CN113660022B - Transceiver of air computing system under non-ideal CSI and IRS optimization design method - Google Patents

Transceiver of air computing system under non-ideal CSI and IRS optimization design method Download PDF

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CN113660022B
CN113660022B CN202110916731.0A CN202110916731A CN113660022B CN 113660022 B CN113660022 B CN 113660022B CN 202110916731 A CN202110916731 A CN 202110916731A CN 113660022 B CN113660022 B CN 113660022B
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CN113660022A (en
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许威
张雯惠
黄纯
禹树文
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Southeast University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/145—Passive relay systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/20—TPC being performed according to specific parameters using error rate
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H04W52/346—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/38—TPC being performed in particular situations
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    • 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
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Abstract

The invention discloses a transceiver and IRS (inter-reference signal) optimization design method of an air computing system under non-ideal CSI (channel state information), which comprises the following steps of: (1) according to the uncertain domain of the CSI, solving the error of the CSI under the condition that MSE of the receiving end is the worst
Figure DDA0003205877360000011
And calculating MSE of a receiving end; (2) removing constant modulus constraint of IRS phase, fixing transmitting power t of each sensor by taking minimum receiving end MSE as targetkAnd the combination coefficient m of the receiving end, optimizing the reflection phase vector v of the IRSk(ii) a (3) Reflection phase vector v of fixed IRSkOptimizing the individual sensor transmission power tkAnd a merging coefficient m of the receiving end; (4) repeating the steps (2) - (3) until MSE iteration of the receiving end converges; (5) reflection phase vector v to IRSkAnd carrying out constant modulus constraint. The invention can reduce the receiving error of the system under the condition of non-ideal CSI under the condition of limited total transmitting power.

Description

Transceiver of air computing system under non-ideal CSI and IRS optimization design method
Technical Field
The invention relates to the field of communication, in particular to a transceiver of an air computing system under non-ideal CSI and an IRS optimization design method.
Background
The development of internet of things (IoT) service is vigorous, and the aspects of people's life are related. The remarkable growth of mass data has a crucial significance to the internet of things as the most prominent feature of the current information high-speed era. On the one hand, the number of intelligent devices of the internet of things is continuously increased, and data collection becomes more difficult. On the other hand, information fusion of massive amounts of data, or what we call data aggregation, is more challenging with low spectral efficiency and high latency brought by billions of sensors.
To address this problem, researchers have proposed a promising solution, namely, over-the-air computation (AirComp) that exploits the superposition properties of wireless Multiple Access Channels (MACs). The required data can be directly obtained at the receiving end through the concurrent transmission of the sensors and the weighted average function of distributed local calculation in the system. AirComp is concerned with information theory, signal processing, transceiver design, etc. research has shown that AirComp can improve communication efficiency and reduce required bandwidth. However, the advantage of the above technique is promising, i.e. the MAC is not blocked.
To address this problem, Intelligent Reflective Surfaces (IRS) are considered to improve signal propagation conditions by reflecting an incident signal at a desired angle using passive reflective elements, which is an emerging complementary technology. It can be easily attached to the exterior of a building and is low cost. In view of the real-time reconstruction characteristic of the IRS, the application of the IRS, such as secure communication in the internet of things, synchronous wireless information and power transmission System (SWIPT), edge computing (MEC), etc., has been widely developed. Thus, using the IRS to assist the AirComp system, the MSE of the AirComp can be reduced by optimizing the transceivers and phase shifts. However, all of the above studies are highly dependent on perfect CSI, which is difficult to obtain in applications. Therefore, it is also necessary to introduce stochastic and deterministic robustness to mitigate performance degradation in CSI inaccuracy. Furthermore, the wireless sensors of the AirComp systems are typically powered by beacons with limited power, and the total power constraint is also a big challenge.
