Ultra-large scale MIMO wireless transmission method based on double-time-scale CSI and distributed processing architecture

Abstract

The application discloses a super-large-scale MIMO wireless transmission method based on double-time-scale CSI and a distributed processing architecture. Firstly, user position information and statistical channel state information are acquired. Secondly, user selection is performed based on the user position information and combined with an equivalent distance decision criterion. Subsequently, each distributed processing unit designs an analog beamforming matrix according to the user statistical channel state information, taking the maximization of user rate as a criterion, and processes uplink user signals by using a linear receiver algorithm in the distributed processing unit. Finally, the central processing unit designs a combining coefficient according to the instantaneous channel state information, taking the maximization of system data transmission rate as a target, and combines user data from different distributed processing units. The method provided by the application can realize a spectrum efficiency close to a full-digital architecture with low complexity and hardware overhead, and can also effectively reduce the requirement for channel information acquisition by cooperatively utilizing instantaneous and statistical channel state information.

Description

TECHNICAL FIELD

[0001] The application relates to a super-large-scale MIMO wireless transmission method based on a double-time-scale CSI and a distributed processing architecture, and belongs to the technical field of wireless communication. BACKGROUND

[0002] The fifth generation mobile communication technology is a new generation of mobile communication technology after 2020, which has been commercially used on a large scale worldwide, but the depth and breadth of information interaction are still insufficient to meet the communication business demands of future huge traffic and huge connection. The super-large-scale MIMO technology is an evolved version of the large-scale MIMO technology, which can improve the number of antennas by configuring a super-large-scale array antenna at the base station end, and deeply excavate the spatial resource dimension, so as to support massive user space division multiplexing transmission and further realize a substantial improvement of spectrum efficiency.

[0003] However, if the full-digital architecture in the traditional transceiver is followed, a huge hardware cost will be incurred due to the substantial increase of the array scale. Therefore, under the premise of ensuring the system spectrum efficiency, reducing the number of radio frequency links at the base station end and the cost of the radio frequency front end has become an important challenge for the actual deployment of the super-large-scale MIMO. From the perspective of transmission complexity, the substantial increase of the array scale will correspondingly increase the near-field range. If the existing transmission scheme for the far-field channel condition is adopted, a large performance loss will be caused. In order to make up for the performance loss caused by the far-field transmission scheme, a corresponding codebook and transmission scheme need to be designed for the near-field channel condition, and thus the more complex near-field channel condition introduces additional complexity for the efficient transmission of the system. As can be seen, the hardware cost and the calculation complexity have become the bottleneck problem restricting the actual deployment of the super-large-scale MIMO. In addition, as a prerequisite for the transmission design, the channel estimation overhead of the super-large-scale MIMO system is large, which will seriously impair the system transmission rate. Therefore, the instantaneous and statistical channel state information cooperative transmission scheme is expected to reduce the channel estimation overhead and improve the system spectrum efficiency.

[0004] Therefore, for the super-large-scale MIMO channel condition, a low-cost hardware architecture and a low-complexity transmission scheme suitable for the channel condition are designed, which is beneficial to exert the advantages of the super-large-scale MIMO wireless transmission system. SUMMARY

[0005] The technical problem to be solved by the application is to provide a super-large-scale MIMO wireless transmission method based on a double-time-scale CSI and a distributed processing architecture, to schedule users according to the near-field position information of the users, to design a DFT beamforming vector, and to deploy a low-complexity receiver scheme in the distributed processing unit and the central processing unit, so as to realize low-cost and low-complexity transmission.

[0006] The application adopts the following technical scheme to solve the above technical problem:

[0007] The method for super large scale MIMO wireless transmission based on double time scale CSI and distributed processing architecture, the super large scale MIMO system comprises a base station and a plurality of users, wherein the base station is configured with a distributed processing architecture, the base station comprises a central processing unit and a plurality of distributed units, and there is a bidirectional interaction link between each distributed unit and the central processing unit; the distributed unit comprises an antenna subarray, an analog beamformer, a radio frequency link and a digital beamformer connected in sequence; the method comprises the following steps:

[0008] Step 1: the base station obtains the position information and statistical channel state information of all users according to the uplink pilot signal;

[0009] Step 2: the base station selects users with good spatial separation characteristics to provide uplink transmission services based on the equivalent distance criterion according to the position information of all users obtained in step 1;

[0010] Step 3: the distributed units of the base station design analog beamforming matrices for each user selected in step 2 according to the statistical channel state information of each user, and the maximum user rate is taken as the criterion to configure the analog beamformer;

[0011] Step 4: the digital beamformer of the distributed unit of the base station adopts a linear receiver to process the uplink data of each user selected in step 2;

[0012] Step 5: the central processing unit of the base station designs combining coefficients based on the instantaneous channel state information, and the maximum system data transmission rate is taken as the criterion to combine the user data from each distributed unit.

