WO2023108760A1 - 实现大规模urllc的用户自适应接入方法及装置 - Google Patents

实现大规模urllc的用户自适应接入方法及装置 Download PDF

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WO2023108760A1
WO2023108760A1 PCT/CN2021/140518 CN2021140518W WO2023108760A1 WO 2023108760 A1 WO2023108760 A1 WO 2023108760A1 CN 2021140518 W CN2021140518 W CN 2021140518W WO 2023108760 A1 WO2023108760 A1 WO 2023108760A1
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user
users
signal
urllc
error probability
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曾捷
周世东
武腾
宋雨欣
谷慧敏
张秀军
赵明
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清华大学
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria

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  • the present application relates to the technical field of wireless communication, in particular to a user adaptive access method and device for implementing large-scale URLLC.
  • Ultra-Reliable and Low-Latency Communications plays an indispensable role in the Internet of Things (IoT), where the error probability (EP) is required to be less than 10 - 5 , the delay is less than 0.5 milliseconds.
  • IoT Internet of Things
  • EP error probability
  • 6G mobile communication systems the demand for wide-area IoT with a large number of devices is expected to continue to grow.
  • the coverage of 6G mobile communication system will include cities and remote villages, and it is necessary to provide high-quality communication for a large number of potential users. Therefore, it is crucial to realize large-scale URLLC.
  • the research on realizing URLLC has made considerable progress on the classic fading models (Rice, Rayleigh, etc.).
  • Cell-Free Access Point Cell-Free AP
  • Cell-Free Massive MIMO system can increase the number of system access users while ensuring high-quality communication for users.
  • the required deployment costs and future maintenance costs are huge and unrealistic.
  • the present application provides a user adaptive access method and device for implementing large-scale URLLC, which can realize multiplexing of a large number of users, and reduce interference between users at the same time, thereby ensuring high-quality communication of a large number of users.
  • the embodiment of the first aspect of the present application provides a user adaptive access method for implementing large-scale URLLC, including the following steps: performing user grouping in the power domain for multiple users with URLLC requirements according to the distance from the access point, and obtaining multiple A NOMA group; use the least squares channel estimation method to perform channel estimation, and obtain the detection signal of each user of each NOMA group; based on the detection signal of each user, calculate the corresponding posterior signal-to-noise ratio, and according to The a posteriori signal-to-noise ratio calculates the error probability corresponding to the user, and in the case that the error probability is less than the preset threshold, continue to allocate according to the number of channels available for dynamic allocation of each group at present, so as to realize URLLC user adaptive access .
  • the formula for calculating the number of available channels for dynamic allocation of the current groups is:
  • K is a parameter representing the number of users
  • N max is the maximum number of channels available for dynamic allocation.
  • each NOMA group includes strong users who are closer to the access point and weak users who are farther from the access point, and the detection of each user in each NOMA group is obtained
  • the signal includes: using a serial interference cancellation method, after subtracting the signal of the strong user from the received signal, using linear detection to obtain the detection signal of the weak user.
  • the calculation of the error probability corresponding to the user according to the a posteriori signal-to-noise ratio includes: using the finite block length information theory, and according to the preset number of available channels, the pilot length and the Posteriori signal-to-noise ratio, calculating the error probability corresponding to the user.
  • it further includes: when the error probability is greater than or equal to the preset threshold, additionally assigning a preset number of available channels to users greater than or equal to the preset threshold, Users in the remaining groups equally share the remaining number of available channels.
  • the embodiment of the second aspect of the present application provides a user adaptive access device for implementing large-scale URLLC, including: a division module, which is used to group multiple users with URLLC needs in the power domain according to the distance from the access point , to obtain a plurality of NOMA groups; the estimation module is used to perform channel estimation using the least squares channel estimation method, and obtain the detection signal of each user of each NOMA group; the access module is used to base each user on the basis of Detect the signal, calculate the corresponding posterior signal-to-noise ratio, and calculate the error probability corresponding to the user according to the posteriori signal-to-noise ratio, and when the error probability is less than the preset threshold, use the current dynamic allocation of each group
  • the number of channels continues to be allocated to realize URLLC user-adaptive access.
  • the formula for calculating the number of available channels for dynamic allocation of the current groups is:
  • K is a parameter representing the number of users
  • N max is the maximum number of channels available for dynamic allocation.
  • each NOMA group includes strong users who are closer to the access point and weak users who are farther from the access point, and the detection of each user in each NOMA group is obtained
  • the signal includes: using a serial interference cancellation method, after subtracting the signal of the strong user from the received signal, using linear detection to obtain the detection signal of the weak user.
