CN114095318B - Channel estimation method for intelligent super-surface-assisted mixed configuration millimeter wave communication system - Google Patents
Channel estimation method for intelligent super-surface-assisted mixed configuration millimeter wave communication system Download PDFInfo
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Abstract
The invention discloses a channel estimation method of an intelligent super-surface-assisted mixed configuration millimeter wave communication system. The method is characterized in that a channel between a base station and an intelligent reflecting surface is researched and decomposed based on a singular value decomposition method, and an analog beam forming matrix at the base station end is designed. Secondly, by activating the intelligent units on the intelligent reflecting surface in sequence, the obtained signals are estimated and accumulated at the base station end, virtual beam searching is carried out based on the accumulated data, and the arrival angle (AoA) value of the channel between the user end and the intelligent reflecting surface is measured. The invention only needs to start part of intelligent units on the intelligent reflecting surface, and can fully utilize the existing accumulated information to assist in estimating the strongest arrival angle, so that the time cost and the energy consumption of channel estimation can be greatly reduced. In addition, the channel estimation algorithm fully considers the influence of system hardware errors and priori information matrix errors on subsequent real angle estimation, and the robustness of the algorithm is verified through simulation.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a multi-user channel estimation method of a novel mixed configuration millimeter wave communication system under the auxiliary condition of an intelligent reflecting surface.
Background
With the shortage of low frequency resources, the fifth generation mobile communication technology (5G) needs to exploit available spectrum resources to a higher frequency band range. Millimeter waves are considered to be one of the most potential technologies for research in 5G. For high carrier frequency communication systems, high gain beams typically have a narrow beamwidth and are easily blocked, which has an impact on the robustness of the received signal. To solve this problem, reconfigurable intelligent reflective surface Reconfigurable Intelligent Surface (RIS) proposes to improve mobile communication performance by dynamically controlling the electromagnetic propagation environment. Under millimeter wave frequency band communication, when the base station end and the user end are blocked by barriers in the propagation space, such as wall surfaces, trees and the like, the intelligent reflecting surface can be reconfigured through proper configuration, the reflecting factors on the intelligent reflecting surface are designed, and a reflecting link from the user end to the intelligent reflecting surface and then to the base station end can be established, so that effective communication is carried out between the user end and the base station end.
However, the lack of a radio frequency link on the reconfigurable smart reflective surface for signal processing makes acquisition of channel state information difficult, and thus channel estimation for smart reflective surface assisted communication is an important challenge. The design of the reflection factor of the intelligent reflection surface is based on obtaining accurate channel state information, but most of researches are based on the known channel state information to design the reflection factor coefficient so as to optimize the communication performance. Most of the current channel estimation researches aiming at the intelligent reflecting surface are to jointly estimate the cascade channels from the user to the intelligent reflecting surface and then to the base station. The invention designs the corresponding base station to receive the analog wave beam by utilizing the singular value decomposition information of the base station and the intelligent reflecting surface channel, and obtains the channel state information from the user terminal to the intelligent reflecting surface. Under the condition that part of channel information is acquired, the corresponding arrival angle is searched and estimated through the virtual wave beam.
Disclosure of Invention
Aiming at a reconfigurable intelligent reflecting surface assisted mixed configuration millimeter wave communication system, the invention provides a novel multi-user channel estimation method under the intelligent reflecting surface assisted condition. The invention firstly carries out research and decomposition on the channel between the base station and the intelligent reflecting surface based on a singular value decomposition method, and designs an analog beam forming matrix at the base station end and unit division of the intelligent reflecting surface. Secondly, by activating the intelligent unit groups on the intelligent reflecting surface in sequence, the obtained signals are estimated and accumulated at the base station end, virtual beam searching is carried out based on the accumulated data, and the arrival angle value of the channel between the user end and the intelligent reflecting surface is measured. The invention only needs to start part of intelligent units on the intelligent reflecting surface, and can fully utilize the existing accumulated information to assist in estimating the strongest arrival angle, so that the time cost and the energy consumption of channel estimation can be greatly reduced. In addition, the channel estimation algorithm fully considers the influence of system hardware errors and priori information matrix errors on subsequent real angle estimation, and the robustness of the algorithm is verified through simulation.
