A method for low-latency transmission of IoT using a smart reflector and superimposed pilot signals

Abstract

This invention discloses a method for low-latency transmission of Internet of Things (IoT) assisted by a smart reflector and superimposed pilot signals, relating to the field of communication performance optimization technology. The method includes establishing a system model of URLLC assisted by a smart reflector and superimposed pilot signals to obtain a hybrid signal of direct and reflected links; performing LMMSE channel estimation on the obtained hybrid signal; based on the channel estimation result, performing data detection on the hybrid signal to obtain a lower bound of a closed-form expression for the achievable rate with a finite block length; based on the lower bound of the closed-form expression, constructing a joint optimization problem model of superimposed pilot power and smart reflector phase shift with the objective of maximizing user URLLC and rate; and designing an algorithm using a block coordinate descent method to jointly optimize the optimization problem and obtain the maximum weighted sum rate. This invention can improve transmission rate while reducing URLLC transmission latency.

Description

Technical Field

[0001] This invention relates to the field of communication performance optimization technology, and in particular to a method for low-latency transmission of the Internet of Things assisted by a smart reflector and superimposed pilot. Background Technology

[0002] Ultra-reliable and low-latency communications (URLLC) is one of the three major application scenarios of 5G, characterized by extremely low transmission latency and extremely high reliability. To ensure latency requirements are met, short packet transmission strategies are commonly used in URLLC scenarios. However, this strategy leads to a decrease in transmission rate, the degree of which is proportional to the length of the data packet. Therefore, how to improve the transmission rate of short packets while maintaining low latency is a problem worth considering in URLLC.

[0003] Intelligent Reflecting Surface (IRS) technology is an emerging wireless communication technology designed to improve the performance and efficiency of wireless networks. The core component of an IRS is a panel composed of numerous adjustable reflective elements. These elements can be phase-tunable materials or components, enabling precise control over the direction of signal reflection, refraction, or scattering. As a wireless signal propagates from the transmitter to the receiver, the IRS optimizes the signal propagation path by adjusting the state of its reflective elements, thereby increasing data transmission rates and improving network throughput and user experience. Therefore, IRS can be used to enhance the transmission rate of short packets in URLLC (URLLocal Communication Communication).

[0004] Superimposed Pilot (SP) technology can be used to reduce transmission latency in URLLC. By transmitting pilots and data simultaneously on the same frequency band, SP completely avoids the transmission latency caused by pilot overhead in Regular Pilot (RP) transmission, making it ideal for short packet transmission. Therefore, by combining the advantages of SP and IRS technologies, it is possible to increase transmission rate while reducing transmission latency. Summary of the Invention

[0005] In view of the problems existing in the background art, the present invention is proposed.

[0006] Therefore, the problem to be solved by this invention is how to provide a joint optimization scheme for phase and power for IRS and SP-assisted uplink URLLC massive multi-input multi-output (mMIMO) systems, so that the system can improve the transmission rate while reducing transmission latency.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a method for low-latency transmission of Internet of Things (IoT) assisted by a smart reflector and superimposed pilot, comprising: establishing a system model of a smart reflector and superimposed pilot-assisted URLLC to obtain a hybrid signal of a direct link and a reflected link; performing LMMSE channel estimation on the obtained hybrid signal; performing data detection on the hybrid signal based on the channel estimation result to obtain a lower bound of a closed-form expression for the achievable rate with a finite block length; constructing a joint optimization problem model of superimposed pilot power and smart reflector phase shift based on the lower bound of the closed-form expression, with the goal of maximizing user URLLC and rate; and designing an algorithm using a block coordinate descent method to jointly optimize the optimization problem to obtain the maximum weighted sum rate.

[0009] As a preferred embodiment of the intelligent reflector and superimposed pilot-assisted low-latency transmission method for Internet of Things (URLLC) described in this invention, the system model of the intelligent reflector and superimposed pilot-assisted URLLC includes: a base station, an intelligent reflector, and... One user; the intelligent reflective surface is composed of It consists of a passive reflector element; the user configures a single antenna; the base station configures... The communication link between the base station and the user is called the direct link, and the communication link that reaches the base station through the intelligent reflector is called the reflective link. The user sends a superimposed pilot signal to the base station, and the superimposed pilot signal reaches the base station through the direct link and the reflective link.

