的情况，其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项(个)”或其类似表达，是指的这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a、b和c中的至少一项(个)，可以表示：a，或，b，或，c，或，a和b，或，a和c，或，b和c，或，a、b和c。其中a、b和c分别可以是单个，也可以是多个。
在本申请实施例中，“当……时”、“在……的情况下”、“若”以及“如果”等描述均指在某种客观情况下设备(如，终端设备或者网络设备)会做出相应的处理，并非是限定时间，且也不要求设备(如，终端设备或者网络设备)在实现时一定要有判断的动作，也不意味着存在其它限定。
本申请提供的技术方案可以应用于各种通信系统，例如：长期演进(Long Term Evolution，LTE)系统、LTE频分双工(frequency division duplex，FDD)系统、LTE时 分双工(time division duplex，TDD)、通用移动通信系统(universal mobile telecommunication system，UMTS)、全球互联微波接入(worldwide interoperability for microwave access，WiMAX)通信系统、未来的第五代(5th Generation，5G)移动通信系统或新无线接入技术(new radio access technology，NR)。其中，5G移动通信系统可以包括非独立组网(non-standalone，NSA)和/或独立组网(standalone，SA)。
本申请提供的技术方案还可以应用于机器类通信(machine type communication，MTC)、机器间通信长期演进技术(Long Term Evolution-machine，LTE-M)、设备到设备(device-to device，D2D)网络、机器到机器(machine to machine，M2M)网络、物联网(internet of things，IoT)网络或者其他网络。其中，IoT网络例如可以包括车联网。其中，车联网系统中的通信方式统称为车到其他设备(vehicle to X，V2X，X可以代表任何事物)，例如，该V2X可以包括：车辆到车辆(vehicle to vehicle，V2V)通信，车辆与基础设施(vehicle to infrastructure，V2I)通信、车辆与行人之间的通信(vehicle to pedestrian，V2P)或车辆与网络(vehicle to network，V2N)通信等。
本申请提供的技术方案还可以应用于未来的通信系统，如第六代移动通信系统等。本申请对此不作限定。
本申请实施例中，网络设备可以是任意一种具有无线收发功能的设备。该设备包括但不限于：演进型节点B(evolved Node B，eNB)、无线网络控制器(radio network controller，RNC)、节点B(Node B，NB)、基站控制器(base station controller，BSC)、基站收发台(base transceiver station，BTS)、家庭基站(例如，home evolved NodeB，或home Node B，HNB)、基带单元(baseband unit，BBU)，无线保真(wireless fidelity，WiFi)系统中的接入点(access point，AP)、无线中继节点、无线回传节点、传输点(transmission point，TP)或者发送接收点(transmission and reception point，TRP)等，还可以为5G，如，NR，系统中的gNB，或，传输点(TRP或TP)，5G系统中的基站的一个或一组(包括多个天线面板)天线面板，或者，还可以为构成gNB或传输点的网络节点，如基带单元(BBU)，或，分布式单元(distributed unit，DU)等。
在一些部署中，gNB可以包括集中式单元(centralized unit，CU)和DU。gNB还可以包括有源天线单元(active antenna unit，AAU)。CU实现gNB的部分功能，DU实现gNB的部分功能，比如，CU负责处理非实时协议和服务，实现无线资源控制(radio resource control，RRC)，分组数据汇聚层协议(packet data convergence protocol，PDCP)层的功能。DU负责处理物理层协议和实时服务，实现无线链路控制(radio link control，RLC)层、介质接入控制(medium access control，MAC)层和物理(physical，PHY)层的功能。AAU实现部分物理层处理功能、射频处理及有源天线的相关功能。由于RRC层的信息最终会变成PHY层的信息，或者，由PHY层的信息转变而来，因而，在这种架构下，高层信令，如RRC层信令，也可以认为是由DU发送的，或者，由DU+AAU发送的。可以理解的是，网络设备可以为包括CU节点、DU节点、AAU节点中一项或多项的设备。此外，可以将CU划分为接入网(radio access network，RAN)中的网络设备，也可以将CU划分为核心网(core network，CN)中的网络设备，本申请对此不做限定。
网络设备为小区提供服务，终端设备通过网络设备分配的传输资源(例如，频域资源，或者说，频谱资源)与小区进行通信，该小区可以属于宏基站(例如，宏eNB或宏gNB 等)，也可以属于小小区(small cell)对应的基站，这里的小小区可以包括：城市小区(metro cell)、微小区(micro cell)、微微小区(pico cell)、毫微微小区(femto cell)等，这些小小区具有覆盖范围小、发射功率低的特点，适用于提供高速率的数据传输服务。
在本申请实施例中，终端设备也可以称为用户设备(user equipment，UE)、接入终端、用户单元、用户站、移动站、移动台、远方站、远程终端、移动设备、用户终端、终端、无线通信设备、用户代理或用户装置。
终端设备可以是一种向用户提供语音/数据连通性的设备，例如，具有无线连接功能的手持式设备、车载设备等。目前，一些终端的举例可以为：手机(mobile phone)、平板电脑(pad)、带无线收发功能的电脑(如笔记本电脑、掌上电脑等)、移动互联网设备(mobile internet device，MID)、虚拟现实(virtual reality，VR)设备、增强现实(augmented reality，AR)设备、工业控制(industrial control)中的无线终端、无人驾驶(self driving)中的无线终端、远程医疗(remote medical)中的无线终端、智能电网(smart grid)中的无线终端、运输安全(transportation safety)中的无线终端、智慧城市(smart city)中的无线终端、智慧家庭(smart home)中的无线终端、蜂窝电话、无绳电话、会话启动协议(session initiation protocol，SIP)电话、无线本地环路(wireless local loop，WLL)站、个人数字助理(personal digital assistant，PDA)、具有无线通信功能的手持设备、计算设备或连接到无线调制解调器的其它处理设备、车载设备、可穿戴设备，5G网络中的终端设备或者未来演进的公用陆地移动通信网络(public land mobile network，PLMN)中的终端设备等。
其中，可穿戴设备也可以称为穿戴式智能设备，是应用穿戴式技术对日常穿戴进行智能化设计、开发出可以穿戴的设备的总称，如眼镜、手套、手表、服饰及鞋等。可穿戴设备即直接穿在身上，或是整合到用户的衣服或配件的一种便携式设备。可穿戴设备不仅仅是一种硬件设备，更是通过软件支持以及数据交互、云端交互来实现强大的功能。广义穿戴式智能设备包括功能全、尺寸大、可不依赖智能手机实现完整或者部分的功能，例如：智能手表或智能眼镜等，以及只专注于某一类应用功能，需要和其它设备如智能手机配合使用，如各类进行体征监测的智能手环、智能首饰等。
此外，终端设备还可以是物联网(internet of things，IoT)系统中的终端设备。IoT是未来信息技术发展的重要组成部分，其主要技术特点是将物品通过通信技术与网络连接，从而实现人机互连，物物互连的智能化网络。IoT技术可以通过例如窄带(narrow band，NB)技术，做到海量连接，深度覆盖，终端省电。
此外，终端设备还可以包括智能打印机、火车探测器、加油站等传感器，主要功能包括收集数据(部分终端设备)、接收网络设备的控制信息与下行数据，并发送电磁波，向网络设备传输上行数据。
为便于理解本申请实施例，下面对本申请实施例中涉及到的相关术语进行简单介绍。
1、最大似然检测算法(Maximum Likelihood，ML)
