Visual area and channel estimation method and device in super-large-scale MIMO (Multiple Input Multiple Output) system

Abstract

The invention discloses a visual area and channel estimation method, device and equipment in a super-large-scale MIMO system and a storage medium, and belongs to the technical field of wireless communication multi-antenna transmission. The method comprises the following steps: determining visual state information and channel state information according to a plurality of received signal samples received by each antenna; determining an estimation result by adopting an iteration method according to the plurality of received signal samples, the visual state information and the channel state information; the process of each iteration comprises the following steps: determining visual state posterior information according to a plurality of received signal samples, visual state information and channel state information; the visual state posterior information represents the posterior probability of the antenna in the user visual area; and optimizing the visual state information and the channel state information according to the visual state posterior information. According to the technical scheme provided by the invention, the visual area and channel estimation of the super-large-scale MIMO system can be realized at the same time, and the communication performance and efficiency of the system are further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication multi-antenna transmission, and particularly relates to a visible region and channel estimation method, device and equipment in a super large scale MIMO system and a storage medium. BACKGROUND

[0002] Super large scale MIMO (Multiple-Input Multiple-Output) technology is an important technology for future 6G communication. In order to achieve effective precoding, accurate channel state information needs to be obtained, and channel estimation is a key to obtaining accurate channel state information. Accurate estimation of the visible region of large scale MIMO is one of the keys to optimizing communication performance and improving communication efficiency and quality. If the visible region is known, the dimension of the transmission channel can be reduced, thereby simplifying the signal processing process and reducing the computational complexity. However, the existing channel estimation methods are difficult to identify the influence of such visible region estimation, resulting in a serious decline in channel estimation accuracy. And most of the existing research schemes are based on the ideal assumption that the visible region information at the receiving and transmitting ends is completely known. This assumption ignores the impact of imperfect visible region estimation on key links such as system performance evaluation and wireless transmission scheme design. In real application scenarios, the visible region information is usually unknown, which makes it difficult to directly apply the existing research conclusions to the design and optimization of actual communication systems. Recent related schemes have begun to focus on the more realistic situation where the visible region information is unknown. However, these studies usually estimate the visible region information first, and then further develop channel estimation based on the estimation results. This step-by-step strategy makes the process more cumbersome and the efficiency is limited.

[0003] Therefore, it is necessary to provide a more reliable scheme. SUMMARY

[0004] The present application aims to overcome the deficiencies in the prior art, and provides a visible region and channel estimation method, device, equipment and storage medium in a super large scale MIMO system, which can simultaneously realize the visible region and channel estimation of the super large scale MIMO system, improve the estimation efficiency, and can improve the performance of the super large scale MIMO system, thereby improving the communication performance and communication efficiency.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] On the one hand, the present application provides a visible region and channel estimation method in a super large scale MIMO system, the method comprising:

[0007] The visible state information and the channel state information are determined according to a plurality of received signal samples received by each antenna; wherein the received signal samples are determined according to a transmitted pilot signal transmitted by the user and a received pilot signal received by the antenna, and the visible state information represents a probability that the antenna is located in a visible area of the user;

[0008] The estimation result is determined by using an iterative method according to the plurality of received signal samples, the visible state information and the channel state information; wherein the estimation result is the optimized visible state information and the optimized channel state information corresponding to a case that the iterative process meets a preset requirement;

[0009] The process of each iteration includes:

[0010] The visible state posterior information is determined according to the plurality of received signal samples, the visible state information and the channel state information; wherein the visible state posterior information represents a posterior probability that the antenna is located in the visible area of the user;

[0011] The visible state information and the channel state information are optimized according to the visible state posterior information, so as to generate the optimized visible state information and the optimized channel state information.

[0012] In some possible implementation manners, the step of optimizing the visible state information and the channel state information according to the visible state posterior information, so as to generate the optimized visible state information and the optimized channel state information, includes:

[0013] A target optimization function is determined according to the information set to be optimized and the plurality of received signal samples, and the target optimization function is shown in the following formula:

[0014] Q ( Θ ′ | Θ ) =  z ∼ p ( z | y ˜ , Θ ) [ l o g ( p ( y ˜ , z | Θ ′ ) ) ] ;

[0015] Wherein, The target optimization function represents a conditional expectation of a log-likelihood function of complete data with respect to a visible state feature z of a hidden variable under a condition of the information set to be optimized; The optimized information set includes the optimized visible state information and the optimized channel state information corresponding to the current iterative process; The information set to be optimized includes the optimized visible state information and the optimized channel state information corresponding to the last iterative process; The probability density function of the received signal sample under the condition of the visible state feature and the optimized information set; The visible state feature; The received signal sample, The S independent and identically distributed received signal samples received by each antenna;

[0016] Based on the objective optimization function, an objective optimization problem is determined, which is used to determine the optimized information set, as shown in the following equation:

[0017] ;

[0018] in, Maximize ;

[0019] Based on the target optimization problem and the visual state posterior information, the visual state information and the channel state information are optimized to generate the optimized visual state information and the optimized channel state information.

