WO2021109420A1 - 大规模mimo波束域统计信道信息获取方法与系统 - Google Patents

大规模mimo波束域统计信道信息获取方法与系统 Download PDF

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WO2021109420A1
WO2021109420A1 PCT/CN2020/086181 CN2020086181W WO2021109420A1 WO 2021109420 A1 WO2021109420 A1 WO 2021109420A1 CN 2020086181 W CN2020086181 W CN 2020086181W WO 2021109420 A1 WO2021109420 A1 WO 2021109420A1
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refined
channel information
beam domain
matrix
domain
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French (fr)
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卢安安
高西奇
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东南大学
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0868—Hybrid systems, i.e. switching and combining
    • H04B7/088—Hybrid systems, i.e. switching and combining using beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0204—Channel estimation of multiple channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the invention belongs to the field of communication technology, and relates to a method and system for acquiring statistical channel information in a large-scale MIMO beam domain.
  • MIMO Multiple-Input Multiple-Ouput
  • massive MIMO greatly improves the system capacity by equipping the base station (BS, Base Station) with a large-scale antenna array, and makes full use of space dimensional resources.
  • B5G 5G
  • the establishment of the channel statistical model is the basis of the theoretical method of massive MIMO precoding transmission.
  • a commonly used channel statistical model in the literature is the traditional beam-domain channel model based on the Discrete Fourier Transform (DFT) matrix.
  • DFT Discrete Fourier Transform
  • the base station is often equipped with large-scale area array antennas and other easier-to-implement antenna arrays, resulting in the number of antennas in a single dimension. Due to this limitation, the same eigenmode matrix in a single dimension of each user channel still uses the DFT matrix based on the traditional beam domain channel model approximation, which will deviate from the actual physical channel model to a considerable extent.
  • the base station in the massive MIMO wireless system is equipped with a large-scale antenna array, and the number of user antennas occupying the same time-frequency resources increases, which limits the time-frequency resources used for pilots. Channel estimation errors cannot be avoided.
  • there are factors such as the aging of the instantaneous channel information obtained at the base station side in medium and high-speed mobile communication scenarios. Therefore, it is of great significance to develop statistical channel models that can describe various typical mobile communication scenarios.
  • Most of the related work in the literature considers large-scale linear array antennas, and uses DFT matrices to convert spatial signals into sparse angle domain signals, and none of them considers a priori statistical model and a posterior statistical model based on instantaneous channel information.
  • the downlink multi-user precoding transmission method is the key to combating multi-user interference and achieving spectral efficiency gain, and therefore is one of the most core problems of the massive MIMO wireless transmission system.
  • the mobility of users in the actual massive MIMO system brings great challenges to the downlink multi-user precoding transmission method.
  • robust multi-user precoding transmission methods have become increasingly important.
  • methods based on statistical channel models are one of the key methods. The method based on the statistical channel model is based on the acquisition of statistical channel information. Therefore, when the traditional DFT matrix-based beam-domain channel model is extended, how to obtain the statistical channel information of the new model becomes very important.
  • the objective of the present invention is to provide a method and system for obtaining massive MIMO beam-domain statistical channel information, which can provide support for a massive MIMO robust precoding transmission method.
  • the method for acquiring priori statistical channel information in the massive MIMO beam domain includes the following steps:
  • the multiplied pilot signal is converted to a refined beam domain through a refined sampling steering vector matrix; the number of steering vectors in the refined sampling steering vector matrix is more than the number of corresponding antennas;
  • the refined beam domain sample statistics are used to obtain the refined beam domain prior statistical channel information of each user terminal.
  • the multiplied pilot signal is converted into the refined beam domain by multiplying the conjugate matrix of the refined sampling steering vector matrix on the left side and the conjugate matrix of the refined sampling steering vector matrix on the receiving side by left multiplying.
  • each user terminal transmits pilot signals on the same time-frequency resource, and the pilot signals of each user terminal are orthogonal to each other.
  • using the refined beam domain sample statistics to obtain the refined beam domain prior statistical channel information of each user terminal is specifically: solving the channel energy according to the refined beam domain sample statistics and the equation of the channel energy matrix function matrix.
  • Matrix in the equation, only the channel energy matrix or the channel amplitude matrix are unknown matrices, and the remaining matrices are known matrices.
  • the method for acquiring priori statistical channel information in the massive MIMO beam domain includes the following steps:
  • the refined beam domain sample statistics are used to obtain the refined beam domain prior statistical channel information of each user terminal.
  • channel information is converted into the refined beam domain by multiplying the conjugate matrix of the refined sampling steering vector matrix on the left side and the conjugate matrix of the refined sampling steering vector matrix on the receiving side by left multiplying.
  • using the refined beam domain sample statistics to obtain the refined beam domain prior statistical channel information of each user terminal is specifically: solving the channel energy according to the refined beam domain sample statistics and the equation of the channel energy matrix function matrix.
  • Matrix in the equation, only the channel energy matrix or the channel amplitude matrix are unknown matrices, and the remaining matrices are known matrices.
  • the refined beam-domain posterior statistical channel information includes a refined beam-domain posterior mean value and a refined beam-domain posterior variance.
  • a computing device includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor.
  • the computer program is loaded into the processor to implement the foregoing method for acquiring massive MIMO beam-domain prior statistical channel information , Or massive MIMO beam domain posterior statistical channel information acquisition method.
  • a massive MIMO communication system includes a base station and multiple user terminals.
  • the base station is used for:
  • obtain the channel information of each user terminal convert the channel information of each user terminal to a refined beam domain through a multiplication operation with a refined sampling steering vector matrix; the number of steering vectors in the refined sampling steering vector matrix is more than that of the base station The number of antennas; using the refined beam domain sample statistics to obtain the refined beam domain prior statistical channel information of each user terminal.
  • a massive MIMO communication system includes a base station and multiple user terminals.
  • the base station is used for:
  • the method for acquiring massive MIMO beam domain prior and posterior statistical channel information proposed by the present invention can establish statistical models of various typical mobile scenarios of massive MIMO systems, and can effectively support massive MIMO
  • the realization of the system's robust precoding transmission method solves the problem of the adaptability of massive MIMO to various typical mobile scenarios.
  • Figure 1 is a flow chart of a method for obtaining priori statistical channel information in a massive MIMO beam domain
  • Figure 2 is a flow chart of a method for obtaining priori statistical channel information in a massive MIMO beam domain when instantaneous channel information is known;
  • Figure 3 is a flow chart of a method for obtaining massive MIMO beam-domain posterior statistical channel information
  • Fig. 4 is a graph of the MSE performance comparison result of the estimated covariance matrix and the sample covariance matrix under the beam domain channel model.