ML算法可以使MIMO系统获得最佳检测性能，是MIMO系统的最优检测算法。ML算法的目标是寻找最佳的发射信号向量
Figure PCTCN2020126381-appb-000001
使
Figure PCTCN2020126381-appb-000002
最小，y表示MIMIO系统的接收信号，H表示MIMO系统的信道矩阵。ML算法的计算复杂度随星座图规模及发射符号数呈指数增长，故ML算法的计算复杂度高。
2、正三角分解法(QR)
它是将矩阵分解成一个正交矩阵Q与上三角形矩阵R，所以称为QR分解法。如果实(复)非奇异矩阵A能够化成正交(酉)矩阵Q与实(复)非奇异上三角矩阵R的乘积，即A＝QR，则称其为A的QR分解。
3、LDL分解法
若A为共轭对称矩阵且其任意k阶主子式不为零，则A存在唯一分解形式：
A＝LDL H
其中L为单位下三角矩阵，D为对角矩阵，L H为L的共轭转置矩阵。
4、树搜索检测算法
树搜索检测算法是一类基于树搜索策略的信号检测算法，该算法能保证接近最大似然检测的条件下，通过缩小候选信号空间范围实现检测算法复杂度的降低，在MIMO检测器的设计中有着广阔的应用前景。
为便于理解本申请实施例，现在结合图1，介绍适用于本申请提供的MIMO系统的检测方法的无线多址通信系统。
图1是本申请提供的一种无线多址通信系统的示意图。在一个示例中，接入点100(access point，AP)包括多个天线群。如图1所示，一个天线群可以包括天线104和天线106，另一个可以包括天线108和天线110，而又一个可以包括天线112和天线114。应理解，图1仅为示意并不构成任何限定，例如，在另一个示例中，每个天线群还可以利用更多或更少数目的天线。在另一个示例中，接入终端116(access termination，AT)可与天线112和天线114正通信，其中天线112和天线114在前向链路120上向接入终端116传送信息，并在反向链路118上从接入终端116接收信息。补充地和/或替换地，接入终端122可与天线104和天线106正通信，其中天线104和天线106在前向链路126上向接入终端122传送信息，并在反向链路124上从接入终端122接收信息。根据一个方面，接入终端116和接入终端122可具有多个天线，使用这些天线可在相应的前向链路120和126和/或反向链路118和124上在接入终端116和接入终端122与接入点100之间建立MIMO通信。此外，在FDD系统中，通信链路118，120，124和126可使用不同的频率来通信。例如，前向链路120可使用与反向链路118所使用的不同的频率。
每一群天线和/或它们被设计成在其中通信的区域可被称作接入点的扇区。根据一方面，天线群可被设计成与落在接入点100所覆盖的区域的一扇区中的诸接入终端通信。在前向链路120和前向链路126上的通信中，接入点100的发射天线可利用波束成形来提高不同接入终端116和接入终端122的前向链路的信噪比。另外，与接入点通过单个天线向其所有接入终端发射相比，接入点使用波束成形向随机散布在其覆盖中各处的诸接入终端发射对邻蜂窝小区中的接入终端造成更小的干扰。例如接入点100等接入点可以是用于与诸终端通信的固定站，并且也可以用基站、B节点、和/或其他合适术语来述及。此外，例如接入终端116或接入终端122的接入终端也可以用移动终端、UE、无线通信设备、终端、无线终端、和/或其他当术语来述及。
下面结合图2，介绍能够应用于图1所示的无线多址通信系统的MIMO系统。
图2是能够应用于本申请提供的方法的MIMO系统200的示意图。在一个示例中，该系统200可以包括接入点AP 210和接入终端AT 220，其中，接入终端AT 220和接入点AP 210可以进行通信(例如，无线通信或有线通信等)。
在一个示例中，AP 210包括数据源212，其可生成或以其他方式获得要传送给一个或多个AT 220的数据。来自数据源212的数据可被发送给编码组件214以处理该数据，从而经由MIMO传输传达给AT 220。在编码组件214处，包括要传送给AT 220的数据的一系列比特可被编组为空间流，以便经由天线218被发射机216同时发射。此外，编码组件可使用一种或多种数字调制技术来调制每个空间流，这些数字调制技术诸如相移键控(phase shift keying，PSK)、二进制相移键控(binary phase shift keying，BPSK)、正交相移键控(quadrature phase shift keying，QPSK)，16点正交调幅(16 quadrature amplitude modulation，16-QAM),64点正交调幅(64 quadrature amplitude modulation，64-QAM)、和/或另一合适的调制技术，在这些调制技术下包括每个流的数据比特可基于星座点集合被映射至一系列调制码元。作为补充和/或替换，正交频分复用(orthogonal frequency division multiplexing，OFDM)可被用于在多个正交副载波当中划分空间流，以使得每个副载波可各自使用一种或多种调制技术来调制。对应每个流的经映射调制码元可在随后被提供给相应发射机216，以便作为已调制模拟信号经由一系列M个天线218传达给AT 220。
在AT 220处，与由AP 210传送的信号相对应的空间流可经由相应天线222被一系列M个接收机224接收。在一个示例中，与在AT 220处接收到的流相对应的M维接收信号向量y可表达如下:
y＝Hx+N
其中，上述各符号的物理意义如下：y是维度为M×1的接收信号，H是维度为M×M的信道矩阵，x是维度为M×1的发送信号，N是具有独立同分布统计特性的高斯噪声，即N～N(0,σ 2I)，且N的维度为M×1，σ 2是噪声方差，I是单位矩阵。
在另一示例中，接收机224接收到的空间流可被通传给信号检测组件226，该信号检测组件226可利用接收机224接收到的流以及关于有效MIMO信道的知识来获得由AP 210传送的流。根据一个方面，信号检测组件226可通过确定接收自AP 210的空间流中的每个比特的预期符号来确定这些比特的硬判决输出。例如，值为1的比特可由硬判决输出+l来表示，值为0的比特可由硬判决输出-1来表示。替换地，信号检测组件226可通过确定接收来自AP 210的空间流中的每个比特的预期符号外加每个比特的相应预期符号已被正确检出的似然性——例如比特作为+l或-1被发送的似然性——来确定这些比特的软判决输出。根据另一方面，信号检测组件226可通过采用如下文所描述的一种或多种接近软输出最大似然检测算法来提供低复杂度软输出检测。在成功检测之后，检出的传送流可被提供给数据阱228以供AT 220使用。
应理解，图2仅为示意并不对本申请提供的MIMO系统的检测方法适用的MIMO系统构成任何限定。在一个示例中，该系统200还可以包括更多数据的AP 210和/或更多数目的AT 220。在另一个示例中，类似组件和技术可被AP 210和/或AT 220用于从AT 220至AP 120的通信(例如，反向链路118和反向链路124上的通信)。
目前，对MIMO系统进行检测时，可以先将MIMO系统的基带等效模型转换为树搜索模型，然后再对该树搜索模型进行处理，以降低算法复杂性。现有技术中，通常使用QR算法将MIMO系统的基带等效模型转换为树搜索模型，然后再对该树模型进行处理。 具体的，先对MIMO系统的基带等效模型进行变换将该模型转换为度量函数的枚举过程，再使用QR算法将该度量函数的枚举过程转换为树搜索过程。
下面，介绍现有技术中使用QR算法对MIMO系统进行检测的流程：
当前的各种无线通信协议，为了兼顾各种应用场景，支持的M-QAM星座规模和天线规模的变化范围较大。例如，802.11ac Wi-Fi协议，支持从BPSK到256QAM的星座映射，MIMO系统的天线配置也支持从1×1到8×8。因此，下文中以M×M维MIMO系统模型为例进行介绍。例如，M的取值可以为2，4，或8等，对此不限定。
在一个示例中，上述图2所示的M×M维的MIMO系统的可以表示为：
y＝Hx+N               (2-1)
上述公式(2-1)中各符号的物理意义如下：y是维度为M×1的接收信号，H是维度为M×M的信道矩阵，x是维度为M×1的发送信号，N是具有独立同分布统计特性的高斯噪声向量，即N～N(0,σ 2I)，且N的维度为M×1，σ 2是噪声方差，I是单位矩阵。
上述M×M维的MIMO系统的第m(m为正整数，且1≤m≤M)维发送信号的第i(i为正整数，且1≤i≤Q m，Q m为第m维发送信号向量的发送比特数)个比特的对数似然比(log-likelihood ratio，LLR)可以通过下列公式表示：
Figure PCTCN2020126381-appb-000003
上述公式(2-2)中各符号的物理意义如下：X +1为
Figure PCTCN2020126381-appb-000004
的发送符号集合，即X +1为第m维发送信号包括的比特为+1的集合，X -1为