[0020] In some possible implementations, the step of optimizing the visual state information and the channel state information based on the target optimization problem and the visual state posterior information to generate the optimized visual state information and the optimized channel state information includes:

[0021] Based on the aforementioned target optimization problem, the visible state information and the channel state information are solved to determine the target visible state information and the target channel state information, as shown in the following equation:

[0022] ;

[0023] in, Indicates the target's visible status information; Represents the posterior information of the visible state; This represents channel state information; S represents the number of independent and identically distributed received signal samples.

[0024] ;

[0025] in, Indicates target channel state information; Represents the posterior information of the visible state; Indicates the length of the pilot signal; This represents the pilot transmit power of the k-th user; Indicates the number of antennas equipped in the base station;

[0026] Based on the target visual state information, the target channel state information, and the visual state posterior information, the optimized visual state information and the optimized channel state information are determined.

[0027] In some possible implementations, determining the visual state information and channel state information based on a plurality of received signal samples received by each antenna includes:

[0028] Determine the statistical characteristics of the plurality of received signal samples;

[0029] The statistical characteristics of the received signal samples are matched with the statistical characteristics of multiple historical received signals to obtain the signal matching result.

[0030] Based on the signal matching results, the visual state information and the channel state information are determined.

[0031] In some possible implementations, determining the visual state posterior information based on the plurality of received signal samples, visual state information, and channel state information includes:

[0032] Based on the received signal samples, visual state information, and channel state information, the visual state posterior information is determined using Bayes' theorem, as shown in the following equation:

[0033] ;

[0034] in, Represents the posterior information of the visible state; Indicates visual status information; This represents the joint probability of the s-th received signal sample and the visible state features under the channel state information condition; Let f(x) represent the probability density function of the s-th received signal sample under the conditions of visible state characteristics and channel state information. include and , This indicates that the antenna is located in the user's line of sight. This indicates that the antenna is not located in the user's line of sight. This includes the probability that the antenna is located within the user's line of sight and the probability that the antenna is not located within the user's line of sight; This includes the joint probability of the s-th received signal sample and the visible state features under channel state information conditions, and the joint probability of the s-th received signal sample and the non-visual state features under channel state information conditions. This includes the probability density function of the s-th received signal sample under the conditions of visible state features and channel state information, and the probability density function of the s-th received signal sample under the conditions of non-visual state features and channel state information. Indicates visible state characteristics; This represents the s-th received signal sample; This represents the number of independent and identically distributed received signal samples. This indicates channel state information.

[0035] In some possible implementations, the received signal samples are determined in the following manner:

[0036] Determine the channel model between the base station and the user;

[0037] Based on the transmit pilot signal sent by the user and the channel model, the receive pilot signal received by the antenna is determined;

[0038] The received signal sample is determined based on the transmitted pilot signal and the received pilot signal.

[0039] In some possible implementations, the method includes:

[0040] If the change in channel capacity between two iterations is less than a preset change threshold, the estimation result is determined, and the channel capacity is determined based on channel state information.

[0041] The channel capacity is determined based on the channel state information, as shown in the following formula:

[0042] ;

[0043] in, Indicates channel capacity, Indicates channel state information, Indicates the user's transmit power; Indicates noise.

[0044] On the other hand, a visible region and channel estimation device for a very large-scale MIMO system is provided, the device comprising:

[0045] The information determination module is used to determine the visibility status information and channel status information based on several received signal samples received by each antenna. The received signal samples are determined based on the transmitted pilot signal sent by the user and the received pilot signal received by the antenna. The visibility status information represents the probability that the antenna is located in the user's line of sight.

[0046] The information estimation module is used to determine the estimation result based on the plurality of received signal samples, visual state information and channel state information using an iterative method; the estimation result is the optimized visual state information and optimized channel state information corresponding to the iteration process meeting preset requirements;

[0047] Each iteration includes the following steps:

[0048] Based on the aforementioned received signal samples, visual state information, and channel state information, visual state posterior information is determined; the visual state posterior information characterizes the posterior probability that the antenna is located in the user's visual area.

[0049] Based on the visual state posterior information, the visual state information and channel state information are optimized to generate optimized visual state information and optimized channel state information.

[0050] On the other hand, an electronic device is provided, the device including a processor and a memory, the memory storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the visible area and channel estimation method in the ultra-large-scale MIMO system as described above.