Disclosure of Invention
The invention aims to provide a transceiver of an air computing system under non-ideal CSI and an IRS optimization design method, wherein the transmitting power of each sensor at the transmitting end of the system of the IRS auxiliary AirComp system, the combining coefficient of the receiving end and the IRS phase are designed, so that the receiving MSE of the receiving end is minimized.
In order to solve the complex non-convex problem, the invention obtains the closed solution of the robust problem and the joint design of the transmitting power of each sensor, the combining coefficient of the receiving end and the IRS phase by utilizing the efficient alternative optimization algorithm and the KKT condition.
In order to achieve the purpose, the invention provides the following technical scheme:
a transceiver and IRS optimization design method of an air computing system under non-ideal CSI comprises the following steps:
(1) according to the uncertain domain of the CSI, solving the error of the CSI under the condition that MSE of the receiving end is the worst
Figure BDA0003205877340000021
Computing MSE of a receiving end, wherein K belongs to {1,2, …, K }, and K represents the number of sensors of a transmitting end in the air computing system;
(2) removing constant modulus constraint of IRS phase, fixing transmitting power t of each sensor by taking minimum receiving end MSE as targetkAnd the combination coefficient m of the receiving end, optimizing the reflection phase vector v of the IRSk；
(3) Reflection phase vector v of fixed IRSkOptimizing the individual sensor transmission power tkAnd a merging coefficient m of the receiving end;
(4) repeating the steps (2) - (3) until MSE iteration of the receiving end converges;
(5) reflection phase vector v to IRSkAnd carrying out constant modulus constraint.
Further, in the step (1), the CSI uncertainty region is
Figure BDA0003205877340000022
I.e. the uncertainty region of the CSI is limited to a radius of eWithin the zone.
Wherein,
Figure BDA0003205877340000023
a CSI error representing the feedback of the channel,
Figure BDA0003205877340000024
representing the true equivalent concatenated channel that the kth sensor reflected via the kth IRS front to the receiving base station,
Figure BDA0003205877340000025
representing the estimated equivalent concatenated channel for the kth sensor to reach the receiving base station via the kth IRS front reflection. Where N represents the number of IRS reflecting units, K ∈ {1,2, …, K }, K represents the number of transmit-end sensors in the airborne computing system, |2Represents the Frobenius norm of the vector ()HRepresenting a matrix conjugate transpose;
the MSE problem of the worst case at the receiving end is expressed as
Figure BDA0003205877340000026
The constraint condition is
Figure BDA0003205877340000027
Where m is the combining coefficient of the receiving end, vkIs the reflection vector of the kth IRS, tkRepresenting the transmission power, σ, of the kth sensor2Is additive white Gaussian noise power, | non-dominant2A square of a modulus representing a complex number;
since the cascade equivalent channels of each sensor are independent of each other, the MSE problem in the worst case translates into
Figure BDA0003205877340000031
Further, in the step (1), the error of the CSI at the receiving end under the condition that the MSE is the worst is
Figure BDA0003205877340000032
Wherein (C)*Which represents the conjugate of the complex number,
Figure BDA0003205877340000033
is an intermediate variable, λkDenotes the KKT (Karush-Kuhn-Tucker) multiplier at equivalent cascade channel error optimization for the kth sensor, and
Figure BDA0003205877340000034
at this time, the CSI is the worst case, and the receiving MSE of the receiving end is
Figure BDA0003205877340000035
Further, in the step (2), the transmission power t of each sensor is fixedkAnd a combination coefficient m of a receiving end, constructing a reflection phase vector v for optimizing IRS (interference rejection ratio) by the following optimization problemk：
The optimization target is as follows:
Figure BDA0003205877340000036
the constraint conditions are as follows: | vk(n)|2＝1，n∈{1,…,N}
Wherein v isk(n) denotes the nth element in the reflection vector of the kth IRS; because the sensors are independent, the summation optimization problem can be decomposed into K sub-optimization problems, K belongs to {1,2, …, K }, and K represents the number of the sensors at the transmitting end in the aerial computing system; to make the problem solvable, the constant modulus constraint of the IRS phase is temporarily removed first and the optimization objective becomes
Figure BDA0003205877340000037
Wherein,
Figure BDA0003205877340000038
is a function of the intermediate variable(s),
Figure BDA0003205877340000039
representing the total power limit of K sensors in the airborne computing system.
