CN117375786A - Pilot frequency resource optimization method for realizing mURLLC - Google Patents

Pilot frequency resource optimization method for realizing mURLLC Download PDF

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CN117375786A
CN117375786A CN202311339463.6A CN202311339463A CN117375786A CN 117375786 A CN117375786 A CN 117375786A CN 202311339463 A CN202311339463 A CN 202311339463A CN 117375786 A CN117375786 A CN 117375786A
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pilot
interference
post
noise ratio
pilot frequency
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CN117375786B (en
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曾捷
张弼茹
陈昌川
郭浩阳
徐卿钦
叶子任
郭捷兴
江昌博
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3911—Fading models or fading generators
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0452—Multi-user MIMO systems
    • 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
    • H04L25/0224—Channel estimation using sounding signals
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Quality & Reliability (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a pilot frequency resource optimization method for realizing mURLLC; the method comprises the following steps: constructing a CF mMIMO system; performing CSI estimation based on the CF mMIMO system to obtain channel parameter estimation; the AP carries out linear detection on the received UE data signals and calculates the post-processing signal-to-interference-and-noise ratio of the UE; under a unified fading model, calculating a probability density function of the post-processing signal-to-interference-and-noise ratio of the UE according to the channel parameter estimation and the post-processing signal-to-interference-and-noise ratio of the UE; calculating the optimal pilot frequency length according to a probability density function of the post-processing signal-to-interference-and-noise ratio of the UE; according to the optimal pilot frequency length, adopting a greedy pilot frequency allocation algorithm to allocate pilot frequencies to users; the invention can effectively solve the problem of insufficient pilot frequency resources caused by the rapid increase of the number of users, reduce the error rate in data transmission and improve the performance of a communication system.

Description

Pilot frequency resource optimization method for realizing mURLLC
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a pilot frequency resource optimization method for realizing mURLLC.
Background
In the internet of things (Internet of Things, ioT), with the rapid growth of internet of things devices, it is an important challenge to realize large-scale low-latency high-reliability communication (mURLLC) requirements under the condition that massive users access simultaneously. mURLLC aims at satisfying the error probability (Error probability, EP) of less than 10 of delay less than 0.5ms and quantized reliability in high density user access environment -5 To support applications in areas such as smart city, smart transportation, industrial automation, etc. Large-scale Multiple-Input Multiple-Output (mimo) technology combined with a Cellular Free (CF) architecture is a promising research direction in sixth generation (The Sixth Generation, 6G) mobile communication systems. The core idea of CF mimo is to deploy a large number of Access Points (APs) in a slice area and support a large number of User Equipment (UE) communications at the same time, which makes the User Equipment closer to the base station, reducing fading on the transmission path. Currently, it has been demonstrated by scholars that CF mimo can support low latency high reliability communications (Ultra-Reliable and Low Latency Communications, URLLC) by optimizing data rates and system energy efficiency. Today, how to realize the mURLLC requirement in the Internet of things in a 6G diverse and complex environment becomes a research hotspot.
The 6G has the characteristic of wide coverage, and the classical fading model assumption is no longer applicable to the future complex and diverse wireless communication systems. The unified fading model (k-mu shadow fading model) unifies the effects of multipath fading, shadow and path loss, and can accurately describe signal propagation in real environments. Thus, based on the kappa-mu shadow fading model, the channel state information (Channel State Information, CSI) estimation model is built up on the uplink to be more realistic. In future mobile communication systems, the number of users accessing will increase explosively, however the pilot resources in the system are limited.
In order to promote the intelligent, highly interconnected, safe and reliable development of the communication ecosystem, the future mobile communication system faces a key challenge: how to effectively manage limited pilot resources while maintaining a balance between communication latency and reliability. This challenge is particularly acute when the number of users exceeds the number of available pilots, because orthogonal pilot sequences cannot be guaranteed to be allocated to each user when the number of users is greater than the pilot length, which can cause serious pilot pollution problems. Pilot pollution can affect the accuracy of channel estimation, the reliability of signal detection, and lead to an increase in bit error rate.
In summary, in the internet of things, under the condition that a large number of users are simultaneously accessed, a pilot frequency resource optimization method for realizing mURLLC under a unified fading model is needed to reduce the pilot frequency pollution problem caused by explosive growth of the accessed users, so that the system performance and the user experience are improved.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a pilot frequency resource optimization method for realizing mURLLC, which comprises the following steps:
s1: constructing a CF mMIMO system;
s2: performing CSI estimation based on the CF mMIMO system to obtain channel parameter estimation;
s3: the AP carries out linear detection on the received UE data signals and calculates the post-processing signal-to-interference-and-noise ratio of the UE;
s4: under a unified fading model, calculating a probability density function of the post-processing signal-to-interference-and-noise ratio of the UE according to the channel parameter estimation and the post-processing signal-to-interference-and-noise ratio of the UE;
s5: calculating the optimal pilot frequency length according to a probability density function of the post-processing signal-to-interference-and-noise ratio of the UE;
s6: and according to the optimal pilot frequency length, adopting a greedy pilot frequency allocation algorithm to allocate pilot frequencies to the users.
