Distributed receiver method for cellular-removed super-large-scale MIMO (Multiple Input Multiple Output) system

Abstract

The invention provides a distributed receiver method for a cellular-removed ultra-large-scale MIMO (Multiple Input Multiple Output) system. The method comprises the following steps that: each base station in the MIMO system estimates instantaneous channel state information from all users to the base station according to a received pilot signal and a pilot distribution strategy; each base station acquires global statistical information from all base stations to all users in the system; according to a pilot frequency distribution strategy of the MIMO system, each base station locally calculates an inter-user relation matrix in the whole system according to global statistical information; and obtaining a distributed receiver scheme of the MIMO system according to the instantaneous channel state information from the users to the base station and the relationship matrix among the users in the whole system. According to the method disclosed by the invention, the performance approaching to the optimal global minimum mean square error receiver can be realized with relatively low calculation complexity, and the method can be applied to numerous MIMO systems represented by a cellular-removed super-large-scale MIMO system.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to a distributed receiver method for a de-cellularized ultra-large-scale MIMO system. Background Art

[0002] Since the 4G (fourth generation) wireless communication network, MIMO (multiple-input multiple-output) has been widely studied as a powerful technology to improve the spectrum efficiency performance of wireless communication networks. With the continuous increase in wireless communication demand and the continuous diversification of wireless communication scenarios, future wireless communication networks, such as 6G (sixth-generation) wireless communication networks, have put forward higher requirements for MIMO technology. The emerging MIMO technologies now mainly include extremely large-scale MIMO (XL-MIMO), cell-free massive MIMO (CF mMIMO) and RIS (reconfigurable intelligent surface) technology. Among them, the basic idea of ​​XL-MIMO technology is to densely deploy a large number of antennas, such as thousands or even tens of thousands, in a relatively compact space to greatly improve the spatial freedom and spectrum efficiency performance of the system.

[0003] The basic idea of ​​CF mMIMO is to deploy a large number of APs (access points) within a certain coverage area to provide uniform services for UEs (user equipment) with a relatively small number of access points. Each AP is connected to the CPU (central processing unit) through a fronthaul link, and cooperative transmission can be carried out between the APs and the CPU. Inspired by the above two emerging technologies, a decellularized very large-scale MIMO system is studied, in which several BSs (base stations) equipped with very large-scale antenna arrays are distributed within a certain coverage area and connected to the CPU through a fronthaul link for cooperative transmission.

[0004] In wireless communication systems, the design of receiver algorithms is crucial to effectively decode user transmission data and achieve high system spectrum efficiency. Generally speaking, the system's receiver algorithm can be designed based on the CSI (channel state information) between the user and the base station. The instantaneous CSI is often obtained through channel estimation based on the pilot sequence. Since each base station is equipped with a very large-scale antenna array in the decellularized ultra-large-scale MIMO system, it has a certain signal processing capability. Therefore, the instantaneous channel estimation from the base station to all users can be performed locally at each base station and a distributed receiver algorithm can be designed based on the instantaneous channel estimation obtained. Furthermore, based on the distributed receiver algorithm, each base station first locally decodes the data sent by the user, and then transmits the decoded signal to the CPU through the fronthaul link, and finally decodes it at the CPU.

[0005] At present, the mainstream distributed receiver algorithm is mainly represented by the LMMSE (local minimum mean-square error) receiver, whose basic idea is to minimize the local user decoding mean square error. However, the design of the LMMSE receiver algorithm involves the inversion of a high-dimensional matrix containing instantaneous channel information. The computational complexity of this inversion is related to the cube of the number of base station antennas, and the matrix inversion needs to be performed for each coherent transmission block (different coherent transmission blocks have different instantaneous channel state information). Therefore, its computational complexity is extremely large, which restricts its application in decellularized ultra-large-scale MIMO systems with a large number of antennas.

[0006] Therefore, there is an urgent need to design a low-complexity distributed receiver solution for de-cellularized ultra-large-scale MIMO systems. Summary of the invention

[0007] The embodiments of the present invention provide a distributed receiver method for a de-cellularized ultra-large-scale MIMO system, so as to effectively improve the spectrum efficiency performance of the system.

[0008] In order to achieve the above object, the present invention adopts the following technical scheme.