The technical scheme of the invention comprises the following steps:
step 1, scene assumption and channel model;
step 2, designing beam forming of the base station end and dividing intelligent reflecting surface unit groups by utilizing a channel singular value decomposition result from the base station end to the intelligent reflecting surface;
step 3, observing and accumulating the channel data of each unit group from the user side to the intelligent reflecting surface;
step 4, virtual beam searching and fine estimation of the arrival angle between the user side and the intelligent reflecting surface are executed according to the observation data;
further, the step 1 specifically comprises the following steps:
scene assumptions and channel models are described as follows: a base station with M antennas communicates with K single antenna users with the aid of an intelligent reflecting surface with N passive reflecting elements. The communication channel model under the smart reflective surface includes three parts: the channel matrix from the base station end of the reflection link to the intelligent reflection surface is expressed asThe reflection channel matrix from the user terminal to the intelligent reflection surface is expressed as +.>The channel matrix from the direct channel user end to the base station end is expressed as +.>The direct channel from the user end to the base station end in the transmission model is supposed to be blocked by the barrier; and the base station end and the intelligent reflecting surface antenna array are both uniform linear arrays. The reflection channel model between the user terminal and the intelligent reflection surface adopts a rice channel. Wherein->Represented as a matrix of reflection element coefficients, beta, on an intelligent reflecting surface n ∈[0,1]And phi n ∈[0,2π]Representing the amplitude and phase adjustment coefficients of the n-th passive reflective element of the RIS, respectively.
Further, the step 2 specifically comprises the following steps:
2-1. To reduce the overhead cost of channel estimation, the number of singular values of the corresponding channel is observed by Singular Value Decomposition (SVD) of channel G. Screening all singular values with singular values larger than a set threshold value, and counting the number of the screened singular values; dividing passive unit groups on the intelligent reflecting surface according to the number P of singular values, and dividing the intelligent reflecting surface by using P passive devices as one passive unit group (the number of the passive devices on the intelligent reflecting surface and the singular value of an observation channel are in a multiple relation with the number that the singular value of the observation channel is larger than a set threshold); so as to simultaneously turn on a plurality of passive devices for channel estimation operation; the following cases exist for dividing the passive unit group on the intelligent reflecting surface by the number P of singular values:
by observing the number of singular values greater than the threshold, it is assumed that the number greater than the threshold is 3, 4, and 5, and setting the passive devices on the intelligent reflecting surface to be a common multiple greater than the threshold is 3×4×5=60;
if the two devices cannot be equally divided, assuming that the 64 devices are divided into groups of every three, the two devices have 21 groups of more than 1, and at the moment, we do not need to measure more than 1, so that the subsequent beam searching of the two devices has no influence;
2-2 when the passive devices on the P intelligent reflecting surfaces are activated simultaneously, the passive units on the intelligent reflecting surfaces are assembled to the channels of the base station end, and the user end is connected toq∈{1,…,Q}，/>The channels of the table passive unit group may be represented in turn as G q The e is shown as a matrix of phase amplitude coefficients of the passive group of cells on the intelligent reflecting surface.
By means of channel G q Singular value decomposition is carried out to obtain:
wherein G is q Is a matrix of singular values of (a)Is a diagonal matrix of nonnegative real numbers arranged in descending order, and the parameter superscript "H" in the formula (1) represents conjugate transposition according to the arrangement from large to small; />Is-> For unitary matrix, the property is +>
2-3, by activating the passive unit groups on the intelligent reflecting surface in sequence, the user side transmits the pilot signal X to the base station side through the passive unit groups on the intelligent reflecting surface, and the transmission model is as follows:
wherein,for pilot sequences transmitted by all clients in T time slots, W RF,q Is an analog beam forming matrix at the base station end, and is synthesized by adopting a phase shifter network.
Is obeyed to mean 0 and variance sigma 2 Additive white gaussian noise of (c).
To maximize the performance of the transmission system, the analog precoding is set to the decomposed unitary matrix U q Conjugate transpose of the preceding P columns, i.e.The base station side received signal is expressed as follows:
Y q ＝W RF,q G q Θ q H r,q X+W RF,q Z BS
＝Λ q Θ q H r,q X+W RF,q Z BS (3)
wherein, define
Further, the step 3 specifically comprises the following steps:
the linear equation can be obtained by the transmission model in the step 2, and the channel from the user to the intelligent reflecting surface is estimated by using a least square method:
can be estimated asNamely:
wherein,the representation is a left pseudo-inverse. During channel estimation, the amplitude of all intelligent reflecting surface passive devices is set to be 1, so that Θ can be obtained q Is an identity matrix. By activating the intelligent reflecting units in sequence and accumulating and observing the channel data, an estimated channel can be obtained, i.e.>
Further, the step 4 specifically comprises the following steps:
according to sparsity of millimeter wave communication channelsAnd 3, the characteristics that the strongest arrival angle from the user end to the intelligent reflecting surface is found only by utilizing the partial channel information estimated in the step 3 to perform virtual beam search. By searching for part of the channelThe sight distance arrival angle value from each user end to the intelligent reflecting surface can be estimated. The arrival angle detection matrix is designed as follows:
which contains J columns of detection vectors, then the ith column of detection vectors, i.e The following are provided:
wherein,possible angles of arrival for the intelligent reflecting surface. d represents the distance between adjacent antennas, and the beam search value of the ith virtual direction is:
wherein,and (5) representing the rice factor weight coefficient matrixes of different user terminals.
