A method and apparatus for estimating a signal-to-interference-and-noise ratio threshold

Abstract

The application discloses a method and device for estimating a signal-to-interference-and-noise ratio threshold. The method comprises the following steps: selecting a channel estimation filtering method according to the computing power and system configuration of a wireless communication system; calculating a channel estimation filtering coefficient according to the selected channel estimation filtering method; and calculating the signal-to-interference-and-noise ratio threshold of a carrier according to the channel estimation filtering coefficient. The method and device for estimating a signal-to-interference-and-noise ratio threshold provided by the application can estimate the signal-to-interference-and-noise ratio threshold with very low computing complexity for various channel estimation filtering methods. The method and device can effectively evaluate the influence of self-excitation effect in channel estimation on the performance of an intelligent reflecting surface system and improve the performance of the intelligent reflecting surface system in various scenarios.

Description

Technical Field

[0001] This invention relates to communication technology, and more particularly to a method and apparatus for estimating the signal-to-interference-plus-noise ratio (SINR) threshold. Background Technology

[0002] 5G's design goals include enhanced mobile broadband, ultra-reliable low-latency communication, and massive machine-type communication. With the commercial deployment of 5G, a research and development boom for 6G has swept across the world.

[0003] Due to limited spectrum resources in low-frequency bands, 6G will utilize higher communication frequency bands than 5G. Millimeter waves, with their high transmission rates and strong directionality, have become highly competitive high-frequency candidate bands. Because of their short wavelengths, millimeter waves can be used with numerous small antennas to create highly directional transmission links between transmitters and receivers. These highly directional links require precise beam alignment, which can be achieved through beam management. The purpose of beam management is to establish and maintain a beam between the transmitter and receiver. The 5G protocol describes the relevant aspects of beam management, which mainly consists of four parts: beam scanning, beam measurement, beam determination, and beam reporting. Beam management periodically repeats these processes to update the optimal transmitter and receiver beam pair over time. The increase in communication frequency makes beam blocking effects during propagation very frequent, which seriously affects the practical application of millimeter waves as a data transmission carrier.

[0004] Intelligent reflectors are considered a potential technology for addressing the aforementioned bottlenecks by adjusting the channel environment to improve wireless coverage and quality. As an auxiliary device in wireless communication networks, intelligent reflectors adjust channel characteristics without requiring significant modifications to existing wireless communication systems. Without increasing the power consumption of the wireless communication system, intelligent reflectors improve channel characteristics by controlling the reflection direction of radio frequency signals to fill signal gaps. By actively modifying the wireless channel between the transmitter and receiver, intelligent reflectors pave the way for intelligent programmable wireless environments. Researching the key theories and methods of intelligent reflector-assisted three-dimensional dense wireless transmission will become a research hotspot for improving the transmission performance of 6G wireless communication.

[0005] Accurate channel estimation is one of the many problems that need to be solved in smart reflectors. We have discovered a self-excitation effect in channel estimation, namely, the estimated signal-to-interference-plus-noise ratio (SIR) is not equal to zero and has a threshold under beam blocking or transmitter silence conditions. It is necessary to estimate this SIR threshold to effectively assess the impact of the self-excitation effect on building a three-dimensional dense wireless network using smart reflectors.

[0006] The existing method and apparatus for estimating the signal-to-interference-plus-noise ratio (SIR) of wireless communication systems (“Method and apparatus for estimating the SIR of wireless communication systems”; authors: Hou Xiaohui, Yang Feng, Lu Qinbo; application number: CN201010524983.0) has the following drawback: it does not consider the flat-bottom phenomenon in the SIR estimation when the number of receiving antennas is large. Summary of the Invention

[0007] This invention provides a method and apparatus for estimating the signal-to-interference-plus-noise ratio (SINR) threshold, which aims to effectively evaluate the impact of self-excitation effects in channel estimation on the performance of intelligent reflector systems, thereby improving the performance of intelligent reflector systems.

[0008] The objective of this invention is achieved by at least one of the following technical solutions.