Further, in the step (2), the reflection phase vector of the optimal IRS is
Figure BDA00032058773400000310
Wherein,
Figure BDA00032058773400000311
represents the Moore-Penrose inverse of the computational matrix.
Further, in the step (3), the reflection phase vector v passing through the fixed IRSkOptimizing the transmission power t of each sensor by constructing an optimization problemkAnd a combining coefficient m of the receiving end:
the optimization target is as follows:
Figure BDA0003205877340000041
obtaining the optimal intermediate variable by derivation
Figure BDA0003205877340000042
And is
Figure BDA0003205877340000043
Figure BDA0003205877340000044
Wherein,
Figure BDA0003205877340000045
represents the Moore-Penrose inverse of the computational matrix.
Further, in the step (5), a reflection phase vector v for the IRSkIs subjected to constant modulus constraint of
Figure BDA0003205877340000046
Wherein |2Representing the Frobenius norm of the vector.
Has the beneficial effects that: according to the invention, the transmitting power of each sensor at the transmitting end of the system of the IRS auxiliary AirComp system, the combining coefficient of the receiving end and the IRS phase are designed, so that the receiving MSE of the receiving end is minimized. In order to solve the complex non-convex problem, a closed solution of a robust problem and a joint design of the transmitting power of each sensor, the combining coefficient of a receiving end and the IRS phase are obtained by utilizing an efficient alternative optimization algorithm and a KKT condition. The method adopts an alternative optimization algorithm to solve the original complex optimization problem of non-convex and very high coupling degree, converts the non-convex problem into the convex problem through an intermediate variable, obtains a closed solution, and has absolute advantages compared with the non-closed solutions of other inventions. In addition, the invention can effectively reduce the receiving error of the system under the condition of non-ideal CSI under the condition of limited total transmitting power.
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FIG. 1 is a schematic diagram of a practical application scenario of the present invention;
FIG. 2 is a flow chart of the present invention;
fig. 3 is a graph of received NMSE versus the number of IRS reflection elements N using the optimization method of the present invention.
Detailed Description
The technical solutions provided by the present invention will be described in detail below with reference to specific examples, and it should be understood that the following specific embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention.
The technical terms involved in the present invention are explained as follows:
CSI: channel state information;
AirComp: an over-the-air computing system;
IRS: an intelligent reflecting surface;
MSE: the minimum mean square error.
The invention relates to a transceiver of an air computing system (AirComp) under non-ideal Channel State Information (CSI) and an Intelligent Reflection Surface (IRS) optimization design method, wherein a transmitting end sends environment information required by an IoT system for K single-antenna sensors, the environment information reaches a receiving end through the transmission of K IRSs with N reflection units, and the receiving end is a single antenna and receives the sum of the information quantity sent by the K single-antenna sensors.
The invention takes the limitation of total transmission power as a constraint condition to research the errors of the MSE and the CSI of the receiving end under the condition that the MSE of the receiving end is the worst CSI
Figure BDA0003205877340000051
And jointly optimize the transmitting power t of each sensor at the transmitting end of the systemkCombining coefficient m and IRS phase v of receiving endkThereby minimizing the received minimum Mean Square Error (MSE) at the receiving end.