  • the calculation of the error probability corresponding to the user according to the a posteriori signal-to-noise ratio includes: using the finite block length information theory, and according to the preset number of available channels, the pilot length and the Posteriori signal-to-noise ratio, calculating the error probability corresponding to the user.
  • an allocation module configured to additionally allocate a preset number of available channels to a channel that is greater than or equal to the preset threshold when the error probability is greater than or equal to the preset threshold. For users who set the threshold, users in other groups equally share the remaining number of available channels.
  • FIG. 1 is a flow chart of a user adaptive access method for realizing large-scale URLLC provided according to an embodiment of the present application
  • FIG. 2 is a schematic flow diagram of a user adaptive access method provided according to an embodiment of the present application
  • FIG. 3 is a grouping model diagram of an uplink NOMA system provided according to an embodiment of the present application.
  • Fig. 4 is an example diagram of a user adaptive access device for implementing large-scale URLLC according to an embodiment of the present application.
  • Non-Orthogonal Multiple Access has been identified as a promising technology.
  • NOMA technology transmits multiple information streams on overlapping channels in the time domain/frequency domain/code domain with different power, and provides wireless services for multiple users on the same wireless resource at the same time.
  • OMA Orthogonal Multiple Access
  • NOMA technology can allocate the same resource to multiple users.
  • NOMA using power multiplexing has obvious performance advantages over traditional OMA, and is more suitable for future system deployment. And while ensuring the same performance as the OMA system, the NOMA system can access more users.
  • the embodiment of the present application combines Cell-Free Massive MIMO and NOMA technology to propose a user-adaptive access method for realizing large-scale URLLC, which can realize multiplexing of a large number of users, and can also reduce the number of connections between multiple users. interference, make full use of communication resources, expand the feasible domain of URLLC design, and ensure that the URLLC requirements of a large number of users are met.
  • the user adaptive access method for realizing large-scale URLLC of the present application will be introduced below with reference to the accompanying drawings and embodiments.
  • the user adaptive access method for realizing large-scale URLLC includes the following steps:
  • step S101 multiple users with URLLC requirements are grouped in the power domain according to the distance from the access point to obtain multiple NOMA groups.
  • FIG. 3 is a system grouping model diagram of the method.
  • the formula for calculating the number of channels available for dynamic allocation in each group is:
  • K is a parameter representing the number of users
  • N max is the maximum number of channels available for dynamic allocation.
  • step S102 the least squares channel estimation method is used for channel estimation, and the detection signal of each user in each NOMA group is obtained.
  • the AP receiving end cannot determine the CSI, and needs to perform channel estimation.
  • the embodiment of the present application adopts the least squares channel estimation method ( Least-Square Channel Estimation, LSCE) for CSI estimation.
  • B diag(p s ,p w ), p s and p w are the average transmission power of strong and weak users respectively, is the channel state information matrix, It is an additive Gaussian noise whose elements are complex Gaussian random variables with mean 0 and variance 1.
  • L is the number of receiving antennas of the AP.
  • the estimated channel state information matrix is obtained is the channel state information vector of user i, and estimates the error matrix
  • the elements of all have a mean of 0 and a variance of The complex Gaussian random variable of .
  • ⁇ i is the large-scale fading coefficient of user i
  • ⁇ i is the variance of small-scale fading of user i, i ⁇ s,w ⁇ .
  • each NOMA group includes strong users who are closer to the access point and weak users who are farther away from the access point
  • obtaining the detection signal of each user in each NOMA group includes: using In the serial interference elimination method, after subtracting the strong user's signal from the received signal, linear detection is used to obtain the weak user's detection signal.
  • the AP performs multi-user detection on the strong user by using the linear detection method to obtain the detection signal of the strong user, and then the AP uses the serial interference cancellation (Successive Interference Cancellation, SIC) technology to convert the detected strong user signal After subtracting from the received signal, linear detection is used to obtain the detection signal of the weak user. Then the AP transmits the detected signal to a central processing unit (Central Processing Unit, CPU).
  • a central processing unit Central Processing Unit, CPU
  • the AP detects the strong users of each NOMA group by using a linear detection method—zero-forcing detection (Zero-Forcing, ZF), to obtain detection signals of the strong users.
  • ZF zero-forcing detection
  • the detection matrix corresponding to the ZF detection method is Then the received signal obtained by ZF detection at the AP is:
  • n' is the number of CUs used by the data signal.
  • the received signal of a strong user after detection is:
  • the AP uses the SIC technology to subtract the detected strong user signal from the received signal, and then uses ZF detection to obtain the detection signal of the weak user. Then the received signal obtained by the weak user after ZF-SIC is:
  • the AP transmits the detected signal to the CPU.