The virtual beam search maximum can be expressed as:
the index corresponding to the maximum search value is the strongest arrival angle value from the user side to the intelligent reflecting surface.
The invention has the following beneficial effects:
the invention only needs to start part of intelligent units on the intelligent reflecting surface, and can fully utilize the existing accumulated information to assist in estimating the strongest arrival angle, so that the time cost and the energy consumption of channel estimation can be greatly reduced. In addition, the channel estimation algorithm fully considers the influence of system hardware errors and priori information matrix errors on subsequent real angle estimation, and the robustness of the algorithm is verified through simulation.
Drawings
FIG. 1 is a communication system with the aid of intelligent reflective surfaces;
FIG. 2 is a flow chart of a reflected channel estimation implementation;
FIG. 3 user side to intelligent reflective surface virtual beam search beam pattern;
FIG. 4 is a base station end configuration of the influence of different numbers of radio frequency links on channel estimation;
FIG. 5 shows the Mean Square Error (MSE) of a user to an intelligent reflector channel as a function of signal-to-noise ratio for different error weights;
FIG. 6 shows the relationship of the change of the line-of-sight angle from the user to the intelligent reflector channel along with the signal-to-noise ratio under different error weights;
FIG. 7 shows the variation of average achievable data transmission rate with SNR for different error weights;
Detailed Description
The following describes the embodiments of the present invention with reference to the drawings.
As shown in fig. 1 and 2, the channel estimation method of the intelligent super-surface-assisted mixed configuration millimeter wave communication system comprises the following specific implementation steps:
step 1, a channel model and an application scene:
considering the millimeter wave communication scene of a base station with multiple antennas and a single antenna and multiple users, a new reflection link is established through a reconfigurable intelligent reflection surface to carry out communication on the assumption that a direct link between a base station end and a user is blocked by an obstacle. The propagation environment is dynamically changed between the base station end and the intelligent reflecting surface through a controller. Initialization setting and model establishment: setting a channel Lees factor theta, the number of base station end antennas M, the number of passive elements N on an intelligent reflecting surface, the number of single antenna users K, the number of channel scattering paths, the setting of signal-to-noise ratio SNR and the design of pilot signals. The millimeter wave system assumes that the channel is a rice channel that fades slowly in one coherent time slot, and thus the channel is constant in one time slot. Fig. 1 shows a transmission model under intelligent reflector-assisted communication.
The channel matrix from the base station end of the reflection link to the intelligent reflection surface can be expressed as The reflection channel matrix from the user terminal to the intelligent reflection surface is expressed as +.>The reflection channels from the kth user end to the intelligent reflection surface and then to the base station end are respectively expressed as follows:
wherein, ignoring the subscript, θ represents the rice factor, which is the ratio of the power of the main path component LoS to the power of the scattering path component NLoS; g LoS ，Respectively representing the main arrival path from the intelligent reflecting surface to the base station end and from the kth user to the intelligent reflecting surface; g NLoS ，Respectively representing scattering paths from the intelligent reflecting surface to the base station section and from the kth user to the intelligent reflecting surface; and the base station end and the intelligent reflecting surface antenna array adopt uniform linear array response. Channel H for all users r ＝[h r,1 … h r,K ]Can be expressed as follows:
H r ＝H L V L +H S V S (3)
wherein, is the rice factor for different users.
Represented as a matrix of reflection element coefficients, beta, on an intelligent reflecting surface n ∈[0,1]And phi n ∈[0,2π]Representing the amplitude and phase adjustment coefficients of the n-th passive reflective element of the RIS, respectively.
