CN115021779A - RIS assisted MIMO system discrete phase shift design method and apparatus - Google Patents

RIS assisted MIMO system discrete phase shift design method and apparatus Download PDF

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CN115021779A
CN115021779A CN202210670834.8A CN202210670834A CN115021779A CN 115021779 A CN115021779 A CN 115021779A CN 202210670834 A CN202210670834 A CN 202210670834A CN 115021779 A CN115021779 A CN 115021779A
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ris
pavilion
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CN115021779B (en
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邓宏贵
张文涓
李幼真
朱早星
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Central South University
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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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
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
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Abstract

The application relates to a method and a device for designing discrete phase shift of an RIS (RIS assisted multiple input multiple output) system in the technical field of wireless communication. The method comprises the following steps: establishing an RIS-assisted MIMO communication system, and acquiring a target function of the MIMO communication system; deducing a discrete phase shift matrix design optimization problem by taking the maximization of the frequency spectrum efficiency of the MIMO communication system as a target; searching an optimal discrete phase shift matrix through a chaotic discrete satin blue gardener optimization algorithm; the invention chaotizes the population of the classic satin blue gardener optimization algorithm, reduces the probability of the algorithm falling into the local optimal solution, accelerates the convergence speed and expands the scene of searching the target discrete domain. Compared with a random RIS phase shift scheme and an LS-based discrete phase shift scheme, the RIS reflecting unit discrete phase shift design method provided by the invention can effectively improve the frequency efficiency performance of a system, and greatly reduces the calculation complexity compared with an exhaustive search method.

