Channel shape factor determination method and device, storage medium and program product

Abstract

The embodiment of the invention provides a channel shape factor determination method and device, a storage medium and a program product, relates to the technical field of communication, and is used for improving the estimation accuracy of a channel shape factor. The method comprises the following steps: acquiring an uplink signal to interference and noise ratio (UL-SINR) data set of at least one communication link in a target communication network; and according to the UL-SINR data set of the at least one communication link, respectively determining interference sensing information of each communication link in the at least one communication link and a Nakagami-m channel shape factor corresponding to each communication link.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, apparatus, storage medium, and program product for determining channel shape factor. Background Technology

[0002] With the rapid growth of wireless communication services and the evolution of mobile communication technology towards high-reliability low-latency communication (HRLLC) scenarios, refined and highly accurate research on wireless channel characteristics has become the core of optimizing system resource allocation and improving network performance. Among these, the Nakagami channel model, which can accurately characterize small-scale fading, is widely used.

[0003] However, the accuracy of rapid estimation of the shape factor m in current related technologies is low, which seriously restricts the deterministic performance analysis and optimization of communication systems such as HRLLC. Summary of the Invention

[0004] This application provides a method, apparatus, storage medium, and program product for determining the channel shape factor, which improves the accuracy of channel shape factor estimation.

[0005] In a first aspect, this application provides a method for determining a channel shape factor, comprising: acquiring an uplink signal-to-interference-plus-noise ratio (UL-SINR) dataset of at least one communication link in a target communication network; and determining, based on the UL-SINR dataset of at least one communication link, the interference sensing information of each communication link in the at least one communication link and the Nakagami-m channel shape factor corresponding to each communication link.

[0006] The technical solution provided in this application offers at least the following benefits: By acquiring the UL-SINR dataset of at least one communication link in the target communication network, and using this dataset as a unified analysis basis, interference sensing information and Nakagami-m channel shape factor are determined separately for each communication link. This avoids the unreasonable assumption that all links have the same shape factor m, thus improving the accuracy of the Nakagami-m channel shape factor. It provides precise parameter support for resource allocation and power control in scenarios such as 5G / 6G ultra-dense networking and HRLLC. For example, it enables deterministic analysis of HRLLC system performance, providing key theoretical support for core functions such as system capacity assessment and resource allocation optimization.

[0007] One possible implementation involves determining the interference sensing information of each communication link in at least one communication link and the corresponding Nakagami-m channel shape factor based on the UL-SINR dataset of at least one communication link. This includes: determining the interference sensing information of each communication link based on the UL-SINR dataset and loss metric function; and determining the corresponding Nakagami-m channel shape factor based on the interference sensing information and the UL-SINR dataset of the communication link.

[0008] Another possible implementation involves determining the Nakagami-m channel shape factor corresponding to the communication link based on the interference sensing information and the UL-SINR dataset of the communication link. This includes: determining multiple sets of first estimation results of the Nakagami-m channel shape factor using the negative log-likelihood function based on the interference sensing information and the UL-SINR dataset of the communication link; and determining the Nakagami-m channel shape factor corresponding to the communication link from the first estimation result with the smallest negative log-likelihood function value among the multiple sets of first estimation results.

[0009] Another possible implementation involves determining a first set of estimation results as follows: Initialization step: Obtain the first channel shape factor; Calculation step: Calculate the negative log-likelihood function value based on the first channel shape factor, interference sensing information, SINR data in the UL-SINR dataset, and the negative log-likelihood function; Parameter update step: Determine the second channel shape factor using a gradient descent optimization algorithm based on the negative log-likelihood function value; If the convergence condition is not met, determine the second channel shape factor as the new first channel shape factor, and re-execute the calculation and parameter update steps to obtain the new second channel shape factor; If the convergence condition is met, determine the second channel shape factor and the corresponding negative log-likelihood function value as a first set of estimation results.

[0010] Another possible implementation is that the Nakagami-m channel shape factor satisfies the following relationship: ;

[0011] ; in, It is the negative log-likelihood function value; Resource allocation instructions for users interfering with the communication link, and satisfying ; Let be the Nakagami-m channel shape factor to be estimated, satisfying ; To interfere with the perceived information, and satisfy ; For the UL-SINR dataset, the first Row data, UL-SINR dataset .

[0012] Another possible implementation involves determining the interference sensing information and the corresponding Nakagami-m channel shape factor of each communication link in at least one communication link, based on the UL-SINR dataset of at least one communication link. This includes: for each communication link, determining multiple sets of second estimation results based on the UL-SINR dataset of the communication link and a multi-objective joint estimation strategy; wherein each set of second estimation results includes the negative log-likelihood function value, the interference sensing information of the communication link, and the corresponding Nakagami-m channel shape factor of the communication link; and determining the interference sensing information and the corresponding Nakagami-m channel shape factor of the communication link from the second estimation result with the smallest negative log-likelihood function value among the multiple sets of second estimation results.

[0013] Another possible implementation involves determining a second set of estimation results as follows: Initialization step: Obtain the third channel shape factor and the third interference sensing information; Calculation step: Calculate the negative log-likelihood function value based on the third channel shape factor, the third interference sensing information, the SINR data in the UL-SINR dataset, and the negative log-likelihood function; Parameter update step: Determine the fourth channel shape factor and the fourth interference sensing information using a gradient descent optimization algorithm based on the negative log-likelihood function value; If the convergence condition is not met, determine the fourth channel shape factor as the new third channel shape factor and the fourth interference sensing information as the new third interference sensing information, and re-execute the calculation step and the parameter update step to obtain the new fourth channel shape factor and the new fourth interference sensing information; If the convergence condition is met, determine the fourth channel shape factor, the fourth interference sensing information, and the negative log-likelihood function value corresponding to the fourth channel shape factor as a set of second estimation results.

[0014] Another possible implementation is that the Nakagami-m channel shape factor satisfies the following relationship: ;

[0015] ; in, It represents the negative log-likelihood function value; it represents the resource allocation indication for interfering users in the communication link, and satisfies... ; For the interference sensing information to be estimated, satisfying ; Let be the Nakagami-m channel shape factor to be estimated, satisfying ; For the UL-SINR dataset, the first Row data, UL-SINR dataset .

[0016] Secondly, this application provides a channel shape factor determination apparatus, comprising: The acquisition module is used to acquire the UL-SINR dataset of at least one communication link in the target communication network.

[0017] The determination module is used to determine the interference sensing information of each communication link in at least one communication link and the Nakagami-m channel shape factor corresponding to each communication link, based on the UL-SINR dataset of at least one communication link.

[0018] One possible implementation involves a determination module, specifically used for: determining the interference sensing information of each communication link based on the UL-SINR dataset and loss metric function of the communication link; and determining the Nakagami-m channel shape factor corresponding to the communication link based on the interference sensing information and the UL-SINR dataset of the communication link.

[0019] Another possible implementation involves a determination module, specifically used to: determine multiple sets of first estimates of the Nakagami-m channel shape factor based on the interference sensing information of the communication link and the UL-SINR dataset of the communication link, using a negative log-likelihood function; and determine the Nakagami-m channel shape factor corresponding to the communication link from the first estimate with the smallest negative log-likelihood function value among the multiple sets of first estimates.

[0020] Another possible implementation involves determining a first set of estimates in the following way: Initialization steps: Obtain the first channel shape factor; Calculation steps: Based on the first channel shape factor, interference sensing information, SINR data in the UL-SINR dataset, and the negative log-likelihood function, calculate the negative log-likelihood function value; Parameter update steps: Determine the second channel shape factor based on the negative log-likelihood function value using the gradient descent optimization algorithm; If the convergence condition is not met, the second channel shape factor is determined as the new first channel shape factor, and the calculation steps and parameter update steps are re-executed to obtain the new second channel shape factor. If the convergence condition is met, the second channel shape factor and the corresponding negative log-likelihood function value are determined as a set of first estimation results.

[0021] Another possible implementation is that the Nakagami-m channel shape factor satisfies the following relationship: ;

[0022] ; in, It is the negative log-likelihood function value; Resource allocation instructions for users interfering with the communication link, and satisfying ; Let be the Nakagami-m channel shape factor to be estimated, satisfying ; To interfere with the perceived information, and satisfy ; For the UL-SINR dataset, the first Row data, UL-SINR dataset .