[0051] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction and at least one program are stored therein, the at least one instruction and the at least one program being loaded and executed by a processor to implement the visible area and channel estimation method in the ultra-large-scale MIMO system as described above.

[0052] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0053] In this invention, visibility state information and channel state information are determined based on several received signal samples received by each antenna. The received signal samples are determined based on the transmit pilot signal sent by the user and the receive pilot signal received by the antenna. Visibility state information represents the probability that the antenna is located within the user's line of sight. Based on the received signal samples, visibility state information, and channel state information, an iterative method is used to determine the estimation result. The estimation result is the optimized visibility state information and optimized channel state information corresponding to when the iteration process meets preset requirements. Each iteration includes: determining the posterior visibility state information based on the received signal samples, visibility state information, and channel state information; the posterior visibility state information represents the probability that the antenna is located within the user's line of sight. Posterior probability; based on the posterior information of the visible state, the visible state information and channel state information are optimized to generate optimized visible state information and optimized channel state information; while ensuring high accuracy, it can simultaneously realize the estimation of visible area information and channel estimation of ultra-large-scale MIMO, improve the effectiveness and system performance of ultra-large-scale MIMO systems, and thus optimize communication performance, communication efficiency and communication quality. Moreover, the process is simple and can improve estimation efficiency. Furthermore, by determining the optimal visible area information and optimal channel state information during the iteration process of visible area and channel estimation, the accuracy and effectiveness of visible area information estimation and channel estimation can be improved, which can further improve communication performance, communication efficiency and communication quality. Attached Figure Description

[0054] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0055] Figure 1 This is a flowchart illustrating a method for estimating the visible area and channel in a very large-scale MIMO system provided by an embodiment of the present invention;

[0056] Figure 2 This is a flowchart illustrating another method for estimating the visible area and channel in a very large-scale MIMO system provided by an embodiment of the present invention.

[0057] Figure 3 This is a schematic diagram of the visible area and channel estimation device in a large-scale MIMO system provided by an embodiment of the present invention. Detailed Implementation

[0058] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0059] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0060] In this embodiment of the invention, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0061] Various exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0062] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0063] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0064] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0065] This application provides a very large-scale MIMO system, comprising a base station and users. The base station is equipped with M antennas arranged in a linear array, using the same time-frequency resources to serve K (M >> K) users. Optionally, the spacing between adjacent antennas in the base station is greater than... ,in The carrier wavelength is represented, so there is no channel correlation between antennas, and the channels of different antennas can be estimated independently. Optionally, during the uplink channel estimation phase, communication resources are orthogonally allocated to different users for pilot signal transmission, thereby allowing the channels of different users to be estimated independently.

[0066] Figure 1This is a flowchart illustrating a method for estimating the visible area and channel in a large-scale MIMO system according to an embodiment of the present invention. This specification provides the method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive methods, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or server product execution, the method can be executed sequentially according to the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown... Figure 1 As shown, the above method may include:

[0067] S101: Determine the visual state information and channel state information based on several received signal samples received by each antenna;

[0068] In one specific embodiment, the received signal sample can be determined based on the transmit pilot signal sent by the user and the receive pilot signal received by the antenna, and the line-of-sight information can characterize the probability that the antenna is located in the user's line-of-sight area; optionally, the user's line-of-sight area can be the part of the VMIMO antenna array that can receive a relatively strong user signal. Optionally, the line-of-sight information can be line-of-sight information used for line-of-sight area and channel estimation; the channel state information can be channel state information used for line-of-sight area and channel estimation.

[0069] In an optional embodiment, the received signal samples are determined in the following manner:

[0070] Determine the channel model between the base station and the user;

[0071] Based on the transmitted pilot signal sent by the user and the channel model, determine the received pilot signal received by the antenna;

[0072] Based on the transmitted and received pilot signals, the received signal sample is determined.

[0073] In one specific embodiment, the user can include multiple users communicating with the base station; the channel model between the base station and the users can be represented as follows: H = [ h 1 , h 2 , ⋅⋅⋅ , h K ] ∈ ℂ M × K ,in, It can represent the channel model between the base station and the target user, specifically... It can be an uplink channel matrix. Indicates the base station and the first Channel model between users. Optionally, the channel model between the base station and each user can be as follows:

[0074] ;

[0075] in, Representing the channel model; , Represents a diagonal matrix function. Indicates the number of antennas equipped in the base station; It is a binary variable. It can represent the first The non-visual state characteristics corresponding to the root antenna, i.e., the first... The antenna is located in the user's non-line-of-sight area; It can represent the first The visible state characteristics corresponding to the root antenna, i.e., the first... The antenna is within the user's line of sight. ; Indicates the user's first Path gain of the path, Indicates the user's first The distance of the path, This can indicate the user's first The angle of the path; This represents the near-field array steering vector.

