User-centric network massive MIMO signal detection method and system

Abstract

The application discloses a user-centric network large-scale MIMO signal detection method and system. Each base station in the user-centric network large-scale MIMO system has a group of main service users, each group of main service users forms a user group in the system, and each user group respectively performs signal detection. When detecting, each user group simultaneously considers interference sparsity and connection base station sparsity, each group of users considers only a part of users as interference users when detecting, and is only associated with a main service base station and a group of auxiliary service base stations. User-centric two-stage detection is implemented for each user group, the first stage is to locally implement matching filtering for each connection base station, and the auxiliary base station sends the matching filtered signal and channel state information to the main service base station; the second stage is to implement interference cancellation for the main service base station on the matching filtered signal to obtain a detection result. The application can effectively reduce the design and implementation complexity of the detector on the basis of ensuring the detection performance.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology and relates to a method and system for detecting large-scale MIMO signals in a user-centric network. Background Technology

[0002] With the rapid development of communication applications, network traffic and the number of user accesses have exploded in recent years, which in turn has driven the advancement of wireless technology. In wireless technology, Massive Multiple-Input Multiple-Output (MIMO) has become one of the key technologies in 5G networks and is expected to continue to play an important role in the 6G era. By equipping a large number of antennas on the base station (BS) side, MIMO can simultaneously serve multiple user terminals (UTs) on the same time-frequency resources, effectively improving spectrum and energy efficiency. In particular, signal detection is crucial for realizing the potential capacity gain of MIMO and has been extensively studied in recent years. As optimal detectors, maximum a posteriori (MAP) or maximum likelihood (ML) detectors face unacceptable computational complexity problems as the number of decision symbols increases. Although some methods have been proposed to reduce complexity, the computational requirements of these nonlinear detectors may still pose a challenge to the practical implementation of MIMO systems. With the increase in the number of base station antennas and user terminals, linear detectors, benefiting from their low complexity, have gradually shown significant advantages and attracted increasing attention.

[0003] In fact, linear detectors have evolved alongside the development of massive MIMO systems. In early communication systems, the number of user terminals was small, and signals from different users could be distinguished using multiple access techniques. Signal detection could be performed efficiently in a single-user manner without causing excessive interference. In this case, matched filters (MF) demonstrated sufficiently satisfactory performance and relatively low computational complexity. However, in practical applications, time-frequency resources and the number of antennas are limited. As the number of users increases, it becomes more beneficial to serve more spatially interfering users on the same time-frequency resources to enhance system capacity. Therefore, single-user detection, which ignores inter-user interference, is no longer effective. In this context, multi-user detection techniques such as zero-forcing (ZF) and minimum mean square error (MMSE) detection become crucial, effectively mitigating inter-user interference and maintaining Quality of Service (QoS).

[0004] Compared to single-user detection, multi-user detection demonstrates a significant performance improvement in cellular massive MIMO systems. However, cellular massive MIMO systems may not be able to meet the growing demand for higher data rates and better quality of service. More critically, cell-edge users in cellular massive MIMO systems suffer from poor service quality due to lower channel gain and higher interference from neighboring cells. These problems will become more severe as the number of users increases and cell sizes continue to shrink, necessitating further exploration of communication systems capable of providing seamless coverage. To address this issue, network massive MIMO systems have been proposed, which can enhance the performance of cell-edge users through joint coherent processing. Specifically, base stations in the system exchange information with each other via backhaul links, and each user can be served by all base stations in the network massive MIMO system, thus ensuring seamless service. Thanks to this, joint detection in network massive MIMO systems can achieve more effective interference management, improve signal quality at the cell edge, and thus provide more stable QoS for users in the system.

