A Distributed Receiver Method for Cellular Decellularized Ultra-Large-Scale MIMO Systems

Abstract

This invention provides a distributed receiver method for decellularized ultra-large-scale MIMO systems. The method includes: each base station in the MIMO system estimating the instantaneous channel state information from all users to the base station based on received pilot signals and a pilot allocation strategy; each base station acquiring global statistical information between all base stations and all users in the system; calculating the user-to-system relation matrix locally based on the global statistical information according to the MIMO system's pilot allocation strategy; and obtaining a distributed receiver scheme for the MIMO system based on the instantaneous channel state information from users to the base station and the user-to-system relation matrix. This invention achieves near-optimal performance with a globally minimum mean square error receiver with relatively low computational complexity and can be applied to numerous MIMO systems, including decellularized ultra-large-scale MIMO systems.

Description

Technical Field

[0001] This invention relates to the field of mobile communication technology, and in particular to a distributed receiver method for decellularized ultra-large-scale MIMO systems. Background Technology

[0002] Since the advent of 4G (fourth generation) wireless communication networks, MIMO (multiple-input multiple-output) has been widely studied as a powerful technology for improving the spectral efficiency of wireless communication networks. With the ever-increasing demands and diversification of wireless communication scenarios, future wireless communication networks, such as 6G (sixth generation) wireless communication networks, place even higher demands on MIMO technology. Emerging MIMO technologies include extremely large-scale MIMO (XL-MIMO), cell-free massive MIMO (CF mMIMO), and reconfigurable intelligent surface (RIS) technology. Among these, XL-MIMO technology's basic idea is to densely deploy a massive number of antennas, such as thousands or even tens of thousands, in a relatively compact space to significantly improve the system's spatial degrees of freedom and spectral efficiency.

[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 service to a relatively small number of UEs (user equipment). Each AP is connected to the CPU (central processing unit) via a fronthaul link, and the APs and the CPU can cooperate in transmission. Inspired by the above two emerging technologies, research is being conducted on decellularized ultra-large-scale MIMO systems, in which several BSs (base stations) equipped with ultra-large-scale antenna arrays are distributed within a certain coverage area and connected to the CPU via fronthaul links for cooperative transmission.

[0004] In wireless communication systems, receiver algorithm design is crucial for effectively decoding user-transmitted data and achieving high system spectral efficiency. Generally, the system's receiver algorithm can be designed based on the instantaneous channel state information (CSI) between the user and the base station. This instantaneous CSI is often obtained through channel estimation based on pilot sequences. Since each base station in a decellularized UMIMO system is equipped with a VMI array, which has a certain signal processing capability, instantaneous channel estimation from that base station to all users can be performed locally at each base station, and a distributed receiver algorithm can be designed based on the obtained instantaneous channel estimation. Furthermore, based on the distributed receiver algorithm, each base station first decodes the user-transmitted data locally, then transmits the decoded signal to the CPU via the fronthaul link, where final decoding is performed.

[0005] Currently, 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 high-dimensional matrix inversion containing instantaneous channel information. The computational complexity of this inversion is related to the cube of the number of base station antennas, and matrix inversion is required for each coherent transport block (different coherent transport blocks have different instantaneous channel state information), resulting in extremely high computational complexity, which limits 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 scheme for decellularized ultra-large-scale MIMO systems. Summary of the Invention

[0007] Embodiments of the present invention provide a distributed receiver method for decellularized ultra-large-scale MIMO systems to effectively improve the system's spectral efficiency performance.

[0008] To achieve the above objectives, the present invention adopts the following technical solution.

[0009] A distributed receiver method for decellularized ultra-large-scale multiple-input multiple-output (MIMO) systems includes:

[0010] In a decellularized ultra-large-scale MIMO system, each base station estimates the instantaneous channel state information of all users to the base station based on the received pilot signals and pilot allocation strategy.

[0011] Each base station acquires global statistical information between all base stations and all users in the system;

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

[0013] The distributed receiver scheme of the MIMO system is obtained based on the instantaneous channel state information from the user to the base station and the user relationship matrix in the entire system.

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

[0015] Within a decellularized ultra-large-scale MIMO system, K single-antenna users and M multi-antenna base stations are randomly and uniformly distributed. Each base station is equipped with N antennas. The users are numbered sequentially in integer order, and the line-of-sight (LoS) channel components from each user to each base station are obtained. and non-line-of-sight (NLoS) channel components Channel space correlation matrix Where m = 1, ..., M are positive integer indices of the base station, and k = 1, ..., K are positive integer indices of the user;

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

[0017]

[0018] in, For any channel estimation strategy matrix determined by statistical information, p k Let k be the transmit power. To receive noise, τ p This represents the number of orthogonal pilots, and also the length of each pilot signal. The pilot allocation strategy represents the subset of users that use the same pilot signal as user k.