Preferably, the CF mimo system includes a CPU, K single-antenna users, and L single-antenna APs; the APs receive the data signals from the UE and all APs are connected to the CPU via a backhaul link.
Preferably, the process of calculating the post-processing signal-to-interference-and-noise ratio of the UE includes: acquiring a data signal received by an AP from UE; processing the data signal by adopting an MMSE linear detection method to obtain an estimated value of an AP received signal; calculating an initial post-processing signal-to-interference-and-noise ratio of the UE according to the estimated value of the AP received signal; and simplifying the initial post-processing signal-to-interference-and-noise ratio of the UE to obtain the final post-processing signal-to-interference-and-noise ratio of the UE.
Further, the expression of the post-processing signal-to-interference-and-noise ratio of the UE is:
wherein,representing the post-processing signal-to-interference-and-noise ratio, beta, of the kth UE lk Represents the large scale fading coefficient between the ith AP and the kth UE, +.>Representing fast fading gain, ">Representing the channel parameter estimate between the ith AP and the jth UE,/and>represents the estimated value of channel parameters between the ith AP and the kth UE, K represents the total number of UEs, a represents the mathematical expectation of theoretical fast fading gain, beta lj Representing the relationship between the ith AP and the jth UELarge scale fading coefficient, n p Indicating pilot length +.>Representing the average transmit power.
Preferably, the process of calculating the probability density function of the post-processing signal-to-interference-and-noise ratio of the UE includes: calculating fast fading coefficient estimation according to the channel parameter estimation, and calculating fast fading gain according to the fast fading coefficient estimation; calculating a probability density function of the fast fading gain; and calculating the probability density function of the post-processing signal-to-interference-and-noise ratio of the UE according to the probability density function of the post-processing signal-to-interference-and-noise ratio of the UE and the fast fading gain.
Preferably, the process of calculating the optimal pilot length includes: acquiring an approximate achievable data rate of the UE; deriving a relationship between error probability and pilot frequency length according to the approximate reachable data rate of the UE; calculating to obtain error probability according to the probability density function of the post-processing signal-to-interference-and-noise ratio of the UE and the relation between the error probability and the pilot frequency length; and adopting a golden section algorithm to minimize the error probability to obtain the optimal pilot frequency length.
Further, the expression of the error probability is:
wherein,represents error probability, B represents channel bandwidth, t D Represents transmission delay, n p Indicating pilot length, C (x) indicating a first intermediate parameter, V (x) indicating a second intermediate parameter, D indicating packet size,/->A probability density function representing the post-processing signal-to-interference-and-noise ratio of the UE.
Preferably, the process of performing pilot frequency allocation on the user by adopting a greedy pilot frequency allocation algorithm comprises the following steps:
s61: setting an iteration number threshold S, initializing the iteration number S to be 1 and enabling the pilot frequency distribution number n to be equal to the optimal pilot frequency length; randomly distributing pilot sequences to each UE from a preset pilot sequence set;
s62: calculating and comparing the reachable data rates of all the UEs, and re-distributing pilot sequences for the UEs with the minimum reachable data rates from the pilot sequence set; computing pilot pollution xi of all APs min ；
S63: subtracting one from the pilot frequency distribution times n, distributing pilot frequency sequences from the pilot frequency sequence set for the UE with the minimum reachable data rate again, and calculating pilot frequency pollution (XI) of all APs;
s64: if n is less than or equal to 0, adding 1 to the iteration number S, judging whether the iteration number S is greater than the iteration number threshold S, if so, stopping iteration to obtain a user pilot frequency distribution result, otherwise, returning to the step S62; if n is greater than 0, judging xi min Whether or not the ratio is greater than Xi, if min Xie, xie min Step S63, if Σ min And (3) directly returning to the step S63.
The beneficial effects of the invention are as follows:
(1) Establishing a channel estimation model: the LSE method is adopted for channel estimation, and the channel state information can be presumed by collecting the pilot frequency sequence transmitted by the user, thereby being beneficial to improving the reliability of the system.