[0005] However, with the expansion of network scale and the increase in the number of users, the joint detection of all users by all base stations can lead to unacceptable computational complexity, making it impractical for real-world applications. To address this issue, User-Centered Network (UCN) massive MIMO systems have recently been proposed. In UCN massive MIMO systems, since most base stations contribute limited energy to a particular user, each user is served by only a few neighboring base stations. This not only eliminates the concept of cell edges, thereby enhancing the performance of cell-edge users, but also facilitates practical applications. More importantly, the system itself adopts a distributed architecture, which can effectively reduce the dimensionality of the observation vector for each user, alleviating design complexity and implementation challenges. However, existing UCN massive MIMO systems only consider that each user is served by a small number of base stations and still require centralized processing. As the network expands and the number of users increases, the system remains difficult to scale. Summary of the Invention

[0006] Purpose of the invention: The purpose of this invention is to provide a method and system for detecting large-scale MIMO signals in a user-centric network, which eliminates the need for global interaction information and greatly reduces computational complexity while ensuring detection performance.

[0007] Technical Solution: To achieve the above objectives, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a user-centric network massive MIMO signal detection method. The wireless communication network side has a set of massive MIMO base stations, and the wireless communication network coverage area has a set of user terminals. Each user has a primary serving base station, each base station has a set of primary serving users, and each set of primary serving users constitutes a user group. Each user group performs signal detection separately. When detecting each user group, interference sparsity and connection base station sparsity are considered. The number of interfering users in each user group is limited, and each user group only connects to one primary serving base station and one set of auxiliary serving base stations. When detecting each user group, a user-centric two-level detection is implemented, and each user group is detected independently using only the channel state information between the connection base station and the interfering users.

[0009] Furthermore, the primary serving user of each base station can be determined based on the large-scale fading information between the user and each base station. Each user preferentially selects the base station with the smallest large-scale fading as the primary serving base station, and users with the same primary serving base station form the primary serving user group of that base station.

[0010] Preferably, the large-scale fading information is characterized by average channel energy, and each user selects the base station with the largest average channel energy as the main serving base station; based on the main serving base station, a set of connected base stations for each user is selected according to the average channel energy difference threshold or a specified number.

[0011] Furthermore, the aforementioned interference sparsity means that the vast majority of interference for each user group is caused by a subset of users within the coverage area of ​​the wireless communication network. This subset of users constitutes the set of interfering users for that user group. The set of interfering users is selected by the wireless communication network based on the interference intensity between users, prioritizing users whose interference intensity with a given user group is greater than a correlation threshold.

[0012] Furthermore, the aforementioned base station sparsity refers to the fact that each user group has strong connectivity with only a subset of base stations in the network, and these subsets constitute the set of connected base stations for that user group. The set of connected base stations for a user group is determined by the union of the set of connected base stations for users within the group and the set of connected base stations for interfering users. The set of connected base stations for each user is selected based on the large-scale fading information of the channel between the base station and the user, prioritizing the selection of base stations with large-scale fading less than a large-scale fading threshold for each user, or a specified number of base stations with the smallest large-scale fading as the connected base stations for that user.

[0013] Furthermore, the primary serving base station in the set of connected base stations of each user group is connected to the secondary serving base stations via a backhaul link.

[0014] Furthermore, the user-centric two-level detection involves the following steps: the first level performs matched filtering locally for each connected base station, and the auxiliary serving base station sends the matched-filtered information and channel state information to the primary serving base station; the second level involves the primary serving base station performing interference cancellation on the matched-filtered signal to complete the signal detection for that user group.

[0015] Secondly, the present invention provides a user-centric network massive MIMO communication system, characterized in that: on the wireless communication network side there is a group of massive MIMO base stations, and within the coverage area of ​​the wireless communication network there is a group of user terminals; on the wireless network side, the system is used to determine the primary serving user of each base station, complete user grouping, and select an interfering user set and a connecting base station set for each user group; each user has a primary serving base station, each base station has a group of primary serving users, each group of primary serving users constitutes a user group, and each user group performs signal detection separately; when detecting each user group, the sparsity of interference and the sparsity of connecting base stations are considered, the number of interfering users in each user group is limited, and each user group is associated with only one primary serving base station and a group of auxiliary serving base stations; when each base station performs user group detection, it implements a user-centric two-level detection, detects each user group independently, and only uses the channel state information between the connecting base stations and the interfering users.

[0016] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the user-centric network large-scale MIMO signal detection method.

[0017] Fourthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the user-centric network large-scale MIMO signal detection method.