  • the method for acquiring priori statistical channel information in the massive MIMO beam domain disclosed in the embodiment of the present invention includes: receiving a pilot signal sent by each user terminal; The user pilot signals are multiplied separately; the multiplied pilot signals are converted to the refined beam domain through the refined sampling steering vector matrix; the refined beam domain sample statistics are used to obtain the refined beam domain prior statistics of each mobile terminal Channel information.
  • the method for acquiring priori statistical channel information in the massive MIMO beam domain disclosed in another embodiment of the present invention is a method for acquiring priori statistical channel information in the massive MIMO beam area when instantaneous channel information is known. Including: obtaining the channel information of each user terminal; converting the channel information of each user terminal into a refined beam domain through a refined sampling steering vector matrix; using the refined beam domain sample statistics to obtain the refined beam domain prior of each user terminal Statistics channel information.
  • the method for acquiring massive MIMO beam-domain posterior statistical channel information disclosed in the embodiment of the present invention includes: acquiring refined beam-domain prior statistical channel information of each user terminal before the current time slot; and acquiring the current time slot Pilot signal sent by each user terminal; use the received pilot signal to estimate the refined beam domain channel matrix, combine the refined beam domain prior statistical channel information and inter-channel correlation factors to obtain the refined beam domain posterior of each user terminal Statistical channel information acquisition.
  • the user terminal in the foregoing embodiment may be a mobile terminal or a fixed terminal such as a mobile phone, a vehicle-mounted device, or a smart device; the conjugate matrix of the refined sampling steering vector matrix on the transmitting side can be multiplied by the conjugate matrix of the refined sampling steering vector matrix on the right side and the refined sampling steering vector matrix on the receiving side can be multiplied by the combined matrix.
  • the yoke matrix converts the pilot signal or channel information into a refined beam domain, where the number of steering vectors in the refined sampling steering vector matrix is more than the number of corresponding antennas.
  • the acquisition of prior statistical channel information in the refined beam domain can solve the channel energy matrix according to the refined beam domain sample statistics and the equation of the channel energy matrix function matrix.
  • the method of the present invention is mainly applicable to a large-scale MIMO system equipped with a large-scale antenna array on the base station side to simultaneously serve multiple users.
  • the specific implementation process of the method for obtaining beam-domain statistical channel information according to the present invention will be described in detail below with reference to specific communication system examples. It should be noted that the method of the present invention is not only applicable to the specific system models mentioned in the following examples, but also applicable to other configurations. System model.
  • the massive MIMO system considered in this embodiment works in a Time Division Duplexing (TDD) mode.
  • TDD Time Division Duplexing
  • the base station In each time slot, the base station only receives the user uplink pilot signal in the first time block.
  • the second to N b time blocks are used by the base station for downlink precoding domain pilot and data signal transmission.
  • the length of the uplink training sequence is the length of the block, that is, T symbol intervals.
  • FDD Frequency Division Duplexing
  • the uplink channel training phase can be replaced with the downlink channel feedback phase, and the downlink transmission phase remains unchanged.
  • the downlink omnidirectional pilot signal is transmitted in the first block, and feedback from the mobile terminal is received.
  • the refined beam domain prior statistical model based on the refined sampling steering vector matrix is the same as the number of antennas.
  • the refined beam domain statistical model of the present invention refers to introducing more steering vectors than the number of antennas in the channel model to better describe channel statistical characteristics.
  • N h and N v the refinement factors on the horizontal and vertical dimensions of the base station side
  • the user-side refinement factor is defined as N k
  • U k correspond to the steering vector matrix of the base station side array and the steering vector matrix of the user side linear array, respectively.
  • the method of the present invention is not only applicable to large-scale uniform area array antennas, but also applicable to other forms of antennas, such as cylindrical array antennas, area array antennas whose array elements are polarized antennas.
  • U k can be changed to the steering vector matrix of the corresponding array.
  • H k, m, n denote the channel of the k-th user on the n-th block of the m-th slot
  • the refined beam-domain prior statistical channel model of the considered massive MIMO system can be expressed as
  • G k,m,n (M k ⁇ W k,m,n ) is the refined beam domain channel matrix of the kth user on the nth block of the mth time slot
  • M k represents the refined beam domain of the kth user Channel amplitude matrix
  • W k, m, n is a random matrix composed of independent and identically distributed complex Gaussian random variables on the nth block of the mth time slot for the kth user.
  • the refined beam-domain statistical model has more statistical characteristic directions, so it can more accurately characterize the actual physical channel model.
  • the obtained uplink channel statistical information can be directly used as the downlink channel statistical information.
  • the user side can obtain downlink statistical channel information and feed it back to the base station.
  • a method for obtaining priori statistical channel information in a refined beam domain is given below. Assuming that X k is the pilot matrix of the k-th user, the pilot matrix can be used to obtain prior statistical channel information.
  • the pilot matrix between users is orthogonal, and the pilots between different antennas do not need to be orthogonal, that is, X k does not need to be unitary matrix.
  • Y m,1 denote the pilot signal received by the base station on the first block of the m-th time slot, and
  • the superscript T represents transpose
  • the superscript * represents conjugate
  • the superscript H represents conjugate transpose
  • Z m,1 is a random matrix composed of independent and identically distributed complex Gaussian random variables. Since the pilot matrix of each user is orthogonal, the Left multiplication Multiply right Available
  • Equation (13) can be expressed as
  • T kr and T t are known matrices, and O kr NO t are also known matrices. Therefore, the only unknown parameter matrix on the right side of the equal sign of the above equation is the refined beam domain channel energy matrix ⁇ k . Therefore, the acquisition of the channel energy matrix ⁇ k is based on the sample statistical matrix ⁇ k and the determination matrix T kr , T t and O kr NO t . T kr ⁇ k T t +O kr NO t is called the function matrix of the channel energy matrix. Equation (16) belongs to the parameter matrix estimation problem.
  • c 0 is a constant independent of M k.
  • J is a matrix of all ones.
  • the first half of the derivation is slightly more complicated, as
  • the iterative formula can be constructed as follows
  • a refined sampling beam domain channel amplitude matrix can be obtained.
  • the steps for obtaining refined beam-domain statistics channel information can be summarized as follows:
  • Step 1 Receive the pilot signal X k sent by each mobile terminal
  • Step 2 Multiply the received pilot signal Y m,1 and each local user pilot signal X k respectively to obtain
  • Step 3 Convert the multiplied pilot signal to the refined beam domain
  • Step 4 Use the refined beam domain sample statistics Perform the refined beam domain prior statistical channel information acquisition for each mobile terminal.
  • step 4 uses the refined beam domain sample statistics ⁇ k to obtain the refined beam domain prior statistical channel information of each mobile terminal.