[0009] A distributed receiver method for a decellularized ultra-large-scale multiple-input multiple-output MIMO system, comprising:

[0010] Each base station in the decellularized ultra-large-scale MIMO system estimates the instantaneous channel state information of all users to the base station based on the received pilot signal and the pilot allocation strategy;

[0011] Each base station obtains global statistical information from all base stations to all users in the system;

[0012] According to the pilot allocation strategy of the MIMO system, each base station locally calculates the user relationship matrix in the entire system according to the global statistical information;

[0013] A distributed receiver solution of the MIMO system is obtained according to the instantaneous channel state information from the user to the base station and the relationship matrix between users in the entire system.

[0014] Preferably, each base station in the decellularized ultra-large-scale MIMO system estimates instantaneous channel state information from all users to the base station based on the received pilot signal and the pilot allocation strategy, including:

[0015] In the range of the decellularized ultra-large-scale MIMO system, K single-antenna users and M multi-antenna base stations are randomly and evenly distributed. Each base station is equipped with N antennas. The users are numbered in integer order to obtain the line-of-sight LoS channel components from each user to each base station. And the non-line-of-sight NLoS channel component The channel spatial correlation matrix Where m=1, ..., M is a positive integer index of a base station, k=1, ..., K is a positive integer index of a user;

[0016] definition is the instantaneous channel state information vector from user k to base station m. At base station m, the local instantaneous channel state information from user k to base station m is estimated as:

[0017]

[0018] in, is an arbitrary channel estimation strategy matrix determined by statistical information, p k is the transmission power of user k, is the received noise, τ p is the number of orthogonal pilots, and also the length of each pilot signal. is the pilot allocation strategy, which represents the user subset that uses the same pilot signal as user k.

[0019] Preferably, the step of acquiring global statistical information from each base station to all users based on instantaneous channel state information from the user to the base station includes:

[0020] Based on the instantaneous channel state information from the user to the base station, each base station obtains the statistical information from all base stations to all users in the decellularized ultra-large-scale MIMO system. The statistical information includes the LoS channel components from all users to all base stations in the system. Global channel spatial correlation matrix And the global channel estimation strategy matrix

[0021] Preferably, the method of obtaining the distributed receiver solution of the MIMO system according to the instantaneous channel state information from the user to the base station and the relationship matrix between users in the entire system includes:

[0022] At base station m, for user k, according to the user subset that shares the pilot with user k Calculate the relationship matrix between users in the entire system For the element [∑] in the kth row and lth column of ∑ kl , k, l = 1, ..., K;

[0023] like Calculated in is the comprehensive correlation matrix for channel estimation,

[0024] like Among them Ψ k =diag(Ψ 1k , ..., Ψ Mk ).

[0025] Preferably, the method of obtaining the distributed receiver solution of the MIMO system according to the instantaneous channel state information from the user to the base station and the relationship matrix between users in the entire system includes:

[0026] At base station m, construct the local instantaneous channel state estimation matrix For user k, the distributed receiver vector v of user k is calculated mk

[0027]

[0028] in is the user power diagonal matrix, e k The unit matrix I K The kth column of .

[0029] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the method of the present invention can achieve the achievable spectral efficiency performance close to the global optimal receiver algorithm of the de-cellularized ultra-large-scale MIMO system with lower computational complexity, thereby improving the spectral efficiency performance of the de-cellularized ultra-large-scale MIMO system.

[0030] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0032] Figure 1 A schematic diagram of a distributed receiver method for a de-cellularized ultra-large-scale MIMO system provided by an embodiment of the present invention;

[0033] Figure 2 A processing flow chart of a low-complexity distributed receiver method for a decellularized ultra-large-scale MIMO system provided by an embodiment of the present invention;

[0034] Figure 3 A spectrum efficiency effect diagram of a low-complexity distributed receiver solution for a de-cellularized ultra-large-scale MIMO system provided by an embodiment of the present invention;

[0035] Figure 4 A computational complexity rendering of a low-complexity distributed receiver solution for a de-cellularized ultra-large-scale MIMO system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0037] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0038] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0039] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0040] Embodiment 1

[0041] A schematic diagram of the implementation principle of a distributed receiver solution for a decellularized ultra-large-scale MIMO system provided in an embodiment of the present invention is shown in FIG. Figure 1 As shown in the specific implementation flow chart Figure 2 As shown, reference Figure 1 and Figure 2 , the method comprises the following processing steps:

[0042] Step S1: Each base station in the decellularized ultra-large-scale MIMO system estimates instantaneous channel state information from all users to the base station based on received pilot signals and pilot allocation strategies.