[0018] Beneficial effects: The user-centric network large-scale MIMO signal detection method proposed in this invention can effectively reduce the dimensions of signal processing on the base station side. Each base station only needs to interact with the auxiliary service base stations of its own primary service user group. While the detection performance is close to the optimal performance, the computational complexity and implementation complexity are greatly reduced, and signal detection is performed more efficiently. Attached Figure Description

[0019] Figure 1 This is a flowchart of the user-centric network large-scale MIMO signal detection system proposed in this invention.

[0020] Figure 2 This is a schematic diagram illustrating the computation and information interaction of the user-centered two-level detection proposed in this invention.

[0021] Figure 3The performance comparison chart of the user-centric network large-scale MIMO signal detection proposed in this invention with signal detectors in other systems is shown.

[0022] Figure 4 Comparison of design complexity between user-centric network massive MIMO signal detectors and network massive MIMO signal detectors.

[0023] Figure 5 Comparison of implementation complexity between user-centric network massive MIMO signal detectors and network massive MIMO signal detectors. Detailed Implementation

[0024] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. These embodiments are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0025] This invention discloses a user-centric method for detecting massive MIMO signals in a network. The wireless communication network has a set of massive MIMO base stations, and a set of user terminals within its coverage area. Each user has a primary serving base station, and each base station has a group of primary serving users. Each group of primary serving users constitutes a user group, and each user group performs signal detection independently. When detecting each user group, interference sparsity is considered, and the number of interfering users in each user group is limited. When detecting each user group, connection base station sparsity is also considered, and each user group is associated with only one primary serving base station and one set of auxiliary serving base stations. During the detection of each user group, a two-level user-centric detection is implemented, with each user group detected independently and only using channel state information between the connecting base stations and interfering users.

[0026] Interference sparsity refers to the fact that the vast majority of interference for each user group is caused by a subset of users within the coverage area of ​​the wireless communication network; these users constitute the set of interfering users for that user group. Connectivity sparsity refers to the fact that each user group has strong connectivity with only a subset of base stations in the network; these base stations constitute the set of connected base stations for that user group.

[0027] Specifically, such as Figure 1 As shown, the user-centric network large-scale MIMO signal detection method includes the following steps: First, the user determines the primary serving base station and connecting base stations based on large-scale fading information, and each base station determines its own primary serving user group; then, the wireless communication network side determines the interfering user group for each user group based on channel correlation, and the base station then determines the auxiliary serving base station for the primary serving user based on the connecting base stations of the primary serving user and the interfering user; finally, the primary serving base station and the auxiliary serving base station perform two-level detection to complete the detection of the user group.

[0028] In some embodiments, each user can preferentially select the base station with the smallest large-scale fading as the primary serving base station, and users with the same primary serving base station form the primary serving user group of that base station. Large-scale fading information can be characterized by average channel energy, and each user selects the base station with the largest average channel energy as the primary serving base station. Based on the primary serving base station, the set of connected base stations for each user can be selected according to an average channel energy difference threshold or a specified number. The set of interfering users can be selected by the wireless communication network side based on the channel correlation between users, preferentially selecting users whose channel correlation with a given user group is greater than a correlation threshold as interfering users of that user group. In some embodiments, the set of connected base stations for a user group is determined by the union of the set of connected base stations for users within the group and the set of interfering users. The set of connected base stations for each user is selected by the large-scale fading information between the base station and the user, preferentially selecting base stations with large-scale fading less than a large-scale fading threshold for each user, or a specified number of base stations with the smallest large-scale fading as the user's connected base stations.

[0029] In some embodiments, in a user-centric two-level detection, the first level performs matched filtering locally for each connected base station, and the auxiliary serving base station sends the matched-filtered information and channel state information to the primary serving base station; the second level is for the primary serving base station to perform interference cancellation on the matched-filtered signal, thus completing signal detection for the user group. Detection methods include, but are not limited to, minimum mean square error detection and zero-forcing detection.

[0030] This invention is primarily applicable to user-centric large-scale MIMO systems where there are multiple base stations and multiple users, each base station has a group of primary serving users, each user group has a selected set of interfering users that cause interference, and a selected set of connected base stations that provide services to them. The following detailed explanation of the specific implementation process of user-centric large-scale MIMO signal detection according to this invention is provided in conjunction with a specific communication system example. It should be noted that this invention is applicable not only to the specific system model illustrated below but also to system models with other configurations.