  • step S103 based on the detection signal of each user, calculate the corresponding a posteriori signal-to-noise ratio, and calculate the corresponding error probability of the user according to the a posteriori signal-to-noise ratio, and if the error probability is less than the preset threshold, according to the current
  • the number of available channels for dynamic allocation of each group continues to be allocated to realize user-adaptive access of URLLC.
  • calculating the error probability corresponding to the user according to the posteriori signal-to-noise ratio includes: using the finite block length information theory, and according to the preset number of available channels, pilot length and posteriori signal-to-noise ratio , to calculate the error probability corresponding to the user.
  • the user adaptive access method for implementing large-scale URLLC further includes: when the error probability is greater than or equal to a preset threshold, additionally assigning a preset number of available channels to a value greater than or equal to Users with a preset threshold, and users in other groups equally share the remaining number of available channels.
  • the CPU calculates the corresponding post-processing signal-to-noise ratio (Post-Processing Signal-to-Noise Ratio, PPSNR) according to the detected received signal of strong and weak users transmitted by each AP. Then, use the finite block length (Finite blocklength, FBL) information theory, and according to NCU (given CU number), pilot length and PPSNR, calculate the EP corresponding to the user. will EP and (the number of CUs of the group with URLLC requirements at this time) is fed back to the sending end.
  • PPSNR post-processing Signal-to-noise Ratio
  • the CPU calculates the corresponding PPSNR according to the detected signals of strong and weak users delivered by each AP. Due to detection matrix and estimated error matrix are independent of each other, so the expression of the PPSNR of the strong user is:
  • the PPSNR of the weak user is:
  • N CU is the number of user CUs
  • ⁇ is the user's EP, for PPSNR.
  • D is the number of bits transmitted in the short packet, and the complementary cumulative distribution function of the standard normal distribution According to formula (8) and the user's PPSNR, given N CU and D, the user's EP at this time can be obtained.
  • EP and (the number of CUs of the group with URLLC requirements at this time) is fed back to the sending end.
  • the user adaptive access method for implementing large-scale URLLC proposed in the embodiment of the present application, under the unified fading model, combined with de-cellular large-scale MIMO and non-orthogonal multiple access technology, through user grouping and dynamic allocation
  • the channels can be used to realize the multiplexing of a large number of users, and at the same time reduce the interference between users, thereby ensuring the high-quality communication of a large number of users.
  • using NOMA technology to distinguish users in the power domain can make full use of communication resources.
  • it can further increase the number of access users, improve frequency utilization, enhance system reliability, and reduce Transmission delay, expanding the feasible domain of URLLC design, so as to realize large-scale URLLC.
  • Fig. 4 is an example diagram of a user adaptive access device for implementing large-scale URLLC according to an embodiment of the present application.
  • the user adaptive access device 10 for implementing large-scale URLLC includes: a division module 100 , an estimation module 200 and an access module 300 .
  • the division module 100 is configured to perform user grouping in the power domain for multiple users with URLLC requirements according to the distance from the access point to obtain multiple NOMA groups.
  • the estimation module 200 is configured to perform channel estimation using the least squares channel estimation method, and obtain a detection signal of each user of each NOMA group.
  • the access module 300 is configured to calculate a corresponding a posteriori signal-to-noise ratio based on each user's detection signal, and calculate an error probability corresponding to the user according to the a posteriori signal-to-noise ratio, and when the error probability is less than a preset threshold, Continue to allocate according to the number of channels available for dynamic allocation in each group at present, so as to realize user-adaptive access of URLLC.
  • the formula for calculating the number of channels available for dynamic allocation in each group is:
  • K is a parameter representing the number of users
  • N max is the maximum number of channels available for dynamic allocation.
  • each NOMA group includes strong users who are closer to the access point and weak users who are farther away from the access point
  • obtaining the detection signal of each user in each NOMA group includes: using In the serial interference elimination method, after subtracting the strong user's signal from the received signal, linear detection is used to obtain the weak user's detection signal.
  • calculating the error probability corresponding to the user according to the posteriori signal-to-noise ratio includes: using the finite block length information theory, and according to the preset number of available channels, pilot length and posteriori signal-to-noise ratio , to calculate the error probability corresponding to the user.
  • the user adaptive access device 10 for implementing large-scale URLLC further includes: an allocation module, configured to additionally allocate a preset number of available The number of channels is given to users greater than or equal to the preset threshold, and the remaining number of available channels is shared equally among users in other groups.
  • the user adaptive access device for implementing large-scale URLLC proposed in the embodiment of the present application, under the unified fading model, combined with de-cellular large-scale MIMO and non-orthogonal multiple access technology, through user grouping and dynamic allocation
  • the channels can be used to realize the multiplexing of a large number of users, and at the same time reduce the interference between users, thereby ensuring the high-quality communication of a large number of users.
  • using NOMA technology to distinguish users in the power domain can make full use of communication resources.