Step 2, designing beam forming of the base station end and dividing intelligent reflecting surface unit groups by using a channel singular value decomposition method from the base station end to the intelligent reflecting surface:
2-1. As the number of passive devices increases, the channel estimation time overhead and the energy consumption overhead of the intelligent reflecting surface continuously increase. In order to reduce the overhead cost of channel estimation, using step 1, the singular value decomposition is performed on channel G as follows:
G＝U∑V H (4)
wherein,as unitary matrix, characteristic is U H U＝I M×M ，V H V＝I N×N The parameter superscript "H" in equation (4) represents the conjugate transpose; singular value matrix of G->Is that the diagonal elements are non-negative real numbers and the diagonal elements are the ordered singular values of the matrix G. The number of singular values of the corresponding channel G is observed. The passive units on the intelligent reflecting surface are designed and divided by the number of the singular values (the number of diagonal elements with the larger values is assumed to be P), so that a plurality of passive devices are simultaneously started for channel estimation operation.
2-2 when the passive devices on the P smart reflective surfaces are activated simultaneously, the channels from the smart unit to the base station, the channels from the user side to the smart unit can be expressed in turn as
Represented as the phase amplitude coefficient of the passive element on the smart reflective surface.
In order to reduce the energy consumption and cost of the system as much as possible and meet the development characteristics of the millimeter wave communication system, the invention designs aiming at the mixed modulus architecture and estimates the channel from the user end to the intelligent reflection unit on the basis of assuming the channel state information between the known intelligent reflection surface and the base station.
To channel G q Singular Value Decomposition (SVD) can be performed to obtain:
wherein G is q Is a matrix of singular values of (a)Diagonal matrix of non-negative real numbers arranged in descending order, < >>Is-> As unitary matrix, characterized by
2-3 by activating the intelligent reflection units in sequenceThe user terminal transmits the pilot signal X to the base station terminal through the unit on the intelligent reflecting surface, and the transmission model is as follows:
wherein,pilot sequences transmitted in T slots for all users. W (W) RF,q The method is the analog beam forming of the base station end, and the phase shifter network is adopted for synthesis, so that a mixed modulus configuration is formed. Z is Z BS Is obeyed to mean 0 and variance sigma 2 Additive white gaussian noise of (c).
To maximize the performance of the transmission system, the analog precoding is set to the decomposed unitary matrix U q Conjugate transpose of the preceding P columns, i.e.The base station side received signal is expressed as follows:
Y q ＝W RF,q G q Θ q H r,q X+W RF,q Z BS (7)
＝Λ q Θ q H r,q X+W RF,q Z BS (8)
wherein, define
Step 3, observing and accumulating channel data from the user end to the unit on the intelligent reflecting surface:
the linear equation can be obtained by the transmission model in the step 2, and the channel from the user terminal to the intelligent reflecting surface is estimated by using a least square method (LS):
can be estimated asNamely:
wherein,the representation is a left pseudo-inverse. The above can be written as:
during channel estimation, the amplitude of all intelligent reflecting surface passive devices is set to be 1, so that Θ can be obtained q Is an identity matrix. By sequentially activating the corresponding intelligent reflecting units, accumulating and observing the channel data, an estimated channel can be obtained, namely
And 4, executing a virtual beam searching and fine estimating method of the arrival angle between the user side and the intelligent reflecting surface according to the observed data:
and (3) according to the sparsity characteristic of the millimeter wave communication channel, finding the strongest arrival angle from the user end to the intelligent reflecting surface only needs to perform virtual beam search by using part of channel information estimated in the step (3). By searching for part of the channelThe line-of-sight arrival angle value of each user to the intelligent reflecting surface can be estimated. The user side performs virtual beam search from 0 degree to 180 degrees, and the angle search step length is +.>The strongest angle of arrival is estimated. Designing the arrival angle detection matrix to be +.>The search matrix contains J columns of detection vectors, then the ith column of detection vector, i.e. +.>The following are provided:
wherein,for the possible angle of arrival of the intelligent reflecting surface, the characteristic is +.>The beam search value of the i-th virtual direction is:
the virtual beam search maximum can be expressed as:
wherein the index corresponding to the maximum search value is the strongest arrival angle value from the user side to the intelligent reflecting surface, i.e. the line-of-sight arrival angle from the user side to the intelligent reflecting surface is
Step 5, according to the virtual wave beam searching and fine estimating method of the arrival angle between the user end of the observed data and the intelligent reflecting surface in the step 4, the sight distance arrival angle from the user end to the intelligent reflecting surface is measured, the phase amplitude coefficient of a device on the intelligent reflecting surface is designed based on the unitary matrix obtained by the singular value decomposition of the channel G and the measured angle value, the downlink data transmission is carried out, and the equivalent channel of the reflecting link of the kth user is as follows if the direct link is blocked by an obstacle:
wherein,the method is a beam forming vector at a base station end, the parameter superscript is conjugated, and the vector is designed as a unitary matrix U corresponding to the largest singular value in ordered singular values of a matrix G; the design of Θ is based on the strongest arrival angle of the user side and the intelligent reflecting surface and G singular value decomposition to obtain the vector of unitary matrix V corresponding to the maximum singular value, and the vector is expressed as follows:
wherein,the arrival angle of the line-of-sight between the kth user and the intelligent reflecting surface channel measured based on the virtual beam in the step 4; v 1 Represented as base station end-to-intelligenceBeam forming vectors of intelligent reflecting surface ends of the reflecting surfaces; the arithmetic symbol "" in the formula indicates the Hadamard product.