  • the method can be further refined as follows:
  • Step 2 Initialize M k ;
  • Step 3 Iterative calculation Among them, A k should be updated as follows with M k:
  • the instantaneous channel information can also be acquired first, and then the instantaneous channel information can be used to refine the beam-domain priori statistical channel information.
  • the following gives a method for obtaining refined beam-domain statistical channel information ⁇ k when the channel information is known. Multiply H k, m, 1 to the left Multiply right Available
  • T kr becomes The KL divergence function of ⁇ k and the channel energy matrix function matrix T kr ⁇ k T t is simplified as
  • Step 1 Obtain the channel matrix H k,m,1 ;
  • Step 2 Convert the channel matrix to the refined beam domain
  • Step 3 Use the refined beam domain sample statistics Perform the refined beam domain prior statistical channel information acquisition for each mobile terminal.
  • step 4 uses the refined beam domain sample statistics ⁇ k to obtain the refined beam domain prior statistical channel information of each mobile terminal.
  • the method can be further refined as follows:
  • Step 1 Calculate based on V Mt
  • Step 2 Initialize M k ;
  • Step 3 Iterative calculation Among them, A k should be updated as follows with M k:
  • the pilot signal received on the first block of the m-1 time slot can still be expressed as
  • the minimum mean square error of the refined beam domain channel vector vec(G k, m-1, 1) can be estimated as
  • the channel information obtained in the first time block on time slot m-1 is used for transmission in the m-th time slot.
  • a first-order Gauss Markov model is used to describe the time-correlation model.
  • the refined beam-domain channel on the n-th time block of the m-th time slot can be expressed as
  • ⁇ k,m (N b +n-1) is the correlation factor function of channels G k,m,n and G k,m-1,1 , and is the time correlation factor related to the user's moving speed.
  • ⁇ k,m is the correlation factor function of channels G k,m,n and G k,m-1,1 .
  • the model in equation (42) is used for channel prediction.
  • precoding is performed on the entire time slot m. For simplicity, without considering the channel estimation error, assuming that accurate channel information of the refined beam domain channel matrix G k,m-1,1 can be obtained, the posterior statistical information of the refined beam channel on time slot m can be obtained as
  • ⁇ k,m and the channel on the entire time slot m are related to the correlation factor ⁇ k,m of H k,m-1,1 .
  • a feasible approach is to take the root mean square of all correlation factors ⁇ k,m on the time slot.
  • the refined beam-domain posterior statistical channel model on time slot m can be obtained as
  • the channel posterior statistical model in equation (44) needs to be further derived based on the channel estimation error model, time correlation model and a priori statistical model.
  • H k,m-1,1 is expressed as Then the fine chemical posterior statistical model can be further expressed as
  • ⁇ k,m G k,m-1,1 is the posterior mean value of the refined beam domain
  • the variance of is the posterior variance of the refined beam domain.
  • G k,m-1,1 can be obtained through feedback, and on this basis, the refined beam-domain a priori statistical information can be combined to obtain the refined beam-domain posterior statistical information.
  • the embodiments of the present invention also disclose a computing device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, which is implemented when the computer program is loaded on the processor.
  • a computing device including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, which is implemented when the computer program is loaded on the processor.
  • the device includes a processor, a communication bus, a memory, and a communication interface.
  • the processor may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present invention.
  • the communication bus may include a path for transferring information between the above-mentioned components.
  • the communication interface uses any device such as a transceiver to communicate with other devices or communication networks.
  • the memory can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or it can be an electronic device.
  • EEPROM Erasing programmable read-only memory
  • CD-ROM Compact Disc-read-only memory
  • disk storage media or other magnetic storage devices
  • EEPROM Erasing programmable read-only memory
  • the memory can exist independently and is connected to the processor through a bus.
  • the memory can also be integrated with the processor.
  • the memory is used to store application program codes for executing the solution of the present invention, and the processor controls the execution.
  • the processor is used to execute the application program code stored in the memory, so as to implement the information acquisition method provided in the foregoing embodiment.
  • the processor may include one or more CPUs, or may include multiple processors, and each of these processors may be a single-core processor or a multi-core processor.
  • the processor here may refer to one or more devices, circuits, and/or processing cores for processing data (for example, computer program instructions).
  • the embodiment of the present invention also discloses a massive MIMO communication system, including a base station and multiple user terminals.
  • the base station is used to: receive pilot signals sent by each user terminal;
  • the pilot signal is multiplied by the pre-stored pilot signals of each user;
  • the pilot signal after the multiplication is converted to the refined beam domain through the multiplication operation of the refined sampling steering vector matrix;
  • the refined sampling steering vector matrix is guided
  • the number of vectors is more than the number of base station antennas; the refined beam domain sample statistics are used to obtain the refined beam domain prior statistical channel information of each user terminal.
  • the embodiment of the present invention also discloses a massive MIMO communication system, including a base station and multiple user terminals.
  • the base station is used to: obtain channel information of each user terminal; and pass the channel information of each user terminal through It is multiplied by the refined sampling steering vector matrix and converted to the refined beam domain; the number of steering vectors in the refined sampling steering vector matrix is more than the number of base station antennas; each user is obtained by using the refined beam domain sample statistics
  • the terminal refines the beam domain priori statistics on channel information.
  • the embodiment of the present invention also discloses a massive MIMO communication system, including a base station and multiple user terminals, the base station is used to: use the massive MIMO beam domain prior statistical channel information to obtain The method obtains the refined beam-domain prior statistical channel information of each user terminal before the current time slot; obtains the pilot signal sent by each user terminal in the current time slot; uses the received pilot signal to estimate the refined beam-domain channel matrix, and combines The refined beam-domain prior statistical channel information and inter-channel correlation factors are used to obtain the refined beam-domain posterior statistical channel information of each user terminal.
  • the embodiment of the present invention also discloses a massive MIMO communication system, which includes a base station and a plurality of user terminals, and the base station is provided with the aforementioned computing device.