[0013] Compared with the prior art, the above technical scheme has the following technical effects:

[0014] 1: By means of the distributed processing architecture and the distributed processing algorithm, the super large scale MIMO near field channel characteristics can be adapted with low hardware cost.

[0015] 2: The position information of the user and the statistical channel state information of the selected user are only used to realize the performance of the nearly full digital architecture and the instantaneous channel state information, and the complexity of the method is effectively reduced.

[0016] 3: The statistical and instantaneous double time scale channel state information are used cooperatively, the requirement for channel information acquisition is effectively reduced, the high channel information acquisition cost is avoided, and the spectrum efficiency is improved. DETAILED DESCRIPTION

[0017] Figure 1 It is the configuration schematic diagram of the super large scale MIMO system architecture based on the distributed processing architecture proposed by the application;

[0018] Figure 2 is a flow chart of the super large scale MIMO wireless transmission method based on the double time scale CSI and the distributed processing architecture. DETAILED DESCRIPTION

[0019] Embodiments of the present application are described in detail below with reference to examples shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as a limitation of the present application.

[0020] The present application proposes a super large scale MIMO wireless transmission method based on a double time scale CSI and a distributed processing architecture, schedules users according to near field position information of the users, designs an analog beamforming matrix, and deploys a low complexity receiver scheme in a distributed processing unit and a central processing unit, so as to realize low cost and low complexity transmission. Meanwhile, the statistical and instantaneous double time scale channel state information is cooperatively utilized, so as to effectively reduce the requirement for channel information acquisition, avoid high channel information acquisition overhead, and be beneficial to improving spectrum efficiency.

[0021] The configuration of the base station and the user is as shown in Figure 1 . The super large scale MIMO system works in a time division duplex mode, wherein the base station adopts a distributed processing architecture and is divided into a distributed unit and a central processing unit; the distributed unit includes an antenna array, an analog beamformer, a radio frequency link and a digital beamformer, and the central processing unit includes a low cost processor; there is a bidirectional interaction link between the distributed unit and the central processing unit.

[0022] The number of base station antennas is N, which is divided into B subarrays, and the number of antennas of each subarray is N B , N RF radio frequency links are configured, N B antenna units are first connected to the analog beamformer, connected to N RF radio frequency links after analog beamforming, and the digital beamformer is connected to the radio frequency link. Among them, the analog beamformer can be realized by a DFT matrix, and the digital beamformer can adopt a linear receiver scheme such as maximum ratio combining or zero-forcing receiver. The user adopts a single antenna or multiple antennas.

[0023] The channel between the base station and the user can be modeled based on a Rician channel model as follows:

[0024]

[0025] Among them, κ u represents a Rician factor, and respectively represent line-of-sight and non-line-of-sight components, r u and denotes the distance and azimuth angle of user u relative to the center of the hyper large scale array, denotes the near-field array response vector, where the nth element can be modeled as denotes the carrier wavelength, denotes the distance between user located at coordinates and the nth antenna, denotes the spatial correlation matrix corresponding to the non-line-of-sight component, g u is a complex Gaussian vector, β u denotes the large-scale fading of the user channel, which can be modeled at the distributed unit level as:

[0026]

[0027] where β u,b denotes the large-scale fading coefficient of user u to subarray b, which can be further expressed as β u,b ∝ 1 / r u,b , r u,b denotes the distance of user u to the center of subarray b. The non-line-of-sight component of the user channel is described by the spatial correlation matrix, which can be further expressed as:

[0028]

[0029] where θ u denotes the azimuth angle of the non-line-of-sight component, and denote the angular range and energy distribution of the non-line-of-sight component, respectively. Then the covariance matrix of the user channel (i.e., the statistical channel state information) can be expressed as:

[0030]

[0031] where,

[0032] Taking the distributed unit b as an example, the analog beamforming matrix can be expressed as F b ; the digital beamforming matrix can be written as W b , and under the maximum ratio combining reception algorithm, there is under the zero-forcing reception algorithm, there is where H eq,b = F b H b , H b = [h 1,b ,..., h U,b ].

[0033] Based on the above description, as shown in Figure 2 , the wireless transmission method proposed by the present application specifically includes the following steps:

[0034] Step one, channel information acquisition stage: the base station segment acquires the position information of all users in the cell and the statistical channel state information according to the uplink pilot signals, including the distance of the user (taking any user u as an example) to the center of each subarray u,b} b＝1,...,B , the azimuth angle of the user to the center of each subarray and the channel covariance matrix u,bb} b＝1,...,B , Θ u,bb is the matrix u the (b, b)th submatrix, which satisfies

[0035] Step two, user selection stage: the base station selects U users with good spatial separation characteristics to provide uplink transmission services based on the equivalent distance criterion based on the position information of all users in the cell, eliminating the interference between multiple users.