[0023] Another possible implementation involves a determination module, specifically used for: for each communication link, based on the UL-SINR dataset of the communication link and a multi-objective joint estimation strategy, determining multiple sets of second estimation results; wherein, one set of second estimation results includes the negative log-likelihood function value, the interference sensing information of the communication link, and the Nakagami-m channel shape factor corresponding to the communication link; and determining the interference sensing information of the communication link and the Nakagami-m channel shape factor corresponding to the communication link from the second estimation result with the smallest negative log-likelihood function value among the multiple sets of second estimation results.

[0024] Another possible implementation involves determining a second set of estimates in the following way: Initialization steps: Obtain the third channel shape factor and third interference sensing information; Calculation steps: Based on the third channel shape factor, the third interference sensing information, the SINR data in the UL-SINR dataset, and the negative log-likelihood function, calculate the negative log-likelihood function value; Parameter update steps: Based on the negative log-likelihood function value, the fourth channel shape factor and the fourth interference sensing information are determined by the gradient descent optimization algorithm; If the convergence condition is not met, the fourth channel shape factor is determined as the new third channel shape factor and the fourth interference sensing information is determined as the new third interference sensing information. The calculation steps and parameter update steps are re-executed to obtain the new fourth channel shape factor and the new fourth interference sensing information. If the convergence condition is met, the fourth channel shape factor, the fourth interference sensing information, and the negative log-likelihood function value corresponding to the fourth channel shape factor are determined as a set of second estimation results.

[0025] Another possible implementation is that the Nakagami-m channel shape factor satisfies the following relationship: ;

[0026] ; in, It represents the negative log-likelihood function value; it represents the resource allocation indication for interfering users in the communication link, and satisfies... ; For the interference sensing information to be estimated, satisfying ; Let be the Nakagami-m channel shape factor to be estimated, satisfying ; For the UL-SINR dataset, the first Row data, UL-SINR dataset .

[0027] Thirdly, this application provides a channel shape factor determination apparatus, which may also be referred to as an electronic device, comprising: a processor and a memory; the memory storing processor-executable instructions; when the processor is configured to execute the instructions, the electronic device implements the method of the first aspect described above.

[0028] Fourthly, this application provides a computer-readable storage medium comprising: computer software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0029] Fifthly, this application provides a computer program product comprising a computer program; when the computer program is run in an electronic device, the electronic device performs the method described in the first aspect.

[0030] The beneficial effects of the second to fifth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0031] Figure 1 A schematic diagram of the architecture of a communication system provided in this application; Figure 2 A flowchart illustrating a method for determining a channel shape factor provided in this application; Figure 3 A schematic diagram of a communication scenario provided in this application; Figure 4 A schematic diagram illustrating the process of determining a channel shape factor provided in this application; Figure 5 A schematic diagram illustrating another process for determining the channel shape factor provided in this application; Figure 6 A schematic diagram illustrating the estimation performance of a channel shape factor provided in this application; Figure 7 A schematic diagram illustrating the estimation performance of another channel shape factor provided in this application; Figure 8 A schematic diagram illustrating the estimation performance of another channel shape factor provided in this application; Figure 9 A schematic diagram illustrating the estimation performance of another channel shape factor provided in this application; Figure 10 A schematic diagram illustrating the estimation performance of another channel shape factor provided in this application; Figure 11 A schematic diagram of a KL divergence provided in this application; Figure 12 A schematic diagram of SINR value and CDF provided for this application; Figure 13 A schematic diagram illustrating the average running time provided in this application; Figure 14 A schematic diagram of the composition of a channel shape factor determination device provided in this application; Figure 15 A schematic diagram of the composition of another channel shape factor determination device provided in this application. Detailed Implementation

[0032] The following is a detailed description of a call detail record (CDR) data recording method provided in this application, with reference to the accompanying drawings.

[0033] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0034] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0035] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0036] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0037] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0038] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0039] In the field of wireless communication systems, a relatively complete research system has been formed around interference sensing and channel parameter estimation based on core physical layer indicators. The existing research directions are mainly divided into two categories. One category is interference sensing problems such as inter-carrier interference (ICI), and the other category is problems related to the estimation of the Nakagami-m channel shape factor m.

[0040] In terms of interference perception, the mainstream approach is to use machine learning-related methods, including deep learning neural network models that rely on multi-layer network structures to automatically extract and classify interference features, and nonlinear regression algorithms that establish a nonlinear mapping relationship between interference features and signal-to-interference-plus-noise ratio (SINR). Although these methods can achieve high recognition accuracy in specific scenarios, they generally ignore the impact of small-scale fading characteristics on interference features. Furthermore, they are limited by the fact that the training data cannot cover all channel state combinations, resulting in insufficient feature extraction. Moreover, due to the complexity of the actual channel environment, the training data is difficult to cover all possible channel state combinations, which limits the generalization performance of the model.

[0041] Regarding the estimation of the shape factor *m*, there are three main approaches: first, the moment estimation method based on low-order moments, which estimates parameters by matching sample moments with theoretical moments; second, the improved high-order moment estimation method, which enhances estimation accuracy by increasing the order of the moments; and third, the maximum likelihood estimation method based on the likelihood function. However, these methods suffer from two significant drawbacks in practical applications: first, they typically rely on the original received signal—a signal that requires complex channel estimation and is susceptible to distortion in HRLLC scenarios; second, existing methods all assume that "all links have the same shape factor *m*", which contradicts the objective fact that "different links may have different fading characteristics" in real communication systems, ultimately leading to systematic biases in the estimation results.

[0042] Currently, while machine learning-based methods have shown some adaptability, they still have many shortcomings in practical applications. These methods often simply model the gain brought by small-scale channels as an error, and fail to fully consider the changing characteristics of various small-scale channel states, especially performing poorly when dealing with fast fading channels. Although deep learning methods such as neural networks can learn the complex mapping relationship between channel features and parameters, they have extremely high requirements for the completeness of training data, making it difficult to cover all possible channel states. More importantly, these methods usually require large computational overhead and long observation windows, which cannot meet the real-time requirements of scenarios such as HRLLC.

[0043] Furthermore, while traditional mathematical methods possess a solid theoretical foundation in estimating the shape factor m of Nakagami-m channels, their application in real-world dynamic channel environments has significant limitations. A more fundamental problem lies in the fact that these methods are based on the simplified assumption that "all communication links have the same shape factor m," which severely contradicts the objective reality that "different links may have different fading characteristics" in actual communication systems. In real-world wireless propagation environments, influenced by factors such as multipath effects, movement speed, and obstacle distribution, the fading characteristics of different links often exhibit significant differences. This assumptional bias leads to systematic errors in the estimation results, especially in heterogeneous network deployment scenarios where macro base stations and small base stations coexist, where the differences in fading characteristics between links within different coverage areas are even more pronounced. Such systematic biases can further affect the accuracy of subsequent key decisions such as resource allocation and power control, ultimately leading to a decline in overall network performance. More seriously, this bias can be amplified as network density increases, and in ultra-dense network scenarios, it may even cause an order-of-magnitude deterioration in estimation accuracy, becoming a major bottleneck restricting the improvement of network performance in the fifth-generation mobile communication technology (5G) and the sixth-generation mobile communication technology (6G).

[0044] Of particular note is the millisecond-level random mutation characteristic of fast fading channels, which makes it difficult for traditional mathematical methods to accurately track channel changes, while machine learning methods cannot achieve a fast response under limited computing resources. Ultimately, this means that existing technologies cannot simultaneously achieve ideal levels in both the estimation accuracy and real-time performance of channel parameters.

[0045] Therefore, how to develop a parameter estimation scheme that is more suitable for small-scale fading channels (i.e., Nakagami-m channels) and improve the estimation accuracy of the shape factor m of Nakagami-m channels is a technical problem that urgently needs to be solved in related technologies.

[0046] To address the aforementioned technical problems, this application provides a method for determining the channel shape factor. The method involves: acquiring the uplink signal-to-interference-plus-noise ratio (UL-SINR) dataset of at least one communication link in the target communication network; and, based on the UL-SINR dataset of the at least one communication link, determining the interference-aware information of each communication link and the corresponding Nakagami-m channel shape factor for each communication link. Thus, by acquiring the UL-SINR dataset of at least one communication link in the target communication network and using this dataset as a unified analysis basis, the interference-aware information and Nakagami-m channel shape factor are determined separately for each communication link, thereby avoiding the unreasonable assumption that all links have the same shape factor m and improving the accuracy of the Nakagami-m channel shape factor. This provides precise parameter support for resource allocation and power control in scenarios such as 5G / 6G ultra-dense networking and HRLLC. For example, it can achieve deterministic analysis of HRLLC system performance, providing key theoretical support for core functions such as system capacity assessment and resource allocation optimization.