(2) Determining the optimal pilot length: the golden section algorithm can be used to obtain the optimal pilot length by deriving a relational expression of pilot length and EP based on FBL information theory. Determining the optimal pilot length may help to improve the accuracy of the channel estimation, thereby reducing the bit error rate in data transmission and improving the performance of the communication system.
(3) Dynamically allocating pilot frequency resources: when the challenge of limited pilot frequency resources is faced, the greedy pilot frequency allocation algorithm executed on the CPU is adopted, so that the problem of pilot frequency pollution caused by the surge of the number of users under the limited pilot frequency resources can be effectively solved.
Drawings
FIG. 1 is a flow chart of a pilot resource optimization method for implementing mURLLC in the present invention;
FIG. 2 is a schematic diagram of a CF mMIMO system architecture according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a pilot frequency resource optimization method for realizing mURLLC, as shown in figures 1 and 2, the method comprises the following steps:
s1: a CF mimo system is constructed.
A CF mMIMO system is constructed, which comprises a CPU, K single-antenna users and L single-antenna APs; the APs receive the data signals from the UE and all APs are connected to the CPU via a backhaul link.
S2: and carrying out CSI estimation based on the CF mMIMO system to obtain channel parameter estimation.
Under imperfect CSI (channel state information), an AP cannot determine the CSI and needs to perform channel estimation; specific:
assume pilot sequencesAnd->n p Is the pilot length. Then, pilot signal received by the first AP +.>The method comprises the following steps:
wherein, representing the average transmit power; />The elements of the noise are independent of each other and obey complex Gaussian distribution with the mean value of 0 and the variance of 1; />Is a channel state information matrix->β lk Represents a large scale fading coefficient, h lk Representing the fast fading coefficients.
According to the pilot signal received by the AP, the estimated value of the channel parameter can be obtained by adopting LSE as follows:
wherein w is lk Representing the estimation error, subject tog lk Can be further expressed as:
thus, channel parameter estimation
Wherein,mathematical expectation representing theoretical fast fading gain, +.>Obeys->
S3: and the AP carries out linear detection on the received UE data signals and calculates the post-processing signal-to-interference-and-noise ratio of the UE.
Suppose the data signal sequence transmitted by the kth UE isn d Representing the length of the data signal sequence; the data signal received by the first AP is:
wherein,the elements of the additive Gaussian white noise are independent of each other and obey complex Gaussian distribution with a mean value of 0 and a variance of 1.
The AP processes the data signal by adopting an MMSE linear detection method, and an estimated value of an AP received signal can be obtained as follows:
wherein,is MMSE detection matrix,/->Represents n d A rank identity matrix; and is also provided with
The initial post-processing signal-to-interference-and-noise ratio of the UE is calculated according to the estimated value of the AP received signal, and the post-processing SINR (signal-to-interference-and-noise ratio) of the kth UE can be expressed as:
wherein,and->
Simplifying the initial post-processing signal-to-interference-and-noise ratio of UE, and enablingThe post-processing SINR for the kth UE is ultimately expressed as:
s4: and under the unified fading model, calculating a probability density function of the post-processing signal-to-interference-and-noise ratio of the UE according to the channel parameter estimation and the post-processing signal-to-interference-and-noise ratio of the UE.
Based on the unified fading model (kappa-mu shadow fading model), the theoretical fast fading gain can be expressed as
Where μ represents the number of clusters. Assuming that there are a scattering component and a dominant component in each cluster, where X i,lk And Y i,lk Is the mean value is 0, the variance is sigma 2 A gaussian random variable representing the first and second scattering components of cluster i; p is p i,lk And q i,lk The first and second dominant components of cluster i are represented as real numbers. Zeta type toy lk Is a nakagami-m random variable with a molding parameter of m,obeying the Γ (m, 1/m) distribution, and +.>Representing the ratio of the dominant component power to the sum of the scattered component powers.
Calculating fast fading coefficient estimation according to channel parameter estimation, namely: due toFast fading coefficient estimation ∈ ->
From the theoretical fast fading gains and the fast fading coefficient estimates, the calculation of the fast fading gain of the i-th cluster can be expressed as:
assume that And->Is 0, the variances are equal, i.e. +.>Thus, fast fading gain +.>Can be further expressed as:
so that the number of the parts to be processed,also obeys the kappa-mu shadow distribution.
Assume thatThe PDF (probability density function) of the fast fading gain is expressed as:
wherein,Γ (&) and 1 F 1 (. Cndot.) Gamma function and confluent super geometric function, respectively, < >>m represents the molding parameters of the nakagami-m random variable.