[0009] A method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold includes the following steps:

[0010] S1. Select a channel estimation filtering method based on the computing power and system configuration of the wireless communication system, wherein the wireless communication system can be a base station or a terminal;

[0011] S2. Calculate the channel estimation filter coefficients according to the selected channel estimation filtering method;

[0012] S3. Calculate the signal-to-interference-plus-noise ratio (SINR) threshold of the carrier based on the channel estimation filter coefficients.

[0013] Furthermore, in step S1, the channel estimation filtering method includes minimum mean square error (MMSE) channel estimation and discrete Fourier transform (DFT) channel estimation; these channel estimation filtering methods are existing technologies, and specific details can be found in references [1-3].

[0014] [Document 1] Y.Liu, Z.Tan, H.Hu, LJCimini and GYLi, "Channel estimation forOFDM", IEEE Commun.Surveys Tuts., vol.16, no.4, pp.1891-1908, 4th Quart.2014.

[0015] [Document 2] MKOzdemir and H.Arslan, "Channel estimation for wireless OFDMsystems", IEEE Commun.Surveys Tuts., vol.9, no.2, pp.18-48, 2nd Quart.2007.

[0016] [Document 3] O. Edfors, M. Sandell, J..-J. van de Beek, SK Wilson and P. O. Borjesson, "OFDM channel estimation by singular value decomposition," IEEE Transactions on Communications, vol. 46, no. 7, pp. 931-939, July 1998, doi: 10.1109 / 26.701321.

[0017] Furthermore, in step S1, when the number of operations performed per second by the wireless communication system is greater than T... x And the number of receiving antennas is less than T y When the mean squared error is reached, the minimum mean squared error (MMSE) channel estimation method is selected; otherwise, the discrete fourier transform (DFT) channel estimation method is selected, where T... x and T y It is a real number greater than 0.

[0018] Further, in step S2, it is assumed that the number of transmit antennas is 1 and the number of receive antennas is r; it is assumed that the starting carrier index is s and the ending carrier index is e; the channel estimation filter coefficients for receive antenna i and carrier j are:

[0019] W ij =[W ijs W ij(s+1) ,…,W ije ]

[0020] Among them, W ij W is the channel estimation filter coefficient for receiving antenna i and carrier j. ijs It is the product coefficient of the channel estimation of the receiving antenna i carrier s when calculating the channel estimation after filtering of antenna i carrier j.

[0021] Furthermore, assume that the channel estimate before filtering for antenna i and carrier j is H. ij Then the channel estimate after filtering for antenna i and carrier j is: Specifically as follows:

[0022]

[0023] Among them, W ijk It is the product coefficient of the channel estimation of the receiving antenna i carrier k when calculating the channel estimation after filtering of antenna i carrier j.

[0024] Further, in step S3, the definition of the signal-to-interference-plus-noise ratio (SINR) threshold is the minimum average value of the SINR when the beam is blocked or the transmitting end is silent. The SINR threshold SINR is calculated according to the channel estimation filtering coefficient. r :

[0025]

[0026] Where, Ψ = {s, s + 1, …, e}, \ represents set difference operation, * represents conjugate operation, || represents absolute value operation, and ∈ represents set membership operation.

[0027] Further, it further includes step S4: constructing a three-dimensional dense wireless network according to the SINR threshold, specifically as follows:

[0028] Assume that when the number of receiving antennas is equal to r, the SINR threshold is equal to SINR r , define the antenna set Λ = {r|SINR r < a}, where a is a real number with a value range of [-40dB, 40dB];

[0029] Then Λ is the set of all receiving antennas with an SINR threshold less than a;

[0030] When constructing a three-dimensional dense wireless network using the intelligent reflecting surface, the number of receiving antennas is selected from the set Λ.

[0031] An apparatus for estimating the signal-to-interference-plus-noise ratio (SINR) threshold includes a channel estimation filtering method selection module, a channel estimation filtering coefficient calculation module, and an SINR threshold calculation module;

[0032] Among them, the channel estimation filtering method selection module is used to select a suitable channel estimation filtering method according to the system computing power and system configuration. The channel estimation filtering coefficient calculation module is used to calculate the channel estimation filtering coefficient according to the selected channel estimation filtering method. The SINR threshold calculation module is used to calculate the SINR threshold of the carrier according to the channel estimation filtering coefficient.