[0010] As a preferred embodiment of the intelligent reflector and superimposed pilot-assisted low-latency transmission method for Internet of Things described in this invention, the system model of the intelligent reflector and superimposed pilot-assisted URLLC further includes: the first... Channel between individual users and the smart reflector and the channel between the smart reflector and the base station The Ricean channel model is represented as follows:

[0011]

[0012]

[0013] in, Indicates the first Large-scale fading coefficient between individual users and smart reflectors This represents the large-scale fading coefficient between the smart reflector and the base station; Indicates the first Large-scale fading coefficient between individual users and base stations; let Indicates the first The Rice factor between individual users and intelligent reflective surfaces The Rice factor represents the distance between the smart reflector and the base station. The phase shift matrix represents the intelligent reflector; the first... The channel between a user and a base station is represented by the Rayleigh fading model:

[0014]

[0015] in, This indicates small-scale fading. and The line-of-sight portion of the channel. and This represents the non-line-of-sight portion of the channel; , and The elements of the array all follow a complex Gaussian distribution with zero mean and unit variance;

[0016] and Specifically, it is expressed as follows:

[0017]

[0018]

[0019]

[0020] in, , , and These represent the spacing between the reflecting elements and the carrier wavelength, respectively. and Indicates from the first The angle of arrival parameter from each user to the smart reflector. and This represents the angle of arrival parameter from the smart reflector to the base station. and This represents the emission angle parameter from the smart reflector to the base station.

[0021] As a preferred embodiment of the intelligent reflector and superimposed pilot-assisted low-latency transmission method for the Internet of Things described in this invention, the mixed signal is composed of superimposed pilot signals from the direct link and superimposed pilot signals from the reflected link, as shown below:

[0022]

[0023] in, Indicates the first Valid channels for each user and They represent the first Pilot power and data power for each user and They represent the first Pilot vectors and data vectors for each user, This indicates the conjugate transpose operation. This represents the additive white Gaussian noise matrix.

[0024] As a preferred embodiment of the intelligent reflector and superimposed pilot-assisted low-latency transmission method for the Internet of Things described in this invention, the following steps are included: based on the channel estimation result, performing data detection on the mixed signal to obtain the lower bound of the closed-form expression for the finite block length achievable rate includes the following steps: preprocessing the mixed signal using the channel estimation result and the known pilot vector, performing detection using an MRC receiver to obtain the data estimation value of the i-th user; calculating the i-th user's data estimation value based on the data estimation value. The signal-to-interference-plus-noise ratio (SIR) of the first user is calculated using Jensen's inequality. The finite block length for each user can reach the lower bound of the rate.

[0025] As a preferred embodiment of the intelligent reflective surface and superimposed pilot-assisted low-latency transmission method for the Internet of Things described in this invention, wherein: the first The data estimates for each user are calculated as follows:

[0026]

[0027] in, Indicates the first Channel estimation results for each user , and They represent the first Pilot power, pilot vector, and channel estimation results for each user.

[0028] As a preferred embodiment of the intelligent reflector and superimposed pilot-assisted low-latency transmission method for the Internet of Things described in this invention, wherein: the joint optimization problem model is based on the lower bound of the achievable rate. The construction and optimization variables include the pilot power of all users. Data power of all users Phase shift matrix of intelligent reflective surface The optimization problem is expressed in the following form:

[0029]

[0030] in, Indicates the first The weights for each user are: C1 represents the rate of each user must be greater than 0, C2 represents the energy consumption constraint of each user, and C3 represents the range of phase shift values ​​for each reflective element of the smart reflector.

[0031] As a preferred embodiment of the intelligent reflector and superimposed pilot-assisted low-latency transmission method for the Internet of Things described in this invention, the joint optimization is based on known large-scale parameters; the known large-scale parameters include large-scale fading coefficient, Rice factor, angle of arrival, and angle of departure parameters.

[0032] In a second aspect, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the intelligent reflective surface and superimposed pilot-assisted low-latency transmission method for the Internet of Things as described in the first aspect of the present invention.

[0033] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the intelligent reflective surface and superimposed pilot-assisted low-latency transmission method for the Internet of Things as described in the first aspect of the present invention.

[0034] The beneficial effects of this invention are as follows: this invention can improve the transmission rate while reducing the transmission delay of URLLC; this invention can reduce the pilot overhead of IRS channel estimation and achieve low-complexity channel estimation; based on the joint optimization of phase and power allocation of large-scale parameters, the computational complexity can be greatly reduced. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A model diagram of an uplink URLLC mMIMO system assisted by IRS and SP.