Figure PCTCN2020126381-appb-000005
的发送符号集合，即X -1为第m维发送信号包括的比特为-1的集合。
对上述公式(2-2)作Max-Log-Map近似处理，可以通过下列公式表示：
Figure PCTCN2020126381-appb-000006
上述公式(2-3)中，|| ||表示矩阵的2-范数。具体的，||y-Hx||表示矩阵y-Hx的2-范数。
根据上述公式(2-3)，可知计算对数似然比可以转换为针对度量函数的枚举，故第m维发送信号向量的第i个比特的度量函数的枚举可以通过下列公式表示：
d(x)＝||y-Hx|| 2            (2-4)
上述公式(2-4)中，x∈X +1，或x∈X -1。
由上述公式(2-4)可知，运算复杂度随发送信号x的维度以及发送信号星座点的规模呈指数增长。为了降低运算复杂度以及提高检测系统性能，通常将上述公式(2-4)表示度量函数的枚举过程转换为树搜索过程，即近似最大似然检测算法(near maximum likelihood，NML)。
下面，介绍使用QR分解将上述公式(2-4)的度量函数的枚举过程转换为树搜索过程，具体转换过程如下：
首先，对上述公式(2-1)所示的M×M维的MIMO系统的基带等效模型进行扩展，扩展后的模型可以可以通过下列公式表示：
Figure PCTCN2020126381-appb-000007
上述公式(2-5)中，
Figure PCTCN2020126381-appb-000008
是维度为(M+M)×M的扩展信道矩阵，且
Figure PCTCN2020126381-appb-000009
Figure PCTCN2020126381-appb-000010
是维度为(M+M)×1的扩展噪音向量，且
Figure PCTCN2020126381-appb-000011
进一步，对上述公式(2-5)中的扩展信道矩阵
Figure PCTCN2020126381-appb-000012
进行QR分解，则
Figure PCTCN2020126381-appb-000013
可以通过下列公式表示：
Figure PCTCN2020126381-appb-000014
上述公式(2-6)中，
Figure PCTCN2020126381-appb-000015
为扩展的酉矩阵(unitary matrix)，且
Figure PCTCN2020126381-appb-000016
Figure PCTCN2020126381-appb-000017
为上三角矩阵。
将上述公式(2-5)和上述公式(2-6)将入上述公式(2-4)，则上述公式(2-4)所示的第m维发送信号向量的第i个比特的距离度量函数可以进一步表示为：
Figure PCTCN2020126381-appb-000018
上述公式(1-7)中：
Figure PCTCN2020126381-appb-000019
与发送信号x无关。也就是说，在计算比特似然概率时忽略该项不会影响比特似然概率的计算。因此，对上述公式(2-7)进一步简化可以得到：
Figure PCTCN2020126381-appb-000020
由上述公式(2-6)可知
Figure PCTCN2020126381-appb-000021
由上述公式(2-7)可知
Figure PCTCN2020126381-appb-000022
进一步z'可以表示为：
Figure PCTCN2020126381-appb-000023
上述公式(2-9)中，() -1表示求逆运算，
Figure PCTCN2020126381-appb-000024
即为线性最小均方误差(linear minimum mean square error，LMMSE)算法的接收信号向量。
由上述公式(2-8)和上述公式(2-9)可知，使用QR算法需要对向量2范数开方求倒数，使得运算复杂度较高。而且，当信道矩阵H的条件数较大时，使用QR算法稳定性较差。
由上述分析可知，现有技术中使用QR算法将MIMO模型的度量函数的枚举过程转换为树搜索过程时，需要引入开平方运算。由于在上述转换过程中引入了开平方运算，使得上述转换过程的运算复杂度较高、转换速率较低。因此，当在物理设备(例如，专用集成电路(application specific integrated circuit，ASIC))中使用上述转换方法对MIMO系统进行检测时，需要消耗较大的芯片资源，且检测效率也较低。
本申请提供了一种MIMO系统的检测方法，在保证检测结果具有较高准确度的前提下，能够有效降低检测方法的复杂度。
下面，结合图3至图6，对本申请提供的MIMO系统的检测方法进行详细介绍。
图3是本申请提供的MIMO系统的检测方法100的示意性流程图。可以理解的是，本申请提供的MIMO系统的检测方法适用于接收端的天线数目小于等于发射端的天线数目的MIMO系统。如图3所示，该方法100可以包括步骤110至步骤140，下面将结合附图对步骤110至步骤140进行介绍。
为便于描述，下面以M×M(M是大于等于2的正整数)维的MIMO系统为例，介绍方法100中的步骤110至步骤140。
步骤110，确定共轭对称矩阵，共轭对称矩阵是根据第一信道矩阵、噪声方差和单位矩阵确定的，第一信道矩阵是对接收信号进行处理得到的。
在一个示例中，上述共轭对称矩阵是根据第一信道矩阵、噪声方差和单位矩阵确定的，可以理解为：共轭对称矩阵是对第一信道矩阵与第一信道矩阵的共轭转置矩阵的乘积，以及噪声方差和单位矩阵的乘积求和确定的，上述共轭对称矩阵G可以通过下列公式表示：
G＝H HH+σ 2I
其中，H是第一信道矩阵，H H是第一信道矩阵的共轭转置矩阵，σ 2是噪声方差，I是单位矩阵。
可以理解的是，在上述步骤110之前，还包括：
获取接收信号，并对接收信号进行处理得到原始信道矩阵；
使用列变换矩阵对原始信道矩阵进行列变换，得到列变换后的M个信道矩阵，M个信道矩阵与M个树搜索模型一一对应，M个树搜索模型与MIMO系统的M层一一对应；
将M个信道矩阵中的一个信道矩阵确定第一信道矩阵，根据第一信道矩阵确定的树搜索模型的最小路径度量小于M个信道矩阵中的其余M-1个信道矩阵确定的树搜索模型的最小路径度量。
上述M个信道矩阵可以通过下列公式表示：
H l＝Hp l
其中，H l是M个信道矩阵中的第l个信道矩阵，且l＝0,2,3,...,M-1，H是原始信道矩阵，p l是列变换矩阵。
上述将M个信道矩阵中的一个信道矩阵确定第一信道矩阵，可以理解的是，上述第一信道矩阵可以是M个信道矩阵中的一个信道矩阵，且根据第一信道矩阵确定的树搜索模型的最小路径度量小于M个信道矩阵中的其余M-1个信道矩阵确定的树搜索模型的最小路径度量。
上述M个信道矩阵与M个树搜索模型一一对应，可以理解为，用M个不相同的信道矩阵将同一接收信号对应的发送信号映射至树搜索模型，可以得到M个不同的树搜索模型。
上述M个树搜索模型与MIMO系统的M层一一对应，可以理解为，该M个树搜索中的每个树搜索模型的根节点与该MIMO系统的一层对应。也就是说，当该MIMO系统的每一层分别作为树搜索模型的根节点时，可以得到该M个树搜索模型。
步骤120，使用预处理矩阵和单位下三角矩阵将接收信号对应的发送信号映射至树搜索模型，预处理矩阵和单位下三角矩阵是对共轭对称矩阵进行LDL分解得到的。
在本申请中，使用LDL算法对上述共轭对称矩阵G＝H HH+σ 2I进行分解，可以得到如下表达式：
Figure PCTCN2020126381-appb-000025
其中，
Figure PCTCN2020126381-appb-000026
是上三角矩阵，
Figure PCTCN2020126381-appb-000027
是
Figure PCTCN2020126381-appb-000028
的共轭转置，L是单位下三角矩阵，L H为L的转置，即L H是单位上三角矩阵；D是预处理矩阵，且是一个对角矩阵。
由
Figure PCTCN2020126381-appb-000029
可以得到，
Figure PCTCN2020126381-appb-000030
可以通过下列公式表示：
Figure PCTCN2020126381-appb-000031
M×M维的MIMO系统的发送信号的距离度量函数可以通过下列公式表示：
d(x)＝||y-Hx|| 2
将
Figure PCTCN2020126381-appb-000032
代入至上述距离度量函数d(x)＝||y-Hx|| 2中，M×M维的MIMO系统的发送信号的近似最大似然检测的距离度量函数可以通过下列公式表示，即上述树搜索模型可以通过下列公式表示：
Figure PCTCN2020126381-appb-000033
其中，z＝D -1L -1H Hy＝L HWy，H Hy为匹配滤波矩阵，且上述对D的开平方运算(即，
Figure PCTCN2020126381-appb-000034
的运算)在路径搜索过程中可以被省略。由上述公式(2-9)可以得到：