[0031] I. System Configuration

[0032] First, consider a multi-user, multi-base station massive multiple-input multiple-output (MIMO) system, where B base stations (BS) serve U single-antenna user terminals (UT), and each base station has N antennas. Let... and These represent the sets of base stations and user terminals, respectively. This represents the information signal transmitted by the i-th user, satisfying... and Where p i It is the average transmit power of user i. Let be the channel vector from the i-th user to the k-th base station. and s = [s1, s2, ..., s U ] T We assume that the base station has perfect channel state information (CSI).

[0033] Consider uplink transmission in a user-centric (UCN) massive MIMO system. This represents the primary serving user of the k-th base station, which naturally constitutes the k-th user group in the network. The k-th user group's signal is detected by the baseband processing unit (BBU) of its primary serving base station. Note that some user groups in the network may be empty sets. Indicating in the Interference user groups (IGs) during signal detection, among which Obviously there is Based on the sparsity of base station connections between each user terminal and the base station, the base stations with strong channel gain with user i constitute the set of base stations connected to user i, denoted as . further, This constitutes a user group The connected base station set (CBSC).

[0034] Compared to traditional network systems, UCN massive MIMO systems operate in a distributed manner, offering better scalability. Unlike cellular massive MIMO systems, this user-centric approach allows each user terminal to obtain service without relying on the cell concept. It's important to note that if each set of connected base stations contains only one base station, the proposed system becomes a cellular massive MIMO system; if... This system is equivalent to a traditional network-based massive MIMO system.

[0035] II. Selection Strategy for Interference User Set and Connection Base Station Set

[0036] In UCN massive MIMO systems, a key issue is how to determine the set of interfering users and the set of connected base stations for each user group. This paper aims to provide several feasible criteria for determining these sets in a specific communication system example.

[0037] First, determine the primary serving base station and the base stations connected to each user. (Definition)

[0038]

[0039] It quantifies the average channel energy between user i and base station k. Note that user i is served by at least one base station, which is [base station name missing].

[0040]

[0041] Specifically, base stations The primary serving base station is referred to as user i, and It can be used for initialization Furthermore, we can define

[0042]

[0043] This is used to measure the difference in channel gain between user i and the primary connected base station and the l-th base station. Then, the set of connected base stations for user i... It can be constructed using the following formula:

[0044]

[0045] in, This is a set threshold. In addition to selecting the connected base stations for each user based on a given threshold, one can also directly limit the number of connected base stations for each user, selecting the set number of base stations that maximize the channel gain for that user. Once the primary serving base station and the set of connected base stations for each user are determined, the primary serving user of the k-th base station can also be determined accordingly. At the same time, all serving users of the k-th base station can also be determined, for Let the primary serving users of each base station constitute a user group in the network, that is, let We put They become secondary serving users of base station k. Each user group completes signal detection by exchanging information between its primary serving base station and secondary serving base station.

[0046] After defining the user groups, the next step is to determine the set of interfering users for each user group. Let... because Service users are more likely to be user groups This causes strong interference, so we... Select user groups Interference with users.

[0047] because It can be done Initialize. Let

[0048]

[0049] Where, ψ k,j,l This reflects the average channel correlation between the serving user j of base station l and the kth user group. If the following conditions are met...

[0050] ψ k,j,l >δ ig (6)

[0051] The correlation threshold δ ig If > 0, then l can be included. Otherwise, l is considered not to be in the interfering user cluster of user i. When the user's speed is low, ψ k,j,l The instantaneous CSI can be obtained by estimating it in the pilot band, and the generated interference user group can be reused over a period of time.