  • it can further increase the number of access users, improve frequency utilization, enhance system reliability, and reduce Transmission delay, expanding the feasible domain of URLLC design, so as to realize large-scale URLLC.
  • first and second are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features.
  • the features defined as “first” and “second” may explicitly or implicitly include at least one of these features.
  • “N” means at least two, such as two, three, etc., unless otherwise specifically defined.
  • Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing a custom logical function or step of a process , and that the scope of preferred embodiments of the present application includes additional implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order depending upon the functions involved, which shall It should be understood by those skilled in the art to which the embodiments of the present application belong.

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Abstract

本申请涉及无线通信技术领域，特别涉及一种实现大规模URLLC的用户自适应接入方法及装置，其中，方法包括：根据与接入点的距离对多个有URLLC需求的用户在功率域进行用户分组，得到多个NOMA小组；利用最小二乘信道估计法进行信道估计，并获取每个NOMA小组的每个用户的检测信号；基于每个用户的检测信号，计算对应的后验信噪比，并根据后验信噪比计算用户对应的错误概率，并在错误概率小于预设阈值的情况下，按照当前各组的动态分配可用信道数继续分配，以实现URLLC的用户自适应接入。由此，可以实现海量用户的多路复用，并同时减少用户间的干扰，从而保证大量用户的高质量通信。

Description

实现大规模URLLC的用户自适应接入方法及装置
相关申请的交叉引用
本申请要求清华大学于2021年12月13日提交的、发明名称为“实现大规模URLLC的用户自适应接入方法及装置”的中国专利申请号“202111516618.X”的优先权。
技术领域
本申请涉及无线通信技术领域，特别涉及一种实现大规模URLLC的用户自适应接入方法及装置。
背景技术
高可靠低时延通信(Ultra-Reliable and Low-Latency Communications，URLLC)在物联网(Internet of Things，IoT)中起着不可或缺的作用，其中要求错误概率(Error probability，EP)小于10 -5，延迟低于0.5毫秒。在第六代(sixth-generation，6G)移动通信系统中，对拥有大量设备的广域物联网的需求预计将持续增长。此外，6G移动通信系统的覆盖范围将包括城市和偏远村庄，并且有必要为海量潜在用户提供高质量的通信。因此，实现大规模URLLC至关重要。有关实现URLLC的研究在经典的衰落模型(莱斯、瑞利等)上，取得了相当大的进展。但是，随着6G无线系统的广域覆盖和无线系统中激活用户数量的增加，系统中快衰落的类型变得复杂多样。这些快衰落是严重、不确定以及难以准确刻画的。而且，这些快衰落具有非高斯特性，使用经典快衰落模型进行刻画，其理论分析值与实际测试结果并不契合。因此，需要使用能够刻画多种快衰落的统一衰落模型，来探究如何实现大规模URLLC。目前研究实现大规模URLLC的主要途径是以正交频分多址接入(Orthogonal Frequency-Division Multiple Access，OFDMA)和多输入多输出(Multiple-Input Multiple-Output，MIMO)系统为基础上，通过大量部署去蜂窝接入点(Cell-Free Access Point，Cell-Free AP)构建Cell-Free massive MIMO系统。虽然Cell-Free Massive MIMO系统能够在保证用户高质量通信的同时增多系统的接入用户。但是，当系统需要容纳海量的接入用户时，如果仅靠大量部署AP来保证用户的高质量通信，所需部署成本以及未来维护成本是巨大的且不符合实际的。