Figure PCTCN2020126381-appb-000035
故由此实现NML算法与LMMSE算法过程的融合。也就是说，
步骤130，基于树搜索方式从树搜索模型的最顶层逐层向下扩展，确定度量集合，度量集合包括根节点的度量集合和多个叶节点的度量集合，根节点的度量集合包含于第一候选集合，第一候选集合是根据第一规则从第一星座点集合中确定的，第一星座点集合是对根节点对应的发送信号进行编码后映射得到的，度量集合使得树搜索模型具有最小的路径度量。
上述树搜索模型包括M层，根节点与第M层对应，多个叶节点分别与第M-1至第1层对应，M为大于等于2的正整数，基于树搜索方式从树搜索模型的最顶层逐层向下扩展，确定度量集合，包括：
确定根节点的第一距离度量函数，第一距离度量函数是根据预处理矩阵、下三角矩阵、接收信号、第一信道矩阵、第一信道矩阵的共轭转置矩阵和噪声方差确定的；
基于第一距离度量函数在第一候选集合中进行搜索，将第四星座点确定为根节点的度量集合包括的星座点，第四星座点对应的发送信号使得第一距离度量函数的值小于等于第一阈值；
确定第i个叶节点的第二距离度量函数，第一距离度量函数是根据预处理矩阵、下三角矩阵、接收信号、第一信道矩阵、第一信道矩阵的共轭转置矩阵和噪声方差确定的，且第二距离度量函数不包括树搜索模型包括的第i+1层至第M层对应的发送信号对第i层的干扰，i＝1,2,3,……,M-1，第二距离度量函数是第M-1至第1层中的任意一层对应叶节点的距离度量函数；
基于第二距离度量函数在第二候选集合中进行搜索，将第五星座点确定为第i个叶节点的度量集合包括的星座点，第五星座点对应的发送信号使得第二距离度量函数的值小于等于第二阈值，第二候选集合是根据第i个叶节点的估计发送信号和第二星座点集合确定的，第二星座点集合是对第i个叶节点对应的发送信号进行编码后映射得到的。
在一个示例中，上述根节点的第一距离度量函数可以通过下列公式表示：
d NML(x) M＝D M||z M-x M|| 2-σ 2||x M|| 2
其中，D M是对共轭对称矩阵进行LDL分解后得到的预处理矩阵D的第M个对角元素，z M＝[Wy] M＝[L -HD -1L -1H Hy] M，为MMSE估计向量的第M个符号，由上述公式(2-9)可以得到：
Figure PCTCN2020126381-appb-000036
x M∈S M，S M为根节点候选集合。
上述第一规则包括将第一星座点集合中的如下星座点确定为第一候选集合包括的星座点：第一星座点，第二星座点和第三星座点，
其中，第一星座点是与根节点的估计发送信号的欧式距离最近的星座点，第二星座点是与第一星座点的欧式距离最近且分别包含第一星座点每个比特对应反比特的星座点，第三星座点是第一区域内包括的除第一星座点和第二星座点之外的星座点，第一区域包含于第一星座点集合对应的区域，第一区域是根据预设复杂度从第一星座点集合对应的区域中确定的，根节点的估计发送信号是根据预处理矩阵、接收信号、第一信道矩阵、噪声方差确定的。
其中，根节点的估计发送信号可以通过下列公式表示：
Figure PCTCN2020126381-appb-000037
上述第二距离度量函数不包括树搜索模型包括的第i+1层至第M层对应的发送信号对第i层的干扰，可以理解为，在计算第i层发送信号的度量函数时需要先删减已遍历层的信号对该第i层发送信号的干扰。在一个示例中，当树搜索模型包括M层且该树搜索模型最顶层第M层为根节点所在层，可以先对该树搜索模型的第M层进行搜索，然后对该树搜索模型的第M-1层进行搜索，再对该树搜索模型的第i层进行搜索，i为小于等于M-1且大于等于1的正整数。当对该树搜索模型的第i层进行搜索时，需要删除该树搜索模型的第i+1层的发送信号至第M层的发送信号对第i层的发送信号的干扰。
在一个示例中，上述第i个叶节点的第二距离度量函数可以通过下列公式表示：
Figure PCTCN2020126381-appb-000038
其中，D i为对共轭对称矩阵进行LDL分解后得到的预处理矩阵D的第i个对角元素，
Figure PCTCN2020126381-appb-000039
为第i层叶节点的等效接收信号，x i∈S i，S i为第i层叶节点候选集合。
上述第二候选集合是根据第i个叶节点的估计发送信号和第二星座点集合确定的，包括：
将第i层叶节点对应的星座图Q i上距离发送符号估计
Figure PCTCN2020126381-appb-000040
最近的星座点确定为S i包括的星座点，其中
Figure PCTCN2020126381-appb-000041
或者，
根据上述确定根节点候选集合S M的方法，确定第i层叶节点候选集合S i。基于此，可以将距离发送符号估计
Figure PCTCN2020126381-appb-000042
最近的星座点，与距离发送符号估计
Figure PCTCN2020126381-appb-000043
距离最近且分别包含每个比特对应反比特的星座点，以及包括在叶节点候选集合S i的度内且未被选择的星座点，确定为叶节点候选集合S i包括的星座点。
上述第i层叶节点的等效接收信号
Figure PCTCN2020126381-appb-000044
可以通过下列公式表示：
Figure PCTCN2020126381-appb-000045
其中，z i＝[Wy] i＝[L -HD -1L -1H Hy] i，为MMSE估计向量的第i个符号，
Figure PCTCN2020126381-appb-000046
为M×M维的MIMO模型的第j层对第i层的干扰，i为正整数，且1≤i≤M-1，i+1≤j≤M。
上述在第一候选集合中确定第四星座点的方法，以及在第二候选集合中确定第五星座点的方法可以是现有的方法，此处不再详细赘述。上述第一阈值和上述第二阈值可以根据具体的应用场景进行设置，对此不进行限定。
可以理解的是，通过上述方法确定的第一距离度量函数与根据线性最小均方误差LMMSE算法估计的第M层对应的发送信号的度量函数相同。
上述树搜索方式包括以下方式中的一种：广义优先树搜索和深度优先树搜索。
步骤140，根据度量集合，确定发送信号中每个信息比特的对数似然比。
其中，上述M×M维的MIMO系统的第m(m为正整数，且1≤m≤M)层发送信号的第i(i为大于等于1的正整数)个比特的对数似然比可以通过下列公式表示：
Figure PCTCN2020126381-appb-000047
其中，X +1为
Figure PCTCN2020126381-appb-000048
的发送符号集合，X -1为
Figure PCTCN2020126381-appb-000049
的发送符号集合，
Figure PCTCN2020126381-appb-000050
为第m层发送信号的第i个比特。
下面，结合下文以M×M维的MIMO系统为例，对上述步骤110至步骤140中未详细介绍的方法进行介绍。
M×M维的MIMO系统模型可以表示为：
y＝Hx+N               (3-1)
上述公式(3-1)中各符号的物理意义如下：y是接收信号，H是信道矩阵，x是发送信号，x对应到Q个正交幅度调制(QAM)的星座点，符号N是具有独立同分布统计特性的高斯噪声，即N～N(0,σ 2I)，σ 2是噪声方差，I是单位矩阵。
上述M×M维的MIMO系统的第m(m为正整数，且1≤m≤M)层发送信号的第i(i为大于等于1的正整数)个比特的对数似然比可以通过下列公式表示：
Figure PCTCN2020126381-appb-000051
上述公式(3-2)中各符号的物理意义如下：X +1为
Figure PCTCN2020126381-appb-000052
的发送符号集合，X -1为
Figure PCTCN2020126381-appb-000053
的发送符号集合，
Figure PCTCN2020126381-appb-000054
为第m层发送信号的第i个比特。
对上述公式(3-2)作Max-Log-Map近似处理，上述公式(3-2)进一步可以通过下列公式表示：
Figure PCTCN2020126381-appb-000055
根据上述公式(3-3)，可知计算M×M维的MIMO系统的发送信号的对数似然比可以转换为针对发送信号的度量函数的枚举，可以确定M×M维的MIMO系统的发送信号的距离度量函数可以通过下列公式表示：
d(x)＝||y-Hx|| 2                (3-4)
将上述枚举过程转换为树搜索模型的过程，可以降低运算复杂度，该转换过程称之为近似最大似然(near maximum-likelihood，NML)
对上述公式(3-1)的等效模型进行扩展，扩展后的模型可以通过下列公式表示：
Figure PCTCN2020126381-appb-000056
上述公式(3-5)中，
Figure PCTCN2020126381-appb-000057
是维度为(M+M)×M的扩展信道矩阵，且
Figure PCTCN2020126381-appb-000058