[0043] Schematically, within the 1000m×1000m decellularized ultra-large-scale MIMO system, there are randomly and evenly distributed K single antenna users and M multi-antenna base stations, each equipped with N Antennas. Number each user in integer order and obtain the LoS (Line-Of-Sight) channel component from each user to each base station. And NLoS (Not-Line-of-Sight) channel component The channel spatial correlation matrix Where m=1,...,M is the positive integer index of the base station, k=1,...,K is the positive integer index of the user, and definition is the instantaneous channel state information vector from user k to base station m.

[0044] At base station m, the local instantaneous channel state information from user k to base station m is estimated as:

[0045]

[0046] in, is any channel estimation strategy matrix determined by statistical information, and any selected channel estimation strategy matrix is ​​within the scope of protection of this patent. p k is the transmission power of user k, is the received noise, τ p is the number of orthogonal pilots, and also the length of each pilot signal. is a pilot allocation strategy, which represents a subset of users (including user k itself) that use the same pilot signal as user k.

[0047] According to the obtained channel estimation vector Further calculate the channel estimation error vector The covariance matrix of And the correlation matrix between channel estimation and channel estimation error in σ 2 is the power of the received noise.

[0048] Through the above processing, each base station obtains locally an estimate of the instantaneous channel state information from all users to the base station.

[0049] Step S2: Each base station obtains global statistical information from all base stations to all users in the system, where the statistical information includes a global LoS channel component, a global channel spatial correlation matrix, and a global channel estimation strategy matrix.

[0050] According to the instantaneous channel state information from the user to the base station Each base station obtains the LoS channel components from all users to all base stations globally Global channel spatial correlation matrix And the global channel estimation strategy matrix Through step S2, each base station obtains global statistical information for designing subsequent steps.

[0051] Step S3: According to the pilot allocation strategy of the de-cellularized ultra-large-scale MIMO system, each base station locally calculates the user relationship matrix in the entire system according to the global statistical information.

[0052] The calculation of the user relationship matrix involves the relevant statistical information of all base stations in the system. Schematically, at base station m, for user k, according to the user subset that shares the pilot with user k, Calculate the relationship matrix between users in the entire system For the element [∑] in the kth row and lth column of ∑ kl ,k,l=1,...,K.

[0053] like Calculated in is the comprehensive correlation matrix for channel estimation,

[0054] like Among them Ψ k =diag(Ψ 1k , ..., Ψ Mk )

[0055] Each base station obtains a user relationship matrix based on global statistical information.

[0056] Step S4: Estimate the local instantaneous channel state information obtained in step S1 And the user relationship matrix Σ obtained in step S3 based on the global statistical information obtains a distributed receiver solution using local instantaneous and global statistical information.

[0057] Schematically, at base station m, a local instantaneous channel state estimation matrix is ​​constructed For user k, the receiver vector v of user k is calculated mk

[0058]

[0059] in is the user power diagonal matrix, e k The unit matrix I K Through step S4, each base station obtains a distributed receiver solution.

[0060] Embodiment 2

[0061] This embodiment adopts the method of the present invention to design a distributed receiver solution for a decellularized ultra-large-scale MIMO system, and specifically includes the following steps:

[0062] Scenario setting: Consider that within a square area of ​​1000m×1000m, 6 base stations are randomly distributed, the number of users is 20, and each base station is equipped with a square panel with a large number of antennas. The number of antennas in the horizontal and vertical directions is represented by N respectively. x and N y , where N x =N y , then the total number of antennas per base station is N = N x ×N y The antenna spacing in the horizontal and vertical directions is λ / 4, where λ is the wavelength. The channel uses the COST 231Walfish-Ikegami model in 3GPP to model the large-scale fading coefficient.

[0063] β mkk [dB] = -35.4 + 26log10 (d mk )+20log 10 (f c )

[0064] where d mk is the distance from user k to base station m, f c represents the carrier frequency (in megahertz MHz). Consider the carrier frequency to be f c =3GHz, the height of the base station is 12.5m, and the height of the user is 1.5m. The channel adopts the near-field spherical wave channel model. In the simulation experiment, the Monte Carlo method is used to randomly generate 50 independent base station / user position distributions for simulation. Under each base station / user position distribution, 800 small-scale instantaneous channel state information is generated.