[0033] Compared with the prior art, the advantages of the present invention are as follows:

[0034] By using the method and apparatus for estimating the signal-to-interference-plus-noise ratio (SINR) threshold provided by the present invention, for various channel estimation filtering methods, the SINR threshold can be estimated with very low computational complexity. Using the method and apparatus of the present invention can effectively evaluate the impact of the self-excitation effect in channel estimation on the performance of the intelligent reflecting surface system, and improve the performance of the intelligent reflecting surface system in various scenarios. Description of the Drawings

[0035] The accompanying drawings are included to provide a further understanding of the embodiments, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and, together with the description, are used to explain the principles of the embodiments. Other embodiments and many anticipated advantages of the embodiments will be readily appreciated, as they will be better understood through reference to the following detailed description. Similar reference numerals denote corresponding similar parts.

[0036] Figure 1 A schematic diagram of intelligent reflective surface-assisted communication is given;

[0037] Figure 2 A flowchart of the steps of the method for estimating the signal-to-interference-plus-noise ratio threshold according to the present invention is provided;

[0038] Figure 3 A schematic diagram of a device for estimating the signal-to-interference-plus-noise ratio threshold according to the present invention is provided;

[0039] Figure 4 A schematic diagram illustrating the relationship between carriers and symbols in common communication systems is provided.

[0040] Figure 5 A schematic diagram of the results of a method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold is provided.

[0041] Figure 6 A schematic diagram of the results of a method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold is provided.

[0042] Figure 7 A schematic diagram of the results for a method of estimating the signal-to-interference-plus-noise ratio (SINR) threshold is provided. Detailed Implementation

[0043] The accompanying drawings are used to describe various aspects of the invention, wherein similar reference numerals are generally used throughout to denote similar elements. For purposes of explanation, numerous specific details are set forth in the following description to provide a thorough understanding of one or more aspects of the embodiments. However, those skilled in the art may practice one or more aspects of the described embodiments with a lesser degree of detail. It should be understood that other embodiments may be utilized, and structural or logical changes may be made without departing from the scope of the invention.

[0044] Example:

[0045] Figure 1 A schematic diagram of intelligent reflector-assisted communication is provided. The intelligent reflector consists of an array of intelligent reflective elements, each capable of independently altering the incident signal. When the quality of the direct channel is poor, the intelligent reflector can intelligently configure the wireless environment to assist the transmitter and receiver in information transmission. When the beam is obstructed by buildings, the base station and terminal can utilize the intelligent reflector to assist communication.

[0046] A method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold, such as Figure 2 As shown, it includes the following steps:

[0047] S1. Select a channel estimation filtering method based on the computing power and system configuration of the wireless communication system, wherein the wireless communication system can be a base station or a terminal;

[0048] The channel estimation and filtering methods mentioned include minimum mean squared error (MMSE) channel estimation and discrete fourier transform (DFT) channel estimation; these channel estimation and filtering methods are existing technologies, and specific details can be found in references [1-3].

[0049] S2. Calculate the channel estimation filter coefficients according to the selected channel estimation filtering method;

[0050] Assume there is 1 transmit antenna and r receive antennas; assume the starting carrier index is s and the ending carrier index is e; the channel estimation filter coefficients for receive antenna i and carrier j are:

[0051] W ij =[W ijs W ij(s+1) ,…,W ije ]

[0052] Among them, W ij W is the channel estimation filter coefficient for receiving antenna i and carrier j. ijs It is the product coefficient of the channel estimation of the receiving antenna i carrier s when calculating the channel estimation after filtering of antenna i carrier j.

[0053] S3. Calculate the signal-to-interference-plus-noise ratio (SINR) threshold of the carrier based on the channel estimation filter coefficients;

[0054] Assume the channel estimate before filtering for antenna i and carrier j is H. ij Then the channel estimate after filtering for antenna i and carrier j is: Specifically as follows:

[0055]

[0056] Among them, W ijk It is the product coefficient of the channel estimation of the receiving antenna i carrier k when calculating the channel estimation after filtering of antenna i carrier j.