[0052] After the set of connected base stations for each user and the interference user groups for each user group are determined, The set of connected base stations is and We make The auxiliary service base station for user group k is represented by... express,

[0053] III. User Center Detector

[0054] make This represents the set of connections from user terminal j to base stations. The superimposed channel matrix of all base stations in the network. Furthermore, let... Indicates belonging to a set But not in the group Among the users, For clarity, we make Indicates from user set to base station collection The channel matrix, let Represents a set of users The transmitted signal vector, where and These represent arbitrary sets of users and base stations, respectively. (Connecting to the set of base stations) Used to detect user groups The joint received signal after matched filtering is

[0055]

[0056] in

[0057]

[0058] This represents the simplified received signal before matched filtering; this signal only considers... and As a set of interfering users and a set of connected base stations, the UCN massive MIMO system significantly reduces the dimensionality of the received signal and the number of interfering user terminals, unlike network massive MIMO systems. It is distributed as Additive noise. The transmitted signal can be transmitted through its connected base station set. recover: in, This is for The designed detector. In the UCN massive MIMO system, the conditional mean square error (MSE) of the k-th user group is used... This can be expressed as:

[0059]

[0060] in It is a diagonal matrix, with the elements on the diagonal being the user set. The signal energy transmitted by each user. Then, minimize. The detector is:

[0061]

[0062] in This is an MMSE detector designed using the result of matched filtering. Its function is interference cancellation, and it is simply called the MMSE-IC matrix. Note that the interference cancellation matrix here is determined by the detection method used after matched filtering. Is base station J related to interference user groups? The channel Gram matrix. Using (10), signal detection can be accomplished by the following equation.

[0063]

[0064] in This represents the local matched filtering result obtained by base station l using the interfering user group of the k-th user group and the channel state information of its own base station. It can be seen that signal detection performs interference cancellation on the output result after local matched filtering. Note that because each base station has the required local received signal and channel state information, local matched filtering can be performed by each base station independently, but the subsequent interference cancellation process requires information exchange between base stations. Specifically, for the k-th user group, the interference cancellation matrix... The design of the primary serving base station requires the Gram matrix of each auxiliary serving base station. In addition, the signal detection process requires the local matched filtering results of each auxiliary service base station. Therefore, the proposed user-centric detection method divides the detection process for each user group into two levels, such as... Figure 2 As shown:

[0065] Level 1: Each connected base station performs local matched filtering and calculates the channel Gram matrix. Then, the auxiliary serving base station of the user group transmits the local matched filtering results and the channel Gram matrix to the primary serving base station.

[0066] Level 2: The main serving base station uses the information obtained from the interaction to complete the MMSE-IC matrix design and applies it to the summarized matched filtering results to complete local detection.

[0067] User-centric detection for the kth user group, such as Figure 2 As shown, where A significant advantage of this efficient implementation for each user group is that the dimension of the transmitted information is independent of the number of antennas. in Furthermore, this strategy supports parallel processing, which can effectively reduce processing latency. The proposed user-centric detection is reflected in the separate detection of each user group, and only the channel state information from the users within the group and interfering users to the connected base station is needed during detection; therefore, (10) is called a user-centric detector (UCD). UCD is equivalent to the optimal MMSE detector designed for network massive MIMO systems, when B k =1, UCD is the MMSE detector in traditional cellular network massive MIMO systems. Because in UCN massive MIMO systems, The number of interfering users is limited, usually U. k ≤I k <B k N. In this way, the design complexity is... The implementation complexity is

[0068] IV. Implementation Results

[0069] To enable those skilled in the art to better understand the present invention, the following provides a comparison of the BER performance of the user-centric network large-scale MIMO signal detection method in this embodiment with that of other systems, as well as a comparison of the algorithm complexity, under a specific system configuration.

[0070] We used the QuaDRiGa channel model to generate the simulation scenario, which considered the "3GPP 38.901 RMa NLOS" model. To ensure better coverage, we adopted a three-sector configuration with seven gNodeBs (gNBs) installed in the system. Each gNB has three co-located base stations, each responsible for a 120-degree coverage area. Therefore, there are a total of B = 21 base stations in the system. The distance between adjacent gNBs was set to 500 meters in our simulation. In the network, user terminals are randomly distributed within a circular area with a radius of 625 meters. For simplicity, we assume that each user terminal has the same transmit power, denoted by p. Let I... ig Let represent the number of interfering users in each user group. This indicates the number of base stations each user can connect to.

