CN115173914A - Multi-intelligent-reflector auxiliary communication active and passive beam forming iterative optimization method - Google Patents
Multi-intelligent-reflector auxiliary communication active and passive beam forming iterative optimization method Download PDFInfo
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- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/04013—Intelligent reflective surfaces
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- H—ELECTRICITY
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- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
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Abstract
The invention discloses an active and passive beamforming iterative optimization method for multi-intelligent reflecting surface auxiliary communication, which comprises the steps that a user side sends pilot signals through an intelligent reflecting surface, and a base station side acquires channel state information of a cascade link according to the pilot signals; then the base station end distributes an intelligent reflecting surface for each user end, the generalized Rayleigh entropy algorithm is adopted to calculate and obtain the optimal active beam forming vector of the base station end serving each user end through the selected intelligent reflecting surface, and the signal-to-noise leakage ratio is calculated; then, the base station side optimizes the passive beam forming of the intelligent reflecting surface selected by the user side by maximizing the minimum signal-to-noise leakage ratio of each user side; and then the base station end performs iterative optimization by combining the active beam forming of the base station end and the passive beam forming of the intelligent reflecting surface. The invention enhances the stability and reliability of the wireless communication system and improves the frequency spectrum efficiency of the wireless communication system.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to an active and passive beam forming iterative optimization method for multi-intelligent reflector auxiliary communication.
Background
The intelligent reflective surface is comprised of an array of low cost passive reflective elements, each capable of independently adjusting phase, amplitude, and frequency to reflect an incident signal and thereby cooperatively alter the wireless channel between the transmitter and receiver. Therefore, the intelligent reflecting surface has the capability of reshaping a wireless propagation environment, and is favorable for signal transmission. Different from the traditional active relay and the active beam forming of a base station end, the intelligent reflecting surface can realize the passive beam forming of full duplex without generating any noise amplification and without any active radio frequency chain for signal transmission, reception and self-interference elimination, thereby greatly reducing the realization cost and energy consumption. Intelligent reflective surfaces can be deployed indoors and outdoors, such as on walls, ceilings of buildings, and even drones, to overcome adverse propagation conditions, increase coverage area, while consuming less energy.
The intelligent reflecting surfaces are arranged at different positions of the wireless communication system, so that a plurality of flexible communication links can be provided for the user side, the signal strength received by the user side can be enhanced through the plurality of links, and the influence of the condition that one or more communication links are blocked by a barrier on the communication quality can be reduced. However, if the passive beamforming of the intelligent reflective surface serving the ue is not set accurately, the direction of the reflective link is not accurate enough, so that the strength of the signal received by the ue served by the intelligent reflective surface is weak, and the strength of the interference signal leaked to other ues through the intelligent reflective surface is increased.
Therefore, in order to ensure that the signal reflected by the intelligent reflecting surface can more accurately reach the user end served by the intelligent reflecting surface, the passive beam forming of the intelligent reflecting surface needs to be accurately set. In order to solve the problem, the invention designs an iterative optimization method of active and passive beam forming for multi-intelligent reflecting surface auxiliary communication, which combines the active beam forming arranged at a base station end with the passive beam forming at an intelligent reflecting surface to carry out iterative optimization, so that the base station end is accurately aligned to a served user side when the base station end is served by the selected intelligent reflecting surface, and interference signals leaked to other user sides when the base station end is served by communication through the intelligent reflecting surface are reduced; meanwhile, the method considers user fairness, and ensures that each user side can carry out stable and reliable communication by optimizing the minimum signal-to-noise leakage ratio of each user side.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides an iterative optimization method for active and passive beamforming of multi-intelligent reflector auxiliary communication, which combines active beamforming arranged at a base station end and passive beamforming at an intelligent reflector to perform iterative optimization.
In order to achieve the purpose, the invention provides an active and passive beamforming iterative optimization method for multi-intelligent-reflector auxiliary communication, which comprises the following steps:
s1: considering the downlink communication of the wireless communication system, the communication system provides communication service for a plurality of user sides simultaneously through a plurality of intelligent reflecting surface reflecting links; the user side sends a pilot signal through the intelligent reflecting surface, and the base station side acquires channel state information of the cascade link according to the pilot signal;
s2: the base station end distributes an unselected and closest intelligent reflecting surface to each user end for downlink communication according to distance factors, and calculates by adopting a generalized Rayleigh entropy algorithm to obtain an optimal active beam forming vector of the base station end serving each user end through the selected intelligent reflecting surface;
s3: the base station end calculates the signal-to-noise leakage ratio of the base station end serving each user end through the selected intelligent reflecting surface according to the obtained optimal active beam forming vector serving each user end;
s4: and the base station side maximizes the minimum signal-to-noise leakage ratio of each user side by optimizing the passive beam forming matrix of the intelligent reflecting surface selected by each user side according to the calculated optimal active beam forming vector.