发明内容
本申请提供一种实现大规模URLLC的用户自适应接入方法及装置，可以实现海量用户 的多路复用，并同时减少用户间的干扰，从而保证大量用户的高质量通信。
本申请第一方面实施例提供一种实现大规模URLLC的用户自适应接入方法，包括以下步骤：根据与接入点的距离对多个有URLLC需求的用户在功率域进行用户分组，得到多个NOMA小组；利用最小二乘信道估计法进行信道估计，并获取每个NOMA小组的每个用户的检测信号；基于所述每个用户的检测信号，计算对应的后验信噪比，并根据所述后验信噪比计算用户对应的错误概率，并在所述错误概率小于预设阈值的情况下，按照当前各组的动态分配可用信道数继续分配，以实现URLLC的用户自适应接入。
根据本申请的一个实施例，所述当前各组的动态分配可用信道数的计算公式为：
Figure PCTCN2021140518-appb-000001
其中，K为表示用户个数的参数，N max为最大动态分配可用信道数。
根据本申请的一个实施例，所述每个NOMA小组包括距离所述接入点较近的强用户和距离所述接入点较远的弱用户，获取每个NOMA小组的每个用户的检测信号，包括：利用串行干扰消除方法，将所述强用户的信号从接收信号消减后，使用线性检测得到所述弱用户的检测信号。
根据本申请的一个实施例，所述并根据所述后验信噪比计算用户对应的错误概率，包括：利用有限块长信息理论，并根据预设的可用信道数、导频长度和所述后验信噪比，计算所述用户对应的错误概率。
根据本申请的一个实施例，还包括：在所述错误概率大于或等于所述预设阈值的情况下，额外分配预设个数的可用信道数给大于或等于所述预设阈值的用户，其余组的用户均分剩下的可用信道数。
本申请第二方面实施例提供一种实现大规模URLLC的用户自适应接入装置，包括：划分模块，用于根据与接入点的距离对多个有URLLC需求的用户在功率域进行用户分组，得到多个NOMA小组；估计模块，用于利用最小二乘信道估计法进行信道估计，并获取每个NOMA小组的每个用户的检测信号；接入模块，用于基于所述每个用户的检测信号，计算对应的后验信噪比，并根据所述后验信噪比计算用户对应的错误概率，并在所述错误概率小于预设阈值的情况下，按照当前各组的动态分配可用信道数继续分配，以实现URLLC的用户自适应接入。
根据本申请的一个实施例，所述当前各组的动态分配可用信道数的计算公式为：
Figure PCTCN2021140518-appb-000002
其中，K为表示用户个数的参数，N max为最大动态分配可用信道数。
根据本申请的一个实施例，所述每个NOMA小组包括距离所述接入点较近的强用户和距离所述接入点较远的弱用户，获取每个NOMA小组的每个用户的检测信号，包括：利用 串行干扰消除方法，将所述强用户的信号从接收信号消减后，使用线性检测得到所述弱用户的检测信号。
根据本申请的一个实施例，所述并根据所述后验信噪比计算用户对应的错误概率，包括：利用有限块长信息理论，并根据预设的可用信道数、导频长度和所述后验信噪比，计算所述用户对应的错误概率。
根据本申请的一个实施例，还包括：分配模块，用于在所述错误概率大于或等于所述预设阈值的情况下，额外分配预设个数的可用信道数给大于或等于所述预设阈值的用户，其余组的用户均分剩下的可用信道数。
本申请实施例的实现大规模URLLC的用户自适应接入方法及装置，具有以下有益效果：
1)通过结合NOMA技术，对用户进行分组并动态分配可用信道(Channel Use，CU)，充分利用时域、频域、空域以及能量域资源，实现海量用户的多路复用；
2)虑了不同用户的需求，可以保证大量用户的高质量通信；
3)结合Cell-Free Massive MIMO，扩展了URLLC的设计可行域，能够实现大规模URLLC。
本申请附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本申请的实践了解到。
附图说明
本申请上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：
图1为根据本申请实施例提供的一种实现大规模URLLC的用户自适应接入方法的流程图；
图2为根据本申请实施例提供的一种用户自适应接入方法流程示意图；
图3为根据本申请实施例提供的一种上行NOMA系统分组模型图；
图4为根据本申请实施例的实现大规模URLLC的用户自适应接入装置的示例图。
具体实施方式
下面详细描述本申请的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本申请，而不能理解为对本申请的限制。