Calculating the post-processing signal-to-interference-and-noise ratio of the UE according to the probability density function of the post-processing signal-to-interference-and-noise ratio and the fast fading gain of the UEProbability density function of (c):
s5: and calculating the optimal pilot frequency length according to the probability density function of the post-processing signal-to-interference-and-noise ratio of the UE.
Aiming at short data packet transmission, obtaining the approximate reachable data rate of the UE and a relational expression of the EP and the pilot frequency length by utilizing a finite block length FBL information theory; specific:
acquiring an approximate reachable data rate R for a UE k Expressed as:
wherein B is the channel bandwidth, t D For transmission delay, gamma k Is the post-processing SINR of the kth UE, C (x) represents a first intermediate parameter, V (x) represents a second intermediate parameter, C (gamma) k )＝ln(1+γ k )，Q -1 (. Cndot.) is the inverse of Q (. Cndot.),. Cndot.,>deriving the relation between the error probability and the pilot frequency length according to the approximate reachable data rate of the UE:
wherein D is the packet size. At a given time delay t D And pilot length n p And calculating to obtain Error Probability (EP) according to a probability density function of the post-processing signal-to-interference-and-noise ratio of the UE and the relation between the error probability and the pilot frequency length:
when a given delay (t D Should meet the demand of mURLLC, i.e. t D Less than or equal to 0.5 ms) using the golden section algorithm to obtain optimal pilot length by minimizing error probabilityNamely:
wherein n is down ＝K，n up ＝Bt D -1。
S6: and according to the optimal pilot frequency length, adopting a greedy pilot frequency allocation algorithm to allocate pilot frequencies to the users.
Since the explosive growth of the access users brings serious pilot pollution problem, greedy pilot allocation is needed to be carried out on the CPU, and then the CPU sends the allocated pilot index to each user; specific:
because in the actual Internet of things scene, K > n p It is impossible to allocate orthogonal pilot sequences for users, i.e Pilot sequence representing kth UE, +.>Representing the pilot sequence of the kth' UE. However using non-orthogonal pilot sequences,serious pilot pollution is generated when pilot detection is performed, and therefore, the estimated value of the channel parameter can be expressed as:
the second term is pilot pollution caused by non-orthogonal pilot sequences. In order to reduce analysis complexity, the invention performs pilot frequency distribution based on large-scale fading, and the variance of pilot frequency pollution can be further expressed as:
the process of pilot frequency distribution to users by adopting a greedy pilot frequency distribution algorithm is as follows:
s61: setting an iteration number threshold S, initializing the iteration number S to be 1 and pilot frequency distribution numbern is equal to the optimal pilot length, i.eFrom a predetermined set of pilot sequences->Pilot sequences are randomly allocated to each UE (K UEs).
S62: calculating and comparing the reachable data rates of all the UEs, and re-distributing pilot sequences for the UEs with the minimum reachable data rates from the pilot sequence set; computing pilot pollution xi of all APs min 。
Find R k The smallest UE and marking the UE as k * ：
From a set of pilot sequencesIn re-for UE k * Allocation of pilot sequences->Pilot pollution for all APs was calculated and noted as:
s63: and subtracting one from the pilot frequency distribution times n, distributing pilot frequency sequences from the pilot frequency sequence set for the UE with the minimum reachable data rate again, and calculating pilot frequency pollution (XI) of all the APs.
Let n=n-1, from the pilot sequence setIs again UE k * Allocation of pilot sequences->Pilot pollution for all APs was calculated and noted as:
s64: if n is less than or equal to 0, adding 1 to the iteration number S, judging whether the iteration number S is greater than the iteration number threshold S, if so, stopping iteration to obtain a user pilot frequency distribution result, otherwise, returning to the step S62; if n is greater than 0, judging xi min Whether or not the ratio is greater than Xi, if min Xie, xie min Step S63, if Σ min And (3) directly returning to the step S63.
After the pilot frequency distribution result of the user is obtained through a greedy pilot frequency distribution algorithm, the system distributes pilot frequency to the user according to the pilot frequency distribution result.
While the foregoing is directed to embodiments, aspects and advantages of the present invention, other and further details of the invention may be had by the foregoing description, it will be understood that the foregoing embodiments are merely exemplary of the invention, and that any changes, substitutions, alterations, etc. which may be made herein without departing from the spirit and principles of the invention.