S5: and the base station end recalculates the optimal active beamforming vector, the updated signal-to-noise leakage ratio of each user end and the reachable rate of all the user ends according to the obtained passive beamforming optimization matrix.
S6: and the base station terminal performs iterative optimization by repeating the steps S4 and S5 until the iteration is completed when the reachable rate of the new iteration is not increased or the set iteration times is reached compared with the reachable rate of the last iteration, and the reachable rates of all the user terminals obtained by calculation are improved.
S7: and the base station end calculates an optimal active beam forming vector by adopting a generalized Rayleigh entropy algorithm according to the passive beam forming matrix of the intelligent reflecting surface after iterative optimization, sets active beam forming and adjusts the passive beam forming at the intelligent reflecting surface, thereby realizing downlink data communication from the base station end to each user side.
Preferably, in step S1, the base station is equipped with a plurality of antennas, and the plurality of intelligent reflection surfaces are distributed between the base station and the user side; specifically, a single base station end provided with M antennas is considered, J intelligent reflecting surfaces are distributed between the base station end and K user ends, each intelligent reflecting surface is provided with N passive reflecting units, the user ends are provided with the single antennas, and the position information of the intelligent reflecting surfaces is known at the base station end; the user side carries out uplink communication, pilot signals are sent to the base station side through the intelligent reflection surface, the base station side controls the intelligent reflection surface to open the N passive reflection units in sequence through the controller to reflect the pilot signals, and the base station side estimates the channel state information of the cascade link according to the received pilot signals; the specific components comprise a cascade channel of a base station end serving a kth user through a jth intelligent reflecting surface
() H Which represents a conjugate transpose matrix of the image,
is an index set of intelligent reflecting surfaces,
index set for user side, wherein
Channel matrix comprising base station end to jth intelligent reflecting surface
Channel vector from jth intelligent reflecting surface to kth user
And phase shift matrix of intelligent reflecting surface
Wherein
α j,n ∈[0,1]Amplitude of nth passive reflecting unit of jth intelligent reflecting surface theta j,n ∈[0,2π]Setting the amplitude alpha of all passive reflection units of all intelligent reflection surfaces for the phase of the nth passive reflection unit of the jth intelligent reflection surface 1,1 ＝α 1,2 ＝...＝α j,n ＝1，n∈{1,2,...,N}。
Preferably, in step S2, specifically, the index of the intelligent reflection plane serving the kth ue allocated by the base station is set to m (k), where m (k) is the index of the intelligent reflection plane serving the kth ue
Is an index set of intelligent reflecting surfaces,
adopting a generalized Rayleigh entropy algorithm for a user side index set, and obtaining the optimal active beam forming vector of the base station side serving the kth user through the selected intelligent reflecting surface when the signal-to-noise leakage ratio of K user sides is maximized
Wherein
For the cascade channel when the base station end services the kth user through the selected intelligent reflecting surface,
it includes the channel matrix when the base station end services the k user through the selected intelligent reflecting surface
Channel vector between selected intelligent reflecting surface and k user
And a phase shift matrix of selected intelligent reflective surfaces
The interference caused to the t user when the base station side serves the k user through the selected intelligent reflecting surface,
additive white Gaussian noise n obeys mean value of 0 and variance of sigma 2 Complex gaussian distribution of (c) () -1 Is an inverse matrix, I M Is an M × M identity matrix.
Preferably, in step S3, the signal-to-noise leakage ratio when the base station serves the kth user through the selected intelligent reflecting plane can be expressed as
Wherein m (k) is the base station end distributing the intelligent reflection surface index serving the kth user end,
is an index set of intelligent reflecting surfaces,
an index set is provided for the user side,
an active beam forming vector when the base station end serves the kth user through the selected intelligent reflecting surface,
for the cascade channel when the base station side serves the k user through the selected intelligent reflecting surface,
it includes the channel matrix when the base station side serves the k user through the selected intelligent reflecting surface
Channel vector between selected intelligent reflecting surface and k-th user
And a phase shift matrix of selected intelligent reflective surfaces
The interference caused to the t user when the base station side serves the k user through the selected intelligent reflecting surface,
additive white Gaussian noise n obeys mean value of 0 and variance of sigma 2 Complex gaussian distribution.