[0037] Figure 2 This is a flowchart of a method for low-latency transmission of the Internet of Things (IoT) assisted by a smart reflective surface and superimposed pilot signals.

[0038] Figure 3 This is a schematic diagram comparing the frame structures of RP and SP.

[0039] Figure 4 The diagram showing the relationship between the system weighted sum rate and the number of reflection units is provided for an embodiment of the present invention. Detailed Implementation

[0040] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0041] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0043] Example 1

[0044] Reference Figures 1-3 This is the first embodiment of the present invention, which provides a method for low-latency transmission of IoT assisted by a smart reflector and superimposed pilots. The present invention provides a joint phase and power optimization scheme for IRS and SP-assisted uplink URLLC mMIMO systems, enabling the system to improve transmission rate while reducing transmission latency. The method includes the following steps:

[0045] S1: Establish a system model of intelligent reflector and superimposed pilot-assisted URLLC to obtain the mixed signal of direct link and reflected link.

[0046] The system model in this embodiment is as follows: Figure 1 As shown, this uplink URLLC mMIMO system includes a base station, a smart reflector, and One user;

[0047] The intelligent reflective surface is composed of It consists of a passive reflector element; the user configures a single antenna; the base station configures... The communication link between the base station and the user is called a direct link, while the communication link that reaches the base station through the intelligent reflector is called a reflective link.

[0048] The user sends a superimposed pilot signal to the base station, and the superimposed pilot signal reaches the base station via a direct link and a reflection link.

[0049] The system model of intelligent reflector and superimposed pilot-assisted URLLC also includes:

[0050] No. Channel between individual users and the smart reflector and the channel between the smart reflector and the base station The Ricean channel model is represented as follows:

[0051]

[0052]

[0053] in, Indicates the first Large-scale fading coefficient between individual users and smart reflectors This represents the large-scale fading coefficient between the smart reflector and the base station; Indicates the first Large-scale fading coefficient between individual users and base stations; let Indicates the first The Rice factor between individual users and intelligent reflective surfaces The Rice factor represents the distance between the smart reflector and the base station. The phase shift matrix represents the intelligent reflective surface;

[0054] No. The channel between a user and a base station is represented by the Rayleigh fading model:

[0055]

[0056] in, This indicates small-scale fading. and The line-of-sight portion of the channel. and This represents the non-line-of-sight portion of the channel; , and The elements of the array all follow a complex Gaussian distribution with zero mean and unit variance;

[0057] and Specifically, it is expressed as follows:

[0058]

[0059]

[0060]

[0061] in, , , and These represent the spacing between the reflecting elements and the carrier wavelength, respectively. and Indicates from the first The angle of arrival parameter from each user to the smart reflector. and This represents the angle of arrival parameter from the smart reflector to the base station. and This represents the emission angle parameter from the smart reflector to the base station.

[0062] S2: Perform LMMSE channel estimation on the obtained mixed signal.

[0063] S2.1: As Figure 3 As shown in the figure, this embodiment of the invention provides a comparative diagram of the frame structures of RP and SP.

[0064] In RP (Pilot Signal) mode, pilot signals and data are transmitted separately; in SP (Short Packet Signal) mode, pilot signals and data are completely superimposed and transmitted simultaneously on the same frequency. For transmitting data of the same length, RP requires splitting the data into two frames, while SP only requires one frame, thus significantly reducing system transmission latency. In the system, all users simultaneously send short packet data to the base station using SP mode. The base station receives a mixed signal from the direct link and the reflected link as follows:

[0065]

[0066] in, Indicates the first Valid channels for each user and They represent the first Pilot power and data power for each user and They represent the first Pilot vectors and data vectors for each user, This indicates the conjugate transpose operation. This represents the additive white Gaussian noise matrix.

[0067] S2.2: The base station performs LMMSE channel estimation on the obtained mixed signal to obtain the channel estimation results for all users. .

[0068] S3: Based on the channel estimation results, perform data detection on the mixed signal to obtain a closed-form expression lower bound for the achievable rate with finite block length.

[0069] S3.1: Using the channel estimation results and the known pilot vector, preprocess the mixed signal, use an MRC receiver for detection, and obtain the data estimate of the i-th user.

[0070] Among them, the The data estimates for each user are calculated as follows:

[0071]

[0072] in, Indicates the first Channel estimation results for each user , and They represent the first Pilot power, pilot vector, and channel estimation results for each user.