[0057] The signal-to-interference-plus-noise ratio (SINR) threshold is defined as the minimum average SINR value when the beam is blocked or the transmitter is silent. The SINR threshold is calculated based on the channel estimation filter coefficients.r :

[0058]

[0059] Among them, Ψ = {s, s + 1, …, e}, \ represents set difference operation, * represents conjugate operation, || represents absolute value operation, and ∈ represents set membership operation.

[0060] Step S4: Construct a three-dimensional dense wireless network according to the signal-to-interference-plus-noise ratio (SINR) threshold, specifically as follows:

[0061] Assume that when the number of receiving antennas is equal to r, the SINR threshold is equal to SINR r , define the antenna set Λ = {r|SINR r < a}, where a is a real number with a value range of [-40dB, 40dB];

[0062] Then Λ is the set of all receiving antennas with an SINR threshold less than a;

[0063] When constructing a three-dimensional dense wireless network using intelligent reflecting surfaces, the number of receiving antennas is selected from the set Λ.

[0064] An apparatus for estimating the SINR threshold, as Figure 3 shown, includes a channel estimation filtering method selection module, a channel estimation filtering coefficient calculation module, and an SINR threshold calculation module;

[0065] Among them, the channel estimation filtering method selection module is used to select a suitable channel estimation filtering method according to the system computing power and system configuration, the channel estimation filtering coefficient calculation module is used to calculate the channel estimation filtering coefficient according to the selected channel estimation filtering method, and the SINR threshold calculation module is used to calculate the SINR threshold of the carrier according to the channel estimation filtering coefficient.

[0066] Figure 4 shows the relationship between carriers and symbols in common communication systems. In the OFDM system, the basic unit in the frequency domain is a carrier, and the basic unit in the time domain is a symbol. When the carrier spacing is 15kHz, a subframe consists of 14 symbols. When the carrier spacing is 30kHz, a subframe consists of 28 symbols. Figure 4 shows the carrier and symbol positions of the detection signal, where the symbol index of the detection signal is l, the starting carrier index of the detection signal is s, and the ending carrier index of the detection signal is e.

[0067] In one embodiment, as Figure 5 shown, the channel estimation method selects DFT channel estimation. A method for estimating the SINR threshold is divided into the following three steps:

[0068] 1) Select a channel estimation filtering method. Based on the system's computing power and configuration, select a DFT channel estimation filtering method.

[0069] 2) Calculate the channel estimation filter coefficients. Based on the selected channel estimation filtering method, calculate the channel estimation filter coefficients. Assume the number of transmit antennas is 1 and the number of receive antennas is r. Assume the starting carrier index is s and the ending carrier index is e. Let F be a Fourier transform matrix with rows corresponding to the carrier indices of the probe signal, and let D be a matrix of the following form.

[0070]

[0071] Where I j Represents an identity matrix of dimension j×j, 0 i×j Let W be an all-zero matrix of dimension i×j. The channel estimation filter coefficients W for receiving antenna i and carrier j are also shown. ij FDF of the matrix H The j-th row, where W ij It has the following forms

[0072] W ij =[W ijs W ij(s+1) ,…,W ije ]

[0073] 3) Calculate the signal-to-interference-plus-noise ratio (SINR) threshold. The SINR threshold is defined as the minimum average SINR value when the beam is blocked or the transmitter is silent. We calculate the SINR threshold based on the channel estimation filter coefficients.

[0074]

[0075] Where Ψ = {s, s+1, ..., e}, and \ denotes set difference operation. When s = 0, e = 2047, and L = 144, the signal-to-interference-plus-noise ratio (SIR) thresholds for different antennas are as follows: Figure 5 As shown.