Figure PCTCN2020126381-appb-000059
是维度为(M+M)×1的扩展噪声向量，且
Figure PCTCN2020126381-appb-000060
接下来使用LDL算法对共轭对称矩阵G＝H HH+σ 2I进行分解，可以得到：
Figure PCTCN2020126381-appb-000061
上述公式(3-6)中，
Figure PCTCN2020126381-appb-000062
是上三角矩阵，
Figure PCTCN2020126381-appb-000063
是
Figure PCTCN2020126381-appb-000064
的共轭转置，L是单位下三角矩阵，L H为L的转置，即L H是单位上三角矩阵；D是对角矩阵。
由上述公式(3-6)可以得到
Figure PCTCN2020126381-appb-000065
可以通过下列公式表示：
Figure PCTCN2020126381-appb-000066
将上述公式(3-7)代入上述公式(3-4)中，故M×M维的MIMO系统的发送信号的近似最大似然检测NML的距离度量函数可以通过下列公式表示：
Figure PCTCN2020126381-appb-000067
上述公式(3-8)中，z＝D -1L -1H Hy＝L HWy，H Hy为匹配滤波矩阵，且上述对D的开平方运算(即，
Figure PCTCN2020126381-appb-000068
的运算)在路径搜索过程中可以被省略。由上述公式(2-9)可以得到：
Figure PCTCN2020126381-appb-000069
故由此实现NML算法与LMMSE算法过程的融合。
在M×M维的MIMO系统中，该系统的发送信号包括的数据符号的所有可能的组合可以映射到一个包括M层的树搜索模型中。具体的，该树搜索模型自顶向下，依次为第M层，第M-1层，以此类推，第1层。其中，第M层记为该树搜索模型根节点所在层，其余层为树搜索模型叶节点所在层。
在上述公式(3-8)中，利用矩阵L H的单位上三角特性，可以通过树搜索模型求解上述公式(3-8)。也就是说，可以通过逐层计算局部最优节点并在下一层删减上一层干扰的 方式逼近全局最优路径。
由上述公式(3-8)可知，M×M维的MIMO系统的发送信号对应的树搜索模型的根节点的近似最大似然检测NML的距离度量函数(即，上述根节点的第一度量函数的一例)可以通过下列公式表示：
d NML(x) M＝D M||z M-x M|| 2-σ 2||x M|| 2           (3-9)
上述公式(3-9)中，z M＝[Wy] M，即z M为MMSE估计向量的第M个符号，x M∈S M，S M为根节点候选集合。集合S M为发送符号星座图Q M(即，上述第一星座图的一例)上与归一化LMMSE估计符号
Figure PCTCN2020126381-appb-000070
(即，上述根节点的估计发送信号的一例)最近的星座点。软输出MIMO检测，需要同时计算
Figure PCTCN2020126381-appb-000071
的最优度量函数和
Figure PCTCN2020126381-appb-000072
的最优度量函数，否则近似最大似然算法不可避免地会产生缺失比特(missing bit)，即没有遍历到包含某个比特符号的路径。对于该问题的处理方法通常是利用本比特已选路径度量集合的最大值、中值、均值或者均值的修正值代替反比特的度量值。不仅实现复杂度较高，而且性能较差。
本申请提供了一种确定根节点候选集合(即，上述第一候选集合的一例)的方法，在保证能够有效避免缺失反比特问题的前提下，可以降低计算复杂度。
下面，结合图4具体介绍本申请提供的确定根节点候选集合S M包括的发送符号的方法。
图4是本申请提供的确定根节点候选集合的示意图。应理解，图4仅为示意并不对本申请提供的确定根节点候选集合S M的方法构成任何限定。例如，本申请提供的确定根节点候选集合S M的方法还适用于更小规模(例如，16QAM星座图)或更大规模(例如，128QAM星座图或256QAM星座图)的星座图。
图4所示的星座图可以理解为是对M×M维的MIMO系统的发送信号x进行编码后映射到64QAM星座图(Q＝64)，该星座图中包括64个符号，每个符号可以通过6比特位表示。例如，64QAM星座图中的第一行第一列的符号可以表示为101111。
具体的，本申请提供的根节点候选集合S M可以根据如下步骤确定：
(1)根据运算复杂度约束确定根节点候选集合S M的度(即，上述第一区域的一例)。
参见图4，根节点候选集合S M的度可以理解为图4中的大圆圈。
上述运算复杂度约束可以是预定义的。具体的，可以根据设备的运算性能等确定运算复杂度。例如，当设备的性能较差时，上述运算复杂度的约束可以设置的较为严格。
作为示例，根节点候选集合S M的运算复杂度约束可以表示为：log 2|Q M|＜|S M|≤|Q M|。
(2)选择距离根节点发送符号估计
Figure PCTCN2020126381-appb-000073
最近的星座点放入集合S M，其中
Figure PCTCN2020126381-appb-000074
表示归一化LMMSE估计符号，
Figure PCTCN2020126381-appb-000075
参见图4，距离发送符号
Figure PCTCN2020126381-appb-000076
最近的星座点为图4中的星座点#1(即，上述第一星座点的一例)。
(3)选择与上述步骤(2)选出的星座点距离最近且分别包含每个比特对应反比特的星座点放入集合S M，例如，图4中的星座点#2(即，上述第二星座点的一例)。
经过步骤(3)可以选出与步骤(2)选出的星座点距离最近且分别包含每个比特对应反比特的log 2|Q M|个星座点。
(4)选择包括在根节点候选集合S M的度内且未被选择的星座点放入集合S M，例如，图4中的星座点#3(即，上述第三星座点的一例)。
经过步骤(4)可以选出|S M|-1-log 2|Q M|个距离
Figure PCTCN2020126381-appb-000077
最近且尚未被选入集合S M的星座点。
应理解的是，上述距离最近，可以理解为星座图Q M上星座点与发送符号估计
Figure PCTCN2020126381-appb-000078
的欧式距离最近。
根据上述选取规则，可以得到本申请实施例提供的根节点候选集合S M包括：距离根节点的估计发送符号
Figure PCTCN2020126381-appb-000079
最近的星座点(例如，图4中的星座点#1)，与距离估计发送符号
Figure PCTCN2020126381-appb-000080
距离最近且分别包含每个比特对应反比特的星座点(例如，图4中的星座点#2)，以及包括在根节点候选集合S M的度内且未被选择的星座点(例如，图4中的星座点#3)
在上述技术方案中，在选择根节点候选集合S M包括的比特时，不仅要包含距离根节点估计发送符号
Figure PCTCN2020126381-appb-000081
最近的星座点，还要包含与估计发送符号
Figure PCTCN2020126381-appb-000082
距离最近且分别包含每个比特对应反比特的星座点，通过上述选取规则可以保证选择的根节点候选集合S M中包含全部比特符号的度量，不存在缺失比特(missing bit)。由于确定的根节点候选集合S M不存在缺少比特的问题，因此，使用本申请提供的方法，可以省去缺失比特度量函数的计算过程(例如，利用本比特已选路径度量集合的最大值、中值、均值或者均值的修正值)，从而可以有效降低计算复杂度。
在确定树搜索模型根节点候选集合后，需要确定该树搜索模型包括的叶节点的度量函数(即，上述第二距离度量函数的一例)以及对应的叶节点候选集合(即，上述第二候选集合的一例)。实际应用中为了消除信号之间的干扰，在计算第m层发送信号的度量函数时需要先删减已遍历层的信号对该第m层发送信号的干扰。在一个示例中，当树搜索模型包括M层且该树搜索模型最顶层第M层为根节点所在层，可以先对该树搜索模型的第M层进行搜索，然后对该树搜索模型的第M-1层进行搜索，再对该树搜索模型的第i层进行搜索，i为小于等于M-1且大于等于1的正整数。当对该树搜索模型的第i层进行搜索时，需要删除该树搜索模型的第i+1层的发送信号至第M层的发送信号对第i层的发送信号的干扰。
在一个示例中，M×M维的MIMO系统对应的树搜索模型的第i层包括的叶节点的等效接收信号
Figure PCTCN2020126381-appb-000083
可以通过下列公式表示：
Figure PCTCN2020126381-appb-000084
上述公式(3-10)中，i为正整数，且1≤i≤M-1；
Figure PCTCN2020126381-appb-000085
为M×M维的MIMO模型的第j层对第i层的干扰，i+1≤j≤M。
因此，根据上述公式(3-8)和上述公式(3-10)，可以得到上述树搜索模型的第i层叶节点的距离度量函数(即，上述第i个叶节点的第二距离度量函数的一例)可以通过下列公式表示：