In order to reduce the receiving MSE of the receiving end, the invention designs the transmitting power of each sensor at the system transmitting end, the combining coefficient and the IRS phase of the receiving end of the IRS auxiliary AirComp system, thereby minimizing the receiving MSE of the receiving end. In order to solve the complex non-convex problem, the invention obtains the closed solution of the robust problem and the joint design of the transmitting power of each sensor, the combining coefficient of the receiving end and the IRS phase by utilizing the efficient alternative optimization algorithm and the KKT condition. The method adopts an alternative optimization algorithm to solve the original complex optimization problem of non-convex and very high coupling degree, converts the non-convex problem into the convex problem through an intermediate variable, obtains a closed solution, and has absolute advantages compared with the non-closed solutions of other inventions.
As shown in fig. 1, a transmitting end transmits environment information required by an IoT system for K single-antenna sensors, and K IRS are used to reconstruct a propagation environment in order to enhance a received signal of a receiving end. The main optimization design idea is that firstly, according to an uncertain domain of the CSI, errors of receiving MSE under the condition that the MSE of a receiving end is the worst and the CSI at the moment are analyzed; then under the MSE of the worst condition, firstly removing the constant modulus constraint of the IRS phase, fixing the transmitting power of each sensor and the combination coefficient of the receiving end, optimizing the reflection phase vector of the IRS, then fixing the reflection phase vector of the IRS, optimizing and fixing the transmitting power of each sensor and the combination coefficient of the receiving end, and repeating the alternating optimization steps of the reflection phase vector of the IRS and the receiving and transmitting end coefficient of the system until the receiving MSE of the system converges; and carrying out constant modulus limitation on the optimization result of the IRS reflection phase vector. The invention can effectively reduce the receiving error of the system under the condition of non-ideal CSI under the condition of limited total transmitting power.
The invention relates to an optimal design method of a transceiver and an IRS of an AirComp system under non-ideal CSI, which comprises the following steps:
(1) according to the uncertain domain of the CSI, solving the error of the CSI under the condition that MSE of the receiving end is the worst
Figure BDA0003205877340000061
Wherein the CSI uncertainty region is
Figure BDA0003205877340000062
Namely, the uncertain domain of the CSI is limited in a region with epsilon as a radius;
wherein,
Figure BDA0003205877340000063
a CSI error representing the feedback of the channel,
Figure BDA0003205877340000064
representing the true equivalent concatenated channel that the kth sensor reflected via the kth IRS front to the receiving base station,
Figure BDA0003205877340000065
representing the estimated equivalent concatenated channel for the kth sensor to reach the receiving base station via the kth IRS front reflection. Where N represents the number of IRS reflection units, K ∈ {1,2, …, K }And K represents the number of transmitting end sensors in the AirComp system, | II2Represents the Frobenius norm of the vector ()HRepresenting a matrix conjugate transpose;
at this time, the MSE problem of the worst case at the receiving end is expressed as
Figure BDA0003205877340000066
The constraint condition is
Figure BDA0003205877340000067
Where m is the combining coefficient of the receiving end, vkIs the reflection vector of the kth IRS, tkRepresenting the transmission power, σ, of the kth sensor2Is additive white Gaussian noise power, | non-dominant2A square of a modulus representing a complex number;
since the cascaded equivalent channels of each sensor are independent of each other, the MSE problem of the worst case scenario described above translates into
Figure BDA0003205877340000068
Wherein K belongs to {1,2, …, K }, and K represents the number of sensors at the transmitting end in the AirComp system;
the error of the CSI in the worst case is
Figure BDA0003205877340000069