[0073] S3.2: Calculate the first value based on the data estimate. The signal-to-interference-plus-noise ratio (SIR) for each user is calculated using the following formula:

[0074]

[0075] in, Indicates the length of the transport block. This indicates the operation of calculating variance. This indicates the operation of finding the expected value. This indicates the operation of finding the Euclidean norm. Indicates the noise term. Indicates the first Large-scale fading coefficient between individual users and base stations and For intermediate values, the specific expression is:

[0076]

[0077] .

[0078] S3.3: Calculate the first inequality using Jensen's inequality. The finite block length for a single user can reach the lower bound of the rate, as shown in the following formula:

[0079]

[0080] in, Indicates the probability of decoding errors. This represents the inverse function of the Gaussian Q-function. .

[0081] S4: Based on the lower bound of the closed expression, construct a joint optimization problem model of superimposed pilot power and intelligent reflector phase shift with the goal of maximizing user URLLC and rate.

[0082] Preferably, the joint optimization problem model is based on the lower bound of the achievable rate. The construction and optimization variables include the pilot power of all users. Data power of all users Phase shift matrix of intelligent reflective surface .

[0083] The optimization problem can be expressed as follows:

[0084]

[0085] in, Indicates the first The weights for each user are: C1 represents the rate of each user must be greater than 0, C2 represents the energy consumption constraint of each user, and C3 represents the range of phase shift values ​​for each reflective element of the smart reflector.

[0086] S5: The algorithm is designed using the block coordinate descent method to jointly optimize the optimization problem and obtain the maximum weighted sum rate.

[0087] S5.1: Decompose the optimization problem into a phase shift optimization subproblem and a power allocation optimization subproblem.

[0088] S5.2: Keep the power allocation of users unchanged, and use a genetic algorithm to solve the phase shift optimization subproblem.

[0089] S5.3: Keeping the phase shift matrix of the smart reflector unchanged, the power allocation subproblem is transformed into a geometric programming problem using the log function approximation method and the continuous convex approximation method, and then iteratively solved using the CVX convex optimization toolbox.

[0090] S5.4: Repeat S5.2 and S5.3 until the objective function value converges.

[0091] In summary, this invention can improve the transmission rate while reducing URLLC transmission latency; it can reduce the pilot overhead of IRS channel estimation and achieve low-complexity channel estimation; and it can greatly reduce the computational complexity by jointly optimizing phase and power allocation based on large-scale parameters.

[0092] Example 2

[0093] In a second embodiment of the present invention, a computer device is also provided, applicable to the intelligent reflector and superimposed pilot-assisted low-latency transmission method for the Internet of Things, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the intelligent reflector and superimposed pilot-assisted low-latency transmission method for the Internet of Things as proposed in the above embodiments.

[0094] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0095] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for realizing intelligent reflective surfaces and superimposed pilot-assisted low-latency transmission of the Internet of Things as proposed in the above embodiments.

[0096] Example 3

[0097] Reference Figure 4 This is the third embodiment of the present invention, which provides a method for low-latency transmission of the Internet of Things assisted by a smart reflective surface and superimposed pilot signals. In order to verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0098] Figure 4 A graph showing the relationship between the system weighted sum rate and the number of reflection units is presented, where k is a fixed power allocation factor representing the ratio of data power to pilot power.

[0099] from Figure 4 As can be seen, the joint optimization method provided by this invention can effectively improve the transmission rate of the URLLCmMIMO system under low latency constraints, and the transmission rate increases with the increase of the number of reflection units.