非正交多址接入(Non-Orthogonal Multiple Access，NOMA)已被确定为一项很有前途的技术。NOMA技术以不同功率将多个信息流在时域/频域/码域重叠的信道上传输，在相 同无线资源上为多个用户同时提供无线业务。在正交多址接入(Orthogonal Multiple Access，OMA)系统中，只能为一个用户分配单一的无线资源，例如按频率分割或按时间分割，而NOMA技术可将同一资源分配给多个用户。在某些场景中，比如广覆盖多节点接入的场景，特别是上行传输密集场景，采用功率复用的NOMA较传统的OMA有明显的性能优势，更适合未来系统的部署。并且在保证与OMA系统相同的性能下，NOMA系统能够接入更多的用户。与传统的OFDMA系统相比，NOMA系统还可以有效地提高频谱效率。由此，本申请的实施例结合Cell-Free Massive MIMO与NOMA技术，提出一种实现大规模URLLC的用户自适应接入方法，能够实现海量用户的多路复用，还能够减少多用户之间的干扰，并充分利用通信资源，扩展URLLC的设计可行域，确保大量用户的URLLC需求得到满足。下面结合附图和实施例对本申请的实现大规模URLLC的用户自适应接入方法进行介绍。
具体而言，如图1和图2所示，该实现大规模URLLC的用户自适应接入方法包括以下步骤：
在步骤S101中，根据与接入点的距离对多个有URLLC需求的用户在功率域进行用户分组，得到多个NOMA小组。
假设一个小区内有2K个用户，其中有2M个用户有URLLC需求，将这2M个用户根据用户与AP的距离两两为一组，均分为M个NOMA小组。剩余的2(K-M)个用户同样也根据与AP的距离两两为一组，均分为K-M个NOMA小组。其中，在任意一个NOMA小组中，距离AP较近的用户为强用户，较远的用户为弱用户。图3为该方法的系统分组模型图。
在本申请的一个实施例中，当前各组的动态分配可用信道数的计算公式为：
Figure PCTCN2021140518-appb-000003
其中，K为表示用户个数的参数，N max为最大动态分配可用信道数。
在步骤S102中，利用最小二乘信道估计法进行信道估计，并获取每个NOMA小组的每个用户的检测信号。
具体地，在非完美状态信息(Channel State Information，CSI)下，AP接收端无法确定CSI，需要进行信道估计，作为一种可能实现的方式，本申请的实施例采取最小二乘信道估计法(Least-Square Channel Estimation，LSCE)进行CSI的估计。
假设导频满足
Figure PCTCN2021140518-appb-000004
并且X (p)(X (p)) H＝nI 2，其中n为导频长度。那么在AP端导频接收信号为：
Figure PCTCN2021140518-appb-000005
其中
Figure PCTCN2021140518-appb-000006
B＝diag(p s,p w)，p s和p w分别为强弱用户的平均发 送功率，
Figure PCTCN2021140518-appb-000007
为信道状态信息矩阵，
Figure PCTCN2021140518-appb-000008
为加性高斯噪声，其元素均为均值为0，方差为1的复高斯随机变量。其中L为AP的接收天线数。
根据公式(1)以及最小二乘法估计，得到了估计信道状态信息矩阵
Figure PCTCN2021140518-appb-000009
Figure PCTCN2021140518-appb-000010
为用户i的信道状态信息向量，并且估计误差矩阵
Figure PCTCN2021140518-appb-000011
的元素均为均值为0，方差为
Figure PCTCN2021140518-appb-000012
的复高斯随机变量。其中β i为用户i的大尺度衰落系数，χ i为用户i小尺度衰落的方差，i∈{s,w}。
在本申请的一个实施例中，每个NOMA小组包括距离接入点较近的强用户和距离接入点较远的弱用户，获取每个NOMA小组的每个用户的检测信号，包括：利用串行干扰消除方法，将强用户的信号从接收信号消减后，使用线性检测得到弱用户的检测信号。
可以理解的是，AP通过使用线性检测方法对强用户进行多用户检测，得到强用户的检测信号，然后AP通过使用串行干扰消除(Successive Interference Cancellation，SIC)技术，将检测后的强用户信号从接收信号消减后，再使用线性检测，得到弱用户的检测信号。然后AP将检测后的信号传递到中央处理器(Central Processing Unit，CPU)。
具体地，AP通过使用线性检测方法—迫零检测(Zero-Forcing，ZF)对每一个NOMA组的强用户进行检测，得到强用户的检测信号。在非完美CSI下，ZF检测方法对应的检测矩阵为
Figure PCTCN2021140518-appb-000013
那么在AP经过ZF检测得到的接收信号为：
Figure PCTCN2021140518-appb-000014
其中
Figure PCTCN2021140518-appb-000015
为任意一个NOMA小组发送的数据信息，
Figure PCTCN2021140518-appb-000016
为加性高斯白噪声，n'为数据信号所用CU数。
强用户经过检测后的接收信号为：
Figure PCTCN2021140518-appb-000017
其中，
Figure PCTCN2021140518-appb-000018
和
Figure PCTCN2021140518-appb-000019
分别为
Figure PCTCN2021140518-appb-000020
和
Figure PCTCN2021140518-appb-000021
的第i列.