Wherein (C)*Which represents the conjugate of the complex number,
Figure BDA00032058773400000610
is an intermediate variable, λkDenotes the KKT (Karush-Kuhn-Tucker) multiplier at equivalent cascade channel error optimization for the kth sensor, and
Figure BDA00032058773400000611
at this time, the CSI is the worst case, and the receiving MSE of the receiving end is
Figure BDA0003205877340000071
(2) Removing constant modulus constraint of IRS phase, fixing transmitting power t of each sensor by taking minimum receiving end MSE as targetkAnd the combination coefficient m of the receiving end, optimizing the reflection phase vector v of the IRSk. By fixing the transmission power t of the individual sensorskAnd a combination coefficient m of a receiving end, constructing a reflection phase vector v for optimizing IRS (interference rejection ratio) by the following optimization problemk：
The optimization target is as follows:
Figure BDA0003205877340000072
the constraint conditions are as follows: | vk(n)|2＝1，n∈{1,…,N}
Wherein v isk(n) denotes the nth element in the reflection vector of the kth IRS; because the sensors are independent, the summation optimization problem is decomposed into K sub-optimization problems, K belongs to {1,2, …, K }, and K represents the number of the sensors at the transmitting end in the air computing system; to make the problem solvable, the constant modulus constraint of the IRS phase is temporarily removed first and the optimization objective becomes
Figure BDA0003205877340000073
Wherein,
Figure BDA0003205877340000074
is the intermediate variable(s) of the variable,
Figure BDA0003205877340000075
representing the total power limit of K sensors in the AirComp system;
wherein the reflection phase vector of the optimal IRS is
Figure BDA0003205877340000076
Wherein,
Figure BDA0003205877340000077
represents the Moore-Penrose inverse of the computational matrix.
(3) Reflection phase vector v of fixed IRSkOptimizing the individual sensor transmission power tkAnd a merging coefficient m of the receiving end; reflection phase vector v by fixed IRSkOptimizing the transmission power t of each sensor by constructing an optimization problemkAnd a combining coefficient m of the receiving end:
the optimization target is as follows:
Figure BDA0003205877340000078
obtaining the optimal intermediate variable by derivation
Figure BDA0003205877340000079
And is
Figure BDA0003205877340000081
Figure BDA0003205877340000082
Wherein,
Figure BDA0003205877340000083
represents the Moore-Penrose inverse of the computational matrix.
(4) And (4) repeating the steps (2) to (3) until the MSE iteration of the receiving end converges.
(5) Reflection phase vector v to IRSkPerforming constant modulus confinement, i.e.
Figure BDA0003205877340000084
Wherein |2Representing the Frobenius norm of the vector.
As shown in fig. 2, the main process of the present invention is to analyze the MSE received by the receiving end under the condition that the MSE is the worst and the error of the CSI at this time according to the uncertainty domain of the CSI; then under the MSE of the worst condition, firstly removing the constant modulus constraint of the IRS phase, fixing the transmitting power of each sensor and the combination coefficient of the receiving end, optimizing the reflection phase vector of the IRS, then fixing the reflection phase vector of the IRS, optimizing and fixing the transmitting power of each sensor and the combination coefficient of the receiving end, and repeating the alternating optimization steps of the reflection phase vector of the IRS and the receiving and transmitting end coefficient of the system until the receiving MSE of the system converges; and carrying out constant modulus limitation on the optimization result of the IRS reflection phase vector.
As shown in fig. 3, the optimized design scheme proposed by the present invention can effectively reduce the receiving error of the system under the non-ideal CSI condition under the condition that the total transmission power is limited. Compared with the traditional scheme which does not consider the robust design, the method has the performance advantage.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and such improvements and modifications are also considered to be within the scope of the present invention.