[0047] For example, the Nakagami technology described above can use a mini wireless development platform (minibee) as its hardware foundation, and run a community enterprise operating system (CentOS) to build an integrated development environment that includes matrix laboratory software (MATLAB), Xilinx programmable logic device and development tool (XILINX), baseband processing system (BPS), etc. After completing the association of software tools and configuration of environment variables, a test system consisting of a signal transmitting unit, a signal receiving unit, a MATLAB unit, and a user graphical interface is constructed. Each unit achieves coordinated control through software control parameter interface, data input / output interface, field programmable gate array (FPGA) configuration interface, and hardware configuration interface. The signal processing link follows a fixed process: the transmitter generates a baseband signal of a specified sequence through software, which is then converted into a 245.76MHz intermediate frequency (IF) signal after square root raised cosine filtering, interpolation, and carrier modulation by the FPGA module. This IF-to-high frequency (HF-to-HF) module then performs digital-to-analog conversion, modulation, filtering, and RF amplification to generate a 2.4576GHz RF signal. After conversion according to the RF gain and quantization accuracy, the signal is transmitted via a wireless channel. Upon receiving the 2.4576GHz RF signal, the receiver sequentially performs low-pass filtering, demodulation, variable gain amplification, and analog-to-digital conversion by the HF-to-IF module, restoring the 245.76MHz IF signal. After conversion according to the corresponding accuracy, the signal is output in two paths: one path is stored in the FPGA data memory for software retrieval, and the other path undergoes FPGA matched filtering and down-conversion preprocessing. In the algorithm verification phase, the Matlab unit is responsible for collecting the intermediate frequency signal voltage values ​​at the transmitting and receiving ends, running the Nakagami channel m-parameter estimation algorithm based on mathematical methods such as moment estimation, and judging the effectiveness and accuracy of the algorithm by comparing the measured results with the theoretical values. Subsequently, the FPGA hardware structure or algorithm parameters can be flexibly adjusted according to the verification results to adapt to the needs of different communication scenarios.

[0048] The technical solutions provided in this application can be applied to various mobile communication networks, including but not limited to wireless local area network (WLAN) systems such as wireless fidelity (Wi-Fi) or ambient power (AMP), long term evolution (LTE) systems, various versions based on LTE evolution, 5G systems, 6th generation mobile communication technology (6G) systems, HRLLC communication networks, future mobile communication networks, or multiple converged communication systems. Furthermore, the technical solutions provided in this application can also be applied to future-oriented communication systems.

[0049] For example, the data transmission method provided in the embodiments of this application can be applied to, for example, Figure 1 In communication systems, such as Figure 1 As shown, the communication system includes one or more terminals 10 and one or more access network devices 20. It should be understood that... Figure 1 The number of terminals 10 and access network devices 20 is just an example; there could be more or fewer.

[0050] In one possible implementation, the access network device 20, the terminal 10, or other possible devices may execute the above-described method for determining the channel shape factor. The specific implementation of this method and its related technical effects can be found in the subsequent method embodiments, and will not be repeated here.

[0051] The terminal in this application embodiment can be a device for implementing wireless communication functions, such as a terminal or a chip, module, etc. that can be used in the terminal. The terminal can be a user equipment (UE), access terminal, terminal unit, terminal station, mobile station, mobile station, remote station, remote terminal, mobile device, wireless communication device, terminal agent, or terminal device in a 5G network or a future evolved public land mobile network (PLMN). Access terminals can be cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to a wireless modem, in-vehicle devices or wearable devices, virtual reality (VR) terminals, augmented reality (AR) terminals, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical care, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, etc. In one possible implementation, the terminal can be mobile or fixed. The terminal can also be referred to as a user interface (UE), terminal device, etc.

[0052] The access network equipment in this application embodiment can be a device that communicates with the terminal. For example, it can include an evolved Node B (NodeB, eNB, or e-NodeB) in an LTE system or an enhanced LTE (LTE-Advanced, LTE-A) system, such as a traditional macro base station (eNB) and a micro base station (eNB) in a heterogeneous network scenario. Alternatively, it can include a next-generation node B (gNB) in a new radio (NR) system. Or, it can include a transmission reception point (TRP), a home base station (e.g., a home evolved NodeB, or a home Node B, HNB), a base band unit (BBU), a base band pool (BBU pool), or a WiFi access point (AP), etc. Alternatively, it can include access network devices or access equipment in non-terrestrial networks (NTNs), that is, access network devices or access equipment that can be deployed on flight platforms or satellites. In NTNs, access network devices or access equipment can act as Layer 1 (L1) relays, base stations, or integrated access and backhaul (IAB) nodes. Alternatively, access network devices can be devices that implement base station functions in Internet of Things (IoT) devices, such as devices that implement base station functions in drone communication, vehicle-to-everything (V2X), device-to-device (D2D), or machine-to-machine (M2M) communication.

[0053] In some possible scenarios, the access network device in this application embodiment can also be a module or unit capable of implementing some functions of a base station. For example, the access network device can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc. The CU and DU can be set up separately, or they can be included in the same network element, such as in a baseband unit (BBU). The RU can be included in a radio frequency device or radio frequency unit, such as in a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0054] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, an access network device can be an access network device or a module of an access network device in an Open Radio Access Network (ORAN) system. In an ORAN system, CU can also be called open (O)-CU, DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0055] In some embodiments, the base station in this application may include various forms of base stations, such as: macro base station, micro base station (also known as small station), relay station, access point, home base station, TRP, transmitting point (TP), mobile switching center, etc. This application does not specifically limit these.

[0056] In one possible implementation, both the access network device and the terminal in this application can be configured with multiple antennas to support massive multiple input multiple output (Massive-MIMO) technology. Furthermore, the access network device and the terminal can support both single-user MIMO (SU-MIMO) and multi-user MIMO (MU-MIMO) technology. MU-MIMO technology can be implemented based on space division multiple access (SDMA) technology. Because it is equipped with multiple antennas, access network equipment and terminals can also flexibly support Single Input Single Output (SISO), Single Input Multiple Output (SIMO), and Multiple Input Single Output (MISO) technologies to achieve various diversity (such as, but not limited to, transmit diversity and receive diversity) and multiplexing technologies. The diversity technologies can include, but are not limited to, transmit diversity (TD) and receive diversity (RD) technologies, and the multiplexing technology can be spatial multiplexing technology.

[0057] In one possible implementation, the access network device and terminal in the embodiments of this application may also be referred to as a channel shape factor determination device, which may be a general device or a special device. The embodiments of this application do not specifically limit this.

[0058] In one possible implementation, the related functions of the access network device and terminal in this application embodiment can be implemented by one device, multiple devices working together, or one or more functional modules within a single device. This application embodiment does not specifically limit this. The aforementioned functions can be network elements in hardware devices, software functions running on dedicated hardware, a combination of hardware and software, or virtualization functions instantiated on a platform (e.g., a cloud platform).

[0059] This application also provides a channel shape factor determination device (hereinafter referred to as the determination device for ease of description), which is the execution entity for determining the channel shape factor. The determination device can be an electronic device with data processing capabilities, or a functional module within that electronic device; there is no limitation in this regard. For example, the determination device can be terminal 10 in the aforementioned communication system, or access network device 20 in the aforementioned communication system, or a functional module of terminal 10 or access network device 20, or any computing device connected to terminal 10 or access network device 20, etc. This application does not limit the scope of the determination device in this regard.

[0060] Figure 1 This is just an example framework diagram. Figure 1 The number of communication devices included, and the names of each communication device, are unlimited, except for... Figure 1 In addition to the communication equipment shown, the communication system may also include other communication equipment.

[0061] The application scenarios of the embodiments in this application are not limited. The system architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0062] The method embodiments provided in this application will be described in detail below with reference to the accompanying drawings.

[0063] like Figure 2 As shown in the embodiment of this application, a method for determining the channel shape factor is provided, the method comprising: S101. Obtain the UL-SINR dataset of at least one communication link in the target communication network.

[0064] The target communication network refers to the communication network for which channel shape factor analysis and optimization are currently performed, such as 5G, 6G, HRLLC communication networks, etc.

[0065] A communication link refers to the wireless channel path used to transmit signals between two communication nodes (such as base station and terminal, terminal and terminal) in a target communication network. For example, it is a one-way or two-way wireless transmission path from a transmitter (such as user equipment) to a receiver (such as base station).

[0066] UL-SINR refers to the ratio of signal power to interference power plus noise power in the uplink. It is a core physical layer indicator for measuring uplink communication quality and reflecting the degree of interference and noise affecting the channel.