Figure PCTCN2020126381-appb-000086
上述公式(3-11)中，x i∈S i，S i为第i层叶节点候选集合(即，上述第二候选集合 的一例)。
在本申请中，S i可以包括第i层叶节点对应的星座图Q i(即，上述第二星座图的一例)上距离发送符号估计
Figure PCTCN2020126381-appb-000087
(即，上述第i个叶节点的估计发送信号的一例)最近的星座点，其中
Figure PCTCN2020126381-appb-000088
可选的，还可以根据上述确定根节点候选集合S M的方法，确定第i层叶节点候选集合S i。在此情况下，叶节点候选集合S i可以包括：距离第i层叶节点估计发送符号
Figure PCTCN2020126381-appb-000089
最近的星座点(例如，图4中的星座点#1)，与距离估计发送符号
Figure PCTCN2020126381-appb-000090
距离最近且分别包含每个比特对应反比特的星座点(例如，图4中的星座点#2)，以及包括在叶节点候选集合Si的度内且未被选择的星座点(例如，图4中的星座点#3)。
经过上述步骤后，即完成了对M×M维的MIMO系统对应的树搜索模型的根节点的检测以及对该树搜索模型各个叶子节点的检测。
结合上述公式(3-2)可以得到M×M维的MIMO系统的第m层对应的发送信号LMMSE估计对数似然比可以通过下列公式表示：
Figure PCTCN2020126381-appb-000091
上述公式(3-12)中，
Figure PCTCN2020126381-appb-000092
表示第m层发送信号的LMMSE估计符号，
Figure PCTCN2020126381-appb-000093
φ m表示信号增益，为复数标量，ξ m表示等效噪声，为复数标量，
Figure PCTCN2020126381-appb-000094
表示似然函数。
可以理解的是，在本申请中，树搜索模型的根节点的度量函数还可以被用来计算该根节点所在层的发送信号的LMMSE估计的对数似然比，具体的推导过程可以如下：
通过推导可以得到：
Figure PCTCN2020126381-appb-000095
因此，结合上述公式(3-13)，可以得知上述公式(3-12)中的
Figure PCTCN2020126381-appb-000096
可以可以通过下列公式表示：
Figure PCTCN2020126381-appb-000097
将上述公式(3-14)带入上述公式(3-12)，并做MAX-LOG-MAP近似处理，可得：
Figure PCTCN2020126381-appb-000098
根节点层LMMSE估计的度量函数可以通过下列公式表示：
Figure PCTCN2020126381-appb-000099
由于根据上述公式(2-9)可知：
Figure PCTCN2020126381-appb-000100
可见，若将第m层作为树搜索模型的根节点层，则有：
Figure PCTCN2020126381-appb-000101
将上述公式(3-18)带入上述公式(3-16)可以得到：
Figure PCTCN2020126381-appb-000102
由上述公式(3-19)可知，
Figure PCTCN2020126381-appb-000103
与发送信号x m无关，故在计算第m层根节点的对数似然比时可以将
Figure PCTCN2020126381-appb-000104
省略。因此，上述公式(3-19)进一步可以通过下列公式表示：
d LMMSE(x m)＝D m||z m-x m|| 2-σ 2||x m|| 2         (3-20)
由上述公式(3-20)可以得知，NML算法中第m层发送符号作为根节点时的根节点度量函数可以被用来计算第m层发送符号的LMMSE估计的对数似然比。因此，实现了NML算法与LMMSE算法的深度融合，即仅利用NML算法并通过中间变量的转换就可以得到基于LMMSE算法估计的根节点的对数似然比，避免了直接采用LMMSE算法复杂度高的问题。
为了提高估计的发送信号的准确度，对M×M维的MIMO系统对应的树搜索模型进行搜索之前，需要先对该树搜索模型待遍历层进行排序，以使先搜索的层的信噪比最大。目前，性能最佳的排序方法是贝尔实验室垂直分层空时码V-BLAST排序，该方法复杂度随发送层数指数增长；简化的排序方法，例如排序的QR算法SQRD，由于不能预先估计根节点信噪比，存在鲁棒性风险，且没有考虑根节点遍历星座点集合的度远大于叶节点遍历星座点集合的度。
本申请提供的MIMO检测方法，树搜索模型进行搜索时不需要根据信噪比大小对树搜索模型对应的各层进行排序。
具体的，对M×M维的MIMO系统的发送信号进行检测时，可以使用列变换矩阵p l,l＝0,1,2,...,M-1对上述公式(3-1)中的信道矩阵H进行M次列变换，以得到经过列置换后的M个不同的信道矩阵H l。再根据每个信道矩阵H l采用上述提供的方法对M×M维的MIMO系统进行检测。最后，综合每个信道矩阵H l对应的估计结果，确定M×M维的MIMO系统的最优的发送信号。
使用列变换矩阵p l,l＝0,1,2,...,M-1对上述公式(3-1)中的信道矩阵H进行M次列变换，可以通过下列公式表示经过列置换后的信道矩阵H l:
H l＝Hp l                  (3-21)
当M＝4时，列变换矩阵p l,l＝0,1,2,3满足如下结构：
Figure PCTCN2020126381-appb-000105
其中，e l,l＝0,1,2,3为4维单位矩阵I的第l个列向量。可以理解的是在上述公式(3-22)中，p l,l＝0,1,2,3的最后一列对应树搜索模型的根节点，如p 0中的e 3对应树搜索模型的根节点，故根据通过上述公式(3-21)和上述公式(3-22)对4×4的MIMO系统的信道矩阵进行列置换后，该4×4的MIMO系统发送符号的每一层都有机会处于根节点位置。
当M＝8时，列变换矩阵p l,l＝0,1,2,3,4,5,6,7满足如下结构：
Figure PCTCN2020126381-appb-000106
其中，e l,l＝0,1,2,3,4,5,6,7为8维单位矩阵I的第l个列向量。可以理解的是，在上述公式(3-23)中，p l,l＝0,1,2,3,4,5,6,7的最后一列对应树搜索模型的根节点，如p 0中的e 7对应树搜索模型的根节点，故根据通过上述公式(3-21)和上述公式(3-22)对8×8的MIMO系统的信道矩阵进行列置换后，该8×8的MIMO系统发送符号的每一层都有机会处于根节点位置。
在本申请实施例中，为了得到最优的发送信号估计，可以依次根据经过列置换后的信道矩阵H l,l＝0,1,2,3,...,M-1确定一个树搜索模型，共可以确定M个树搜索模型并根据搜索结果估计M×M维的MIMO系统的发送信号。
应理解的是，上述公式(3-22)和上述公式(3-23)仅示出了M＝4或M＝8对应的列变换矩阵的结构，当M取其它值时(M＝16)，还可以根据上述公式(3-21)得到对应的列变换矩阵的结构。
由上述公式(3-22)或上述公式(3-23)可知，发送信号的每一层都有机会处于根节点位置，且MIMO系统包括的各层对应的树搜索模型的路径搜索顺序对称，从而可以避免简化排序引起的鲁棒风险。由于NML算法中根节点的度量函数可以被用来计算该层LMMSE估计的对数似然比，因此上述公式(3-22)或上述公式(3-23)设定的树搜索分支顺序，可以保证同时得到发送信号x的NML估计对数似然比和LMMSE估计对数似然比，从而实现NML算法与LMMSE算法的深度融合。
应理解的是，图3仅为示意并不对本申请提供的检测方法构成任何限定。例如，上述方法100还可以用于对MU-MIMO系统进行发射信号检测及干扰层发射信道调制阶数ML估计。
下面结合图5和图6，对本申请提供的MIMO检测的方法200进行介绍。
图5是本申请提供的MIMO系统的检测方法200的示意性流程图。如图5所示，方法200包括步骤210至步骤270，下面对步骤210至步骤270进行介绍。
在本申请实施例中，以4×4维的MIMO系统为例，介绍本申请提供的MIMO检测方法。例如，该4×4维的MIMO系统的结构可以如图6所示。图6中包括选择模块610，用于选择检测结果620输出的检测结果是基于NML检测的结果还是基于LMMSE检测的结果；检测结果620，用于输出估计的发送信号中每个信息比特的对数似然比；其中，LDD -1和ZDL用于表示对4×4维的MIMO系统进行检测的中间变量。具体的，LDD -1可以用于表示对信道矩阵进行LDL分解后得到的矩阵，可以包括上述方法100得到的预处理矩阵、预处理矩阵的逆矩阵以及单位上三角矩阵等；ZDL用于表示上述方法100中的单位上三角矩阵、接收信号以及L -HD -1L -1H Hy的乘积，LLR Calc用于表示计算对数似然比。