Preferably, in step S4, the specific steps of passive beamforming optimization are as follows:
t1: expressing the minimum signal-to-noise-and-leakage ratio of each user terminal as a non-convex problem
Wherein SLNR m(k),k Clothes with intelligent reflecting surface selected for base station endSignal to noise leakage ratio, theta, for the kth user m(k) The phase matrix of the intelligent reflecting surface of the k-th user is selected for the base station,
problem P1 satisfies the conditions
Wherein N belongs to {1, 2.,. N }, m (k) is the intelligent reflection surface index for the base station end to service the kth user end,
is an index set of intelligent reflecting surfaces,
for the user side index set, α m(k),n Amplitude theta of nth passive reflection unit of intelligent reflection surface for serving k user side selected by base station side m(k),n And selecting the phase of the nth passive reflection unit serving the intelligent reflection surface of the kth user side for the base station side.
T2: because the problem and the condition of P1 are non-convex and the problem P1 is a quadratic programming problem of quadratic constraint, the problem P1 is approximately solved by utilizing a semi-definite relaxation technology, and the problem P1 is simplified into a passive beamforming optimization problem taking delta as an auxiliary variable
Problem P2 satisfies the condition
T3: introduce the auxiliary variable x to rewrite the problem P2 into a problem
Problem P3 satisfies the condition
Wherein
An active beam forming vector when the base station end serves the kth user through the selected intelligent reflecting surface,
the channel matrix when the base station side serves the k-th user through the selected intelligent reflecting surface,
for the channel vector between the selected intelligent reflecting surface to the kth user,
t4: due to the fact that
Order to
Satisfy V m(k) Greater than or equal to 0 and rank (V) m(k) ) =1, is a loose non-protruding strip rank (V) m(k) ) =1, problem P3 is rewritten as
Problem P4 satisfies the Condition
[V m(k) ] n,n ＝1,n＝1,2,...,N+1，V m(k) ≧ 0, where tr () represents a trace of the matrix and rank () represents the rank of the matrix.
T5: due to the sum of
Are all of the variables of the process,problem P4 is still non-convex. Therefore, the maximum value of δ is iteratively obtained by adopting the idea of binary search. In the iteration, the following feasibility problem P5 is solved: find V m(k) Problem P5 satisfies the condition
At the moment, the feasibility problem P5 is solved by adopting a convex optimization method. When the problem P5 is feasible, an optimization matrix V is obtained m(k) Otherwise, skipping to the step T1 to solve again.
T6: adopting a Gaussian randomization method to obtain an optimization matrix V according to the solution m(k) Further solving to obtain the product satisfying rank (V) m(k) ) Passive beamforming optimization vector V of =1 m(k),opt ，
Let phi m(k) ＝diag(V m(k),opt ) Obtaining a passive beamforming optimization matrix phi m(k) 。
And step T5, iterating to obtain the maximum value of delta which can be reached by adopting the thought of dichotomy search. The dichotomy search comprises the following specific steps:
r1: setting maximum value delta of auxiliary variable delta optimized by passive beam forming max Minimum value delta min And a set minimum positive value epsilon.
R2: calculating delta mid ＝(δ max +δ min )/2。
R3: let auxiliary variable δ = δ in problem P5 conditional inequality mid The convex optimization method is adopted to solve the problem P5: find V m(k) 。
R4: if the feasibility problem P5 can be solved, δ min ＝δ mid Else delta max ＝δ mid 。
R5: when delta max -δ min If the situation is more than or equal to epsilon or the feasibility problem P5 is not solved, jumping to the step R2, otherwise outputting delta mid At this time, delta mid I.e. the maximum value of delta.
Preferably, in said step S5, the specific sum rate
γ k Is the SINR, SINR of the kth user terminal
Wherein m (k) is the base station end distributing the intelligent reflecting surface index for serving the k user end,
is an index set of intelligent reflecting surfaces,
the index set is set for the user terminal,
the channel matrix when the base station side serves the k-th user through the selected intelligent reflecting surface,
for the channel vector between the selected intelligent reflecting surface to the kth user,
for the phase shift matrix of the selected intelligent reflecting surface,
and an active beamforming vector when the kth user is served by the base station end through the selected intelligent reflecting surface.