Figure PCTCN2021140518-appb-000022
和
Figure PCTCN2021140518-appb-000023
分别为
Figure PCTCN2021140518-appb-000024
和X (d)的第i行，i∈{s,w}。
对于弱用户，AP通过使用SIC技术，将检测后的强用户信号从接收信号消减后，再使用ZF检测，得到弱用户的检测信号。那么弱用户经过ZF-SIC之后得到的接收信号为：
Figure PCTCN2021140518-appb-000025
然后AP将检测后的信号传递到CPU。
在步骤S103中，基于每个用户的检测信号，计算对应的后验信噪比，并根据后验信噪比计算用户对应的错误概率，并在错误概率小于预设阈值的情况下，按照当前各组的动态分配可用信道数继续分配，以实现URLLC的用户自适应接入。
在本申请的一个实施例中，并根据后验信噪比计算用户对应的错误概率，包括：利用有限块长信息理论，并根据预设的可用信道数、导频长度和后验信噪比，计算用户对应的错误概率。
在本申请的一个实施例中，实现大规模URLLC的用户自适应接入方法还包括：在错误概率大于或等于预设阈值的情况下，额外分配预设个数的可用信道数给大于或等于预设阈值的用户，其余组的用户均分剩下的可用信道数。
可以理解的是，CPU根据各AP传递来的强弱用户检测后的接收信号，求出对应的后验信噪比(Post-Processing Signal-to-Noise Ratio，PPSNR)。接着利用有限块长(Finite blocklength，FBL)信息理论，并根据N CU(给定的CU数)，导频长度和PPSNR，求出用户对应的EP。将EP以及
Figure PCTCN2021140518-appb-000026
(此时的具有URLLC需求的小组的CU数)反馈到发送端。
具体地，CPU根据各AP传递来的强弱用户检测后的信号，求出对应的PPSNR。由于检测矩阵
Figure PCTCN2021140518-appb-000027
与估计误差矩阵
Figure PCTCN2021140518-appb-000028
相互独立，所以强用户的PPSNR的表达式为：
Figure PCTCN2021140518-appb-000029
弱用户的PPSNR为：
Figure PCTCN2021140518-appb-000030
其中，
Figure PCTCN2021140518-appb-000031
Figure PCTCN2021140518-appb-000032
以及
Figure PCTCN2021140518-appb-000033
然后根据FBL信息理论，可以得到系统最大可达的信息速率可近似为：
Figure PCTCN2021140518-appb-000034
其中n′＝N CU-n为用户发送信号所占CU数，N CU为用户CU数，ε为用户的EP，
Figure PCTCN2021140518-appb-000035
为PPSNR。 根据公式(7)，可以得到用户对应的EP的表达式为：
Figure PCTCN2021140518-appb-000036
其中，D为短包传输比特数，
Figure PCTCN2021140518-appb-000037
以及标准正态分布的互补累计分布函数
Figure PCTCN2021140518-appb-000038
根据公式(8)以及用户的PPSNR，给定N CU和D可以求出该用户此时的EP。将EP以及
Figure PCTCN2021140518-appb-000039
(此时的具有URLLC需求的小组的CU数)反馈到发送端。
在发送端，根据CPU反馈的EP进行判决。如果M组具有URLLC需求的用户的错误概率均小于10 -5，那么发送端会按照当前各组的CU数继续分配CU。如果这M组用户的错误概率不能满足URLLC的需求，那么发送端会分别分配
Figure PCTCN2021140518-appb-000040
个CU给这M组用户，其余K-M组用户均分剩余的CU，然后重复上述步骤。
根据本申请实施例提出的实现大规模URLLC的用户自适应接入方法，在统一衰落模型下，结合去蜂窝大规模多输入多输出与非正交多址接入技术，通过用户分组和动态分配可用信道，来实现海量用户的多路复用，并同时减少用户间的干扰，从而保证大量用户的高质量通信。并且使用NOMA技术在功率域进行用户区分，能够充分利用通信资源，在保证大量用户实现URLLC的前提下，可以进一步的增加接入用户个数，还能够提高频率利用率，增强系统可靠性，降低传输时延，扩展URLLC设计可行域，从而实现大规模URLLC。
其次参照附图描述根据本申请实施例提出的实现大规模URLLC的用户自适应接入装置。
图4为根据本申请实施例的实现大规模URLLC的用户自适应接入装置的示例图。
如图4所示，该实现大规模URLLC的用户自适应接入装置10包括：划分模块100、估计模块200和接入模块300。
其中，划分模块100，用于根据与接入点的距离对多个有URLLC需求的用户在功率域进行用户分组，得到多个NOMA小组。估计模块200，用于利用最小二乘信道估计法进行信道估计，并获取每个NOMA小组的每个用户的检测信号。接入模块300，用于基于每个用户的检测信号，计算对应的后验信噪比，并根据后验信噪比计算用户对应的错误概率，并在错误概率小于预设阈值的情况下，按照当前各组的动态分配可用信道数继续分配，以实现URLLC的用户自适应接入。
在本申请的一个实施例中，当前各组的动态分配可用信道数的计算公式为：
Figure PCTCN2021140518-appb-000041
其中，K为表示用户个数的参数，N max为最大动态分配可用信道数。