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Abstract

本发明公开了一种大规模MIMO波束域统计信道信息获取方法与系统。所公开方法中涉及的精细化波束域信道模型基于精细化采样导向矢量矩阵，与传统基于DFT矩阵的波束域信道模型相比，在天线尺寸有限时，更加接近于物理信道模型，为解决天线尺寸受限下大规模MIMO对各种典型移动场景的普适性问题提供模型基础。本发明给出了大规模MIMO精细化波束域先验统计信道信息与后验统计信道信息获取方法，其中后验统计信道信息包括后验信道均值和方差信息。本发明方法具有较低复杂度，能够应用于实际大规模MIMO系统，为鲁棒预编码传输方法提供支撑，具有较大的应用价值。

Description

大规模MIMO波束域统计信道信息获取方法与系统 技术领域
本发明属于通信技术领域，涉及大规模MIMO波束域统计信道信息获取方法与系统。
背景技术
为提升用户体验，应对无线数据业务需求的快速增长以及新业务需求带来的挑战，未来新一代移动网络需要支持更高质量、更高传输率、更高移动性、更高用户密度、更低时延、更低能耗等场景。近年来，为大幅提高无线频谱的频谱效率和功率效率，大规模多输入多输出(MIMO，Multiple-Input Multiple-Ouput)技术被广泛研究。目前，大规模MIMO已被确认为5G的关键技术之一。大规模MIMO通过在基站(BS，Base Station)配备大规模天线阵列极大的提高系统容量，充分利用了空间维度资源。未来，大规模MIMO仍将是Beyond 5G(B5G)的研究热点。
信道统计模型的建立是大规模MIMO预编码传输理论方法的基础。文献中一个常用的信道统计模型是基于离散傅里叶变换(DFT，Discrete Fourier Transform)矩阵的传统波束域信道模型。然而，在实际大规模MIMO无线系统中，有限的天线尺寸限制了大规模线阵天线的应用，基站侧常配置大规模面阵天线等更易于实现的天线阵列，进而造成单一维度上的天线数量受限，在此限制下各用户信道单一维度上同一特征模式矩阵仍采用基于DFT矩阵传统波束域信道模型近似会在相当程度上偏离实际物理信道模型。另一方面，大规模MIMO无线系统中基站配置大规模天线阵列，且占据相同时频资源的用户天线数量增多，限制了用于导频的时频资源，在导频资源受限的情况下瞬时信道估计误差无法避免，同时在中高速移动通信场景还存在基站侧所获瞬时信道信息老化等因素，因此发展能描述各种典型移动通信场景的统计信道模型具有重要意义。文献中相关工作大都考虑大规模线阵天线，多采用DFT矩阵将空间信号转为稀疏角度域信号，且都没有考虑基于先验统计模型和瞬时信道信息的后验统计模型。
在大规模MIMO无线传输系统中，下行多用户预编码传输方法是对抗多用户干扰并实现频谱效率增益的关键，因而是大规模MIMO无线传输系统最为核心的问题之一。实际大规模MIMO系统中用户的移动性为下行多用户预编码传输方法带来了极大挑战。为解决这一问题，鲁棒多用户预编码传输方法变得日益重要。对于鲁棒传输方法，基于统计信道模型的方法是一类关键方法。而基于统计信道模型的方法的基础则在于统计信道信息的获取。因此，在将传统基于DFT矩阵的波束域信道模型进行扩展时，如何获得新模型的统计信道信息变得至关重要。
发明内容
发明目的：针对现有技术的不足，本发明目的在于提供大规模MIMO波束域统计信道信息获取方法与系统，能够为大规模MIMO鲁棒预编码传输方法提供支撑。
技术方案：为了达到上述目的，本发明提供如下技术方案：
大规模MIMO波束域先验统计信道信息获取方法，包括如下步骤：
接收各用户终端发送的导频信号；
将接收到的导频信号与预先保存的各用户导频信号分别相乘；
将相乘后导频信号通过精细化采样导向矢量矩阵转换到精细化波束域；所述精细化采样导向矢量矩阵中导向矢量个数多于对应的天线个数；
利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息。
进一步地，将相乘后导频信号通过左乘发送侧精细化采样导向矢量矩阵共轭矩阵和右乘接收侧精细化采样导向矢量矩阵共轭矩阵转换到精细化波束域。
进一步地，各用户终端在同一时频资源上发送导频信号，各用户终端的导频信号相互正交。
进一步地，所述利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息具体为：根据精细化波束域样本统计量和信道能量矩阵函数矩阵的方程求解信道能量矩阵；所述方程中只有信道能量矩阵或信道幅度矩阵为未知矩阵，其余矩阵为已知矩阵。
大规模MIMO波束域先验统计信道信息获取方法，包括如下步骤：
获取各用户终端的信道信息；
将各用户终端信道信息通过精细化采样导向矢量矩阵转换到精细化波束域；所述精细化采样导向矢量矩阵中导向矢量个数多于对应的天线个数；
利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息。
进一步地，将信道信息通过左乘发送侧精细化采样导向矢量矩阵共轭矩阵和右乘接收侧精细化采样导向矢量矩阵共轭矩阵转换到精细化波束域。
进一步地，所述利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息具体为：根据精细化波束域样本统计量和信道能量矩阵函数矩阵的方程求解信道能量矩阵；所述方程中只有信道能量矩阵或信道幅度矩阵为未知矩阵，其余矩阵为已知矩阵。
大规模MIMO波束域后验统计信道信息获取方法，包括如下步骤：
利用上述大规模MIMO波束域先验统计信道信息获取方法获取当前时隙之前的各用户终端的精细化波束域先验统计信道信息；
获取当前时隙各用户终端发送的导频信号；
利用接收到的导频信号估计精细化波束域信道矩阵，结合精细化波束域先验统计信道信息以及信道间相关因子获取各用户终端的精细化波束域后验统计信道信息。
进一步地，所述精细化波束域后验统计信道信息包括精细化波束域后验均值和精细化波束域后验方差。
一种计算设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述计算机程序被加载至处理器时实现上述大规模MIMO波束域先验统计信道信息获取方法，或者大规模MIMO波束域后验统计信道信息获取方法。
一种大规模MIMO通信系统，包括基站和多个用户终端，所述基站用于：
接收各用户终端发送的导频信号；将接收到的导频信号与预先保存的各用户导频信号分别相乘；将相乘后导频信号通过与精细化采样导向矢量矩阵相乘运算转换到精细化波束域；所述精细化采样导向矢量矩阵中导向矢量个数多于基站天线个数；利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息；
或者，获取各用户终端的信道信息；将各用户终端信道信息通过与精细化采样导向矢量矩阵相乘运算转换到精细化波束域；所述精细化采样导向矢量矩阵中导向矢量个数多于基站天线个数；利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息。
一种大规模MIMO通信系统，包括基站和多个用户终端，所述基站用于：
利用上述大规模MIMO波束域先验统计信道信息获取方法获取当前时隙之前的各用户终端的精细化波束域先验统计信道信息；
获取当前时隙各用户终端发送的导频信号；