Claims (8)

1. A method for implementing pilot resource optimization of a msurllc, comprising:
s1: constructing a CF mMIMO system;
s2: performing CSI estimation based on the CF mMIMO system to obtain channel parameter estimation;
s3: the AP carries out linear detection on the received UE data signals and calculates the post-processing signal-to-interference-and-noise ratio of the UE;
s4: under a unified fading model, calculating a probability density function of the post-processing signal-to-interference-and-noise ratio of the UE according to the channel parameter estimation and the post-processing signal-to-interference-and-noise ratio of the UE;
s5: calculating the optimal pilot frequency length according to a probability density function of the post-processing signal-to-interference-and-noise ratio of the UE;
s6: and according to the optimal pilot frequency length, adopting a greedy pilot frequency allocation algorithm to allocate pilot frequencies to the users.
2. The method for optimizing pilot resources of a mlllc according to claim 1, wherein the CF mlmo system includes a CPU, K single antenna users, and L single antenna APs; the APs receive the data signals from the UE and all APs are connected to the CPU via a backhaul link.
3. The method for optimizing pilot resources of a mlllc according to claim 1, wherein the process of calculating the post-processing signal-to-interference-and-noise ratio of the UE includes: acquiring a data signal received by an AP from UE; processing the data signal by adopting an MMSE linear detection method to obtain an estimated value of an AP received signal; calculating an initial post-processing signal-to-interference-and-noise ratio of the UE according to the estimated value of the AP received signal; and simplifying the initial post-processing signal-to-interference-and-noise ratio of the UE to obtain the final post-processing signal-to-interference-and-noise ratio of the UE.
4. The method for implementing pilot resource optimization of mURLLC according to claim 3, wherein the expression of post-processing signal-to-interference-and-noise ratio of the UE is:
wherein,representing the post-processing signal-to-interference-and-noise ratio, beta, of the kth UE lk Represents the large scale fading coefficient between the ith AP and the kth UE, +.>Representing fast fading gain, ">Representing the channel parameter estimate between the ith AP and the jth UE,/and>represents the estimated value of channel parameters between the ith AP and the kth UE, K represents the total number of UEs, a represents the mathematical expectation of theoretical fast fading gain, beta lj Representing the large-scale fading coefficient between the ith AP and the jth UE, n p Indicating pilot length +.>Representing the average transmit power.
5. The method for implementing the pilot resource optimization of the mURLLC according to claim 1, wherein the process of calculating the probability density function of the post-processing signal-to-interference-and-noise ratio of the UE comprises: calculating fast fading coefficient estimation according to the channel parameter estimation, and calculating fast fading gain according to the fast fading coefficient estimation; calculating a probability density function of the fast fading gain; and calculating the probability density function of the post-processing signal-to-interference-and-noise ratio of the UE according to the probability density function of the post-processing signal-to-interference-and-noise ratio of the UE and the fast fading gain.
6. The method for optimizing pilot resources for implementing a mhrblc of claim 1, wherein the process of calculating the optimal pilot length includes: acquiring an approximate achievable data rate of the UE; deriving a relationship between error probability and pilot frequency length according to the approximate reachable data rate of the UE; calculating to obtain error probability according to the probability density function of the post-processing signal-to-interference-and-noise ratio of the UE and the relation between the error probability and the pilot frequency length; and adopting a golden section algorithm to minimize the error probability to obtain the optimal pilot frequency length.
7. The method for implementing the pilot resource optimization of the mhrblc of claim 6, wherein the error probability is expressed as:
wherein,represents error probability, B represents channel bandwidth, t D Represents transmission delay, n p Indicating pilot length, C (x) indicating a first intermediate parameter, V (x) indicating a second intermediate parameter, D indicating packet size,/->A probability density function representing the post-processing signal-to-interference-and-noise ratio of the UE.
8. The method for implementing the pilot resource optimization of the mURLLC according to claim 1, wherein the process of using the greedy pilot allocation algorithm to allocate the pilot to the user is:
s61: setting an iteration number threshold S, initializing the iteration number S to be 1 and enabling the pilot frequency distribution number n to be equal to the optimal pilot frequency length; randomly distributing pilot sequences to each UE from a preset pilot sequence set;
s62: calculating and comparing the reachable data rates of all the UEs, and re-distributing pilot sequences for the UEs with the minimum reachable data rates from the pilot sequence set; computing pilot pollution xi of all APs min ；
S63: subtracting one from the pilot frequency distribution times n, distributing pilot frequency sequences from the pilot frequency sequence set for the UE with the minimum reachable data rate again, and calculating pilot frequency pollution (XI) of all APs;
s64: if n is less than or equal to 0, adding 1 to the iteration number S, judging whether the iteration number S is greater than the iteration number threshold S, if so, stopping iteration to obtain a user pilot frequency distribution result, otherwise, returning to the step S62; if n is greater than 0, judging xi min Whether or not the ratio is greater than Xi, if min Xie, xie min Step S63, if Σ min And (3) directly returning to the step S63.
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