[0071] Figure 3 The document presents a comparison of the BER performance of a detector designed for a user-centric network-based massive MIMO system, a cellular massive MIMO system, and the optimal detector designed for a network-based massive MIMO system. It can be seen that the user-centric network-based massive MIMO system has a significant performance improvement over the cellular massive MIMO system, and its performance is close to that of the network-based massive MIMO system at low to medium transmission power, with only a 3dB performance loss at high signal-to-noise ratios.

[0072] Figure 4 The paper presents a comparison of the optimal detector design complexity for a user-centric network-based massive MIMO system and a network-based massive MIMO system, as shown in the examples. It can be seen that the detector design complexity for a user-centric network-based massive MIMO system is significantly lower than that for a network-based massive MIMO system.

[0073] Figure 5 A comparison of the implementation complexity of the detector for the user-centric network massive MIMO system and the optimal detector for the network massive MIMO system is presented in the embodiments. It can be seen that the implementation complexity of the detector for the user-centric network massive MIMO system is lower than that for the network massive MIMO system.

[0074] Based on the same inventive concept, an embodiment of the present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the steps of the above-described user-centric network massive MIMO signal detection method.

[0075] In a specific implementation, the device includes a processor, a communication bus, a memory, and a communication interface. The processor can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. The communication bus may include a path for transmitting information between the aforementioned components. The communication interface, using any transceiver-like device, is used for communicating with other devices or communication networks. The memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), read-only optical disc (CD-ROM) or other optical disc storage, disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory can exist independently and be connected to the processor via a bus. The memory can also be integrated with the processor.

[0076] The memory stores application code that executes the present invention and is controlled by a processor. The processor executes the application code stored in the memory to implement the user-centric network massive MIMO signal detection method provided in the above embodiments. The processor may include one or more CPUs, or multiple processors, each of which may be a single-core processor or a multi-core processor. Here, "processor" may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0077] This invention also discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the user-centric network massive MIMO signal detection method.

[0078] The program code used to implement the method of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the steps of the method of the present invention to be performed. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a standalone software package, or entirely on a remote machine or server. All aspects not detailed in this invention are well-known to those skilled in the art.

[0079] Based on the same inventive concept, this invention discloses a user-centric network massive MIMO signal detection system. On the wireless communication network side, there is a set of massive MIMO base stations, and within the coverage area of ​​the wireless communication network, there is a set of user terminals. Each user has a primary serving base station, and each base station has a group of primary serving users. Each group of primary serving users constitutes a user group, and each user group performs signal detection independently. When detecting each user group, interference sparsity is considered, and the number of interfering users in each user group is limited. When detecting each user group, connection base station sparsity is also considered, and each user group is associated with only one primary serving base station and one set of auxiliary serving base stations. When each base station performs user group detection, it implements a user-centric two-level detection system, detecting each user group independently and using only the channel state information between the connecting base station and the interfering users.

[0080] In the embodiments provided in this application, it should be understood that the disclosed methods can be implemented in other ways without departing from the spirit and scope of this application. The current embodiments are merely exemplary examples and should not be considered limiting, nor should the specific content given limit the purpose of this application. For example, some features may be omitted or not performed.

[0081] The technical means disclosed in this invention are not limited to those disclosed in the above embodiments, but also include technical solutions composed of any combination of the above technical features. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications are also considered within the scope of protection of this invention.