利用接收到的导频信号估计精细化波束域信道矩阵，结合精细化波束域先验统计信道信息以及信道间相关因子获取各用户终端的精细化波束域后验统计信道信息。
一种大规模MIMO通信系统，包括基站和多个用户终端，所述基站设有上述计算设备。
有益效果：与现有技术相比，本发明提出的大规模MIMO波束域先验及后验统计信道信息获取方法能够建立大规模MIMO系统各种典型移动场景的统计模型，能有效支撑大规模MIMO系统鲁棒预编码传输方法的实现，解决大规模MIMO对于各种典型移动场景的适应性问题。
附图说明
图1为大规模MIMO波束域先验统计信道信息获取方法流程图；
图2为已知瞬时信道信息情形大规模MIMO波束域先验统计信道信息获取方法流程图；
图3为大规模MIMO波束域后验统计信道信息获取方法流程图；
图4为波束域信道模型下估计出的协方差矩阵与样本协方差矩阵的MSE性能比较结果图。
具体实施方式
以下将结合具体实施例对本发明提供的技术方案进行详细说明，应理解下述具体实施方式仅用于说明本发明而不用于限制本发明的范围。
如图1所示，本发明实施例公开的大规模MIMO波束域先验统计信道信息获取方法，包括：接收各用户终端发送的导频信号；将接收到的导频信号与本地预先保存的各用户导频信号分别相乘；将相乘后导频信号通过精细化采样导向矢量矩阵转换到精细化波束域；利用所述精细化波束域样本统计量获取各移动终端精细化波束域先验统计信道信息。
如图2所示，本发明另一实施例公开的大规模MIMO波束域先验统计信道信息获取方法，是在已知瞬时信道信息情形下的大规模MIMO波束域先验统计信道信息获取方法，包括：获得各用户终端的信道信息；将各用户终端信道信息通过精细化采样导向矢量矩阵转换到精细化波束域；利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息。
如图3所示，本发明实施例公开的大规模MIMO波束域后验统计信道信息获取方法，包括：获取当前时隙之前的各用户终端精细化波束域先验统计信道信息；获取当前时隙各用户终端发送的导频信号；利用接收到的导频信号估计精细化波束域信道矩阵，结合精细化波束域先验统计信道信息以及信道间相关因子获取各用户终端的精细化波束域后验统计信道信息获取。
上述实施例中的用户终端可以是手机、车载设备、智能装备等移动终端或固定终端；可通过左乘发送侧精细化采样导向矢量矩阵共轭矩阵和右乘接收侧精细化采样导向矢量矩阵共轭矩阵将导频信号或信道信息转换到精细化波束域，其中精细化采样导向矢量矩阵中导向矢量个数多于对应的天线个数。精细化波束域先验统计信道信息的获取可以根据精细化波束域样本统计量和信道能量矩阵函数矩阵的方程求解信道能量矩阵。
本发明方法主要适用于基站侧配备大规模天线阵列以同时服务多个用户的大规模MIMO系统。下面结合具体的通信系统实例对本发明涉及波束域统计信道信息获取方法的具体实现过程作详细说明，需要说明的是本发明方法不仅适用于下面示例所举的具体系统模型，也同样适用于其它配置的系统模型。
一、系统配置
考虑一平坦块衰落大规模MIMO系统，假设系统中各移动终端信道在T个符号间隔内保持不变。该大规模MIMO系统由一个基站和K个移动终端构成。基站配置大规模均匀面阵天线，每行和每列的天线数量分别为M h'和M v'，总发送天线数量为M t＝M h'M v'。简便起见， 假设每个用户配置的天线数为M k。将系统时间资源分为若干时隙，每一时隙包括N b个时间块，而每个时间块则包含T个符号间隔。本实施例中所考虑大规模MIMO系统工作于时分双工(TDD，Time Division Duplexing)模式。简便起见，假设只存在上行信道训练和下行传输阶段，下行传输包括预编码域导频和数据信号发送。在每一时隙中，基站只在第一时间块接收用户上行导频信号。第2至N b时间块则用于基站进行下行预编码域导频和数据信号传输。上行训练序列的长度为块的长度，即T个符号间隔。对于频分双工(FDD，Frequency Division Duplexing)模式，可以将上行信道训练阶段替换为下行信道反馈阶段，下行传输阶段则保持不变。具体来说，在第一块传输下行全向导频信号，并接收移动终端反馈。
二、精细化波束域先验统计信道模型
下面对基于精细化采样导向矢量矩阵的精细化波束域先验统计模型进行详细阐述。传统的波束域信道模型中导向矢量个数与天线个数相同，本发明所述精细化波束域统计模型指在信道模型中引入比天线数量更多的导向矢量来更好的描述信道统计特性。定义基站侧水平和垂直维度上的精细化因子分别为N h和N v，其取值为大于1的整数或分数，令M h＝N hM h'和M v＝N vM v'分别表示水平和垂直维度上精细化采样导向矢量数量。进一步，定义
Figure PCTCN2020086181-appb-000001
和
Figure PCTCN2020086181-appb-000002
则水平和垂直维度上的导向矢量矩阵可以分别表示为
Figure PCTCN2020086181-appb-000003
和
Figure PCTCN2020086181-appb-000004
其中v n＝(n-1)/M h和u m＝(m-1)/M v。令
Figure PCTCN2020086181-appb-000005
表示基站侧精细化采样导向矢量矩阵。相似的，定义用户侧精细化因子为N k，令M k＝N kM k'表示用户侧精细化采样导向矢量数量。进一步，定义
Figure PCTCN2020086181-appb-000006
则用户侧精细化采样导向矢量矩阵定义为
Figure PCTCN2020086181-appb-000007
上述
Figure PCTCN2020086181-appb-000008
和U k的形式分别对应基站侧面阵的导向矢量矩阵以及用户侧线阵的导向矢量矩阵。需要说明的是，本发明方法不仅适用于大规模均匀面阵天线，也适用于其他形式的天线，如圆柱阵列天线、阵元为极化天线的面阵天线，当基站侧或者用户侧使用的天线阵列发生变化时，
Figure PCTCN2020086181-appb-000009
或者U k改为相应阵列的导向矢量矩阵即可。令H k,m,n表示第k用户在第m时隙第n块上的信道，则所考虑大规模MIMO系统精细化波束域先验统计信道模型可以表示为
Figure PCTCN2020086181-appb-000010
其中G k,m,n＝(M k⊙W k,m,n)为第k用户在第m时隙第n块上精细化波束域信道矩阵，M k为表示第k用户精细化波束域信道幅度矩阵，W k,m,n为第k用户在第m时隙第n块上独立同分布复高斯随机变量组成的随机矩阵。与基于DFT矩阵的传统波束域先验统计信道模型相比，该精细化波束域统计模型有着更多的统计特征方向，因此能更准确地表征实际物理信道模型。定义大规模MIMO系统信道精细化波束域能量矩阵Ω k为Ω k＝M k⊙M k，该波束域能量矩阵常常具有稀疏特性。
三、精细化波束域信道模型先验统计信道信息获取方法
对于所考虑工作于TDD模式的大规模MIMO系统，由于上下行信道具有互易性，获得的上行信道统计信息可以直接作为下行信道统计信息使用。对于FDD系统信道瞬时互易性不存在，可以由用户侧进行下行统计信道信息获取并反馈给基站。下面给出一种精细化波束域先验统计信道信息获取的方法。假设X k为第k用户的导频矩阵，该导频矩阵可用于获取先验统计信道信息，用户间导频矩阵正交，不同天线间的导频不需要正交，即X k不必为酉矩阵。令Y m,1表示基站在第m时隙第1块上的接收到的导频信号，有