[0076] In one embodiment, such as Figure 6 As shown, the channel estimation method chosen is MMSE channel estimation. A signal-to-interference-plus-noise ratio (SINR) threshold estimation method consists of the following three steps:

[0077] 1) Select the channel estimation filtering method. Based on the system's computing power and configuration, select the MMSE channel estimation filtering method.

[0078] 2) Calculate the channel estimation filter coefficients. Based on the selected channel estimation filtering method, calculate the channel estimation filter coefficients. Assume the number of transmit antennas is 1 and the number of receive antennas is r. Assume the starting carrier index is s and the ending carrier index is e. Let R be the cross-correlation matrix of different carriers of the probe signal. The channel estimation filter coefficient W for receive antenna i and carrier j is... ij R(R+I) of the matrix e-s+1 / SNR) -1 The j-th row, where W ij It has the following forms

[0079] W ij =[W ijs W ij(s+1) ,…,W ije ]

[0080] 3) Calculate the signal-to-interference-plus-noise ratio (SINR) threshold. The SINR threshold is defined as the minimum average SINR value when the beam is blocked or the transmitter is silent. We calculate the SINR threshold based on the channel estimation filter coefficients.

[0081]

[0082] Where Ψ = {s, s+1, ..., e}, and \ denotes set difference operation. When s = 0, e = 119, and SNR = -30dB, the signal-to-interference-plus-noise ratio (SNR) thresholds for different antennas are as follows: Figure 6 As shown.

[0083] In one embodiment, such as Figure 7 As shown, the channel estimation method chosen is MMSE channel estimation. A signal-to-interference-plus-noise ratio (SINR) threshold estimation method consists of the following three steps:

[0084] 1) Select the channel estimation filtering method. Based on the system's computing power and configuration, select the MMSE channel estimation filtering method.

[0085] 2) Calculate the channel estimation filter coefficients. Based on the selected channel estimation filtering method, calculate the channel estimation filter coefficients. Assume the number of transmit antennas is 1 and the number of receive antennas is r. Assume the starting carrier index is s and the ending carrier index is e. Let R be the cross-correlation matrix of different carriers of the probe signal. The channel estimation filter coefficient W for receive antenna i and carrier j is... ij R(R+I) of the matrix e-s+1 / SNR) -1 The j-th row, where W ij It has the following forms

[0086] W ij =[W ijs W ij(s+1) ,…,W ije ]

[0087] 3) Calculate the signal-to-interference-plus-noise ratio (SINR) threshold. The SINR threshold is defined as the minimum average SINR value when the beam is blocked or the transmitter is silent. We calculate the SINR threshold based on the channel estimation filter coefficients.

[0088]

[0089] Where Ψ = {s, s+1, ..., e}, and \ denotes set difference operation. When s = 0, e = 1199, and SNR = -30dB, the signal-to-interference-plus-noise ratio (SNR) thresholds for different antennas are as follows: Figure 7 As shown.

[0090] The embodiments of the present invention have been described above with reference to the accompanying drawings, but this is not intended to limit the scope of the invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the invention should be considered within the scope of the invention.

Claims

1. A method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold, characterized in that, Includes the following steps: S1. Select a channel estimation filtering method based on the computing power and system configuration of the wireless communication system; S2. Calculate the channel estimation filter coefficients according to the selected channel estimation filtering method; S3. Calculate the signal-to-interference-plus-noise ratio (SINR) threshold of the carrier based on the channel estimation filter coefficients; The signal-to-interference-plus-noise ratio (SINR) threshold is defined as the minimum average SINR value under conditions of beam blocking or transmitter silence. The SINR threshold is calculated based on the channel estimation filter coefficients. : in, , The operator `*` represents the set difference operation, `*` represents the conjugate operation, and `||` represents the absolute value operation. The set belongs to the operation; the number of receiving antennas is r The starting carrier index is s The end carrier index is e ; Receiving antenna i carrier j The channel estimation filter coefficients are , It is calculating the antenna i carrier j When estimating the channel after filtering, the receiving antenna... i carrier The product coefficients for channel estimation; It is calculating the antenna carrier j When estimating the channel after filtering, the receiving antenna carrier The product coefficients for channel estimation; It is calculating the antenna carrier j When estimating the channel after filtering, the receiving antenna carrier The product coefficients for channel estimation.