Claims

1. A method for detecting large-scale MIMO signals in a user-centric network, characterized in that, On the wireless communication network side, there is a set of massive MIMO base stations, and within the coverage area of ​​the wireless communication network, there is a set of user terminals. Each user has a primary serving base station, each base station has a group of primary serving users, and each group of primary serving users constitutes a user group. Each user group performs signal detection separately. When detecting each user group, interference sparsity and connection base station sparsity are considered. The number of interfering users in each user group is limited, and each user group is associated with only one primary serving base station and one set of auxiliary serving base stations. When detecting each user group, a two-level detection centered on the user is implemented. Each user group is detected independently and only the channel state information between the connecting base stations and interfering users is used. The primary serving users of each base station are determined based on the large-scale fading information between the user and each base station. Each user preferentially selects the base station with the smallest large-scale fading as the primary serving base station. Users with the same primary serving base station form the primary serving user group of that base station. The large-scale fading information is characterized by average channel energy, and each user selects the base station with the largest average channel energy as the primary serving base station. The set of connected base stations for each user is selected based on the average channel energy difference threshold or a specified number from the primary serving base station. The interference sparsity refers to the fact that most of the interference in each user group is caused by a portion of users within the coverage area of ​​the wireless communication network, and these users constitute the set of interfering users for that user group. The set of interfering users is selected by the wireless communication network side based on the correlation between channels among users, and users whose channel correlation with a given user group is greater than the correlation threshold are preferentially selected as the interfering users of that user group. The aforementioned connection base station sparsity refers to the fact that each user group has strong connectivity with only a portion of the base stations in the network, and these base stations constitute the connection base station set of the user group. The connection base station set of the user group is determined by the union of the connection base station sets of users within the group and interfering users. The connection base station set of each user is selected by the large-scale fading information of the channel between the base station and the user, and priority is given to selecting base stations with large-scale fading less than the large-scale fading threshold for each user, or a specified number of base stations with the smallest large-scale fading as the connection base stations of that user. The user-centric two-level detection involves the first level performing local matching filtering for each connected base station, and the auxiliary serving base station sending the matched filtering information and channel state information to the primary serving base station. The second level involves the primary serving base station performing interference cancellation on the matched-filtered signal to complete signal detection for the user group.

2. The method for detecting massive MIMO signals in a user-centric network according to claim 1, characterized in that, The primary serving base station in the set of connected base stations for each user group is connected to the secondary serving base stations via backhaul links.

3. A user-centric network massive MIMO signal detection system, characterized in that, On the wireless communication network side, there is a set of massive MIMO base stations, and within the coverage area of ​​the wireless communication network, there is a set of user terminals. On the wireless network side, the primary serving user of each base station is determined, user groups are formed, and a set of interfering users and a set of connected base stations are selected for each user group. Each user has a primary serving base station, each base station has a set of primary serving users, and each set of primary serving users constitutes a user group. Each user group performs signal detection separately. When detecting each user group, the sparsity of interference and the sparsity of connected base stations are considered. The number of interfering users in each user group is limited, and each user group is associated with only one primary serving base station and a set of auxiliary serving base stations. When each base station performs user group detection, it implements a user-centric two-level detection, detecting each user group independently and using only the channel state information between connected base stations and interfering users. The primary serving user of each base station is determined based on the large-scale fading information between the user and each base station. Each user preferentially selects the base station with the smallest large-scale fading as the primary serving base station. Users with the same primary serving base station form the primary serving user group of that base station. The large-scale fading information is characterized by average channel energy, and each user selects the base station with the largest average channel energy as the primary serving base station. The set of connected base stations for each user is selected based on the average channel energy difference threshold or a specified number of the primary serving base station. The aforementioned interference sparsity refers to the fact that most of the interference in each user group is caused by a portion of users within the coverage area of ​​the wireless communication network. These users constitute the set of interfering users for that user group. The set of interfering users is selected by the wireless communication network side based on the correlation between channels among users, with priority given to users whose channel correlation with a given user group is greater than a correlation threshold as the interfering users of that user group. The aforementioned connection base station sparsity refers to the fact that each user group has strong connectivity with only a portion of the base stations in the network, and these base stations constitute the connection base station set of the user group. The connection base station set of the user group is determined by the union of the connection base station sets of users within the group and interfering users. The connection base station set of each user is selected by the large-scale fading information of the channel between the base station and the user, and priority is given to selecting base stations with large-scale fading less than the large-scale fading threshold for each user, or a specified number of base stations with the smallest large-scale fading as the connection base stations of that user. The user-centric two-level detection involves the first level performing local matching filtering for each connected base station, and the auxiliary serving base station sending the matched filtering information and channel state information to the primary serving base station. The second level involves the primary serving base station performing interference cancellation on the matched-filtered signal to complete signal detection for the user group.

4. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the user-centric network large-scale MIMO signal detection method according to claim 1 or 2.

5. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the user-centric network large-scale MIMO signal detection method according to claim 1 or 2.

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