Figure PCTCN2020086181-appb-000011
进一步，有
Figure PCTCN2020086181-appb-000012
其中上标T表示转置，上标*表示共轭，上标H表示共轭转置，Z m,1为独立同分布复高斯随机变量组成的随机矩阵。由于各用户导频矩阵正交，将
Figure PCTCN2020086181-appb-000013
左乘
Figure PCTCN2020086181-appb-000014
并右乘
Figure PCTCN2020086181-appb-000015
可得
Figure PCTCN2020086181-appb-000016
其中⊙表示Hadmard乘积。令
Figure PCTCN2020086181-appb-000017
进一步，有
Figure PCTCN2020086181-appb-000018
令矩阵T kr表示
Figure PCTCN2020086181-appb-000019
矩阵T t表示
Figure PCTCN2020086181-appb-000020
矩阵O kr表示
Figure PCTCN2020086181-appb-000021
以及矩阵O t表示
Figure PCTCN2020086181-appb-000022
可以得到
Figure PCTCN2020086181-appb-000023
在噪声方差矩阵N已知情况下，则O krNO t为已知矩阵。简洁起见，令
Figure PCTCN2020086181-appb-000024
N＝N hN v为
Figure PCTCN2020086181-appb-000025
由于实际系统中只能获取样本平均，所以用重新定义Φ k为精细化波束域样本统计矩阵
Figure PCTCN2020086181-appb-000026
其中M表示样本数量。式(13)可按元素表示为
Figure PCTCN2020086181-appb-000027
利用Φ k，式(11)可以变为
Φ k＝T krΩ kT t+O krNO t                (16)
上式中，T kr和T t为已知矩阵，O krNO t同样为已知矩阵。因此，上述方程等号右边唯一的未知参数矩阵为精细化波束域信道能量矩阵Ω k。所以信道能量矩阵Ω k的获取基于样本统计矩阵Φ k和确定矩阵T kr、T t和O krNO t。将T krΩ kT t+O krNO t称做信道能量矩阵的函数矩阵。式(16)属于参数矩阵估计问题，为求解Ω k可根据式(16)建立优化问题，然后对优化问题利用梯度下降法、共轭梯度法、牛顿迭代法或者由KKT条件获得的迭代公式等方法进行求解。为了更加清晰说明该问题，下面给出一种具体估计方法。为估计出信道能量矩阵Ω k或者信道幅度矩阵M k，利用精细化波束域样本统计矩阵Φ k和信道能量矩阵函数矩阵T krΩ kT t+O krNO t的KL散度(divergence)定义目标函数为
Figure PCTCN2020086181-appb-000028
上式中c 0为和M k无关常数。为进行优化获得KL散度最小的M k，首先对目标函数求导，式(17)中后半部分的导数为
Figure PCTCN2020086181-appb-000029
其中J为全1矩阵。前半部分的求导稍微复杂，为
Figure PCTCN2020086181-appb-000030
其中，
Figure PCTCN2020086181-appb-000031
Figure PCTCN2020086181-appb-000032
综上，可得对g(M k)求导有，
Figure PCTCN2020086181-appb-000033
令g(M k)＝0，可得最优点必要条件为
(T tJT kr) T⊙M k-(T tQ TT kr) T⊙M k＝0          (22)
进一步有
(TtJT kr) T⊙M k＝(T tQ TT kr) T⊙M k             (23)
基于必要条件，可构造迭代公式如下
Figure PCTCN2020086181-appb-000034
其中，
Figure PCTCN2020086181-appb-000035
根据所提迭代公式可获得精细化采样波束域信道幅度矩阵。综上，精细化波束域统计信道信息获取的步骤可总结为：
步骤1：接收各移动终端发送的导频信号X k；
步骤2：将接收到的导频信号Y m,1与本地各用户导频信号X k分别相乘获得
Figure PCTCN2020086181-appb-000036
步骤3：将相乘后导频信号转换到精细化波束域
Figure PCTCN2020086181-appb-000037
步骤4：利用所述精细化波束域样本统计量
Figure PCTCN2020086181-appb-000038
进行各移动终端精细化波束域先验统计信道信息获取。
其中步骤4利用精细化波束域样本统计量Φ k进行各移动终端精细化波束域先验统计信道信息的获取方法可进一步细化为：
步骤1：计算
Figure PCTCN2020086181-appb-000039
及
Figure PCTCN2020086181-appb-000040
步骤2：初始化M k；
步骤3：迭代计算
Figure PCTCN2020086181-appb-000041
其中A k要随着M k做如下更新：
Figure PCTCN2020086181-appb-000042
Ω k＝M k⊙M k
前面讲述了利用导频信号进行精细化波束域先验统计信道信息获取的方法。在实际系统中也可先进行瞬时信道信息获取，然后利用瞬时信道信息进行精细化波束域先验统计信道信息。下面给出一种在信道信息已知情况下，精细化波束域统计信道信息Ω k获取的方法。将H k,m,1左乘
Figure PCTCN2020086181-appb-000043
并右乘
Figure PCTCN2020086181-appb-000044
可得
Figure PCTCN2020086181-appb-000045
进一步，有
Figure PCTCN2020086181-appb-000046
此时，精细化波束域样本统计矩阵
Figure PCTCN2020086181-appb-000047
变为
Figure PCTCN2020086181-appb-000048
或者按元素表示为
Figure PCTCN2020086181-appb-000049
进一步，可以得到
Φ k＝T krΩ kT t                   (30)
此时，T kr变为
Figure PCTCN2020086181-appb-000050
Φ k和信道能量矩阵函数矩阵T krΩ kT t的KL散度函数简化为
Figure PCTCN2020086181-appb-000051
上式中c 0为和M k无关常数。同样，为进行优化获得KL散度最小的M k，首先对目标函数求导，式(31)中后半部分的导数变为
Figure PCTCN2020086181-appb-000052
其中J为全1矩阵，。前半部分的求导变为
Figure PCTCN2020086181-appb-000053
其中，
Figure PCTCN2020086181-appb-000054