[0067] A UL-SINR dataset consists of a set of UL-SINR values ​​obtained through measurement or simulation for one or more communication links in a target communication network.

[0068] For example, the target communication network is a 3GPP dual-strip HRLLC wireless network. In a small-scale wireless service hotspot area, to ensure the service has ultra-high reliability and low latency characteristics, the operator will deploy network equipment to form a cloud radio access network (C-RAN) wireless network based on HRLLC. Figure 3 As shown, there are two rows of rooms on each side of the corridor, each containing a small base station and a user. For example, each row consists of... It consists of several rooms, each containing a small base station. There are [number] users, therefore a total of [number] users. Small base stations and One user.

[0069] In the uplink direction of the target communication network, any serving user The transmitted uplink signal reaches the home cell. The base station's signal reception power is: .

[0070] in, User The transmission power, For users To the assigned community The channel gain caused by large-scale fading, where For users To the assigned community The channel gain caused by small-scale fading.

[0071] In this embodiment of the application, small-scale fading can be regarded as a Nakagami-m distribution, and the channel gain Then obey Distribution, and the normalization parameter can be set. , It can be represented as:

[0072] in, Indicates user To the assigned community The shape factor of the Nakagami-m channel.

[0073] Without considering small-scale fading, serving users The transmitted uplink signal reaches the home cell. The large-scale signal reception power of the base station is:

[0074] in, For users To the assigned community The channel gain caused by large-scale fading.

[0075] Without considering small-scale fading, and service users Interfering users occupying the same wireless resources Arrival at the community The large-scale interference signal received power of the base station is:

[0076] in, For users Arrive at the community The channel gain caused by large-scale fading.

[0077] Thus, serving users The signal in the home cell The instantaneous UL-SINR at the base station can be:

[0078] in, In order to serve users A set of interfering users that reuse the same radio resources. This represents interference with users. Whether with service users Occupying the same wireless resources, This represents noise power.

[0079] The aforementioned UL-SINR can also be expressed as having Distribution pattern:

[0080] in, Indicates service user To the assigned community The shape factor of the Nakagami-m channel, Indicates interference with users Arrive at the community The shape factor of the Nakagami-m channel. Since the noise power follows a one-sided Gaussian distribution, the noise shape factor is denoted as 0.5.

[0081] In some embodiments, the above It can also be obedience Distributed, denoted as:

[0082]

[0083]

[0084] Assuming users are independent, because The probability density function of the distribution is relatively simple, so the service users can be directly calculated. The signal in the home cell The probability density function of instantaneous UL-SINR at the base station:

[0085]

[0086]

[0087] As can be observed from the above formula, the probability density function of instantaneous UL-SINR is only related to the following variables: large-scale signal received power. Large-scale interference signal received power Serving users To the assigned community The shape factor of the Nakagami-m channel Interfering with users Arrive at the community The shape factor of the Nakagami-m channel .

[0088] In some embodiments, the above formula can be further rewritten in a form that includes the received power ratio:

[0089]

[0090]

[0091] make The UL-SINR formula can be further expressed as:

[0092]

[0093] For example, based on the formula above, it is usually necessary to estimate the following four types of parameters: large-scale interference-to-signal ratio (ISR). Large-scale noise-to-signal ratio (NSR) Serving users To the assigned community The shape factor of the Nakagami-m channel Interfering with users Arrive at the community The shape factor of the Nakagami-m channel .

[0094] It should be noted that this application can be based on the core idea of ​​maximum likelihood estimation, which is to find the parameter to be estimated that is "most likely to generate the current observation data". This needs to be achieved through the following logic: First, construct an objective function related to the probability of the observation data (the most classic one is the negative log-likelihood function, the minimization of which is equivalent to the maximization of the likelihood function), thereby determining the service users. The negative log-likelihood function:

[0095]

[0096]

[0097] in, To serve users The linear form of the UL-SINR dataset, the first row data, To serve users The total number of rows in the linear form of the UL-SINR dataset. Based on the idea of ​​maximum likelihood estimation, we need to find a value that makes the negative log-likelihood function... The parameter corresponding to the minimum value is the final result of the parameter to be estimated.

[0098] In some embodiments, the composition of the interfering user set may differ in different orthogonal resource sharing systems. For example, in orthogonal frequency division multiple access (OFDMA) or time division multiple access (TDMA) systems, users located in the same cell do not interfere with each other; therefore, the interfering user set only includes users not in the same cell as the serving user. However, in code division multiple access (CDMA) and non-orthogonal multiple access (NOMA) systems, interference may also exist between users located in the same cell; therefore, the interfering user set includes all users in the system except the serving user. Since the above derivation of interference relationships is not specific to any particular orthogonal resource sharing method, by constructing different interfering user sets, the uplink interference modeling scheme between users obtained from the above analysis can also be applied to various wireless systems employing different orthogonal resource sharing methods.

[0099] In some embodiments, the structure of the UL-SINR dataset of user U1 can be as shown in Table 1, or it can be understood that the structure of the UL-SINR dataset of a communication link (corresponding to user U1) can be as shown in Table 1.

[0100] Table 1

[0101] For example, with users For example, each time a user uses an RB, the dataset (which can also be called the training dataset) will store the resource allocation indicator variables that interfere with the user on that RB, as well as the user's measurements at the base station. The instantaneous UL-SINR in dB form on this RB is taken as a single data point. Therefore, the UL-SINR dataset for this user can be denoted as... .

[0102] It should be noted that the UL-SINR dataset shown in Table 1 above is based on Figure 3 The dataset shown is from a communication scenario. Since intra-cell interference may not exist in this scenario, the UL-SINR dataset may not include data related to the user. Data for each user within the same cell. If the resource reuse method allows users within the same cell to reuse resources (such as CDMA, NOMA, etc.), then the UL-SINR dataset can also contain corresponding data. Data of all other users in the same community.

[0103] Furthermore, the structure of the aforementioned UL-SINR dataset considers both large-scale and small-scale fading. When generating this dataset through simulation, it is necessary to set the shape factor m of the Nakagami-m channel for each link. For example, for users... The shape factors of the Nakagami-m channel from the signal user to the current home cell link, and the shape factors of the Nakagami-m channel from the interfering user to the current home cell link, both need to be randomly selected from the 20 random numbers {0.5, 1.0, 1.5, 2.0, ..., 10.0}, and considered as... To ensure the diversity of the dataset and minimize the error of the maximum likelihood estimation, the m value for each link needs to traverse all 20 values ​​mentioned above. Therefore, 20×5 sets of datasets of the same size should be generated.

[0104] S102. Based on the UL-SINR dataset of at least one communication link, determine the interference sensing information of each communication link in the at least one communication link and the Nakagami-m channel shape factor corresponding to each communication link.

[0105] Among them, the Nakagami-m channel shape factor, or m factor for short, refers to the parameter m that describes the fading depth of the Nakagami distribution. Its value range is usually m>0. The larger the value of m, the more gradual the channel fading is; the smaller the value of m, the more severe the channel fading is.

[0106] Interference perception information refers to interference characteristic information related to uplink transmission of the communication link obtained from UL-SINR data parsing. Its core includes interference type (such as ICI, neighboring cell interference, etc.), interference intensity, interference source, and the variation law of interference over time. It is an important basis for judging the cause of link communication quality degradation and optimizing channel parameter estimation.

[0107] For example, the above UL-SINR data can be represented as:

[0108] It can also be called a service user. The signal in the home cell Instantaneous UL-SINR at the base station.

[0109] Further expressed as:

[0110] make Then the above formula simplifies to:

[0111] Due to channel gain and The mean is 1 ( In this interference sensing scheme, large-scale fading and path loss will be the main considerations, while small-scale fading will be treated as an error term and will not be predicted.

[0112] The formulas above show that regression-based machine learning algorithms can be used to perceive the ISR (Inter-User Ratio) between users and the NSR (Non-Responsible User Ratio) of service users. However, there are numerous regression-based machine learning algorithms. Neural network algorithms are not suitable for real-world network scenarios due to their long offline training time. Traditional regression models can be divided into linear regression and non-linear regression based on the model function they use.

[0113] Add an error term to the above formula We can obtain:

[0114] This is a linear regression model. However, small-scale fading is a multiplicative fading, which cannot be captured by an additive error term; at the same time, UL-SINR is usually used in dB, while linear regression uses its natural value as the optimization objective, which is inconsistent with the actual use case.

[0115] Therefore, this application introduces nonlinear regression as its model function to solve the above problems:

[0116] in, This is the error term.