下面，结合步骤210至步骤270详细介绍对该图6所示的MIMO检测的方法。应理解，下文中的检测方法也适用于其它规模的MIMO系统。例如，8×8维或16×16维的MIMO系统。
步骤210，根据MIMO系统的接收信号确定信道矩阵。
在本申请实施例中，以4×4维的MIMO系统为例，上述步骤210即可以理解为，根据4×4维的MIMO系统的接收信号确定信道矩阵。
具体的，本申请实施例中确定信道矩阵的方法可以参见方法100，信道矩阵H可以通过下列公式表示：
y＝Hx+N                    (5-1)
上述公式(5-1)中，各符号的物理含义参见方法100中的公式(3-1)，此处不再详细赘述。
利用列变换矩阵p l,l＝0,1,2,3对信道矩阵H进行列变换，可以得到变换后的4个信道矩阵H l＝Hp l，其中列变换矩阵p l,l＝0,1,2,3的结构可以通过下列公式表示：
Figure PCTCN2020126381-appb-000107
可以理解的是，在本申请实施例中，步骤210中的信道矩阵为经过列置换后的信道矩阵H l,l＝0,1,2,3中的一个信道矩阵。
步骤220，根据接收信号和信道矩阵，确定共轭对称矩阵G和匹配滤波矩阵。
其中，共轭对称矩阵是根据信道矩阵、该信道矩阵的共轭转置和噪声得到的矩阵，该共轭对称矩阵可以通过下列公式表示：
G＝H HH+σ 2I             (5-3)
在上述公式(5-3)中，H为信道矩阵，H H为信道矩阵的共轭转置，σ 2为噪声方差，I为单位矩阵。
匹配滤波矩阵是根据信道矩阵的共轭转置和接收信号得到的矩阵，该匹配滤波矩阵可以通过下列公式表示：
X MF＝H Hy                (5-4)
在上述公式(5-4)中，H H为信道矩阵的共轭转置，y为接收信号。
可以理解的是，上述步骤220中的H∈H l,l＝0,1,2,3。
步骤230，对共轭对称矩阵进行LDL分解，得到预处理矩阵和单位下三角矩阵。
具体的，对共轭对称矩阵进行LDL分解，可以表示为：
Figure PCTCN2020126381-appb-000108
其中，
Figure PCTCN2020126381-appb-000109
为预处理矩阵，
Figure PCTCN2020126381-appb-000110
为单位下三角矩阵。根据上述公式(5-1)可知，l＝0,1对应的LDL分解的过程可以复用，l＝2,3对应的LDL分解的过程可以复用，进一步，对共轭对称矩阵进行LDL分解，可以通过下列公式表示：
Figure PCTCN2020126381-appb-000111
步骤240，根据预处理矩阵、单位下三角矩阵和匹配滤波矩阵，确定MIMO系统的等效接收信号。
在本申请实施例中，等效接收信号可以通过下列公式表示：
Figure PCTCN2020126381-appb-000112
上述公式(5-7)中，p l∈{p 0,p 1,p 2,p 3}。
步骤250，根据等效接收信号，确定MIMO系统的对应的树模型的根节点进行搜索，确定根节点度量集合。
具体的，上述步骤250的确定根节点度量集合的方法与方法100中确定根节点度量集合的方法相同，此处不再详细赘述。
步骤260，确定MIMO系统的对应的树模型的叶节点进行搜索，确定叶节点度量集合。
具体的，上述步骤260的确定叶节点度量集合的方法与方法100中确定叶节点度量集合的方法相同，此处不再详细赘述。
步骤270，根据根节点度量集合和叶节点度量集合，确定MIMO系统的发送信号的对数似然比。
具体的，上述步骤270根据确定叶节点和根节点的度量集合确定发送信号的对数似然比的方法与方法100中的方法相同，此处不再详细赘述。
可以理解的是，根据上述公式(5-6)可以得到该MIMO系统的发送信号的对数似然比的4个估计结果，进一步可以根据应用需求选取对应的估计结果。
可选的，在一些实施例中，为了降低系统功耗，还可以仅使用H l,l＝0作为MIMO系统的信道矩阵。基于此，经过上述步骤240至上述步骤270处理后，可以得到该MIMO系统的发送信号的对数似然比的1个估计结果。
应理解，图5仅为示意并不对本申请提供的MIMO检测的方法构成任何限定。例如，上述方法200还可以用于对MU-MIMO系统进行发射信号检测和干扰层发射信道调制阶数估计及检测。在使用上述方法200对MU-MIMO系统进行检测时，当获知干扰层调制阶数时，遍历该层叶节点星座点并累和度量函数，否则不遍历。可以推导得知遍历层进行了NML检测，非遍历层进行了LMMSE检测，如此则实现了MU-MIMO场景下根据调制阶数估计结果灵活切换NML和LMMSE检测算法。例如，上述方法200还可以用于对8×8维的MIMO系统进行检测，当对8×8维的MIMO系统进行检测时，上述的列变换矩阵可以替换为上述公式(3-23)表示的列变换矩阵。
上文，结合图1至图6详细介绍了本申请提供的MIMO系统的检测方法。下面，结合图7和图8详细介绍本申请提供的MIMO系统的检测装置和MIMO系统的检测设备。
图7是本申请提供的MIMO系统的检测装置700的示意性框图。图7所示的MIMO系统的检测装置700包括获取单元701、处理单元702，
该处理单元702，用于确定共轭对称矩阵，该共轭对称矩阵是根据第一信道矩阵、噪声方差和单位矩阵确定的，该第一信道矩阵是对接收信号进行处理得到的该处理单元702，用于构建训练数据集；
该处理单元702，还用于使用预处理矩阵和单位下三角矩阵将该接收信号对应的发送信号映射至树搜索模型，该预处理矩阵和该单位下三角矩阵是对该共轭对称矩阵进行LDL分解得到的；
该处理单元702，还用于基于树搜索方式从该树搜索模型的最顶层逐层向下扩展，确定度量集合，该度量集合包括根节点的度量集合和多个叶节点的度量集合，该根节点的度量集合包含于第一候选集合，该第一候选集合是根据第一规则从第一星座点集合中确定的，该第一星座点集合是对该根节点对应的发送信号进行编码后映射得到的，该度量集合使得该树搜索模型具有最小的路径度量；
该处理单元702，还用于根据该度量集合，确定该发送信号中每个信息比特的对数似然比。
可选的，在一些实施例中，该第一规则包括将该第一星座点集合中的如下星座点确定为该第一候选集合包括的星座点：
第一星座点，第二星座点和第三星座点，该第一星座点是与该根节点的估计发送信号的欧式距离最近的星座点，该第二星座点是与该第一星座点的欧式距离最近且分别包含该第一星座点每个比特对应反比特的星座点，该第三星座点是第一区域内包括的除该第一星座点和该第二星座点之外的星座点，该第一区域包含于该第一星座点集合对应的区域，该第一区域是根据预设复杂度从该第一星座点集合对应的区域中确定的。
可选的，在一些实施例中，
该获取单元701，还用于获取该接收信号；
该处理单元702，还用于对该接收信号进行处理得到原始信道矩阵；
该处理单元702，还用于：
使用列变换矩阵对该原始信道矩阵按列进行列变换，得到列变换后的M个信道矩阵，该M个信道矩阵与M个树搜索模型一一对应，该M个树搜索模型与该MIMO系统的M层一一对应；
将该M个信道矩阵中的一个信道矩阵确定该第一信道矩阵，根据该第一信道矩阵确定的树搜索模型的最小路径度量小于该M个信道矩阵中的其余M-1个信道矩阵确定的树搜索模型的最小路径度量。
可选的，在一些实施例中，该M个信道矩阵通过下列公式表示：
H l＝Hp l
其中，H l是该M个信道矩阵中的第l个信道矩阵，且l＝0,2,3,...,M-1，H是该原始信道矩阵，p l是该列变换矩阵。
可选的，在一些实施例中，该处理单元702还用于：
确定该根节点的第一距离度量函数；
基于该第一距离度量函数在该第一候选集合中进行搜索，将第四星座点确定为该根节点的度量集合包括的星座点，该第四星座点对应的发送信号使得该第一距离度量函数的值小于等于第一阈值；
确定第i个叶节点的第二距离度量函数，该第二距离度量函数不包括该树搜索模型包括的第i+1层至第M层对应的发送信号分别对该第i层的干扰，i＝1,2,3,...,M-1；
基于该第二距离度量函数在第二候选集合中进行搜索，将第五星座点确定为该第i个叶节点的度量集合包括的星座点，该第五星座点对应的发送信号使得该第二距离度量函数的值小于等于第二阈值，该第二候选集合是根据该第i个叶节点的估计发送信号和第二星座点集合确定的，该第二星座点集合是对该第i个叶节点对应的发送信号进行编码后映射得到的。
可选的，在一些实施例中，该第一距离度量函数与根据线性最小均方误差LMMSE算法估计的该第M层对应的发送信号的度量函数相同。
可选的，在一些实施例中，该树搜索方式包括以下方式中的一种：广义优先树搜索和深度优先树搜索。
图8是本申请提供的MIMO系统的检测设备800的示意性框图。如图8所示，该MIMO系统的检测设备800包括：收发器810、处理器820和存储器830。其中，收发器810、处理器820和存储器830之间通过内部连接通路互相通信，传递控制和/或数据信号，该存储器830用于存储计算机程序，该处理器820用于从该存储器830中调用并运行该计算机程序，以控制该收发器810收发信号。