[0036] Specifically, based on the iterative method, the equivalent distance is initialized as u = 1,..., U tot , b = 1,..., B. For the i-th iteration, the selected users are based on the following criteria:

[0037]

[0038] For the (i+1)th iteration, the equivalent distance needs to be updated first. When the distributed unit uses a zero-forcing receiver, since the interference between different users is eliminated, the equivalent distance of each user remains consistent with the true distance, i.e. When the distributed unit uses a maximum ratio combining receiver, it can be updated according to the following criteria:

[0039]

[0040] where C(·) and S(·) represent the Fresnel integral function, I l,u,b and Δ u,b are defined as:

[0041]

[0042] where θ l represents the spatial azimuth angle corresponding to the selected DFT beam, which can be assumed to be the closest to the true user azimuth angle, d A represents the antenna spacing, and λ represents the carrier wavelength. The iterative algorithm is executed until i = N RF , and U = N RF .

[0043] Step three, analog beamforming stage: each distributed processing unit at the base station designs an analog beamforming matrix for each user according to the selected U users' statistical channel state information, for configuring the radio frequency front end.

[0044] In particular, each distributed processing unit at the base station allocates a DFT beam for each user from the predefined DFT beams. When the distributed processing unit configures the maximum ratio combining and the zero-forcing receiver algorithm respectively, the approximate expression of the user u's uplink rate can be written as:

[0045]

[0046] wherein,

[0047]

[0048] ζ u,b represents the signal combining coefficient, η u,b = |ζ u,b | 2 , and are the (b, b)th sub-matrix of the matrix and respectively. It can be seen that when the analog beamforming is completed at the distributed processing unit, the system capacity is mainly related to tr(Q b Θ u,bb ). Therefore, the analog beamforming vector (i.e. the DFT beam number) of the user u at the distributed processing unit b is:

[0049]

[0050] wherein, U is the N B dimensional discrete Fourier matrix.

[0051] Step four, distributed processing stage: each distributed processing unit at the base station processes the uplink user data using a linear receiver. The base station can process the user data using the maximum ratio combining or the zero-forcing receiver algorithm.

[0052] Step five, central combining stage: the central processing unit at the base station designs the combining coefficients based on the instantaneous channel state information, with the user rate maximization as the criterion to combine the user data from each distributed processing unit.

[0053] Taking the user u as an example, assuming that the combining coefficients are {ζ u,b} b＝1,...,B When the distributed processing unit adopts the maximum ratio combining or the zero-forcing receiver, the combining coefficients satisfy:

[0054] ζ u = c(∑ \u N u ) -1 χ u

[0055] where c denotes a normalization constant, w b,u is the u-th row of matrix W b , matrix matrix N u is a diagonal matrix satisfying N u = diag(||h u,1 F1‖ 2 ,...,‖h u,B F B ‖ 2 ).

[0056] When the distributed processing unit employs a ZF receiver, the combining coefficients can also be calculated by the following simplified rule:

[0057]

[0058] where γ u,b,ZF = |n u,b | 2 ,

[0059] The above examples only illustrate the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made according to the technical idea of the present application on the basis of the technical scheme falls within the protection scope of the present application.