Compared with the prior art, the invention has the beneficial effects that:
(1) In the wireless communication system with a plurality of intelligent reflecting surfaces, the invention distributes a proper intelligent reflecting surface for each user side to assist communication, thereby reducing the energy consumption of the base station side and the user side and improving the energy efficiency of the wireless communication system.
(2) The invention distributes a plurality of intelligent reflecting surfaces in the wireless communication system, provides a plurality of flexible communication links for the user terminal, enhances the strength of signals received by the user terminal, reduces the influence on the communication quality caused by the condition that one or more communication links are blocked by obstacles, and enhances the stability of the wireless communication system.
(3) By designing the generalized Rayleigh entropy active beam forming of the base station end, the invention inhibits the signal interference between the user ends, reduces the noise influence in the wireless communication system and improves the accessibility and the speed of the wireless communication system.
(4) The invention combines the active beam forming arranged at the base station end with the passive beam forming at the intelligent reflecting surface to carry out iterative optimization, so that the base station end is accurately aligned to the served user end when the base station end is served by the selected intelligent reflecting surface, interference signals leaked to other user ends are reduced, and the frequency spectrum efficiency of a wireless communication system is improved.
(5) The invention considers the fairness of the users, ensures that each user side has enough signal intensity to carry out communication by optimizing the minimum signal-to-noise leakage ratio of each user side, improves the communication quality of the user sides and improves the reliability of a wireless communication system.
Drawings
FIG. 1 is a flow chart of the steps of the present invention.
Fig. 2 is a model diagram of a wireless communication system with multiple intelligent reflectors according to the present invention.
Fig. 3 is a flow chart of the passive beamforming optimization of the intelligent reflecting surface in the present invention.
Fig. 4 is a flowchart of searching the maximum value of the auxiliary variable δ for passive beamforming optimization of the intelligent reflecting surface by using a dichotomy method in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-4, an active and passive beamforming iterative optimization method for multi-intelligent reflector auxiliary communication includes the following steps:
s1: considering the downlink communication of the wireless communication system, the communication system provides communication service for a plurality of user sides simultaneously through a plurality of intelligent reflecting surface reflecting links; a user side sends a pilot signal through an intelligent reflecting surface, and a base station side acquires channel state information of a cascade link according to the pilot signal;
s2: the base station end distributes an unselected and closest intelligent reflecting surface to each user end for downlink communication according to distance factors, and calculates by adopting a generalized Rayleigh entropy algorithm to obtain an optimal active beam forming vector of the base station end serving each user end through the selected intelligent reflecting surface;
s3: the base station end calculates the signal-to-noise leakage ratio of the base station end serving each user end through the selected intelligent reflecting surface according to the obtained optimal active beam forming vector when serving each user end;
s4: and the base station side maximizes the minimum signal-to-noise leakage ratio of each user side by optimizing the passive beam forming matrix of the intelligent reflecting surface selected by each user side according to the calculated optimal active beam forming vector.
S5: and the base station end recalculates the optimal active beamforming vector, the updated signal-to-noise leakage ratio of each user end and the reachable rate of all the user ends according to the obtained passive beamforming optimization matrix.
S6: and the base station terminal performs iterative optimization by repeating the steps S4 and S5 until the iteration is completed when the reachable rate of the new iteration is not increased or the set iteration times is reached compared with the reachable rate of the last iteration, and the reachable rates of all the user terminals obtained by calculation are improved.
S7: and the base station end calculates an optimal active beam forming vector, sets active beam forming and adjusts the passive beam forming at the intelligent reflecting surface by adopting a generalized Rayleigh entropy algorithm according to the passive beam forming matrix of the intelligent reflecting surface after iterative optimization, so that downlink data communication from the base station end to each user end is realized.
In this embodiment, referring to fig. 1, in the method, a user side sends a pilot signal through an intelligent reflecting surface, and a base station side obtains channel state information of a cascade link according to the pilot signal; then the base station end distributes an intelligent reflecting surface for each user end, the generalized Rayleigh entropy algorithm is adopted to calculate and obtain the optimal active beam forming vector of the base station end serving each user end through the selected intelligent reflecting surface, and the signal-to-noise leakage ratio is calculated; then, the base station side optimizes the passive beam forming of the intelligent reflecting surface selected by the user side by maximizing the minimum signal-to-noise leakage ratio of each user side; then the base station end combines the active beam forming of the base station end and the passive beam forming of the intelligent reflecting surface to carry out iterative optimization, and a group of active beam forming and passive beam forming optimization results for improving the system reaching rate are obtained after the iteration is completed; and finally, the base station end adjusts the active beam forming and the intelligent reflecting surface according to the iteration result to carry out downlink data communication.