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

[0020] Based on the instantaneous channel state information from users to base stations, each base station acquires statistical information between all base stations and all users in the decellularized ultra-large-scale MIMO system. This statistical information includes the Loss of Space (LoS) channel components from all users to all base stations globally in the system. Global channel space correlation matrix and the global channel estimation strategy matrix

[0021] Preferably, the step of obtaining the distributed receiver scheme of the MIMO system based on the instantaneous channel state information from the user to the base station and the user relationship matrix of the entire system includes:

[0022] At base station m, for user k, based on the subset of users sharing the pilot with user k. Calculate the user relationship matrix in the entire system. For the element [∑] in the k-th row and l-th column... kl k, l = 1, ..., K;

[0023] like Calculated in To estimate the comprehensive correlation matrix for the channel,

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

[0025] Preferably, the step of obtaining the distributed receiver scheme of the MIMO system based on the instantaneous channel state information from the user to the base station and the user relationship matrix of 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 For the user power diagonal matrix, e k For the unit matrix I K The kth column.

[0029] As can be seen from the technical solutions provided by the embodiments of the present invention above, the method of the present invention can achieve the achievable spectral efficiency performance of the globally optimal receiver algorithm for decellularized ultra-large-scale MIMO systems with low computational complexity, thereby improving the spectral efficiency performance of decellularized ultra-large-scale MIMO systems.

[0030] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram illustrating the implementation principle of a distributed receiver method for a decellularized ultra-large-scale MIMO system provided in an embodiment of the present invention.

[0033] Figure 2 A flowchart illustrating a low-complexity distributed receiver method for a decellularized ultra-large-scale MIMO system provided in this embodiment of the invention;

[0034] Figure 3 A spectral efficiency diagram of a low-complexity distributed receiver scheme for a decellularized ultra-large-scale MIMO system provided in an embodiment of the present invention;

[0035] Figure 4 The diagram illustrates the computational complexity of a low-complexity distributed receiver scheme for a decellularized ultra-large-scale MIMO system, as provided in an embodiment of the present invention. Detailed Implementation

[0036] 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 denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0037] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated 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 say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0038] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0039] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0040] Example 1

[0041] A schematic diagram illustrating the implementation principle of a distributed receiver scheme for a decellularized ultra-large-scale MIMO system provided in this embodiment of the invention is shown below. Figure 1 As shown, the specific implementation flowchart is as follows: Figure 2 As shown, for reference Figure 1 and Figure 2 The method includes the following processing steps:

[0042] Step S1: In a decellularized ultra-large-scale MIMO system, each base station estimates the instantaneous channel state information of all users to that base station based on the received pilot signals and pilot allocation strategy.

[0043] Schematic illustration: within a 1000m × 1000m decellularized ultra-large-scale MIMO system, randomly and uniformly distributed... K Single antenna user and M Multiple antenna base stations, each equipped with N Each user is assigned an antenna in integer order to obtain the Line-of-Sight (LoS) channel component from each user to each base station. and NLoS (Not-Line-of-Sight) channel components Channel space correlation matrix Where m = 1, ..., M are positive integer indices of the base station, and k = 1, ..., K are positive integer indices of the user, defined as follows: This is the instantaneous channel state information vector from user k to base station m.

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

[0045]

[0046] in, The channel estimation strategy matrix is ​​determined by statistical information, and any chosen channel estimation strategy matrix is ​​within the scope of protection of this patent. p k Let k be the transmit power. To receive noise, τ p This represents the number of orthogonal pilots, and also the length of each pilot signal. The pilot allocation strategy represents the 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 covariance matrix And the correlation matrix between channel estimation and channel estimation error in σ 2 The power for receiving noise.

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

[0049] Step S2: Each base station obtains global statistical information between all base stations and all users in the system. This statistical information includes global Loss channel components, global channel spatial correlation matrix, and global channel estimation strategy matrix.

[0050] Based on the instantaneous channel state information from the user to the base station Each base station acquires the Loss of Space (LoS) channel components from all users to all base stations globally. Global channel space correlation matrix and the global channel estimation strategy matrix In step S2, each base station obtains global statistical information for use in the design of subsequent steps.

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

[0052] The calculation of the user relationship matrix involves relevant statistical information from all base stations in the system. Illustratively, at base station m, for user k, based on the subset of users sharing pilot signals with user k... Calculate the user relationship matrix in the entire system. For the element [∑] in the k-th row and l-th column... kl , k, l=1, ..., K.