在本申请的一个实施例中，每个NOMA小组包括距离接入点较近的强用户和距离接入点较远的弱用户，获取每个NOMA小组的每个用户的检测信号，包括：利用串行干扰消除方法，将强用户的信号从接收信号消减后，使用线性检测得到弱用户的检测信号。
在本申请的一个实施例中，并根据后验信噪比计算用户对应的错误概率，包括：利用有限块长信息理论，并根据预设的可用信道数、导频长度和后验信噪比，计算用户对应的错误概率。
在本申请的一个实施例中，实现大规模URLLC的用户自适应接入装置10还包括：分配模块，用于在错误概率大于或等于预设阈值的情况下，额外分配预设个数的可用信道数给大于或等于预设阈值的用户，其余组的用户均分剩下的可用信道数。
需要说明的是，前述对实现大规模URLLC的用户自适应接入方法实施例的解释说明也适用于该实施例的实现大规模URLLC的用户自适应接入装置，此处不再赘述。
根据本申请实施例提出的实现大规模URLLC的用户自适应接入装置，在统一衰落模型下，结合去蜂窝大规模多输入多输出与非正交多址接入技术，通过用户分组和动态分配可用信道，来实现海量用户的多路复用，并同时减少用户间的干扰，从而保证大量用户的高质量通信。并且使用NOMA技术在功率域进行用户区分，能够充分利用通信资源，在保证大量用户实现URLLC的前提下，可以进一步的增加接入用户个数，还能够提高频率利用率，增强系统可靠性，降低传输时延，扩展URLLC设计可行域，从而实现大规模URLLC。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或N个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本申请的描述中，“N个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更N个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分， 并且本申请的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本申请的实施例所属技术领域的技术人员所理解。

Claims (10)

  1. 一种实现大规模URLLC的用户自适应接入方法，其特征在于，包括以下步骤：
    根据与接入点的距离对多个有URLLC需求的用户在功率域进行用户分组，得到多个NOMA小组；
    利用最小二乘信道估计法进行信道估计，并获取每个NOMA小组的每个用户的检测信号；以及
    基于所述每个用户的检测信号，计算对应的后验信噪比，并根据所述后验信噪比计算用户对应的错误概率，并在所述错误概率小于预设阈值的情况下，按照当前各组的动态分配可用信道数继续分配，以实现URLLC的用户自适应接入。
  2. 根据权利要求1所述的方法，其特征在于，所述当前各组的动态分配可用信道数的计算公式为：
    Figure PCTCN2021140518-appb-100001
    其中，K为表示用户个数的参数，N max为最大动态分配可用信道数。
  3. 根据权利要求1所述的方法，其特征在于，所述每个NOMA小组包括距离所述接入点较近的强用户和距离所述接入点较远的弱用户，获取每个NOMA小组的每个用户的检测信号，包括：
    利用串行干扰消除方法，将所述强用户的信号从接收信号消减后，使用线性检测得到所述弱用户的检测信号。
  4. 根据权利要求1所述的方法，其特征在于，所述并根据所述后验信噪比计算用户对应的错误概率，包括：
    利用有限块长信息理论，并根据预设的可用信道数、导频长度和所述后验信噪比，计算所述用户对应的错误概率。
  5. 根据权利要求1或4所述的方法，其特征在于，还包括：
    在所述错误概率大于或等于所述预设阈值的情况下，额外分配预设个数的可用信道数给大于或等于所述预设阈值的用户，其余组的用户均分剩下的可用信道数。
  6. 一种实现大规模URLLC的用户自适应接入装置，其特征在于，包括：
    划分模块，用于根据与接入点的距离对多个有URLLC需求的用户在功率域进行用户分组，得到多个NOMA小组；
    估计模块，用于利用最小二乘信道估计法进行信道估计，并获取每个NOMA小组的每个用户的检测信号；以及
    接入模块，用于基于所述每个用户的检测信号，计算对应的后验信噪比，并根据所述 后验信噪比计算用户对应的错误概率，并在所述错误概率小于预设阈值的情况下，按照当前各组的动态分配可用信道数继续分配，以实现URLLC的用户自适应接入。
  7. 根据权利要求6所述的装置，其特征在于，所述当前各组的动态分配可用信道数的计算公式为：
    Figure PCTCN2021140518-appb-100002
    其中，K为表示用户个数的参数，N max为最大动态分配可用信道数。
  8. 根据权利要求6所述的装置，其特征在于，所述每个NOMA小组包括距离所述接入点较近的强用户和距离所述接入点较远的弱用户，获取每个NOMA小组的每个用户的检测信号，包括：
    利用串行干扰消除方法，将所述强用户的信号从接收信号消减后，使用线性检测得到所述弱用户的检测信号。
  9. 根据权利要求6所述的装置，其特征在于，所述并根据所述后验信噪比计算用户对应的错误概率，包括：
    利用有限块长信息理论，并根据预设的可用信道数、导频长度和所述后验信噪比，计算所述用户对应的错误概率。
  10. 根据权利要求6或9所述的装置，其特征在于，还包括：
    分配模块，用于在所述错误概率大于或等于所述预设阈值的情况下，额外分配预设个数的可用信道数给大于或等于所述预设阈值的用户，其余组的用户均分剩下的可用信道数。
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