[0076] In one specific embodiment, the transmit pilot signal sent by the user may include multiple uplink orthogonal pilot signals sent by the user, which can be represented as follows: Φ = [ ϕ 1 , ϕ 2 , ⋅⋅⋅ , ϕ K ] ∈ ℂ τ × K ,in, It can represent uplink orthogonal pilot signals sent by multiple users. Indicates the first Uplink orthogonal pilot signals sent by individual users Indicates the length of the pilot signal; This indicates the number of users served by a base station using the same time-frequency resources. Optionally, all uplink orthogonal pilot signals sent by users satisfy... ,in, This indicates the conjugate transpose. Represents the identity matrix.

[0077] In one specific embodiment, the received pilot signal received by the antenna can be the pilot signal received by the base station. Based on the transmitted pilot signal... With channel model Determine the received pilot signal received by the antenna. It can be represented by the following formula:

[0078] ;

[0079] in, This can indicate the reception of pilot signals; This indicates the pilot transmit power of multiple users, specifically... , Indicates the first Pilot transmission power of each user ; Let represent the noise matrix, whose elements are independent and identically distributed, and follow a Gaussian distribution. ,in, This represents the variance of the noise.

[0080] In one specific embodiment, by receiving pilot signals With transmitting pilot signals Related, determine the received signal sample As shown in the following formula:

[0081] ;

[0082] in, Indicates the received signal sample; Represents equivalent noise, specifically, Optionally, for any antenna of the base station, the signal sample received by any user is a complex Gaussian variable with the following distribution:

[0083] ;

[0084] in, Indicates the received signal sample; This indicates that the m-th antenna belongs to the line-of-sight area of ​​the k-th user; This indicates that the m-th antenna belongs to the non-visual area of ​​the k-th user; Indicates the pilot transmission power; This represents channel state information. Optionally, in a communication scenario, each antenna can receive S independent and identically distributed received signal samples. , This represents the s-th received signal sample. .

[0085] In an optional embodiment, determining the visual state information and channel state information based on a plurality of received signal samples received by each antenna may include:

[0086] Determine the statistical characteristics of several received signal samples;

[0087] The statistical characteristics of several received signal samples are matched with the statistical characteristics of multiple historical received signals to obtain the signal matching results.

[0088] Based on the signal matching results, the initial visual state information and the initial channel state information are determined.

[0089] In one specific embodiment, the multiple historical received signals can be pilot signals historically received by the base station, and these signals can be obtained from a historical database. Matching the statistical characteristics of the received signal sample with the statistical characteristics of the multiple historical received signals determines the similarity between the statistical characteristics of the received signal sample and the statistical characteristics of each historical received signal. The signal matching result can characterize the ranking result of increasing or decreasing similarity between the statistical characteristics of the received signal sample and the statistical characteristics of the historical received signals. Based on the signal matching result, the statistical characteristics of the target historical received signal with a similarity greater than a preset similarity threshold can be determined. At this point, the historical received signal is most similar to the received signal sample. Then, the visual state information and channel state information corresponding to the target historical received signal can be determined as the initial visual state information and initial channel state information. Optionally, the initial visual state information and initial channel state information can be used as initial parameter values ​​for the iterative optimization process.

[0090] In the above embodiments, determining good initial visual state information and initial channel state information can improve the stability of subsequent iterative optimization. Using the uplink orthogonal pilot signal sent by the user to determine the received signal sample, and then using the received signal sample to determine the initial visual state information and initial channel state information, can effectively identify spatial non-stationary effects, improve adaptability to spatial non-stationarity, and improve the efficiency of iterative optimization and the accuracy of subsequent iterative optimization results.

[0091] S102: Based on several received signal samples, visual state information, and channel state information, an iterative method is used to determine the estimation result; the estimation result is the optimized visual state information and optimized channel state information corresponding to when the iterative process meets the preset requirements.

[0092] Each iteration includes the following steps:

[0093] Based on several received signal samples, visual state information, and channel state information, determine the visual state posterior information;

[0094] Based on the visual state posterior information, the visual state information and channel state information are optimized to generate optimized visual state information and optimized channel state information.

[0095] In one specific embodiment, the posterior information of the visible state can characterize the posterior probability that the antenna is located in the user's visible area.