Specifically, referring to fig. 2, in step S1, a base station is equipped with a plurality of antennas, and a plurality of intelligent reflection surfaces are distributed between the base station and a user side; specifically, a single base station end provided with M antennas is considered, J intelligent reflecting surfaces are distributed between the base station end and K user ends, each intelligent reflecting surface is provided with N passive reflecting units, the user ends are provided with the single antennas, and the position information of the intelligent reflecting surfaces is known at the base station end; the user side carries out uplink communication, pilot signals are sent to the base station side through the intelligent reflection surface, the base station side controls the intelligent reflection surface to open the N passive reflection units in sequence through the controller to reflect the pilot signals, and the base station side estimates the channel state information of the cascade link according to the received pilot signals; the specific components comprise a cascade channel of a base station end serving a kth user through a jth intelligent reflecting surface
() H A conjugate transpose matrix is represented that,
is an index set of intelligent reflecting surfaces,
index set for user side, wherein
Channel matrix including base station end to jth intelligent reflecting surface
Channel vector from jth intelligent reflecting surface to kth user
And phase shift matrix of intelligent reflecting surface
Wherein
α j,n ∈[0,1]Amplitude of the nth passive reflection element of the jth intelligent reflection surface, theta j,n ∈[0,2π]Setting the amplitude alpha of all passive reflection units of all intelligent reflection surfaces for the phase of the nth passive reflection unit of the jth intelligent reflection surface 1,1 ＝α 1,2 ＝...＝α j,n ＝1，n∈{1,2,...,N}。
Specifically, in step S2, the index of the intelligent reflection plane that the base station allocates to serve the kth ue is set to be m (k), where m (k) is the index of the intelligent reflection plane
Is an index set of intelligent reflecting surfaces,
adopting generalized Rayleigh entropy algorithm for index set of user terminal, and maximizing signal-to-noise leakage ratio of K user terminals when consideringThen, the optimal active beam forming vector of the base station end serving the kth user through the selected intelligent reflecting surface is obtained as
Wherein
For the cascade channel when the base station side serves the k user through the selected intelligent reflecting surface,
it includes the channel matrix when the base station end serves the k user through the selected intelligent reflecting surface
Channel vector between selected intelligent reflecting surface and k user
And a phase shift matrix of selected intelligent reflective surfaces
The interference caused to the t user when the base station side serves the k user through the selected intelligent reflecting surface,
additive white Gaussian noise n obeys mean value of 0 and variance of sigma 2 Complex gaussian distribution of (c) () -1 Is an inverse matrix, I M Is an M × M identity matrix.
Specifically, in step S3, the signal-to-noise leakage ratio when the base station serves the kth user through the selected intelligent reflecting surface can be expressed as
Wherein m (k) is the base station end distributing the intelligent reflection surface index serving the kth user end,
is an index set of intelligent reflecting surfaces,
an index set is provided for the user side,
an active beam forming vector when the base station end serves the kth user through the selected intelligent reflecting surface,
for the cascade channel when the base station side serves the k user through the selected intelligent reflecting surface,
it includes the channel matrix when the base station side serves the k user through the selected intelligent reflecting surface
Channel vector between selected intelligent reflecting surface and k-th user
And a phase shift matrix of selected intelligent reflective surfaces
The interference caused to the t user when the base station side serves the k user through the selected intelligent reflecting surface,
additive white Gaussian noise n obeys mean value of 0 and variance of sigma 2 Complex gaussian distribution.
Specifically, referring to fig. 3, in step 4, the specific steps of passive beamforming optimization are as follows:
t1: expressing the minimum signal-to-noise-and-leakage ratio of each user terminal as a non-convex problem
Wherein SLNR m(k),k Signal-to-noise leakage ratio, theta, for the base station serving the k-th user via the selected intelligent reflecting surface m(k) The phase matrix of the intelligent reflecting surface of the k-th user is selected for the base station,
problem P1 satisfies the condition
Wherein N belongs to {1, 2.,. N }, m (k) is the intelligent reflection surface index for the base station end to service the kth user end,
is an index set of intelligent reflecting surfaces,
for the user side index set, α m(k),n Amplitude theta of nth passive reflection unit of intelligent reflection surface for serving k user side selected by base station side m(k),n And selecting the phase of the nth passive reflection unit serving the intelligent reflection surface of the kth user side for the base station side.