Equivalent channel H eq,k The system comprises a sight path and a scattering path, wherein the scattering path is an interference source and is expressed as follows:
thus, the received signal to interference plus noise ratio (SINR) for user k is:
the data transmission rate of each user at this time can be expressed as:
R k ＝log2(1+SINR k )，k＝1,…,K
the total data transmission rate achievable by the system is:
and 6, considering errors of subsequent channel information estimation and angle estimation and influences on downlink transmission efficiency due to system hardware errors and prior information matrix errors. Under the condition that errors exist between the base station end and the channel state information of the intelligent reflecting surface, namely:
where ρ is the error weight,is a source of channel errors, subject to a standard normal gaussian distribution. Under the condition that errors exist in the step 4, virtual beam searching of channels between the user side and the intelligent reflecting surface can be obtainedAnd (3) measuring an angle of arrival error. The virtual beam search is used for measuring the sight distance arrival angle between the user terminal and the intelligent reflecting surface under different error weights, and has influence on the design of the downlink data transmission Θ. The impact on the average achievable data transfer rate can be studied according to the different Θ designs.
Examples:
in the simulation, the base station is provided with a uniform linear array with 16 half-wavelength antenna intervals, 8 single-antenna users and an intelligent reflecting surface is provided with 64 passive reflecting elements. In the example where the transmit pilot channel slot is 8, the signal-to-noise ratio SNR is at 5dB intervals, ranging from 5dB to 20dB. The other parameters were set as follows: the number of scattering paths between the base station end and the intelligent reflecting surface is 11, and the number of scattering paths between the intelligent reflecting surface and a single user is 5.
Fig. 3 shows a beam pattern of a virtual beam search of an estimated channel for known channel state information G. Different colors in the figure represent different users.
Fig. 4 shows that, in the case of the known channel state information G, when the base station configures different radio frequency links, i.e., G singular value decomposition, a unitary matrix is obtainedAnd observing the number of the singular values of the corresponding channel G, and respectively taking vectors in the unitary matrix U corresponding to the number of the larger singular values to design beam forming of the base station. The base station end is shown to be configured with different radio frequency links, and the Mean Square Error (MSE) of the user to the intelligent reflector channel is changed along with the signal to noise ratio.
Fig. 5 shows the performance of testing different error weights on channel estimation of the user side and the intelligent reflection surface under different signal-to-noise ratios. Wherein, black lines represent channel state information known by the base station end and the intelligent reflecting surface, and red lines represent influences on channel estimation under different error weights.
Fig. 6 shows the error of the angle value of the measured line of sight by performing virtual beam search on the estimated channel under different error weights. Even when the error weight is 0.1, the measured angle error is 2.5 ° or less. Fig. 7 shows the variation of the average achievable data transmission rate with SNR for different error weights. Based on the virtual beam searching and fine estimating method of the arrival angle between the user side of the observed data and the intelligent reflecting surface in the step 4, the element on the intelligent reflecting surface is designed by the measured line-of-sight angle value, the change diagram of the average reachable data transmission rate under different weights is simulated through downlink data transmission, and the robustness of the algorithm can be seen from the curve change of the simulation diagram.