具体的，收发器810的功能与图7所示的获取单元701的具体功能相对应，此处不再赘述。
具体的，处理器820的功能与图7所示的处理单元702的具体功能相对应，此处不再赘述。
本申请实施例中的芯片可以是编程门阵列(field programmable gate array，FPGA)，可以是专用集成芯片(application specific integrated circuit，ASIC)，还可以是系统芯片(system on chip，SoC)，还可以是中央处理器(central processor unit，CPU)，还可以是网络处理器(network processor，NP)，还可以是数字信号处理电路(digital signal processor，DSP)，还可以是微控制器(micro controller unit，MCU)，还可以是可编程控制器(programmable logic device，PLD)、其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件，或其他集成芯片。
在实现过程中，上述方法的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。结合本申请实施例所公开的方法的步骤可以直接体现为硬件处理器执行完成，或者用处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。为避免重复，这里不再详细描述。
应注意，本申请实施例中的处理器可以是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法实施例的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合本申请实施例所公开的方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。
可以理解，本申请实施例中的存储器可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(read-only memory，ROM)、可编程只读存储器(programmable ROM，PROM)、可擦除可编程只读存储器(erasable PROM，EPROM)、电可擦除可编程只读存储器(electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(random access memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器(static RAM，SRAM)、动态随机存取存储器(dynamic RAM，DRAM)、同步动态随机存取存储器(synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(double data rate SDRAM，DDR SDRAM)、增强型同步动态随机存取存储器(enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(synchlink DRAM，SLDRAM)和直接内存总线随机存取存储器(direct rambus RAM，DR RAM)。应注意，本文描述的系统和方法的存储器旨在包括但不限于这些和任意其它适合类型的存储器。
根据本申请实施例提供的方法，本申请还提供一种计算机程序产品，该计算机程序产品包括：计算机程序代码，当该计算机程序代码在计算机上运行时，使得该计算机执行图3和图5所示实施例中任意一个实施例的方法。
根据本申请实施例提供的方法，本申请还提供一种计算机可读介质，该计算机可读介质存储有程序代码，当该程序代码在计算机上运行时，使得该计算机执行图3和图5所示实施例中任意一个实施例的方法。
根据本申请实施例提供的方法，本申请还提供一种系统，其包括前述的一个或多个第一模型以及一个或多个第二模型。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (11)

  1. 一种多输入多输出MIMO系统的检测方法，其特征在于，所述方法包括：
    确定共轭对称矩阵，所述共轭对称矩阵是根据第一信道矩阵、噪声方差和单位矩阵确定的，所述第一信道矩阵是对接收信号进行处理得到的；
    使用预处理矩阵和单位下三角矩阵将所述接收信号对应的发送信号映射至树搜索模型，所述预处理矩阵和所述单位下三角矩阵是对所述共轭对称矩阵进行LDL分解得到的；
    基于树搜索方式从所述树搜索模型的最顶层逐层向下扩展，确定度量集合，所述度量集合包括根节点的度量集合和多个叶节点的度量集合，所述根节点的度量集合包含于第一候选集合，所述第一候选集合是根据第一规则从第一星座点集合中确定的，所述第一星座点集合是对所述根节点对应的发送信号进行编码后映射得到的，所述度量集合使得所述树搜索模型具有最小的路径度量；
    根据所述度量集合，确定所述发送信号中每个信息比特的对数似然比。
  2. 根据权利要求1所述的方法，其特征在于，所述第一规则包括将所述第一星座点集合中的如下星座点确定为所述第一候选集合包括的星座点：
    第一星座点，第二星座点和第三星座点，所述第一星座点是与所述根节点的估计发送信号的欧式距离最近的星座点，所述第二星座点是与所述第一星座点的欧式距离最近且分别包含所述第一星座点每个比特对应反比特的星座点，所述第三星座点是第一区域内包括的除所述第一星座点和所述第二星座点之外的星座点，所述第一区域包含于所述第一星座点集合对应的区域，所述第一区域是根据预设复杂度从所述第一星座点集合对应的区域中确定的。
  3. 根据权利要求1或2所述的方法，其特征在于，所述MIMO系统是M×M维的MIMO系统，M为大于等于2的正整数，所述确定共轭对称矩阵之前，所述方法还包括：
    获取所述接收信号，并对所述接收信号进行处理得到原始信道矩阵；
    使用列变换矩阵对所述原始信道矩阵列变换，得到列变换后的M个信道矩阵，所述M个信道矩阵与M个树搜索模型一一对应，所述M个树搜索模型与所述MIMO系统的M层一一对应；
    将所述M个信道矩阵中的一个信道矩阵确定所述第一信道矩阵，根据所述第一信道矩阵确定的树搜索模型的最小路径度量小于所述M个信道矩阵中的其余M-1个信道矩阵确定的树搜索模型的最小路径度量。
  4. 根据权利要求3所述的方法，其特征在于，所述M个信道矩阵通过下列公式表示：
    H l＝Hp l
    其中，H l是所述M个信道矩阵中的第l个信道矩阵，且l＝0,2,3,...,M-1，H是所述原始信道矩阵，p l是所述列变换矩阵。
  5. 根据权利要求1-4任一项所述的方法，其特征在于，所述MIMO系统是M×M维的MIMO系统，所述树搜索模型包括M层，所述根节点与第M层对应，所述多个叶节点分别与第M-1至第1层对应，M为大于等于2的正整数，所述基于树搜索方式从所述树搜索模型的最顶层逐层向下扩展，确定度量集合，包括：
    确定所述根节点的第一距离度量函数；
    基于所述第一距离度量函数在所述第一候选集合中进行搜索，将第四星座点确定为所述根节点的度量集合包括的星座点，所述第四星座点对应的发送信号使得所述第一距离度量函数的值小于等于第一阈值；
    确定第i个叶节点的第二距离度量函数，所述第二距离度量函数不包括所述树搜索模型包括的第i+1层至第M层对应的发送信号分别对所述第i层的干扰，i＝1,2,3,...,M-1；
    基于所述第二距离度量函数在第二候选集合中进行搜索，将第五星座点确定为所述第i个叶节点的度量集合包括的星座点，所述第五星座点对应的发送信号使得所述第二距离度量函数的值小于等于第二阈值，所述第二候选集合是根据所述第i个叶节点的估计发送信号和第二星座点集合确定的，所述第二星座点集合是对所述第i个叶节点对应的发送信号进行编码后映射得到的。
  6. 根据权利要求5所述的方法，其特征在于，
    所述第一距离度量函数与根据线性最小均方误差LMMSE算法估计的所述第M层对应的发送信号的度量函数相同。
  7. 根据权利要求1-6任一项所述的方法，其特征在于，所述树搜索方式包括以下方式中的一种：广义优先树搜索和深度优先树搜索。
  8. 一种通信装置，其特征在于，包括至少一个处理器和通信接口，所述至少一个处理器，用于执行计算机程序或指令，以使得所述通信装置执行如权利要求1至7中任一项所述的方法。
  9. 根据权利要求8所述的通信装置，其特征在于，所述装置还包括至少一个存储器，所述至少一个存储器与所述至少一个处理器耦合，所述计算机程序或指令存储在所述至少一个存储器中。
  10. 一种计算机可读存储介质，其特征在于，用于存储计算机指令，当所述计算机指令被执行时，如权利要求1至7中任一项所述的方法被实现。
  11. 一种通信系统，其特征在于，包括如权利要求8或9所述的通信装置。
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