T2: because the problem and the condition of P1 are non-convex, and the problem P1 is a quadratic programming problem of quadratic constraint, the problem P1 is approximately solved by utilizing a semi-definite relaxation technology, and the problem P1 is simplified into a passive beamforming optimization problem P2 taking delta as an auxiliary variable:
problem P2 satisfies the condition
T3: introduce the auxiliary variable x to rewrite the problem P2 into a problem
Problem P3 satisfies the condition
Wherein
An active beam forming vector when the base station end serves the kth user through the selected intelligent reflecting surface,
the channel matrix when the base station side serves the k-th user through the selected intelligent reflecting surface,
for the channel vector between the selected intelligent reflecting surface to the kth user,
t4: due to the fact that
Order to
Satisfy V m(k) Greater than or equal to 0 and rank (V) m(k) ) =1, is a loose non-protruding strip rank (V) m(k) ) =1, problem P3 is rewritten as
Problem P4 satisfies the condition
[V m(k) ] n,n ＝1,n＝1,2,...,N+1，V m(k) ≧ 0, where tr () represents the trace of the matrix and rank () represents the rank of the matrix.
T5: due to the sum of
Are all variables, the problem P4 is still non-convex. Therefore, the maximum value of δ is iteratively obtained by adopting the idea of binary search. In the iteration, the following feasibility problem P5 is solved: find V m(k) Problem P5 satisfies the condition
At the moment, the feasibility problem P5 is solved by adopting a convex optimization method. When the problem P5 is feasible, an optimization matrix V is obtained m(k) Otherwise, skipping to the step T1 to solve again.
T6: adopting a Gaussian randomization method to obtain an optimization matrix V according to the solution m(k) Further solving to obtain the product satisfying rank (V) m(k) ) Passive beamforming optimization vector V of =1 m(k),opt ，
Let phi m(k) ＝diag(V m(k),opt ) Obtaining a passive beamforming optimization matrix phi m(k) 。
Referring to fig. 4, in step T5, the maximum value that δ can reach is iteratively found by using the concept of binary search. The dichotomy search comprises the following specific steps:
r1: setting maximum value delta of auxiliary variable delta optimized by passive beam forming max Minimum value delta min And a set minimum positive value epsilon.
R2: calculating delta mid ＝(δ max +δ min )/2。
R3: let auxiliary variable δ = δ in problem P5 conditional inequality mid The convex optimization method is adopted to solve the problem P5: find V m(k) 。
R4: if the feasibility problem P5 can be solved, δ min ＝δ mid Else delta max ＝δ mid 。
R5: when delta max -δ min If the problem is more than or equal to epsilon or the feasibility problem P5 is not solved, jumping to the step R2, and if not, jumping to the step R2Then output delta mid At this time, delta mid I.e. the maximum value of delta.
Specifically, in the step S5, the rate is neutralized
γ k Is the SINR, SINR of the kth user terminal
Wherein m (k) is the base station end distributing the intelligent reflection surface index serving the kth user end,
is an index set of intelligent reflecting surfaces,
an index set is provided for the user side,
the channel matrix when the base station side serves the k-th user through the selected intelligent reflecting surface,
for the channel vector between the selected intelligent reflecting surface to the kth user,
for the phase shift matrix of the selected intelligent reflecting surface,
and an active beamforming vector when the kth user is served by the base station end through the selected intelligent reflecting surface.
The description and practice of the disclosure herein will be readily apparent to those skilled in the art from consideration of the specification and understanding, and may be modified and modified without departing from the principles of the disclosure. Therefore, modifications or improvements made without departing from the spirit of the invention should also be considered as the protection scope of the invention.