Claims

1. A very large-scale MIMO wireless transmission method based on dual-time-scale CSI and distributed processing architecture, characterized in that: A very large-scale MIMO system includes a base station and several users. The base station is configured with a distributed processing architecture. The base station includes a central processing unit and several distributed units. Each distributed unit has a bidirectional interactive link with the central processing unit. The distributed units include antenna subarrays, analog beamformers, radio frequency links, and digital beamformers connected in sequence. The number of base station antennas is N, which is divided into B antenna subarrays. The number of distributed units is B. In each distributed unit, the number of antenna units in the antenna subarray is N. B , configure N RF RF links, N B The antenna units are connected to the analog beamformer and connected to N RF The digital beamformer is connected to each RF link, and a bidirectional interactive link exists between the digital beamformer and the central processing unit. The analog beamformer is implemented through a discrete Fourier transform matrix. The digital beamformer uses a maximum ratio combining receiver or a zero-forcing receiver. The user uses a single antenna or multiple antennas. The channel between the base station and the user is established based on the Rice channel model, which is expressed as follows: Among them, h u represents the channel between the base station and user u, κ u represents the Rice factor, r u and denote the distance and azimuth of user u relative to the center of the ultra-large array, represents the near-field array response vector, where the nth element is modeled as represents the imaginary unit, λ represents the carrier wavelength, Indicates that the coordinates The distance between the user and the nth antenna, g u is a complex Gaussian vector; β u The large-scale fading vector representing the user channel is modeled at the distributed unit level as: Among them, β u,b represents the large-scale fading coefficient from user u to antenna subarray b, b=1,...,B, Indicates length N B All-1 vector of ; The spatial correlation matrix corresponding to the non-line-of-sight component is expressed as: Among them, θ u represents the azimuth of the non-line-of-sight component, and f(θ u ) represent the angular range and energy distribution of the non-line-of-sight component, b(r u ,θ u ) represents the near-field array response vector, and the superscript H represents the conjugate transpose of the matrix or vector; The ultra-large-scale MIMO wireless transmission method comprises the following steps: Step 1: The base station obtains the location information and statistical channel state information of all users based on the uplink pilot signal; In step 2, the base station selects users with good spatial separation characteristics based on the location information of all users obtained in step 1 and the equivalent distance judgment criterion to provide uplink transmission services. The specific process is as follows: Initialize the equivalent distance to r u,b ,u=1,...,U tot , r u,b represents the distance from user u to the center of antenna subarray b, U tot is the number of all users; users with good spatial separation characteristics are selected based on an iterative method. For the i-th iteration, the selected users are based on the following criteria: Among them, u (i) represents the user selected in the i-th iteration, represents the equivalent distance of the i-th iteration; Before each iteration, the equivalent distance is updated. Then, at the i+1th iteration, if the digital beamformer adopts a zero-forcing receiver, the equivalent distance of each user is consistent with the actual distance, that is, represents the equivalent distance of the i+1th iteration; If the digital beamformer uses a maximum ratio combining receiver, the equivalent distance is updated according to the following formula: in, C(·) and S(·) both represent Fresnel integral functions, represents the imaginary unit, represents the azimuth angle from user u to the center of antenna subarray b, represents the spatial orientation angle corresponding to the selected discrete Fourier beam, d A Indicates the antenna spacing; When i=N RF The iteration ends when the number of selected users U=N RF ; Step 3: Based on the statistical channel state information of each user selected in step 2, each distributed unit of the base station designs an analog beamforming matrix for each user, with the user rate being maximized, to configure the analog beamformer. Step 4: The digital beamformer of each distributed unit of the base station uses a linear receiver to process the uplink data of each user selected in step 2; Step 5: The central processing unit of the base station designs a combining coefficient based on the instantaneous channel state information and takes maximizing the system data transmission rate as a criterion to combine the user data from each distributed unit.

2. The ultra-large-scale MIMO wireless transmission method based on dual-time-scale CSI and distributed processing architecture according to claim 1, characterized in that: The specific process of step 3 is as follows: When the digital beamformer in the distributed unit adopts a maximum ratio combining receiver or a zero-forcing receiver, the uplink rate expressions of user u are as follows: where tr(·) represents the trace of the matrix, The definition is as follows: They represent the uplink rate of user u when using the maximum ratio combining receiver and the zero forcing receiver, p represents the user's transmit power, ζ u,b represents the signal combining coefficient, η u,b =|ζ u,b | 2 , Indicates ζ u,b′ The conjugate of F b and F b′ are the simulated beamforming matrices for the bth and b′th distributed units, Θ u,bb 、Θ u,bb′ 、Θ u,b′b are the channel covariance matrices Θ for user u respectively. u The (b,b), (b,b′), and (b′,b)th submatrices, Θ j,bb′ is the channel covariance matrix Θ of user j j The (b,b′)th submatrix, and The matrices and The (b,b)th submatrix of Taking maximizing the user rate as the criterion, the simulated beamforming vector of user u in the bth distributed unit is: Where U is N B dimensional discrete Fourier matrix.

3. The ultra-large-scale MIMO wireless transmission method based on dual-time-scale CSI and distributed processing architecture according to claim 2, characterized in that: The specific process of step 5 is as follows: When the digital beamformer in the distributed unit adopts a maximum ratio combining receiver or a zero-forcing receiver, the combining coefficient satisfy: g u =c(Σ \u +N u ) -1 x u Among them, u,1 ,ζ u,B represents the signal combining coefficient of user u on the central processing unit for distributed units 1 and B, c represents the normalization constant, w b,u represents the digital beamforming vector corresponding to user u in distributed unit b, F b represents the analog beamforming matrix, h j,b represents the channel vector between user j and distributed unit b, N u is a diagonal matrix, satisfying N u =diag(‖h u,1 F1‖ 2 ,...,‖h u,B F B ‖ 2 ), b=1,...,B,h u,1 、h u,B denote the channel vectors between user u and distributed units 1 and B respectively; When the digital beamformer in the distributed unit adopts a zero-forcing receiver, the simplified calculation formula of the combining coefficient is as follows: Among them, γ u,b,ZF =|n u,b | 2 ,

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