[0096] In an optional embodiment, determining the visual state posterior information based on several received signal samples, visual state information, and channel state information may include:

[0097] Based on several received signal samples, visual state information, and channel state information, the posterior information of the visual state is determined using Bayes' theorem, as shown in the following equation:

[0098] ;

[0099] in, Represents the posterior information of the visible state; Indicates visual status information; This represents the joint probability of the s-th received signal sample and the visible state features under the channel state information condition; Let f(x) represent the probability density function of the s-th received signal sample under the conditions of visible state characteristics and channel state information. include and , This indicates that the antenna is located in the user's line of sight. This indicates that the antenna is not located in the user's line of sight. This includes the probability that the antenna is located within the user's line of sight and the probability that the antenna is not located within the user's line of sight; This includes the joint probability of the s-th received signal sample and the visible state features under channel state information conditions, and the joint probability of the s-th received signal sample and the non-visual state features under channel state information conditions. This includes the probability density function of the s-th received signal sample under the conditions of visible state features and channel state information, and the probability density function of the s-th received signal sample under the conditions of non-visual state features and channel state information. Indicates visible state characteristics; This represents the s-th received signal sample; This represents the number of independent and identically distributed received signal samples. This indicates channel state information.

[0100] In one specific embodiment It can represent the posterior probability that the antenna is located in the user's line of sight given the s-th received signal sample and channel state information; This indicates visual status information, specifically... It can be ,Right now Indicates visual status information; and This represents non-visual state information, which characterizes the probability that the antenna is not located within the user's line of sight; specifically, Represents visible state characteristics, i.e. This indicates that the antenna is located within the user's line of sight; while This indicates that the antenna is not located in the user's line of sight.

[0101] In a specific embodiment, the above-mentioned visual state posterior information can be specifically expressed as the following formula:

[0102] ;

[0103] In one specific embodiment This represents the joint probability of the s-th received signal sample and non-visual state features under the condition of channel state information. This represents the joint probability of the s-th received signal sample and the visible state features under the channel state information condition; Let f(x) represent the probability density function of the s-th received signal sample under conditions of non-visual state characteristics and channel state information. Let f(s) represent the probability density function of the s-th received signal sample under the conditions of visible state characteristics and channel state information.

[0104] For each defined visible state feature and channel state information, the received signal samples follow a Gaussian distribution; specifically, in In this case, the characterization antenna is located in the user's line of sight, and the received signal sample... Follow the mean The variance is The complex Gaussian distribution is shown in the following equation:

[0105] ;

[0106] exist In this case, the characterization antenna is located in a non-user line-of-sight area, and the received signal sample... It follows a mean of 0 and a variance of . The complex Gaussian distribution is shown in the following equation:

[0107] .

[0108] In an optional embodiment, the above-described optimization of the visual state information and channel state information based on the visual state posterior information to generate optimized visual state information and optimized channel state information may include:

[0109] Based on the information set to be optimized and several received signal samples, the target optimization function is determined as follows:

[0110] Q ( Θ ′ | Θ ) =  z ∼ p ( z | y ˜ , Θ ) [ l o g ( p ( y ˜ , z | Θ ′ ) ) ] ;

[0111] in, Let represent the objective optimization function, which characterizes the conditional expectation of the log-likelihood function of the complete data with respect to the visible state feature z of the hidden variable, given the information set to be optimized. This represents the optimized information set, which includes the optimized visual state information and optimized channel state information corresponding to the current iteration process. This represents the set of information to be optimized, which includes the optimized visual state information and optimized channel state information corresponding to the previous iteration process. It represents the probability density function of the received signal sample under the conditions of visible state features and optimized information set; Indicates visible state characteristics; Indicates the received signal sample. This represents the S independent and identically distributed received signal samples received by each antenna;

[0112] Based on the objective optimization function, the objective optimization problem is determined. This objective optimization problem is used to determine the optimized information set, as shown in the following equation:

[0113] ;

[0114] in, Maximize ;

[0115] Based on the target optimization problem and the a posteriori information of the visible state, the visible state information and the channel state information are optimized to generate optimized visible state information and optimized channel state information.

[0116] In one specific embodiment, the information set to be optimized may include information that needs to be optimized during the current iteration optimization process. Optionally, the information set to be optimized is... , For visual status information, The channel state information is used. Optionally, the visual state information and channel state information can be optimized using the maximum likelihood estimation method based on the visual state posterior information. Optionally, the above objective optimization function specifically expresses the log joint likelihood after performing a probability-weighted average of all possible cases of the hidden state visual state features under the received signal samples and the information set to be optimized.

[0117] In a specific embodiment, the above objective optimization function can be further expressed as follows:

[0118] Q ( Θ ′ | Θ ) = ∑ s = 1 S ∑ z = 0 1 {log[ p ( z | Θ ′ ) ] + l o g [ p ( y ˜ s | z , Θ ′ ) ] } ⋅ p ( z | y ˜ s , Θ ) ;

[0119] in, This represents the visible state information under the optimized information set conditions; Let f(s) represent the probability density function of the s-th received signal sample under the conditions of visible state features and optimized information set. This represents the visual state posterior information under the conditions of the s-th received signal sample and the information set to be optimized; This represents the s-th received signal sample.