Claims (6)
1. A multi-intelligent-reflector auxiliary communication active and passive beam forming iterative optimization method is characterized by comprising the following steps:
s1: considering the downlink communication of the wireless communication system, the communication system provides communication service for a plurality of user sides simultaneously through a plurality of intelligent reflecting surface reflecting links; the user side sends a pilot signal through the intelligent reflecting surface, and the base station side acquires channel state information of the cascade link according to the pilot signal;
s2: the base station end distributes an unselected and nearest intelligent reflecting surface to each user end for downlink communication according to distance factors, and calculates by adopting a generalized Rayleigh entropy algorithm to obtain an optimal active beam forming vector of the base station end serving each user end through the selected intelligent reflecting surface;
s3: the base station end calculates the signal-to-noise leakage ratio of the base station end serving each user end through the selected intelligent reflecting surface according to the obtained optimal active beam forming vector serving each user end;
s4: the base station side maximizes the minimum signal-to-noise leakage ratio of each user side by optimizing the passive beam forming matrix of the intelligent reflecting surface selected by each user side according to the calculated optimal active beam forming vector;
s5: the base station end recalculates the optimal active beamforming vector, the updated signal-to-noise leakage ratio of each user end and the reachable rate of all the user ends according to the obtained passive beamforming optimization matrix;
s6: the base station terminal carries out iterative optimization by repeating the steps S4 and S5 until the reaching rate of a new iteration is not increased compared with the reaching rate of the last iteration or the iteration is completed for a set number of times, and the reaching rates of all the user terminals obtained by calculation are improved;
s7: and the base station end calculates an optimal active beam forming vector, sets active beam forming and adjusts the passive beam forming of the intelligent reflecting surface by adopting a generalized Rayleigh entropy algorithm according to the passive beam forming matrix of the intelligent reflecting surface after iterative optimization, so that downlink data communication from the base station end to each user side is realized.
2. The multi-intelligent-reflector-assisted communication active and passive beamforming iterative optimization method according to claim 1, characterized in that: in the step S1, a base station end is provided with a plurality of antennas, and a plurality of intelligent reflecting surfaces are distributed between the base station end and a user side;
the method comprises the steps that a single base station end of M antennas is equipped, J intelligent reflecting surfaces are distributed between the base station end and K user ends, N passive reflecting units are installed on each intelligent reflecting surface, the user ends are equipped with the single antennas, and position information of the intelligent reflecting surfaces is known at the base station end; the user side carries out uplink communication, pilot signals are sent to the base station side through the intelligent reflection surface, the base station side controls the intelligent reflection surface to open the N passive reflection units in sequence through the controller to reflect the pilot signals, and the base station side estimates the channel state information of the cascade link according to the received pilot signals;
the specific components comprise a cascade channel of a base station end serving a kth user through a jth intelligent reflecting surface
() H Which represents a conjugate transpose matrix of the image,
is an index set of intelligent reflecting surfaces,
index set for user side, wherein
Channel matrix comprising base station end to jth intelligent reflecting surface
Channel vector from jth intelligent reflecting surface to kth user
And phase shift matrix of intelligent reflecting surface
Wherein
α j,n ∈[0,1]Amplitude of nth passive reflecting unit of jth intelligent reflecting surface theta j,n ∈[0,2π]Setting the amplitude alpha of all passive reflection units of all intelligent reflection surfaces for the phase of the nth passive reflection unit of the jth intelligent reflection surface 1,1 ＝α 1,2 ＝...＝α j,n ＝1，n∈{1,2,...,N}。
3. The multi-intelligent-reflector-assisted communication active and passive beamforming iterative optimization method according to claim 1, wherein the method comprises the following steps: in step S2, the base station allocates the index of the intelligent reflection plane serving the kth ue as m (k), where m is the index of the intelligent reflection plane serving the kth ue
Is an index set of intelligent reflecting surfaces,
adopting generalized Rayleigh entropy algorithm for index set of user sideWhen the signal-to-noise leakage ratio of the K user sides is maximized, the optimal active beam forming vector serving the kth user through the selected intelligent reflecting surface at the base station side is obtained as
Wherein
For the cascade channel when the base station side serves the k user through the selected intelligent reflecting surface,
it includes the channel matrix when the base station end serves the k user through the selected intelligent reflecting surface
Channel vector between selected intelligent reflecting surface and k-th user
And phase shift matrix of selected intelligent reflective surfaces
The interference caused to the t user when the base station side serves the k user through the selected intelligent reflecting surface,
additive white Gaussian noise n obeys mean value of 0 and variance of sigma 2 Complex gaussian distribution of (c) () -1 Is an inverse matrix, I M Is an M × M identity matrix.
4. The method of claim 1, wherein the iterative optimization method of active and passive beamforming for auxiliary communication with multiple intelligent reflective surfaces is characterized in thatThe method comprises the following steps: in step S3, the signal-to-noise leakage ratio when the base station serves the kth user through the selected intelligent reflector can be expressed as
Wherein m (k) is the base station end distributing the intelligent reflecting surface index for serving the k user end,
is an index set of intelligent reflecting surfaces,
an index set is provided for the user side,
an active beam forming vector when the base station end serves the kth user through the selected intelligent reflecting surface,
for the cascade channel when the base station end services the kth user through the selected intelligent reflecting surface,
it includes the channel matrix when the base station side services the k user through the selected intelligent reflecting surface
Channel vector between selected intelligent reflecting surface and k-th user
And phase shift matrix of selected intelligent reflective surfaces
The interference caused to the t user when the base station side serves the k user through the selected intelligent reflecting surface,
additive white Gaussian noise n obeys mean value of 0 and variance of sigma 2 Complex gaussian distribution.