[0117] After taking the logarithm of both sides of the equation, the multiplicative small-scale fading becomes additive, which can be well captured by the error term; in addition, the optimization objective of the algorithm also becomes the dB value of UL-SINR, which is more in line with the actual use scenario.

[0118] Based on the above derivation, this application can select a nonlinear regression algorithm to determine the interference sensing information.

[0119] For example, the nonlinear model function is:

[0120] in, The independent variables of the model function (i.e., the set of resource allocation indicator variables that interfere with users). For unknown parameters representing the intensity of interference in the model function, the nonlinear regression algorithm can infer their values ​​by mining historical datasets.

[0121] In some embodiments, the unknown parameter representing the interference intensity directly corresponds to the dB values ​​of the ISR between the serving user and the interfering user, and the NSR of the serving user. This makes the physical meaning of the unknown parameter clearer and more explicit.

[0122] Implementation method 1: The above step S102 can be specifically implemented as the following steps S11-S12: S11. For each communication link, determine the interference sensing information of the communication link based on the UL-SINR dataset and loss metric function of the communication link.

[0123] In some embodiments, the Huber function can be used as a loss metric to determine interference-aware information of the communication link.

[0124] For example, all unknown parameters representing the intensity of interference can be solved based on model training, i.e., interference-aware information (such as ISR between users and NSR of service users). That is, the following equation is solved.

[0125]

[0126] The loss metric function also needs to be determined. Only in this form can the algorithm be trained. The most common loss metric is the squared error function:

[0127] The prediction error for each data point is the squared difference between the predicted value and the label value. Regression problems using squared error are also known as least squares regression. The advantage of using least squares regression is that it can make full use of the data, but it has a significant drawback: it is highly susceptible to outliers.

[0128] In practical wireless networks, small-scale fading fluctuations are often quite drastic, resulting in numerous outliers in the dataset. Therefore, the squared error function is ill-suited to the rapidly changing characteristics of the wireless network channel environment. To address this, this application selects the Huber function as the loss metric:

[0129]

[0130] The Huber function works when the error value is less than the split point. The squared loss function is used when the threshold is large, while a linear function is used when the threshold is large. This special construction of the Huber function can greatly reduce the impact of outliers on the final training result, making it more suitable for real-world wireless networks.

[0131] After determining the loss metric function, the algorithm uses iterative algorithms such as trust regions to solve the aforementioned minimization problem, obtaining the unknown parameter value that minimizes the sum of prediction biases for each data point in the dataset as the solution to the problem.

[0132] Therefore, based on the UL-SINR dataset of each communication link and combined with the Huber function mentioned above, the interference sensing information of each communication link can be determined separately.

[0133] For example, it can also be understood as sensing the interference intensity of all potential interfering users, training the interference intensity prediction model of the current signal user using the instantaneous UL-SINR dataset; after the training of a single user is completed, the sensing results of all users are summarized to obtain the interference intensity sensing results of all links in the entire wireless system.

[0134] S12. Based on the interference sensing information of the communication link and the UL-SINR dataset of the communication link, determine the Nakagami-m channel shape factor corresponding to the communication link.

[0135] In some embodiments, the UL-SINR dataset described above can also be converted to a linear form.

[0136] For example, the probability density function and negative log-likelihood function of UL-SINR both use the true value of UL-SINR. Alternatively, the aforementioned UL-SINR dataset can be used, but the dB-form UL-SINR values ​​need to be converted to linear form. The dataset is denoted as... From the user For example, the UL-SINR dataset can be shown in Table 2: Table 2

[0137] In some embodiments, step S12 above can be specifically implemented as follows: Based on the interference sensing information of the communication link and the UL-SINR dataset of the communication link, multiple sets of first estimates of the Nakagami-m channel shape factor are determined by the negative log-likelihood function. The Nakagami-m channel shape factor corresponding to the communication link is determined from the first estimation result with the smallest negative log-likelihood function value among multiple sets of first estimation results.

[0138] For example, all unknown parameters (i.e., shape factors) can be solved iteratively. That is, to obtain the shape factor when the following formula holds. .

[0139] In some embodiments, the above set of first estimation results are determined in the following manner: Initialization step: Obtain the first channel shape factor. For example, an initial value for the channel shape factor can be used in the first iteration, such as... .

[0140] Calculation steps: The negative log-likelihood function value is calculated based on the first channel shape factor, interference sensing information, SINR data in the UL-SINR dataset, and the negative log-likelihood function.

[0141] For example, each row of the dataset can be... Initial value (If the updated shape factor is included in the first iteration, it is not the first iteration) and the interference sensing information obtained above. Substituting into the negative log-likelihood function In the process, after traversing all the data in the dataset, the current iteration is obtained. value.

[0142] For example, the negative log-likelihood function It can be: ;

[0143] ; in, It is the negative log-likelihood function value; Resource allocation instructions for users interfering with the communication link, and satisfying ; Let be the Nakagami-m channel shape factor to be estimated, satisfying ; To interfere with the perceived information, and satisfy ; For the UL-SINR dataset, the first Row data, UL-SINR dataset .

[0144] Therefore, by combining the linear form of the dataset, interference-perceived information (such as the ISR and NSR parameter sets) is introduced. It can be converted into a linear form. By performing maximum likelihood estimation, the shape factor of the Nakagami-m channel can be easily estimated.

[0145] Parameter update steps: Determine the second channel shape factor based on the negative log-likelihood function value using the gradient descent optimization algorithm.

[0146] If the convergence condition is not met, the second channel shape factor is determined as the new first channel shape factor, and the calculation steps and parameter update steps are re-executed to obtain the new second channel shape factor. If the convergence condition is met, the second channel shape factor and the corresponding negative log-likelihood function value are determined as a set of first estimation results.

[0147] For example, the classic L-BFGS optimizer can be selected, based on the second step. As a result, the updated shape factor was calculated using the gradient descent update strategy. Then return to step two and recalculate. Continue until the preset convergence condition (function between two iterations) is met. The difference is less than 10 -6 Stop iterating and record the current iteration. Final value and corresponding shape factor .

[0148] Furthermore, the learning rate parameter can be adjusted (setting multiple sets of different learning rates), and the above complete process can be repeated to obtain multiple sets of convergence results (i.e., the first estimation results) (each set contains...). Final value and corresponding shape factor ).

[0149] Therefore, parameter selection is performed. From the above multiple sets of first estimation results, the parameter that makes the parameter selection result... Minimum, that is The corresponding shape factor is used as the final estimate. .Should Includes the signal user Shape factor of the Nakagami-m channel to the current home cell link and interfering users The shape factor of the Nakagami-m channel to the current home cell link.

[0150] For example, the above implementation can also be as follows: Figure 4 As shown, taking the wireless network in the HRLLC scenario as an example, the device can first determine the interference intensity sensing result of each communication link, i.e., the interference sensing information, based on the dB form of the UL-SINR dataset through the interference sensing module. Then, based on the interference intensity sensing result of each communication link and the linear form of the UL-SINR dataset, the shape factor of the Nakagami-m channel of each communication link can be determined through the parameter estimation module (based on the maximum likelihood parameter estimation method).

[0151] For example, after obtaining the interference intensity perception results for all links, the interference intensity perception results for the current signal user and the user's dataset are input together into the parameter estimation module based on maximum likelihood. Two sets of shape factors can be estimated: the Nakagami-m channel shape factor of the link from the signal user to the current home cell, and the Nakagami-m channel shape factor of the link from the interfering user to the current home cell. After the estimation of a single user is completed, the estimation results of all users are summarized to obtain the Nakagami-m channel shape factor of all links in the entire target communication system.

[0152] Thus, this implementation adopts an "interference perception first" technical logic for a step-by-step shape factor m estimation process: First, an interference perception algorithm is introduced, based on real-time collected wireless network UL-SINR datasets, to accurately extract interference information under complex interference environments; then, using the perceived interference information as input, combined with the constructed univariate likelihood function of the shape factor m, a gradient descent algorithm is used to finally achieve an accurate estimation of the shape factor m. This "interference perception first, then m parameter estimation" process enables the shape factor estimation to adapt to different interference scenarios, improving the accuracy of parameter estimation.

[0153] Implementation method 2: The above step S102 can be specifically implemented as the following steps S21-S22: S21. For each communication link, based on the UL-SINR dataset of the communication link, determine multiple sets of second estimation results based on the multi-objective joint estimation strategy.

[0154] Among them, a second set of estimation results includes the negative log-likelihood function value, interference-aware information of the communication link, and the Nakagami-m channel shape factor corresponding to the communication link.

[0155] The UL-SINR dataset can be the linear form of UL-SINR data described above, for example... This will not be elaborated upon here.