[0100] Furthermore, it can be seen that when the optimization method is not adopted, increasing the number of reflection units will actually lead to a decrease in system performance, which further illustrates the importance of the proposed method to the system.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for low-latency transmission of the Internet of Things assisted by a smart reflective surface and superimposed pilot signals, characterized in that: include, Establish a system model of intelligent reflector and superimposed pilot-assisted URLLC to obtain the mixed signal of direct link and reflected link; LMMSE channel estimation is performed on the obtained mixed signal; Based on the channel estimation results, data detection is performed on the mixed signal to obtain a lower bound of the closed-form expression for the achievable rate with a finite block length; Based on the lower bound of the closed expression, a joint optimization problem model of superimposed pilot power and smart reflector phase shift is constructed with the goal of maximizing user URLLC and rate. An algorithm is designed using a block coordinate descent method to jointly optimize the optimization problem and obtain the maximum weighted sum rate. The hybrid signal consists of superimposed pilot signals from the direct link and superimposed pilot signals from the reflected link, and is represented as follows: in, Indicates the first Valid channels for each user Indicates the first The channel between users and base stations Indicates the first The channel between individual users and the intelligent reflector The phase shift matrix represents the intelligent reflector. This represents the channel between the smart reflector and the base station. and They represent the first Pilot power and data power for each user and They represent the first Pilot vectors and data vectors for each user, This indicates the conjugate transpose operation. This represents the additive white Gaussian noise matrix; Based on the channel estimation results, data detection is performed on the mixed signal to obtain the lower bound of the closed-form expression for the achievable rate with a finite block length, including the following steps: Using the channel estimation results and known pilot vectors, the mixed signal is preprocessed and detected using an MRC receiver to obtain the first... Data estimates for each user; Calculate the first value based on the data estimate. Signal-to-interference-to-noise ratio for each user; Calculate the 1st inequality using Jensen's inequality. The finite block length of a single user can reach the lower bound of the rate; The first The data estimates for each user are calculated as follows: in, Indicates the first Channel estimation results for each user , and They represent the first Pilot power, pilot vector, and channel estimation results for each user; Calculate the first value based on the data estimate. The signal-to-interference-plus-noise ratio (SIR) for each user is calculated using the following formula: in, Indicates the length of the transport block. This indicates the operation of calculating variance. This indicates the operation of finding the expected value. This indicates the operation of finding the Euclidean norm. Indicates the noise term. Indicates the first Large-scale fading coefficient between individual users and base stations and For intermediate values, the specific expression is: in, Indicates the first Large-scale fading coefficient between individual users and smart reflectors This represents the large-scale fading coefficient between the smart reflector and the base station. Indicates the first The Rice factor between individual users and intelligent reflective surfaces The Rice factor represents the distance between the smart reflector and the base station. and This represents the emission angle parameter from the smart reflector to the base station. The line-of-sight portion of the channel. denoted by array steering vector, and N represents the number of passive reflective elements.

2. The intelligent reflective surface and superimposed pilot-assisted low-latency transmission method for the Internet of Things as described in claim 1, characterized in that: The system model of the intelligent reflector and superimposed pilot-assisted URLLC includes: A base station, a smart reflector and One user; The intelligent reflective surface is composed of It consists of a passive reflector element; the user configures a single antenna; the base station configures... The communication link between the base station and the user is called a direct link, while the communication link that reaches the base station through the intelligent reflector is called a reflective link. The user sends a superimposed pilot signal to the base station, and the superimposed pilot signal reaches the base station via a direct link and a reflection link.

3. The intelligent reflective surface and superimposed pilot-assisted low-latency transmission method for the Internet of Things as described in claim 2, characterized in that: The system model of the intelligent reflector and superimposed pilot-assisted URLLC also includes: No. Channel between individual users and the smart reflector and the channel between the smart reflector and the base station The Ricean channel model is represented as follows: in, The phase shift matrix represents the intelligent reflective surface; No. The channel between a user and a base station is represented by the Rayleigh fading model: in, This indicates small-scale fading. and The line-of-sight portion of the channel. and This represents the non-line-of-sight portion of the channel; , and The elements of the array all follow a complex Gaussian distribution with zero mean and unit variance; and Specifically, it is expressed as follows: in, , , and These represent the spacing between the reflecting elements and the carrier wavelength, respectively. and Indicates from the first The angle of arrival parameter from each user to the smart reflector. and This represents the angle of arrival parameter from the smart reflector to the base station.

4. The intelligent reflective surface and superimposed pilot-assisted low-latency transmission method for the Internet of Things as described in claim 3, characterized in that: The joint optimization problem model is based on the lower bound of the achievable rate. The construction and optimization variables include the pilot power of all users. Data power of all users Phase shift matrix of intelligent reflective surface ; The optimization problem is expressed in the following form: in, Indicates the first The weights for each user are: C1 represents the rate of each user must be greater than 0, C2 represents the energy consumption constraint of each user, and C3 represents the range of phase shift values ​​for each reflective element of the smart reflector.

5. The intelligent reflective surface and superimposed pilot-assisted low-latency transmission method for the Internet of Things as described in claim 4, characterized in that: The joint optimization is based on known large-scale parameters; The known large-scale parameters include the large-scale fading coefficient, Rice factor, angle of arrival, and angle of departure.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent reflective surface and superimposed pilot-assisted low-latency transmission method for the Internet of Things as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent reflective surface and superimposed pilot-assisted low-latency transmission method for the Internet of Things as described in any one of claims 1 to 5.

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