Figure PCTCN2020086181-appb-000055
综上，可得对g(M k)求导有，
Figure PCTCN2020086181-appb-000056
令g(M k)＝0，可得最优点必要条件为
(T tJT kr) TM k-( T tQ TT kr) T⊙M k＝0         (36)
进一步有
(T tJT kr) TM k＝(T tQ TT kr) T⊙M k         (37)
基于必要条件，构造迭代公式如下
Figure PCTCN2020086181-appb-000057
其中，
Figure PCTCN2020086181-appb-000058
综上，已知信道信息情形精细化波束域统计信道信息获取的步骤可总结为：
步骤1：获得信道矩阵H k,m,1；
步骤2：将信道矩阵转换到精细化波束域
Figure PCTCN2020086181-appb-000059
步骤3：利用所述精细化波束域样本统计量
Figure PCTCN2020086181-appb-000060
进行各移动终端精细化波束域先验统计信道信息获取。
其中步骤4利用精细化波束域样本统计量Φ k进行各移动终端精细化波束域先验统计信道信息的获取方法可进一步细化为：
步骤1：根据V Mt计算
Figure PCTCN2020086181-appb-000061
步骤2：初始化M k；
步骤3：迭代计算
Figure PCTCN2020086181-appb-000062
其中A k要随着M k做如下更新：
Figure PCTCN2020086181-appb-000063
Ω k＝M k⊙M k
四、精细化波束域后验统计信道模型
在基于前述方法获得了精细化波束域先验统计信道信息后可用其来进一步获取精细化波束域后验统计信道信息。首先，第m-1时隙第1块上接收到的导频信号仍然可以表示为
Figure PCTCN2020086181-appb-000064
将
Figure PCTCN2020086181-appb-000065
向量化，可以得到
Figure PCTCN2020086181-appb-000066
由于不同用户的导频正交，可以得到精细化波束域信道向量vec(G k,m-1,1)的最小均方误差估计 为
Figure PCTCN2020086181-appb-000067
其中
Figure PCTCN2020086181-appb-000068
为精细化波束域信道协方差矩阵，
Figure PCTCN2020086181-appb-000069
为噪声矩阵
Figure PCTCN2020086181-appb-000070
元素方差。
假设时隙m-1上第1时间块获得的信道信息用于第m时隙的传输。为描述大规模MIMO时间相关特性，采取一阶高斯马尔可夫模型来描述时间相关模型。在该模型下，第m时隙第n时间块上的精细化波束域信道可以表示为
Figure PCTCN2020086181-appb-000071
其中α k,m(N b+n-1)为信道G k,m,n和G k,m-1,1的相关因子函数，为和用户移动速度有关的时间相关因子。相关因子α k,m的获得方法有多种，这里假设相关因子已知。实际中，可以采用信道样本的经验相关因子，也可以采用文献中常用的基于Jakes自相关模型的相关因子α k,m的计算方法，即α k,m(n)＝J 0(2πv kf cnTτ/c)，其中J 0(·)表示第一类零阶Bessel函数，τ表示一个时间间隔对应的时间，v k表示第k用户速度，f c表示载波频率，c为光速。式(42)中模型用来进行信道预测。本实施例中，为考虑系统实现复杂度，在整个时隙m上进行预编码。简便起见，不考虑信道估计误差，假设可以获得精细化波束域信道矩阵G k,m-1,1的准确信道信息，可以得到时隙m上精细化波束信道的后验统计信息为
Figure PCTCN2020086181-appb-000072
其中β k,m和整个时隙m上信道与H k,m-1,1相关因子α k,m有关，一个可行的做法是取时隙上所有相关因子α k,m的均方根。进一步，则可以得到时隙m上的精细化波束域后验统计信道模型为
Figure PCTCN2020086181-appb-000073
当考虑信道估计误差时，式(44)中信道后验统计模型需要根据信道估计误差模型、时间相关模型和先验统计模型进一步得出。为便于在精细化波束域进行计算，将H k,m-1,1表示为
Figure PCTCN2020086181-appb-000074
则精细化后验统计模型可进一步表示为
Figure PCTCN2020086181-appb-000075
其中β k,mG k,m-1,1为精细化波束域后验均值，
Figure PCTCN2020086181-appb-000076
的方差为精细化波束域后验 方差。对于FDD系统，G k,m-1,1可以通过反馈获得，在此基础上结合精细化波束域先验统计信息可以获得精细化波束域后验统计信息。
五、实施效果
为了使本技术领域的人员更好地理解本发明方案，下面给出一种具体系统配置下的本实施例中利用精细化波束域模型进行协方差阵估计与样本协方差阵性能比较。
考虑一配置为M t＝64的大规模MIMO系统，其中基站天线配置为M h'＝8,M v'＝8，用户侧为单天线。随机生成一信道能量矩阵Ω k，并在此基础上根据精细化波束域信道模型生成2000个信道样本。利用本发明所提信道已知情形下精细化波束域统计信道信息获取方法进行信道能量矩阵Ω k的估计并进而计算出协方差矩阵估计。图4给出了该方法估计出的协方差矩阵MSE性能与样本协方差矩阵的MSE性能比较。从图4中，可以看出采用本发明所提精细化波束域先验统计信道信息获取方法所得协方差矩阵估计MSE性能要显著优于样本协方差矩阵MSE性能。
基于相同的发明构思，本发明实施例还公开了一种计算设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述计算机程序被加载至处理器时实现上述的大规模MIMO波束域先验统计信道信息获取方法，或者大规模MIMO波束域后验统计信道信息获取方法。
在具体实现中，该设备包括处理器，通信总线，存储器以及通信接口。处理器可以是一个通用中央处理器(CPU)，微处理器，特定应用集成电路(ASIC)，或一个或多个用于控制本发明方案程序执行的集成电路。通信总线可包括一通路，在上述组件之间传送信息。通信接口，使用任何收发器一类的装置，用于与其他设备或通信网络通信。存储器可以是只读存储器(ROM)或可存储静态信息和指令的其他类型的静态存储设备，随机存取存储器(RAM)或者可存储信息和指令的其他类型的动态存储设备，也可以是电可擦可编程只读存储器(EEPROM)、只读光盘(CD-ROM)或其他光盘存储、盘存储介质或者其他磁存储设备、或者能够用于携带或存储具有指令或数据结构形式的期望的程序代码并能够由计算机存取的任何其他介质，但不限于此。存储器可以是独立存在，通过总线与处理器相连接。存储器也可以和处理器集成在一起。
其中，存储器用于存储执行本发明方案的应用程序代码，并由处理器来控制执行。处理器用于执行存储器中存储的应用程序代码，从而实现上述实施例提供的信息获取方法。处理器可以包括一个或多个CPU，也可以包括多个处理器，这些处理器中的每一个可以是一个单核处理器，也可以是一个多核处理器。这里的处理器可以指一个或多个设备、电路、和/或用于处理数据(例如计算机程序指令)的处理核。