[0156] For example, all unknown parameters (i.e., interference sensing information) can be solved iteratively. and shape factor That is, to obtain the interference strength when the following formula holds true. and shape factor , .

[0157] In some embodiments, a set of second estimation results is determined in the following manner: Initialization steps: Obtain the third channel shape factor and the third interference sensing information. For example, in the first iteration, the third interference sensing information can be set to the initial value of the interference sensing information. The third channel shape factor is the initial value of the channel shape factor, i.e. .

[0158] Calculation steps: The negative log-likelihood function value is calculated based on the third channel shape factor, the third interference sensing information, the SINR data in the UL-SINR dataset, and the negative log-likelihood function.

[0159] For example, each row of the dataset can be... Initial value and initial value (If the updated interference strength is included in the first iteration, it is not the first iteration) and shape factor Substituting this into the negative log-likelihood function In the process, after traversing all the data in the dataset, the current iteration is obtained. value.

[0160] For example, the negative log-likelihood function It can include: ;

[0161] ; in, It represents the negative log-likelihood function value; it represents the resource allocation indication for interfering users in the communication link, and satisfies... ; For the interference sensing information to be estimated, satisfying ; Let be the Nakagami-m channel shape factor to be estimated, satisfying ; For the UL-SINR dataset, the first Row data, UL-SINR dataset .

[0162] Parameter update steps: Based on the negative log-likelihood function value, the fourth channel shape factor and the fourth interference sensing information are determined by the gradient descent optimization algorithm.

[0163] If the convergence condition is not met, the fourth channel shape factor is determined as the new third channel shape factor and the fourth interference sensing information is determined as the new third interference sensing information. The calculation steps and parameter update steps are re-executed to obtain the new fourth channel shape factor and the new fourth interference sensing information. If the convergence condition is met, the fourth channel shape factor, the fourth interference sensing information, and the negative log-likelihood function value corresponding to the fourth channel shape factor are determined as a set of second estimation results.

[0164] For example, the classic L-BFGS optimizer can be selected, based on the second step. As a result, the updated disturbance intensity was calculated using the gradient descent update strategy. and shape factor Then return to step two and recalculate. Continue until the preset convergence condition (function between two iterations) is met. The difference is less than 10 -6 Stop iterating and record the current iteration. Final value and corresponding interference intensity and shape factor .

[0165] S22. Determine the interference sensing information of the communication link and the Nakagami-m channel shape factor corresponding to the communication link from the second estimation result with the smallest negative log-likelihood function value among multiple sets of second estimation results.

[0166] For example, the learning rate parameter can be adjusted (setting multiple sets of different learning rates), and the entire process described above can be repeated to obtain multiple sets of convergence results, i.e., the second estimation results (each set contains...). Final value and corresponding interference intensity and shape factor ).

[0167] Then, parameter selection is performed. From multiple sets of second estimation results, the one that makes the parameter selection result the most suitable is selected. Minimum, that is Corresponding interference intensity and shape factor As the final estimate. Includes current signal users The results of the interference intensity perception. Includes the signal user Shape factor of the Nakagami-m channel to the current home cell link and interfering users The shape factor of the Nakagami-m channel to the current home cell link.

[0168] For example, the above implementation can also be as follows: Figure 5 As shown, taking the wireless network in the HRLLC scenario as an example, the determination device can directly input the UL-SINR dataset into the online estimation module. Based on the joint estimation algorithm of maximum likelihood (including interference sensing and shape factor m estimation), two results can be obtained simultaneously: interference sensing information of all communication links in the entire wireless system, and Nakagami-m channel shape factor of all communication links.

[0169] Thus, this implementation breaks away from the traditional step-by-step logic of "first perturbation sensing, then shape factor m estimation," and innovatively incorporates perturbation information and shape factor m into the same optimization objective function: by constructing a multivariate joint likelihood function that includes perturbation information and shape factor m, and using the same gradient descent algorithm, the perturbation information and shape factor m are solved synchronously and iteratively. This allows for the simultaneous output of perturbation intensity assessment results and shape factor m estimates without the need for staged processing. This simplifies the estimation process, improves the synergistic adaptability between the parameters of perturbation sensing information and shape factor m, and consequently improves estimation accuracy.

[0170] Based on the technical solution provided in this application, by acquiring the UL-SINR dataset of at least one communication link in the target communication network, and using this dataset as a unified analysis basis, interference sensing information and Nakagami-m channel shape factor are determined separately for each communication link. This avoids the unreasonable assumption that all links have the same shape factor m, and improves the accuracy of the Nakagami-m channel shape factor. It provides precise parameter support for resource allocation and power control in scenarios such as 5G / 6G ultra-dense networking and HRLLC. For example, it can achieve deterministic analysis of HRLLC system performance, providing key theoretical support for core functions such as system capacity assessment and resource allocation optimization.

[0171] In some embodiments, the algorithm performance of the two implementation methods can be evaluated using three key indicators: interference perception accuracy, shape factor m estimation accuracy, and training time, thereby quantifying their overall performance. The probability density function of UL-SINR described above can effectively characterize the instantaneous UL-SINR distribution.

[0172] For example, the simulation parameters used can be shown in Table 3: Table 3

[0173] Regarding implementation method 1: For example, by comparing the difference between the estimated and true values ​​of the shape factor m, the parameter m estimation performance of implementation method 1 can be reflected more completely. The parameter m estimation results for the signal user and the interference user are as follows: Figure 6 As shown, the relationship between the true value of parameter m and the estimated value of parameter m is illustrated, as well as the relationship between the true value of parameter m and the estimated value of parameter m for interfering users at different ISR (Interference Signal-to-Ratio) levels.

[0174] from Figure 6It can be seen that implementation method 1 has extremely high accuracy in estimating the Nakagami-m channel shape factor m for signal users; for interfering users, the interference level has been divided into six levels according to their interference intensity (ISR) (the specific level division is as follows). Figure 6 As shown in the figure, the ISR values ​​are below -10 dB, between -10 and -7 dB (inclusive), between -7 and -3 dB (inclusive), between -3 and 0 dB (inclusive), between 0 and 3 dB (inclusive), and between 3 and 7 dB (inclusive). On the one hand, the accuracy of the shape factor m estimation decreases as the ISR decreases. This phenomenon can be intuitively explained by the UL-SINR formula—the larger the ISR, the higher the proportion of interference in the overall signal, and the better the estimation effect of the shape factor m. On the other hand, the estimated m values ​​of interfering users all deviate from the asymptote of y=x. This is because an approximation method is used when combining interference and noise, which leads to the aforementioned deviation in the estimated m values ​​of interfering users.

[0175] Since the shape factor m of the signal user is accurately estimated, only the error between the estimated and true values ​​of the shape factor m of the interfering user is calculated, which can be measured using the root-mean-square error (RMSE).

[0176] in, To interfere with the user set.

[0177] For example, see Figure 7 , which shows the average RMSE of parameter m estimation results for all interfering users. Figure 7 Figure (a) shows the relationship between ISR (dB) and the root mean square error (MSE) with the proposed algorithm. The estimation performance of the Nakagami-m channel shape factor m for interfering users is shown at I=500000. The results show that the root mean square error (RMSE) of the estimation results decreases significantly with increasing interference intensity (ISR)—this indicates that when interference accounts for a higher proportion of the signal, the estimation accuracy of the interfering user shape factor m by implementation method 1 will improve, consistent with the above conclusions regarding the impact of ISR. Figure 7Figure (b) illustrates the relationship between dataset size and the root mean square error (RMSE with Algorithm) of the proposed algorithm. When 3dB ≤ ISR(dB) ≤ 7dB, the estimation performance of the interfering user m is shown in relation to the size of the UL-SINR dataset. As the dataset size increases, the RMSE gradually decreases and eventually stabilizes at around 0.15—this indicates that increasing the amount of data can effectively improve estimation accuracy, and that when the data size reaches a certain threshold, the estimation error can converge to a lower level, verifying the stability of the proposed framework when data is sufficient.

[0178] Regarding implementation method 2: the performance of parameter m estimation in implementation method 2 can also be more fully reflected by comparing the difference between the estimated and true values ​​of the shape factor m. Similarly... Figure 6 ,like Figure 8 As shown, implementation method 2 achieves extremely high accuracy in estimating the Nakagami-m channel shape factor m for signal users; for interfering users, the interference level has been divided into 10 levels based on their interference intensity (ISR) (the specific level division is as follows). Figure 8 shown), including ISR (dB) ∈ (-∞, -23]dB, ISR (dB) ∈ (-23, -20]dB, ISR (dB) ∈ (-20, -17]dB, ISR (dB) ∈ (-17, -13]dB, ISR (dB) ∈ (- 13, -10]dB, ISR (dB) ∈ (-10, -7]dB, ISR (dB) ∈ (-7, -3]dB, ISR (dB) ∈ (-3, 0]dB, ISR (dB) ∈ (0, 3]dB, ISR (dB) ∈ (3, 7]dB.