[0065] Figure 3 A spectrum efficiency effect diagram of a low-complexity distributed receiver solution for a de-cellularized ultra-large-scale MIMO system provided by an embodiment of the present invention, Figure 3 The horizontal axis is the number of antennas N in the horizontal direction of each base station x , the vertical axis is the average spectrum efficiency of the system, where the global minimum error receiver is a method designed in the central processor using the global instantaneous channel state information, and the local minimum mean square error receiver is a method designed in each base station using the local instantaneous channel state information. It can be observed that the method of the present invention has a significant improvement in spectrum efficiency compared with the local minimum mean square error receiver and is close to the spectrum efficiency performance achieved by the optimal global minimum mean square error receiver.

[0066] Figure 4 A computational complexity rendering of a low-complexity distributed receiver solution for a decellularized ultra-large-scale MIMO system provided by an embodiment of the present invention, Figure 4 The horizontal axis in is the total number of antennas N of each base station, and the vertical axis is the computational complexity. It can be observed that the computational complexity of the method proposed in the present invention is greatly reduced compared with the optimal global minimum mean square error receiver.

[0067] In summary, the method of the present invention provides a distributed receiver algorithm based on global statistical information and local instantaneous channel information. The global statistical information used in the algorithm can remain unchanged during a very large number of updates of the instantaneous channel information. Therefore, the proposed scheme has strong robustness and practical application value, and can be applied to many MIMO systems represented by de-cellular ultra-large-scale MIMO systems; at the same time, the algorithm design idea of ​​replacing instantaneous information items with statistical information items can also be regarded as an important enabling idea for the design of receivers and precoding algorithms in future MIMO systems.

[0068] The method of the embodiment of the present invention can achieve performance close to that achieved by an optimal global minimum mean square error receiver with lower computational complexity.

[0069] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0070] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0071] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0072] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A distributed receiver method for a decellularized ultra-large-scale multiple-input multiple-output MIMO system, characterized in that: include: Each base station in the decellularized ultra-large-scale MIMO system estimates the instantaneous channel state information of all users to the base station based on the received pilot signal and the pilot allocation strategy; Each base station obtains global statistical information from all base stations to all users in the system; According to the pilot allocation strategy of the MIMO system, each base station locally calculates the user relationship matrix in the entire system according to the global statistical information; A distributed receiver solution of the MIMO system is obtained according to the instantaneous channel state information from the user to the base station and the relationship matrix between users in the entire system.

2. The method according to claim 1, characterized in that Each base station in the decellularized ultra-large-scale MIMO system estimates instantaneous channel state information from all users to the base station based on the received pilot signal and the pilot allocation strategy, including: In the range of the decellularized ultra-large-scale MIMO system, K single-antenna users and M multi-antenna base stations are randomly and evenly distributed. Each base station is equipped with N antennas. The users are numbered in integer order to obtain the line-of-sight LoS channel components from each user to each base station. And the non-line-of-sight NLoS channel component The channel spatial correlation matrix Where m=1, ..., M is a positive integer index of a base station, k=1, ..., K is a positive integer index of a user; definition is the instantaneous channel state information vector from user k to base station m. At base station m, the local instantaneous channel state information from user k to base station m is estimated as: in, is an arbitrary channel estimation strategy matrix determined by statistical information, p k is the transmission power of user k, is the received noise, τ p is the number of orthogonal pilots, and also the length of each pilot signal. is the pilot allocation strategy, which represents the user subset that uses the same pilot signal as user k.

3. The method according to claim 2, characterized in that The method of obtaining global statistical information from each base station to all users based on the instantaneous channel state information from the user to the base station includes: Based on the instantaneous channel state information from the user to the base station, each base station obtains the statistical information from all base stations to all users in the decellularized ultra-large-scale MIMO system. The statistical information includes the LoS channel components from all users to all base stations in the system. Global channel spatial correlation matrix And the global channel estimation strategy matrix 4. The method according to claim 2, characterized in that: The method of obtaining the distributed receiver solution of the MIMO system according to the instantaneous channel state information from the user to the base station and the relationship matrix between users in the entire system includes: At base station m, for user k, according to the user subset that shares the pilot with user k Calculate the relationship matrix between users in the entire system For the element [∑] in the kth row and lth column of Σ kl , k, l = 1, ..., K; like Calculated in is the comprehensive correlation matrix for channel estimation, if among them k =diag(Ψ 1k ,...,Ψ Mk )。 5. The method according to claim 4, characterized in that The method of obtaining the distributed receiver solution of the MIMO system according to the instantaneous channel state information from the user to the base station and the relationship matrix between users in the entire system includes: At base station m, construct the local instantaneous channel state estimation matrix For user k, the distributed receiver vector v of user k is calculated mk in is the user power diagonal matrix, e k The unit matrix I K The kth column of .

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