[0120] In a specific embodiment, based on the objective optimization problem, the objective optimization function is maximized, and combined with the visual state posterior information, the visual state information and the channel state information are optimized respectively to obtain the optimized visual state information and the optimized channel state information.

[0121] In an optional embodiment, the above-described optimization process of visual state information and channel state information based on the target optimization problem and visual state posterior information to generate optimized visual state information and optimized channel state information may include:

[0122] Based on the target optimization problem, the visible state information and channel state information are solved to determine the target's visible state information and target's channel state information, as shown in the following equation:

[0123] ;

[0124] in, Indicates the target's visible status information; Represents the posterior information of the visible state; This represents channel state information; S represents the number of independent and identically distributed received signal samples.

[0125] ;

[0126] in, Indicates target channel state information; Represents the posterior information of the visible state; Indicates the length of the pilot signal; This represents the pilot transmit power of the k-th user; Indicates the number of antennas equipped in the base station;

[0127] Based on the target visual state information, target channel state information, and visual state posterior information, the optimized visual state information and optimized channel state information are determined.

[0128] In one specific embodiment, the target visual state information can be the optimal solution of the visual state information that can be determined based on the target optimization problem; the target channel state information can be the optimal solution of the channel state information that can be determined based on the target optimization problem. Optionally, in the process of determining the optimized visual state information and the optimized channel state information, after determining the target visual state information and the target channel state information, subsequent iterative optimization processes can optimize the visual state information and the channel state information based on the target visual state information and the target channel state information.

[0129] In a specific embodiment, the preset requirement can be set according to the actual application. Optionally, the preset requirement can be that the change in channel capacity between two iterations is less than a preset change threshold. Optionally, if the preset requirement is not met, the iteration process is repeated; if the preset requirement is met, the estimation result is determined. The estimation result can include optimal visual state information and optimal channel state information. Determining the estimation result can realize visual area estimation and channel estimation. Based on the optimized visual state information and the known visual area, the dimensionality of the transmission channel can be reduced, the signal processing process can be simplified, the computational complexity can be reduced, and the performance and reliability of the communication link can be improved, thereby improving communication efficiency and quality. Based on the optimized channel state information, effective precoding can be achieved, thereby improving communication performance and communication quality.

[0130] In one specific implementation, the estimation result can be determined based on the EM (Expectation-Maximization) algorithm.

[0131] In the above embodiments, the visible area estimation and channel estimation of ultra-large-scale MIMO can be combined, and the visible state information and channel state information can be optimized at the same time. This can improve the efficiency and performance of visible area and channel estimation, thereby achieving performance optimization of the ultra-large-scale MIMO system.

[0132] In an optional embodiment, the above method may include:

[0133] If the change in channel capacity between two iterations is less than a preset change threshold, the estimation result is determined, and the channel capacity is determined based on channel state information.

[0134] The channel capacity is determined based on the channel state information, as shown in the following formula:

[0135] ;

[0136] in, Indicates channel capacity, Indicates channel state information, Indicates the user's transmit power; Indicates noise.

[0137] In one specific embodiment, the preset change threshold can be set according to the actual application requirements.

[0138] In one specific embodiment Figure 2 This is a flowchart illustrating another method for estimating the visible area and channel in a very large-scale MIMO system provided in this application embodiment; as follows: Figure 2 As shown, the method includes:

[0139] S201: Based on several received signal samples received by each antenna, determine the initial visual state information and the initial channel state information; determine the initial visual state information as the current visual state information, and determine the initial channel state information as the current channel state information;

[0140] S202: Determine the visual state posterior information based on several received signal samples, current visual state information, and current channel state information; and determine the current channel capacity based on the current channel state information;

[0141] S203: Based on the visual state a posteriori information, optimize the current visual state information and the current channel state information to generate optimized visual state information and optimized channel state information, and determine the updated channel capacity based on the optimized channel state information.

[0142] S204: Determine the change in channel capacity based on the updated channel capacity and the current channel capacity;

[0143] S205: Determine whether the change in channel capacity is less than a preset change threshold; if the change in channel capacity is greater than or equal to the preset change threshold, determine the optimized visual state information as the current visual state information, determine the optimized channel state information as the current channel state information, and jump to S202; if the change in channel capacity is less than the preset change threshold, jump to S206.

[0144] S206: Determine the estimation result.