[0179] Compared to implementation method 1, the estimation results of the disturbing user m in implementation method 2 are more chaotic, unlike implementation method 1 which always deviates upward from the asymptote of y=x. This difference is due to the fact that implementation method 2 optimizes two types of parameters at the same time.

[0180] Since the shape factor m of the signal user is accurately estimated, only the error between the estimated and true values ​​of the shape factor m of the interfering user is calculated, which can be measured using the root-mean-square error (RMSE).

[0181] in, To interfere with the user set.

[0182] For example, see Figure 9The figure shows the average RMSE of parameter m estimation results for all interfering users, including the relationship between dataset size and average RMSE, and the relationship between ISR (dB) and average RMSE, including MLE1 and E2. Specifically, as the size of the UL-SINR dataset gradually increases, the RMSE of the estimation results continuously decreases, and for the same data size, the larger the ISR value, the smaller the RMSE.

[0183] Furthermore, when the dataset I=500000, the RMSE of the m-estimate of both implementation method 1 and implementation method 2 decreases as the ISR increases, but there are differences between the two: implementation method 2 has a lower RMSE in the small dataset scenario, while implementation method 1 shows a lower RMSE in the large dataset scenario.

[0184] Next, the accuracy of interference sensing is measured, and RMSE is still used as the evaluation metric:

[0185] in, To interfere with the user set.

[0186] like Figure 10 As shown, the average RMSE of interference perception results for all users with interference is illustrated, along with the relationship between dataset size and average RMSE, including NLRE and MLE. As the size of the UL-SINR dataset gradually increases, the RMSE continuously decreases, and the perception accuracy of interference is relatively high in small dataset scenarios.

[0187] In some embodiments, both implementation method 1 and implementation method 2 can effectively achieve interference sensing and shape factor m estimation. In subsequent analysis, both implementation methods can also be used... The experimental data was combined for verification – that is, half of the experimental data was selected from each method (50% for implementation method 1 and 50% for implementation method 2).

[0188] By showcasing the characterization results of UL-SINR, the accuracy of the probability density function modeling of the instantaneous UL-SINR of the proposed scheme can be reflected. (Based on user...) For example.

[0189] The accuracy of this modeling can be measured using KL divergence (KL divergence), the specific process of which is as follows:

[0190] In this proposal, For users Instantaneous UL-SINR data of a certain extracted dataset (interference user is) , , The dataset is input into Implementation Method 1 and Implementation Method 2 proposed in this application to obtain interference sensing and parameter estimation results. These two results are then substituted into the aforementioned probability density function, and the data discretized from this probability density function is obtained. Be careful to maintain and The amount of data is the same.

[0191] like Figure 11 This displays the KL divergence calculation results, including a histogram of KL divergence (horizontal axis: KL divergence, vertical axis: number of test samples), and a bar chart showing the KL divergence distribution for 100 test samples (horizontal axis: test sample number, vertical axis: KL divergence). For users... KL divergence was calculated on 100 datasets (50 datasets and results from implementation method 1, and 50 datasets and results from implementation method 2). The average KL divergence was 0.042, and most of the KL divergences were between 0.00 and 0.10, which demonstrates the modeling accuracy of the proposed probability density function.

[0192] like Figure 12 The diagram illustrates the relationship between the linear SINR value and the CDF (cumulative distribution function), and the relationship between the SINR value (dB) and log10(CDF) (the logarithmic form of CDF). For example, a randomly selected dataset (size...) is shown. A comparison of CDF curves was performed, revealing strong alignment between the two methods when the SINR value was greater than -36 dB, confirming the accuracy of the proposed framework under most conditions. However, for extremely low SINR values ​​less than -36 dB, the estimated CDF cannot fully represent the distribution. For the analysis of HRLLC networks, this bias is negligible because these low SINR values ​​are rarely encountered in practical applications. Therefore, under most conditions, the probability density function provided in this application can effectively simulate the instantaneous UL-SINR distribution.

[0193] like Figure 13 As shown, the average execution time of the two implementation methods is illustrated in possible operating scenarios. Implementation method 1 consistently has a higher time complexity than implementation method 2. However, based on the aforementioned information, implementation method 1 has a slightly better accuracy in estimating the shape factor m than implementation method 2. Therefore, the choice between implementation method 1 and implementation method 2 can be made by comprehensively considering the priority of "estimation accuracy" and "time complexity" requirements in the actual application scenario.

[0194] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0195] This application embodiment can divide the channel shape factor determination device into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0196] In some embodiments, this application also provides an apparatus for determining a channel shape factor. This apparatus may include one or more functional modules for implementing the channel shape factor determination method of the above method embodiments.

[0197] For example, Figure 14 This is a schematic diagram illustrating the composition of a channel shape factor determination device provided in an embodiment of this application. Figure 14 As shown, the channel shape factor determination device 140 includes: an acquisition module 1401 and a determination module 1402.

[0198] The acquisition module 1401 is used to acquire the uplink signal-to-interference-plus-noise ratio (UL-SINR) dataset of at least one communication link in the target communication network.

[0199] The determination module 1402 is used to determine the interference sensing information of each communication link in the at least one communication link and the Nakagami-m channel shape factor corresponding to each communication link based on the UL-SINR dataset of the at least one communication link.

[0200] In some embodiments, the determining module 1402 is specifically used to: determine the interference sensing information of each communication link based on the UL-SINR dataset and the loss metric function of the communication link; and determine the Nakagami-m channel shape factor corresponding to the communication link based on the interference sensing information and the UL-SINR dataset of the communication link.

[0201] In some embodiments, the determining module 1402 is specifically configured to: determine multiple sets of first estimation results of the Nakagami-m channel shape factor by means of a negative log-likelihood function based on the interference sensing information of the communication link and the UL-SINR dataset of the communication link; and determine the Nakagami-m channel shape factor corresponding to the communication link from the first estimation result with the smallest negative log-likelihood function value among the multiple sets of first estimation results.

[0202] In some embodiments, a set of first estimates is determined in the following manner: Initialization steps: Obtain the first channel shape factor; Calculation steps: Based on the first channel shape factor, interference sensing information, SINR data in the UL-SINR dataset, and the negative log-likelihood function, calculate the negative log-likelihood function value; Parameter update steps: Determine the second channel shape factor based on the negative log-likelihood function value using the gradient descent optimization algorithm; If the convergence condition is not met, the second channel shape factor is determined as the new first channel shape factor, and the calculation steps and parameter update steps are re-executed to obtain the new second channel shape factor. If the convergence condition is met, the second channel shape factor and the corresponding negative log-likelihood function value are determined as a set of first estimation results.

[0203] In some embodiments, the Nakagami-m channel shape factor satisfies the following relationship: ;

[0204] ; in, It is the negative log-likelihood function value; Resource allocation instructions for users interfering with the communication link, and satisfying ; Let be the Nakagami-m channel shape factor to be estimated, satisfying ; To interfere with the perceived information, and satisfy ; For the UL-SINR dataset, the first Row data, UL-SINR dataset .

[0205] In some embodiments, the determining module 1402 is specifically configured to: for each communication link, based on the UL-SINR dataset of the communication link and a multi-objective joint estimation strategy, determine multiple sets of second estimation results; wherein, a set of second estimation results includes a negative log-likelihood function value, interference sensing information of the communication link, and the Nakagami-m channel shape factor corresponding to the communication link; and determine the interference sensing information of the communication link and the Nakagami-m channel shape factor corresponding to the communication link from the second estimation result with the smallest negative log-likelihood function value among the multiple sets of second estimation results.

[0206] In some embodiments, a set of second estimation results is determined in the following manner: Initialization steps: Obtain the third channel shape factor and third interference sensing information; Calculation steps: Based on the third channel shape factor, the third interference sensing information, the SINR data in the UL-SINR dataset, and the negative log-likelihood function, calculate the negative log-likelihood function value; Parameter update steps: Based on the negative log-likelihood function value, the fourth channel shape factor and the fourth interference sensing information are determined by the gradient descent optimization algorithm; If the convergence condition is not met, the fourth channel shape factor is determined as the new third channel shape factor and the fourth interference sensing information is determined as the new third interference sensing information. The calculation steps and parameter update steps are re-executed to obtain the new fourth channel shape factor and the new fourth interference sensing information. If the convergence condition is met, the fourth channel shape factor, the fourth interference sensing information, and the negative log-likelihood function value corresponding to the fourth channel shape factor are determined as a set of second estimation results.