5. The multi-intelligent-reflector-assisted communication active and passive beamforming iterative optimization method according to claim 1, wherein the method comprises the following steps: in step S4, the specific steps of passive beamforming optimization are as follows:
t1: the minimum signal-to-noise-and-leakage ratio of each user terminal is maximized and is expressed as a non-convex problem P1:
wherein SLNR m(k),k Signal-to-noise leakage ratio, theta, for the base station serving the k-th user via the selected intelligent reflecting surface m(k) The phase matrix of the intelligent reflecting surface of the k-th user is selected for the base station,
problem P1 satisfies the condition
Wherein N belongs to {1, 2.,. N }, m (k) is the intelligent reflection surface index for the base station end to service the kth user end,
is an index set of intelligent reflecting surfaces,
for the user side index set, α m(k),n Amplitude theta of nth passive reflection unit of intelligent reflection surface for serving k user side selected by base station side m(k),n Selecting the phase of the nth passive reflection unit serving the intelligent reflection surface of the kth user side for the base station side;
t2: the problem P1 is simplified into a passive beamforming optimization problem P2 with delta as an auxiliary variable:
problem P2 satisfies the condition SLNR m(k),k ≥δ,
T3: introduce the auxiliary variable x, rewrite the question P2 to question P3:
problem P3 satisfies the condition
Wherein
An active beam forming vector when the base station end serves the kth user through the selected intelligent reflecting surface,
the channel matrix when the base station side serves the k-th user through the selected intelligent reflecting surface,
for the channel vector between the selected intelligent reflecting surface to the kth user,
t4: order to
Satisfy V m(k) Greater than or equal to 0 and rank (V) m(k) ) =1, problem P3 is rewritten to problem P4:
problem P4 satisfies the Condition
V m(k) The value is more than or equal to 0, wherein tr () represents the trace of the matrix, and rank () represents the rank of the matrix;
t5: and (3) iteratively solving the maximum value of delta which can be reached by adopting the thought of binary search, and solving the following feasibility problem P5 in iteration: find V m(k) Problem P5 satisfies the condition
V m(k) Not less than 0; when the problem P5 is feasible, an optimized matrix V is obtained m(k) Otherwise, jumping to the step T1 to solve again;
t6: using the obtained optimization matrix V by using a Gaussian randomization method m(k) Further solving to obtain the product satisfying rank (V) m(k) ) Passive beamforming optimization vector V of =1 m(k),opt ，
Updating phi m(k) ＝diag(V m(k),opt ) Obtaining a passive beamforming optimization matrix phi m(k) ；
In the step T5, a dichotomy search idea is adopted to iteratively calculate the maximum value of delta which can be reached, and the dichotomy search comprises the following specific steps:
r1: setting maximum value delta of auxiliary variable delta optimized by passive beam forming max Minimum value delta min And a set minimum positive value epsilon;
r2: calculating delta mid ＝(δ max +δ min )/2；
R3: let auxiliary variable δ = δ in problem P5 conditional inequality mid The convex optimization method is adopted to solve the problem P5: find V m(k) ；
R4: if the feasibility problem P5 can be solved, δ min ＝δ mid Else delta max ＝δ mid ；
R5: when delta max -δ min If the problem is more than or equal to epsilon or the feasibility problem P5 is not solved, jumping to the step R2, otherwise outputting delta mid At this time, δ mid I.e. the maximum value of delta.
6. The multi-intelligent-reflector-assisted communication active and passive beamforming iterative optimization method according to claim 1, characterized in that: in said step S5, the rate is neutralized
γ k Is the SINR of the kth user terminal
Wherein m (k) is the base station end distributing the intelligent reflecting surface index for serving the k user end,
is an index set of intelligent reflecting surfaces,
the index set is set for the user terminal,
the channel matrix when the base station side serves the k-th user through the selected intelligent reflecting surface,
for the channel vector between the selected intelligent reflecting surface to the kth user,
for the phase shift matrix of the selected intelligent reflecting surface,
and an active beamforming vector when the kth user is served by the base station end through the selected intelligent reflecting surface.
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