Claims (2)

1. A transceiver and IRS optimization design method of an air computing system under non-ideal CSI is characterized in that: the method comprises the following steps:
(1) according to the uncertain domain of the CSI, solving the error of the CSI under the condition that MSE of the receiving end is the worst
Figure FDA0003620580340000011
And computing MSE at a receiving end, wherein K belongs to {1,2, …, K }, and K represents an aerial meterCalculating the number of transmitting end sensors in the system;
the uncertain domain of CSI is
Figure FDA0003620580340000012
Namely, the uncertain domain of the CSI is limited in a region with epsilon as a radius;
wherein,
Figure FDA0003620580340000013
a CSI error representing the feedback of the channel,
Figure FDA0003620580340000014
representing the true equivalent concatenated channel that the kth sensor reflected via the kth IRS front to the receiving base station,
Figure FDA0003620580340000015
an estimated equivalent concatenated channel representing the arrival of the kth sensor at the receiving base station via the kth IRS front reflection; where N represents the number of IRS reflecting units, K ∈ {1,2, …, K }, and K represents the number of transmitting-end sensors in the airborne computing system, |2Represents the vector Frobenius norm, ()HRepresenting a matrix conjugate transpose;
the MSE problem of the worst case at the receiving end is expressed as
Figure FDA0003620580340000016
The constraint condition is
Figure FDA0003620580340000017
Where m is the combining coefficient of the receiving end, vkIs the reflection vector of the kth IRS, tkRepresenting the transmission power, σ, of the kth sensor2Is additive white Gaussian noise power, | non-dominant2A square of a modulus representing a complex number;
since the cascade equivalent channels of each sensor are independent of each other, the MSE problem in the worst case translates into
Figure FDA0003620580340000018
The error of CSI under the condition of the MSE (mean square error) of the receiving end is
Figure FDA0003620580340000019
Wherein (C)*Which represents the conjugate of the complex number,
Figure FDA00036205803400000110
is an intermediate variable, λkRepresents the KKT multiplier in the optimization of the equivalent cascade channel error for the kth sensor, and
Figure FDA00036205803400000111
at this time, the CSI is the worst case, and the receiving MSE of the receiving end is
Figure FDA0003620580340000021
(2) Removing constant modulus constraint of IRS phase, fixing transmitting power t of each sensor by taking minimum receiving end MSE as targetkAnd the combination coefficient m of the receiving end, optimizing the reflection phase vector v of the IRSk；
By fixing the transmission power t of the individual sensorskAnd a combination coefficient m of a receiving end, constructing a reflection phase vector v for optimizing IRS (interference rejection ratio) by the following optimization problemk：
The optimization target is as follows:
Figure FDA0003620580340000022
the constraint conditions are as follows: | vk(n)|2＝1，n∈{1,…,N}
Wherein v isk(n) denotes the nth element in the reflection vector of the kth IRS; because the sensors are independent, the summation optimization problem can be decomposed into K sub-optimization problems, K belongs to {1,2, …, K }, and K represents the number of transmitting end sensors in the aerial computing system; to make the problem solvable, the constant modulus constraint of the IRS phase is temporarily removed first and the optimization objective becomes
Figure FDA0003620580340000023
Wherein,
Figure FDA0003620580340000024
is the intermediate variable(s) of the variable,
Figure FDA0003620580340000025
representing the total power limit of K sensors in the airborne computing system;
the reflection phase vector of the optimal IRS is
Figure FDA0003620580340000026
Wherein,
Figure FDA0003620580340000027
Moore-Penrose inverse representing the computational matrix;
(3) reflection phase vector v of fixed IRSkOptimizing the individual sensor transmission power tkAnd a merging coefficient m of the receiving end;
reflection phase vector v by fixed IRSkThe following optimization problem is constructed to optimize the transmission power t of each sensorkAnd a combining coefficient m of the receiving end:
the optimization target is as follows:
Figure FDA0003620580340000028
obtaining the optimal intermediate variable by derivation
Figure FDA0003620580340000029
And is provided with
Figure FDA0003620580340000031
Figure FDA0003620580340000032
Wherein,
Figure FDA0003620580340000033
Moore-Penrose inverse representing the computational matrix;
(4) repeating the steps (2) - (3) until MSE iteration of the receiving end converges;
(5) reflection phase vector v to IRSkAnd carrying out constant modulus constraint.
2. The method of claim 1, wherein the method comprises: in the step (5), the reflection phase vector v of IRSkIs subjected to constant modulus confinement of
Figure FDA0003620580340000034
Wherein |2Representing the Frobenius norm of the vector.
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