2. The method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold according to claim 1, characterized in that, In step S1, the channel estimation filtering method includes minimum mean squared error (MMSE) channel estimation and discrete fourier transform (DFT) channel estimation.

3. The method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold according to claim 1, characterized in that, In step S1, when the number of operations performed per second by the wireless communication system is greater than... T x And the number of receiving antennas is less than T y When the minimum mean squared error (MMSE) is selected, channel estimation is chosen; otherwise, Discrete Fourier Transform (DFT) channel estimation is chosen. T x and T y It is a real number greater than 0.

4. The method for estimating the signal-to-interference-plus-noise ratio threshold according to claim 1, characterized in that, In step S2, assume the number of transmitting antennas is 1 and the number of receiving antennas is... r Assume the starting carrier index is s The end carrier index is e ; Receiving antenna i carrier j The channel estimation filter coefficients are: in, It is a receiving antenna. i carrier j Channel estimation filter coefficients, It is calculating the antenna i carrier j When estimating the channel after filtering, the receiving antenna i carrier The product coefficients for channel estimation.

5. The method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold according to claim 4, characterized in that, Assuming an antenna i carrier j The channel estimate before filtering is So, antenna i carrier j The filtered channel estimate is Specifically as follows: in, It is calculating the antenna i carrier j When estimating the channel after filtering, the receiving antenna i carrier k The product coefficients for channel estimation.

6. The method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold according to claim 1, characterized in that, It also includes step S4: constructing a three-dimensional dense wireless network based on the signal-to-interference-plus-noise ratio threshold, as follows: Assuming the number of receiving antennas is equal to r When the signal-to-interference-plus-noise ratio threshold is equal to Define a set of antennas that satisfy the following conditions ,in It is a real number whose value ranges from [-40dB, 40dB]. but The signal-to-interference-plus-noise ratio threshold is less than The set of all receiving antennas; When constructing a three-dimensional dense wireless network using smart reflective surfaces, the number of receiving antennas increases from the set... Selected from the options.

7. The method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold according to claim 1, characterized in that, In step S1, the wireless communication system is a base station.

8. The method for estimating the signal-to-interference-plus-noise ratio (SINR) threshold according to claim 1, characterized in that, In step S1, the wireless communication system is a terminal.

9. An apparatus for estimating a signal-to-interference-plus-noise ratio (SINR) threshold, characterized in that, It includes a channel estimation filtering method selection module, a channel estimation filtering coefficient calculation module, and a signal-to-interference-plus-noise ratio (SINR) threshold calculation module; Among them, the channel estimation filtering method selection module is used to select a suitable channel estimation filtering method according to the system computing power and system configuration; the channel estimation filtering coefficient calculation module is used to calculate the channel estimation filtering coefficients according to the selected channel estimation filtering method; and the signal-to-interference-plus-noise ratio (SINR) threshold calculation module is used to calculate the SINR threshold of the carrier according to the channel estimation filtering coefficients. The signal-to-interference-plus-noise ratio (SINR) threshold is defined as the minimum average SINR value under conditions of beam blocking or transmitter silence. The SINR threshold is calculated based on the channel estimation filter coefficients. : in, , The operator `*` represents the set difference operation, `*` represents the conjugate operation, and `||` represents the absolute value operation. This indicates that the set belongs to the operation, where, , The operator `*` represents the set difference operation, `*` represents the conjugate operation, and `||` represents the absolute value operation. The set belongs to the operation; the number of receiving antennas is r The starting carrier index is s The end carrier index is e ; Receiving antenna i carrier j The channel estimation filter coefficients are , It is calculating the antenna i carrier j When estimating the channel after filtering, the receiving antenna i carrier The product coefficients for channel estimation; It is calculating the antenna carrier j When estimating the channel after filtering, the receiving antenna carrier The product coefficients for channel estimation; It is calculating the antenna carrier j When estimating the channel after filtering, the receiving antenna carrier The product coefficients for channel estimation.

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