Claims (1)
1. The channel estimation method of the intelligent super-surface-assisted mixed configuration millimeter wave communication system is characterized by comprising the following steps of:
step 1, scene assumption and channel model;
step 2, designing beam forming of the base station end and dividing intelligent reflecting surface unit groups by utilizing a channel singular value decomposition result from the base station end to the intelligent reflecting surface;
step 3, observing and accumulating the channel data of each unit group from the user side to the intelligent reflecting surface;
step 4, virtual beam searching and fine estimation of the arrival angle between the user side and the intelligent reflecting surface are executed according to the observation data; measuring the sight distance reaching angle from the user end to the intelligent reflecting surface;
the step 1 is specifically as follows:
scene assumptions and channel models are described as follows: a base station provided with M antennas communicates K single-antenna users with the assistance of an intelligent reflecting surface with N passive reflecting elements; the communication channel model under the smart reflective surface includes three parts: the channel matrix from the base station end of the reflection link to the intelligent reflection surface is expressed asThe reflection channel matrix from the user terminal to the intelligent reflection surface is expressed as +.>The channel matrix from the direct channel user end to the base station end is expressed as +.>The direct channel from the user end to the base station end in the transmission model is supposed to be blocked by the barrier; the base station end and the intelligent reflecting surface antenna array are both uniform linear arrays; the reflection channel model between the user terminal and the intelligent reflection surface adopts a rice channel; wherein-> Represented as a matrix of reflection element coefficients, beta, on an intelligent reflecting surface n ∈[0,1]And phi n ∈[0,2π]Respectively representing the amplitude and phase adjustment coefficients of the nth passive reflection element of the RIS;
the step 2 is specifically as follows:
2-1, in order to reduce the overhead cost of channel estimation, using Singular Value Decomposition (SVD) to the channel G, observing the number of singular values of the corresponding channel; screening all singular values with singular values larger than a set threshold value, and counting the number of the screened singular values; dividing passive unit groups on the intelligent reflecting surface according to the number P of singular values, and dividing the intelligent reflecting surface by taking P passive devices as a passive unit group; so as to simultaneously turn on a plurality of passive devices for channel estimation operation;
2-2 when the passive devices on the P smart reflective surfaces are activated simultaneously, the channels from the passive cell group on the smart reflective surfaces to the base station side, the channels from the user side to the passive cell group can be represented in turn as, q∈{1,…,Q}，/> a matrix of phase amplitude coefficients represented as a set of passive elements on the intelligent reflective surface;
by means of channel G q Singular value decomposition is carried out to obtain:
wherein G is q Is a matrix of singular values of (a)Is a diagonal matrix of nonnegative real numbers arranged in descending order, and the parameter superscript "H" in the formula (1) represents conjugate transposition according to the arrangement from large to small; />Is->For unitary matrix, the property is +>
2-3, by activating the passive unit groups on the intelligent reflecting surface in sequence, the user side transmits the pilot signal X to the base station side through the passive unit groups on the intelligent reflecting surface, and the transmission model is as follows:
wherein,for pilot sequences transmitted by all clients in T time slots, W RF,q Is the analog beam at the base stationForming a matrix, and synthesizing by adopting a phase shifter network; />Is obeyed to mean 0 and variance sigma 2 Additive white gaussian noise of (2);
to maximize the performance of the transmission system, the analog precoding is set to the decomposed unitary matrix U q Conjugate transpose of the preceding P columns, i.e.The base station side received signal is expressed as follows:
Y q ＝W RF,q G q Θ q H r,q X+W RF,q Z BS
＝Λ q Θ q H r,q X+W RF,q Z BS (3)
wherein, define
The step 3 is specifically as follows:
the linear equation can be obtained by the transmission model in the step 2, and the channel from the user to the intelligent reflecting surface is estimated by using a least square method:
can be estimated asNamely:
wherein,the representation is a left pseudo-inverse; during channel estimation, the amplitude of all intelligent reflecting surface passive devices is set to be 1, so that Θ can be obtained q Is a unit matrix; by activating the intelligent reflecting units in sequence and accumulating and observing the channel data, an estimated channel can be obtained, i.e.>
The step 4 is specifically as follows:
according to sparsity characteristics of the millimeter wave communication channel, the strongest arrival angle from the user side to the intelligent reflecting surface is found, and virtual beam searching is carried out only by utilizing part of channel information estimated in the step 3; by searching for part of the channelThe sight distance arrival angle value from each user end to the intelligent reflecting surface can be estimated and obtained; the arrival angle detection matrix is designed as follows:
which contains J columns of detection vectors, then the ith column of detection vectors, i.eThe following are provided:
wherein,possible arrival angles for the intelligent reflecting surface; d represents the distance between the transmitting antenna and the receiving antenna, and the beam search value in the ith virtual direction is:
wherein,the rice factor weight coefficient matrixes of different user terminals are represented;
the virtual beam search maximum can be expressed as:
the index corresponding to the maximum search value is the strongest arrival angle value from the user side to the intelligent reflecting surface.
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