[0207] In some embodiments, the Nakagami-m channel shape factor satisfies the following relationship: ;

[0208] ; in, It represents the negative log-likelihood function value; it represents the resource allocation indication for interfering users in the communication link, and satisfies... ; For the interference sensing information to be estimated, satisfying ; Let be the Nakagami-m channel shape factor to be estimated, satisfying ; For the UL-SINR dataset, the first Row data, UL-SINR dataset .

[0209] For a more detailed description of the acquisition module 1401, the determination module 1402, and the various technical features therein, as well as the description of the beneficial effects, please refer to the corresponding method embodiment section above, which will not be repeated here.

[0210] Figure 14 If the various units or modules in the present application are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. Storage media for storing computer software products include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0211] In the case of implementing the functions of the integrated module described above in hardware, this application embodiment provides a schematic diagram of the structure of a channel shape factor determination device, which can be the aforementioned channel shape factor determination device 150. For example... Figure 15 As shown, the channel shape factor determining device 150 includes: a processor 1502, a communication interface 1503, and a bus 1504. In some embodiments, the channel shape factor determining device 150 may further include a memory 1501.

[0212] Processor 1502 may implement or execute various exemplary logic blocks, modules, and circuits described in connection with this application. Processor 1502 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in connection with this application. Processor 1502 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a digital signal processor (DSP), and a microprocessor.

[0213] The communication interface 1503 is used to connect to other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0214] The memory 1501 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), 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.

[0215] As one possible implementation, the memory 1501 can exist independently of the processor 1502. The memory 1501 can be connected to the processor 1502 via a bus 1504 and is used to store instructions or program code. When the processor 1502 calls and executes the instructions or program code stored in the memory 1501, it can implement the method provided in the embodiments of this application.

[0216] In another possible implementation, the memory 1501 can also be integrated with the processor 1502.

[0217] The 1504 bus can be an extended industry standard architecture (EISA) bus, etc. The 1504 bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 15The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0218] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment or device can be divided into different functional modules to complete all or part of the functions described above.

[0219] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware. The program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be any of the foregoing embodiments or memory. The computer-readable storage medium can also be an external storage device of the above-mentioned device or apparatus, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the above-mentioned device or apparatus. Further, the computer-readable storage medium can include both internal storage units and external storage devices of the above-mentioned device or apparatus. The computer-readable storage medium is used to store the above-mentioned computer program and other programs and data required by the above-mentioned device or apparatus. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0220] This application also provides a computer program product comprising a computer program that, when run on a computer, causes the computer to perform any of the methods provided in the above embodiments.

[0221] This application also provides a chip, which includes a processor capable of executing any of the methods provided in the above embodiments.

[0222] This application also provides a chip module, including a communication interface and a chip, the chip including a processor, which is capable of executing any of the methods provided in the above embodiments.

[0223] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0224] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

[0225] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining a channel form factor, characterized by, The method comprises: obtaining an uplink signal-to-interference-plus-noise ratio (UL-SINR) data set of at least one communication link in a target communication network; determining, according to the UL-SINR data set of the at least one communication link, interference-aware information of each communication link in the at least one communication link and a Nakagami-m channel shape factor corresponding to each communication link.

2. The method of claim 1, wherein, The method of determining, according to the UL-SINR data set of the at least one communication link, interference-aware information of each communication link in the at least one communication link and a Nakagami-m channel shape factor corresponding to each communication link comprises: for each communication link, determining, according to the UL-SINR data set of the communication link and a loss metric function, interference-aware information of the communication link; determining, according to the interference-aware information of the communication link and the UL-SINR data set of the communication link, a Nakagami-m channel shape factor corresponding to the communication link.

3. The method of claim 2, wherein, The method of determining, according to the interference-aware information of the communication link and the UL-SINR data set of the communication link, a Nakagami-m channel shape factor corresponding to the communication link comprises: determining, according to the interference-aware information of the communication link and the UL-SINR data set of the communication link, a plurality of first estimation results of the Nakagami-m channel shape factor by a negative log-likelihood function; determining, in a first estimation result with the minimum negative log-likelihood function value in the plurality of first estimation results, a Nakagami-m channel shape factor corresponding to the communication link.

4. The method of claim 3, wherein, One of the first estimation results is determined by the following method: an initialization step of obtaining a first channel shape factor; a calculation step of calculating a negative log-likelihood function value according to the first channel shape factor, the interference-aware information, SINR data in the UL-SINR data set, and the negative log-likelihood function; a parameter updating step of determining a second channel shape factor by a gradient descent optimization algorithm according to the negative log-likelihood function value; in a case where a convergence condition is not met, determining the second channel shape factor as a new first channel shape factor, re-executing the calculation step and the parameter updating step to obtain a new second channel shape factor; in a case where the convergence condition is met, determining the second channel shape factor and the negative log-likelihood function value corresponding to the second channel shape factor as one of the first estimation results.

5. The method according to claim 3 or 4, characterized in that, The Nakagami-m channel shape factor satisfies the following relationship: ； ； in, The value is the negative log-likelihood function value; Resource allocation indication for interfering users of the communication link, and satisfying... ; Let the Nakagami-m channel shape factor to be estimated satisfy... ; The interference sensing information is, and satisfies ; For the UL-SINR dataset of the first Row data, the UL-SINR dataset .

6. The method of claim 1, wherein, The method of determining, according to the UL-SINR data set of the at least one communication link, interference-aware information of each communication link in the at least one communication link and a Nakagami-m channel shape factor corresponding to each communication link comprises: For each of the communication links, according to the UL-SINR data set of the communication link, a plurality of groups of second estimation results are determined based on a multi-target joint estimation strategy; wherein a group of the second estimation results comprises a negative log-likelihood function value, interference awareness information of the communication link, and a Nakagami-m channel shape factor corresponding to the communication link; The interference awareness information of the communication link and the Nakagami-m channel shape factor corresponding to the communication link are determined in the second estimation result with the minimum negative log-likelihood function value in the plurality of groups of second estimation results.

7. The method of claim 6, wherein, A group of the second estimation results is determined by the following way: An initialization step: obtaining a third channel shape factor and a third interference awareness information; A calculation step: according to the third channel shape factor, the third interference awareness information, SINR data in the UL-SINR data set, and the negative log-likelihood function, a negative log-likelihood function value is calculated; A parameter updating step: according to the negative log-likelihood function value, a fourth channel shape factor and a fourth interference awareness information are determined by a gradient descent optimization algorithm; In the case of not meeting the convergence condition, the fourth channel shape factor is determined as a new third channel shape factor and the fourth interference awareness information is determined as a new third interference awareness information, the calculation step and the parameter updating step are re-executed to obtain a new fourth channel shape factor and a new fourth interference awareness information; In the case of meeting the convergence condition, the fourth channel shape factor, the fourth interference awareness information, and the negative log-likelihood function value corresponding to the fourth channel shape factor are determined as a group of the second estimation results.

8. The method according to claim 6 or 7, characterized in that, The Nakagami-m channel shape factor satisfies the following relationship: ； ； in, It is the negative log-likelihood function value; it is the resource allocation indication for interfering users of the communication link, and satisfies... ; For the interference sensing information to be estimated, satisfying ; Let the Nakagami-m channel shape factor to be estimated satisfy... ; For the UL-SINR dataset of the first Row data, the UL-SINR dataset .

9. An apparatus for determining a channel shape factor, characterized by Comprise: An acquisition module is configured to acquire an UL-SINR data set of at least one communication link in a target communication network; A determination module is configured to determine, according to the UL-SINR data set of the at least one communication link, interference awareness information of each communication link in the at least one communication link and a Nakagami-m channel shape factor corresponding to each of the communication links.

10. An apparatus for determining a channel shape factor, the apparatus comprising: The computer device comprises a processor and a memory, the processor is coupled with the memory; the memory is used to store computer instructions, the computer instructions are loaded and executed by the processor to enable the computer device to implement the method in any one of claims 1 to 8.

11. A computer readable storage medium, characterized in that, The computer readable storage medium comprises computer execution instructions, when the computer execution instructions run on the computer, make the computer execute the method in any one of claims 1 to 8.

12. A computer program product, characterised in that, The computer program product comprises a computer program, when the computer program runs on the electronic device, makes the electronic device execute the method in any one of claims 1 to 8.

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