[0145] As can be seen from the technical solutions provided in the embodiments of this specification above, this specification determines the visibility state information and channel state information based on several received signal samples received by each antenna; wherein, the received signal samples are determined based on the transmitted pilot signal sent by the user and the received pilot signal received by the antenna, and the visibility state information characterizes the probability that the antenna is located in the user's line of sight; based on several received signal samples, visibility state information, and channel state information, an iterative method is used to determine the estimation result; the estimation result is the optimized visibility state information and optimized channel state information corresponding to the iteration process meeting preset requirements; wherein, each iteration process includes: determining the visibility state posterior information based on several received signal samples, visibility state information, and channel state information; the visibility state posterior information characterizes the antenna The posterior probability of the location within the user's visible area; based on the posterior information of the visible state, the visible state information and channel state information are optimized to generate optimized visible state information and optimized channel state information; while ensuring high accuracy, it is possible to simultaneously achieve visible area information estimation and channel estimation for ultra-large-scale MIMO, improve the effectiveness and system performance of ultra-large-scale MIMO systems, and thus optimize communication performance, communication efficiency, and communication quality. Moreover, this process is simple, can improve estimation efficiency, and by determining the optimal visible area information and optimal channel state information during the iterative process of visible area and channel estimation, the accuracy and effectiveness of visible area information estimation and channel estimation can be improved, which can further improve communication performance, communication efficiency, and communication quality.

[0146] This invention also provides a device for estimating the visible area and channel in a very large-scale MIMO system. Figure 3 This is a schematic diagram of the visible area and channel estimation device in a very large-scale MIMO system provided by an embodiment of the present invention; as shown. Figure 3 As shown, the above-mentioned device includes:

[0147] The information determination module 310 is used to determine the visibility status information and channel status information based on several received signal samples received by each antenna; wherein, the received signal samples are determined based on the transmitted pilot signal sent by the user and the received pilot signal received by the antenna, and the visibility status information characterizes the probability that the antenna is located in the user's line of sight.

[0148] The information estimation module 320 is used to determine the estimation result by using an iterative method based on the plurality of received signal samples, visual state information and channel state information; the estimation result is the optimized visual state information and optimized channel state information corresponding to the iteration process meeting preset requirements;

[0149] Each iteration includes the following steps:

[0150] Based on the aforementioned received signal samples, visual state information, and channel state information, visual state posterior information is determined; the visual state posterior information characterizes the posterior probability that the antenna is located in the user's visual area.

[0151] Based on the visual state posterior information, the visual state information and channel state information are optimized to generate optimized visual state information and optimized channel state information.

[0152] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0153] This invention also provides an electronic device, comprising: a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the visible area and channel estimation method in any of the method embodiments.

[0154] Embodiments of the present invention also provide a computer storage medium, which may be disposed in a server to store at least one instruction, at least one program, code set, or instruction set for implementing the method embodiments. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the visible area and channel estimation method in any of the method embodiments.

[0155] Optionally, in embodiments of the present invention, the storage medium may be located at at least one of a plurality of network servers in a computer network. Optionally, in embodiments of the present invention, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0156] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more flowcharts and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0158] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more flowcharts and / or boxes Figure 1 The function specified in one or more boxes.