基于相同的发明构思，本发明实施例还公开了一种大规模MIMO通信系统，包括基站和多个用户终端，所述基站用于：接收各用户终端发送的导频信号；将接收到的导频信号与预先保存的各用户导频信号分别相乘；将相乘后导频信号通过与精细化采样导向矢量矩阵相乘运算转换到精细化波束域；所述精细化采样导向矢量矩阵中导向矢量个数多于基站天线个数；利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息。
基于相同的发明构思，本发明实施例还公开了一种大规模MIMO通信系统，包括基站和多个用户终端，所述基站用于：获取各用户终端的信道信息；将各用户终端信道信息通过与精细化采样导向矢量矩阵相乘运算转换到精细化波束域；所述精细化采样导向矢量矩阵中导向矢量个数多于基站天线个数；利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息。
基于相同的发明构思，本发明实施例还公开了一种大规模MIMO通信系统，包括基站和多个用户终端，所述基站用于：利用所述的大规模MIMO波束域先验统计信道信息获取方法获取当前时隙之前的各用户终端的精细化波束域先验统计信道信息；获取当前时隙各用户终端发送的导频信号；利用接收到的导频信号估计精细化波束域信道矩阵，结合精细化波束域先验统计信道信息以及信道间相关因子获取各用户终端的精细化波束域后验统计信道信息。
基于相同的发明构思，本发明实施例还公开了一种大规模MIMO通信系统，包括基站和多个用户终端，所述基站设有上述的计算设备。
在本申请所提供的实施例中，应该理解到，所揭露的方法，在没有超过本申请的精神和范围内，可以通过其他的方式实现。当前的实施例只是一种示范性的例子，不应该作为限制，所给出的具体内容不应该限制本申请的目的。例如，一些特征可以忽略，或不执行。本申请中未详细说明的内容均为现有技术。
本发明方案所公开的技术手段不仅限于上述实施方式所公开的技术手段，还包括由以上技术特征任意组合所组成的技术方案。应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也视为本发明的保护范围。

Claims (13)

  1. 大规模MIMO波束域先验统计信道信息获取方法，其特征在于，包括如下步骤：
    接收各用户终端发送的导频信号；
    将接收到的导频信号与预先保存的各用户导频信号分别相乘；
    将相乘后导频信号通过精细化采样导向矢量矩阵转换到精细化波束域；所述精细化采样导向矢量矩阵中导向矢量个数多于对应的天线个数；
    利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息。
  2. 根据权利要求1所述的大规模MIMO波束域先验统计信道信息获取方法，其特征在于，将相乘后导频信号通过左乘发送侧精细化采样导向矢量矩阵共轭矩阵和右乘接收侧精细化采样导向矢量矩阵共轭矩阵转换到精细化波束域。
  3. 根据权利要求1所述的大规模MIMO波束域先验统计信道信息获取方法，其特征在于，各用户终端在同一时频资源上发送导频信号，各用户终端的导频信号相互正交。
  4. 根据权利要求1所述的大规模MIMO波束域先验统计信道信息获取方法，其特征在于，所述利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息具体为：根据精细化波束域样本统计量和信道能量矩阵函数矩阵的方程求解信道能量矩阵；所述方程中只有信道能量矩阵或信道幅度矩阵为未知矩阵，其余矩阵为已知矩阵。
  5. 大规模MIMO波束域先验统计信道信息获取方法，其特征在于，包括如下步骤：
    获取各用户终端的信道信息；
    将各用户终端信道信息通过精细化采样导向矢量矩阵转换到精细化波束域；所述精细化采样导向矢量矩阵中导向矢量个数多于对应的天线个数；
    利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息。
  6. 根据权利要求5所述的大规模MIMO波束域先验统计信道信息获取方法，其特征在于，将信道信息通过左乘发送侧精细化采样导向矢量矩阵共轭矩阵和右乘接收侧精细化采样导向矢量矩阵共轭矩阵转换到精细化波束域。
  7. 根据权利要求5所述的大规模MIMO波束域先验统计信道信息获取方法，其特征在于，所述利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息具体为：根据精细化波束域样本统计量和信道能量矩阵函数矩阵的方程求解信道能量矩阵；所述方程中只有信道能量矩阵或信道幅度矩阵为未知矩阵，其余矩阵为已知矩阵。
  8. 大规模MIMO波束域后验统计信道信息获取方法，其特征在于，包括如下步骤：
    利用根据权利要求1-7任一项所述的大规模MIMO波束域先验统计信道信息获取方法获取当前时隙之前的各用户终端的精细化波束域先验统计信道信息；
    获取当前时隙各用户终端发送的导频信号；
    利用接收到的导频信号估计精细化波束域信道矩阵，结合精细化波束域先验统计信道信息以及信道间相关因子获取各用户终端的精细化波束域后验统计信道信息。
  9. 根据权利要求8所述的大规模MIMO波束域后验统计信道信息获取方法，其特征在于，所述精细化波束域后验统计信道信息包括精细化波束域后验均值和精细化波束域后验方差。
  10. 一种计算设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述计算机程序被加载至处理器时实现根据权利要求1-7任一项所述的大规模MIMO波束域先验统计信道信息获取方法，或者根据权利要求8或9所述的大规模MIMO波束域后验统计信道信息获取方法。
  11. 一种大规模MIMO通信系统，包括基站和多个用户终端，其特征在于，所述基站用于：
    接收各用户终端发送的导频信号；将接收到的导频信号与预先保存的各用户导频信号分别相乘；将相乘后导频信号通过与精细化采样导向矢量矩阵相乘运算转换到精细化波束域；所述精细化采样导向矢量矩阵中导向矢量个数多于基站天线个数；利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息；
    或者，
    获取各用户终端的信道信息；将各用户终端信道信息通过与精细化采样导向矢量矩阵相乘运算转换到精细化波束域；所述精细化采样导向矢量矩阵中导向矢量个数多于基站天线个数；利用所述精细化波束域样本统计量获取各用户终端精细化波束域先验统计信道信息。
  12. 一种大规模MIMO通信系统，包括基站和多个用户终端，其特征在于，所述基站用于：
    利用根据权利要求1-7任一项所述的大规模MIMO波束域先验统计信道信息获取方法获取当前时隙之前的各用户终端的精细化波束域先验统计信道信息；
    获取当前时隙各用户终端发送的导频信号；
    利用接收到的导频信号估计精细化波束域信道矩阵，结合精细化波束域先验统计信道信息以及信道间相关因子获取各用户终端的精细化波束域后验统计信道信息。
  13. 一种大规模MIMO通信系统，包括基站和多个用户终端，其特征在于，所述基站设有根据权利要求10所述的计算设备。
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