[0053] like Calculated in To estimate the comprehensive correlation matrix for the channel,

[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 based on the local instantaneous channel state information obtained in step S1 And the user relationship matrix Σ obtained from global statistical information in step S3, a distributed receiver scheme utilizing local instantaneous and global statistical information is obtained.

[0057] Schematic representation: 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 For the user power diagonal matrix, e k For the unit matrix I K The k-th column. Through step S4, each base station obtains a distributed receiver scheme.

[0060] Example 2

[0061] This embodiment describes the design of a distributed receiver scheme for a decellularized ultra-large-scale MIMO system using the method of the present invention, specifically including the following steps:

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

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

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

[0065] Figure 3 The spectral efficiency diagram shows a low-complexity distributed receiver scheme for a decellularized ultra-large-scale MIMO system provided in an embodiment of the present invention. Figure 3 The horizontal axis N represents the number of horizontal antennas for each base station. x The vertical axis represents the system's average spectral efficiency. The global minimum error receiver is a method designed using global instantaneous channel state information at the central processing unit, while the local minimum mean square error receiver is a method designed using local instantaneous channel state information at each base station. It can be observed that the method of the present invention has a significant improvement in spectral efficiency compared to the local minimum mean square error receiver and is close to the spectral efficiency performance achieved by the optimal global minimum mean square error receiver.

[0066] Figure 4 This diagram illustrates the computational complexity of a low-complexity distributed receiver scheme for a decellularized ultra-large-scale MIMO system, as provided in an embodiment of the present invention. Figure 4 The horizontal axis represents the total number of antennas N for each base station, and the vertical axis represents the computational complexity. It can be observed that the computational complexity of the method proposed in this invention is significantly reduced compared to the optimal global minimum mean square error receiver.

[0067] In summary, the present invention provides a distributed receiver algorithm based on global statistical information and local instantaneous channel information. The global statistical information used in this algorithm remains unchanged through numerous updates of instantaneous channel information, thus the proposed scheme has strong robustness and practical application value, and can be applied to many MIMO systems, such as decellularized 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 this invention can achieve performance close to that of an optimal global minimum mean square error receiver with lower computational complexity.

[0069] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0070] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0071] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0072] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

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

2. The method according to claim 1, characterized in that, In the aforementioned decellularized ultra-large-scale MIMO system, each base station estimates the instantaneous channel state information from all users to that base station based on the received pilot signals and pilot allocation strategy, including: Within a decellularized ultra-large-scale MIMO system, K single-antenna users and M multi-antenna base stations are randomly and uniformly distributed. Each base station is equipped with N antennas. The users are numbered sequentially in integer order, and the line-of-sight (LoS) channel components from each user to each base station are obtained. and non-line-of-sight (NLoS) channel components Channel space correlation matrix Where m = 1, ..., M are the positive integer indices of the base station, and k = 1, ..., K are the positive integer indices of the user; definition Let be the instantaneous channel state information vector from user k to base station m. At base station m, the local instantaneous channel state information of user k to base station m is estimated as follows: in, For any channel estimation strategy matrix determined by statistical information, p k Let k be the transmit power. To receive noise, τ p This represents the number of orthogonal pilots, and also the length of each pilot signal. The pilot allocation strategy represents the subset of users that use the same pilot signal as user k.

3. The method according to claim 2, characterized in that, The method of obtaining global statistical information between each base station and all users based on instantaneous channel state information from users to base stations includes: Based on the instantaneous channel state information from users to base stations, each base station acquires statistical information between all base stations and all users in the decellularized ultra-large-scale MIMO system. This statistical information includes the Loss of Space (LoS) channel components from all users to all base stations globally in the system. Global channel space correlation matrix and the global channel estimation strategy matrix 4. The method according to claim 2, characterized in that, The method for obtaining the distributed receiver scheme of the MIMO system based on the instantaneous channel state information from the user to the base station and the user relationship matrix of the entire system includes: At base station m, for user k, based on the subset of users sharing the pilot with user k. Calculate the user relationship matrix in the entire system. For the element [∑] in the k-th row and l-th column... kl k, l = 1, ..., K; like Calculated in To estimate the comprehensive correlation matrix for the channel, if among them k =diag(Ψ 1k ,...,Ψ Mk )。 5. The method according to claim 4, characterized in that, The method for obtaining the distributed receiver scheme of the MIMO system based on the instantaneous channel state information from the user to the base station and the user relationship matrix of 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 For the user power diagonal matrix, e k For the unit matrix I K The kth column.

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