[0159] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more flowcharts and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0160] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0161] Finally, it should be noted that the embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for visual region and channel estimation in a massive MIMO system, the method comprising: The method comprises: determining visible state information and channel state information according to a plurality of received signal samples received by each antenna; wherein the received signal samples are determined according to a transmitted pilot signal transmitted by a user and a received pilot signal received by the antenna, and the visible state information represents a probability that the antenna is located in a visible area of the user; determining an estimation result by using an iterative method according to the plurality of received signal samples, the visible state information and the channel state information; the estimation result is optimized visible state information and optimized channel state information corresponding to a preset requirement in an iterative process; wherein the process of each iteration comprises: determining visible state posterior information according to the plurality of received signal samples, the visible state information and the channel state information; the visible state posterior information represents a posterior probability that the antenna is located in the visible area of the user; optimizing the visible state information and the channel state information according to the visible state posterior information to generate the optimized visible state information and the optimized channel state information. 2.The method of estimating a visible region and a channel in a massive MIMO system according to claim 1, wherein, The optimization of the visible state information and the channel state information according to the visible state posterior information to generate the optimized visible state information and the optimized channel state information comprises: determining a target optimization function according to a set of information to be optimized and the plurality of received signal samples, as shown in the following formula: ； wherein, represents a target optimization function, the target optimization function representing a conditional expectation of a full-data log-likelihood function with respect to a hidden variable visible state feature z under a condition of the information set to be optimized; represents an optimized information set, the optimized information set including optimized visible state information and optimized channel state information corresponding to a current iteration process; represents the information set to be optimized, the information set to be optimized including optimized visible state information and optimized channel state information corresponding to a previous iteration process; represents a probability density function of a received signal sample under a condition of a visible state feature and the optimized information set; represents the visible state feature; represents the received signal sample, represents S independent and identically distributed received signal samples received by each antenna; determining a target optimization problem based on the target optimization function, the target optimization problem being used to determine the set of optimized information, as shown in the following formula: ； wherein represents maximizing ; optimizing the visible state information and the channel state information based on the target optimization problem and the visible state posterior information to generate the optimized visible state information and the optimized channel state information. 3.The method of estimating the visible region and channel in a massive MIMO system according to claim 2, wherein, The optimization of the visible state information and the channel state information based on the target optimization problem and the visible state posterior information to generate the optimized visible state information and the optimized channel state information comprises: solving the visible state information and the channel state information based on the target optimization problem to determine target visible state information and target channel state information, as shown in the following formula: ； wherein, represents target visual state information; represents visual state posterior information; represents channel state information; S represents the number of received independent and identically distributed received signal samples; ； wherein, represents target channel state information; represents visual state posterior information; represents pilot signal length; represents the pilot transmission power of the kth user; represents the number of antennas equipped by the base station; determining the optimized visible state information and the optimized channel state information based on the target visible state information, the target channel state information and the visible state posterior information. 4.The method of estimating a visible region and a channel in a massive MIMO system according to claim 1, wherein, The determination of the visible state information and the channel state information according to the plurality of received signal samples received by each antenna comprises: determining statistical characteristics of the plurality of received signal samples; performing matching processing on the statistical characteristics of the plurality of received signal samples and statistical characteristics of a plurality of historical received signals to obtain a signal matching result; determining the visible state information and the channel state information based on the signal matching result. 5.The method for estimating a visible region and a channel in a massive MIMO system according to claim 1, wherein, The determination of the visible state posterior information according to the plurality of received signal samples, the visible state information and the channel state information comprises: determining the visible state posterior information by using Bayes' theorem according to the plurality of received signal samples, the visible state information and the channel state information, as shown in the following formula: ； wherein represents the visual state posterior information; represents the visual state information; represents the joint probability of the s-th received signal sample and the visual state feature conditioned on the channel state information; represents the probability density function of the s-th received signal sample conditioned on the visual state feature and the channel state information; comprises and , represents that the antenna is located in the user's visual area, represents that the antenna is not located in the user's visual area, comprises the probability that the antenna is located in the user's visual area and the probability that the antenna is not located in the user's visual area; comprises the joint probability of the s-th received signal sample and the visual state feature conditioned on the channel state information and the joint probability of the s-th received signal sample and the non-visual state feature conditioned on the channel state information; comprises the probability density function of the s-th received signal sample conditioned on the visual state feature and the channel state information and the probability density function of the s-th received signal sample conditioned on the non-visual state feature and the channel state information; represents the visual state feature; represents the s-th received signal sample; represents the number of received independent and identically distributed received signal samples; represents the channel state information. 6.The method of estimating the visible region and channel in a massive MIMO system according to claim 1, wherein, The received signal samples are determined in the following manner: determining a channel model between the base station and the user; determining a received pilot signal received by the antenna based on a transmitted pilot signal transmitted by the user and the channel model; determining the received signal sample based on the transmitted pilot signal and the received pilot signal. 7.The method of estimating the visible region and channel in a massive MIMO system according to claim 1, wherein, The method comprises: determining an estimation result in a case that a change value of a channel capacity based on the channel state information is less than a preset change threshold between two iterations; wherein the channel capacity is determined according to the channel state information, as shown in the following formula: ； wherein, denotes a channel capacity, denotes channel state information, denotes a transmit power of a user; denotes noise.

8. A device for estimating the visible area and channel in a very large-scale MIMO system, characterized in that, The device comprises: an information determining module configured to determine visible state information and channel state information according to a plurality of received signal samples received by each antenna; wherein the received signal sample is determined according to a transmitted pilot signal transmitted by the user and a received pilot signal received by the antenna, and the visible state information represents a probability that the antenna is located in a visible area of the user; an information estimating module configured to determine an estimation result by using an iterative method according to the plurality of received signal samples, the visible state information and the channel state information; the estimation result is the optimized visible state information and the optimized channel state information corresponding to a case that the iterative process meets a preset requirement; wherein the process of each iteration comprises: determining visible state posterior information according to the plurality of received signal samples, the visible state information and the channel state information; the visible state posterior information represents a posterior probability that the antenna is located in the visible area of the user; optimizing the visible state information and the channel state information according to the visible state posterior information to generate the optimized visible state information and the optimized channel state information. 9.An electronic device, the device comprising a processor and a memory, the memory storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the method for estimating a visible area and a channel in a super large scale MIMO system according to any one of claims 1 to 7. 10.A computer storage medium, the computer storage medium storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by a processor to implement the method for estimating a visible area and a channel in a super large scale MIMO system according to any one of claims 1 to 7.

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