Description

RIS assisted MIMO system discrete phase shift design method and apparatus
Technical Field
The present application relates to the field of wireless communication technologies, and in particular, to a method and an apparatus for designing discrete phase shift of a RIS-assisted MIMO system.
Background
With the development of a new generation of wireless communication network, millimeter waves have become a research focus, and path loss thereof is very severe due to high frequency and short wavelength thereof, and Multiple Input Multiple Output (MIMO) equipped with multiple antennas can compensate for the path loss of a millimeter wave (mmWave) system. Although the MIMO technology can effectively solve the propagation problem of the millimeter wave system and improve the spectrum efficiency, its high computation complexity, high energy consumption and high hardware cost are difficult to solve.
Among the 6G candidates, Reconfigurable Intelligent Surface (RIS) becomes an efficient green solution. Each low-cost reflecting element of the RIS has adjustable electromagnetic properties, such as phase, capable of passively and independently reflecting incident signals, so that the RIS reflecting channels are organized and considered as a supplemental link to enhance the wireless communication system. Completely different from mature technologies such as large-scale Multiple Input Multiple Output (MIMO), Amplified Forward (AF) relay, and backscatter communication, the RIS can flexibly configure a wireless propagation environment in real time by programming to improve signal transmission performance between the base station and the UE. In addition, due to the lightness of the RIS, it can be easily installed on the building surface. However, due to hardware limitation, the reflection unit of the RIS usually adopts a discrete reflection phase shifter, and the problem of maximizing the spectral efficiency of the RIS-assisted MIMO system with the discrete reflection unit is a non-convex mixed integer optimization problem, and the solution difficulty is large. Aiming at the problem, Qingqing Wu et al researches the frequency efficiency maximization problem of an RIS (RIS) assisted MISO (MISO) system with a continuous reflection unit, and quantifies a continuous constraint solution to a discrete domain by a closest point projection method to solve the problem, but when the quantification precision is low, a large error is caused; tan et al transform the target problem into a discrete domain search problem and select the optimal discrete phase shift using an exhaustive search method, but as the number of reflecting units increases, the algorithm computation complexity increases exponentially; wenjie Chen et al propose that an improved local search algorithm reduces the computational complexity and can find the optimal discrete phase shift solution more quickly, but this method is easy to fall into the local optimal solution and the frequency efficiency performance is not ideal enough, so there is a lot of space for the frequency efficiency optimization research of the RIS-assisted MIMO system with discrete reflection units.
Disclosure of Invention
In view of the above, it is necessary to provide a method and apparatus for designing discrete phase shift of a RIS-assisted MIMO system.
A method of discrete phase shift design for a RIS-assisted MIMO system, the method comprising:
constructing an RIS-assisted MIMO system; the receiving end channel of the MIMO system comprises 3 sub-channels adopting Saleh-Vallenzuela channel models based on angle expansion, which are respectively as follows: base station-RIS channel, RIS-user channel, and base station-user channel.
And constructing a discrete phase shift matrix design optimization problem by taking the spectrum efficiency maximization of the MIMO system as a target according to the base station-RIS channel, the RIS-user channel and the base station-user channel, and acquiring an objective function of an optimization algorithm.
Generating nPop pavilion vectors as an initial population based on Logistic chaotic mapping, wherein the nPop is an integer larger than 1.
Setting the initial value of the iteration times to be 0, calculating the objective function value of the pavilion vector, and taking the pavilion vector with the minimum objective function value as the historical optimal pavilion vector.
And calculating the fitness value and the selection probability value of each pavilion vector in the initial population according to the objective function.
And selecting the current best pavilion vector by adopting a roulette method according to the selection probability value.
And carrying out position updating and population variation according to the current optimal pavilion vector and the historical optimal pavilion vector to generate a new pavilion population.
And evaluating a population combined by the initial population and the new wayside pavilion population according to the objective function, updating the historical best wayside pavilion vector into a wayside pavilion vector with the minimum objective function value, and taking the nPop wayside pavilion vectors with smaller objective function values as the initial population of the next iteration.
And setting the iteration times to be self-added by 1, and generating an optimal discrete phase shift matrix according to the historical optimal pavilion vector when the iteration times are larger than the preset maximum iteration times.
In one embodiment, according to the bs-RIS channel, the RIS-user channel, and the bs-user channel, constructing a discrete phase shift matrix design optimization problem with the spectrum efficiency maximization of the MIMO system as a target, and obtaining an objective function expression of an optimization algorithm, where an objective function of the optimization algorithm in the step is:
Figure BDA0003694524640000031
wherein: h eff ＝H r ΦG+H d Is an equivalent channel matrix, H r Is RIS-user channel, G is base station-RIS channel, H d Is a base station-user channel, phi is a discrete phase shift matrix, N s 、N r Respectively the number of data streams and the number of user antennas; sigma 2 Is the variance of the noise complex signal; p is the transmit signal power.
In one embodiment, a discrete phase shift matrix design optimization problem is constructed based on the base station-RIS channel, the RIS-user channel, and the base station-user channel, with the goal of maximizing the spectral efficiency of the MIMO system, wherein the discrete phase shift matrix design optimization problem in the step is:
Figure BDA0003694524640000032
Figure BDA0003694524640000033
φ m ∈Γ,m＝1,2,...,M.
wherein: h eff ＝H r ΦG+H d Is an equivalent channel matrix, H r Is RIS-user channel, G is base station-RIS channel, H d Is a base station-user channel, phi is a discrete phase shift matrix, N s 、N r M is the number of data streams, the number of user antennas and the number of RIS reflecting unit antennas respectively; sigma 2 Is the variance of the noise complex signal; p is hairThe transmit signal power, Γ, is the set of desirable elements of the discrete phase shift matrix.
In one embodiment, the expression of the pavilion vector is θ ═ θ 1 ,θ 2 ,...,θ M ](ii) a Generating nPop pavilion vectors as initial populations based on Logistic chaotic mapping, wherein the nPop pavilion vectors comprise:
according to the concept of the chaotic sequence, nPop pavilion vectors are randomly generated by adopting a Logistic chaotic mapping expression; the Logistic chaotic mapping expression is as follows:
x(t+1)＝ux(t)(1-x(t))
wherein u belongs to (2.6, 4) as a chaos parameter, t represents the iteration times of the chaos sequence, t belongs to [1, nPop ], and x (t) is a RIS reflecting unit phase shift vector generated by the iteration of the t-th chaos sequence.
In one embodiment, the fitness value of each pavilion vector in the initial population is calculated according to an objective function, and the selection probability value is calculated, wherein the calculation expression of the fitness value of the pavilion vector in the step is as follows:
Figure BDA0003694524640000041
where fit is the fitness value and f (x) is the objective function.
The calculation expression of the selection probability value of the pavilion vector is as follows:
Figure BDA0003694524640000042
where prob is the selection probability value of the pavilion vector.
In one embodiment, location updating and population variation are performed according to the current optimal pavilion vector and the historical optimal pavilion vector to generate a new pavilion population, and the updating expression adopted in location updating in the step is as follows:
Figure BDA0003694524640000043
wherein the content of the first and second substances,
Figure BDA0003694524640000044
alpha is the maximum step length; round (·) denotes round;
Figure BDA0003694524640000045
and
Figure BDA0003694524640000046
the ith element of the ith pavilion vector in the last iteration and the current iteration respectively,
Figure BDA0003694524640000047
and theta elite,l Respectively representing the ith elements of the historical best vector and the current best vector.
The variant expressions used in population variation are:
Figure BDA0003694524640000048
wherein, var max And var min Respectively, an upper limit and a lower limit of each element in the vector theta, z is a preset constant, and N (0,1) represents a random number that follows a normal distribution.
An apparatus for discrete phase shift design for a RIS-assisted MIMO system, the apparatus comprising:
the RIS-assisted MIMO system building module is used for building the RIS-assisted MIMO system, a receiving end channel of the MIMO system comprises 3 sub-channels adopting an Saleh-Valenzuela channel model based on angle expansion, and the sub-channels are respectively as follows: base station-RIS channel, RIS-user channel, and base station-user channel.
And the discrete phase shift matrix design optimization problem construction module is used for constructing a discrete phase shift matrix design optimization problem according to the base station-RIS channel, the RIS-user channel and the base station-user channel by taking the spectrum efficiency of the MIMO system as the maximum target, and acquiring an objective function expression f (x) of an optimization algorithm.
Solving a discrete phase shift matrix design optimization problem module based on a chaos DSBO algorithm, and generating nPop pavilion vectors serving as an initial population based on Logistic chaotic mapping, wherein nPop is an integer greater than 1; setting the initial value of the iteration times as 0, calculating an objective function value of a pavilion vector, and taking the pavilion vector with the minimum objective function value as a history optimal pavilion vector; calculating the fitness value and the selection probability value of each pavilion vector in the initial population according to the target function; selecting the current best pavilion vector by adopting a roulette method according to the selection probability value; performing position updating and population variation according to the current optimal pavilion vector and the historical optimal pavilion vector to generate a new pavilion population; evaluating a population combined by the initial population and the new wayside pavilion population according to the objective function, updating a historical optimal wayside pavilion vector into a wayside pavilion vector with the minimum objective function value, and taking nPop wayside pavilion vectors with smaller objective function values as initial populations of next iteration; and setting the iteration times to be self-added by 1, and generating an optimal discrete phase shift matrix according to the historical optimal pavilion vector when the iteration times are larger than the preset maximum iteration times.
In one embodiment, the objective function of the optimization algorithm in the discrete phase shift matrix design optimization problem construction module is:
Figure BDA0003694524640000051
wherein: h eff ＝H r ΦG+H d Is an equivalent channel matrix, H r Is RIS-user channel, G is base station-RIS channel, H d Is a base station-user channel, phi is a discrete phase shift matrix, N s 、N r Respectively the number of data streams and the number of user antennas; sigma 2 Is the variance of the noise complex signal; p is the transmit signal power.
In one embodiment, the discrete phase shift matrix design optimization problem in the discrete phase shift matrix design optimization problem building module is:
Figure BDA0003694524640000052
Figure BDA0003694524640000053
φ m ∈Γ,m＝1,2,...,M.
wherein: h eff ＝H r ΦG+H d Is an equivalent channel matrix, H r Is RIS-user channel, G is base station-RIS channel, H d Is a base station-user channel, phi is a discrete phase shift matrix, N s 、N r M is the number of data streams, the number of user antennas and the number of RIS reflecting unit antennas respectively; sigma 2 Is the variance of the noise complex signal; p is the transmit signal power and Γ is the set of desirable elements of the discrete phase shift matrix.
In one embodiment, the expression of the pavilion vector is θ ═ θ 1 ,θ 2 ,...,θ M ](ii) a The module is used for solving the optimization problem of the discrete phase shift matrix design based on the chaos DSBO algorithm and is also used for randomly generating nPop pavilion vectors by adopting a Logistic chaos mapping expression according to the idea of a chaos sequence; the Logistic chaotic mapping expression is as follows:
x(t+1)＝ux(t)(1-x(t))
wherein u belongs to (2.6, 4) as a chaos parameter, t represents the iteration times of the chaos sequence, t belongs to [1, nPop ], and x (t) is a RIS reflecting unit phase shift vector generated by the iteration of the t-th chaos sequence.
The above-mentioned RIS assisted MIMO system discrete phase shift design method and apparatus, the said method comprises setting up RIS assisted MIMO communication system; deducing a discrete phase shift matrix design optimization problem by taking the maximization of the frequency spectrum efficiency of the MIMO communication system as a target, and acquiring a target function of an optimization algorithm; searching an optimal discrete phase shift matrix through a chaotic discrete satin blue gardener optimization algorithm; according to the invention, the population of the classic satin blue gardener optimization algorithm is chaotic, the probability that the algorithm falls into the local optimal solution is reduced, the convergence speed is accelerated, and the method is expanded to the scene of searching a target discrete domain. Compared with a random RIS phase shift scheme and an LS-based discrete phase shift scheme, the RIS reflecting unit discrete phase shift design method provided by the invention can effectively improve the frequency efficiency performance of a system, and greatly reduces the calculation complexity compared with an exhaustive search method.
Drawings
FIG. 1 is a schematic flow chart of a method for discrete phase shift design for a RIS assisted MIMO system in one embodiment;
FIG. 2 is a block diagram of a millimeter wave MIMO communication system based on the assistance of an intelligent reflector in one embodiment;
FIG. 3 is a flow diagram of a method for discrete phase shift design for a RIS assisted MIMO system based on chaotic DSBO algorithm in one embodiment;
FIG. 4 is a graph comparing simulation curves of the RIS assisted MIMO system's discrete phase shift design method with two other prior art methods for signal-to-noise ratio variation in another embodiment;
FIG. 5 is a graph comparing simulation curves for the variation of the number of RIS reflecting units (M) for the method of discrete phase shift design of the RIS assisted MIMO system in one embodiment with two other prior methods;
fig. 6 is a block diagram of the mechanism of the discrete phase shift design device of the RIS-assisted MIMO system in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Optimization algorithm of satin blue gardener: SBO algorithm is for short.
The chaos discrete satin blue gardener optimization algorithm is abbreviated as: chaotic DSBO algorithm.
Reconfigurable intelligent surfaces, abbreviated: and (6) RIS.
Spectral Efficiency, SE for short.
An MIMO system: a multiple input multiple output system.
In one embodiment, as shown in fig. 1, there is provided a method of discrete phase shift design for a RIS-assisted MIMO system, the method comprising the steps of:
step 100: constructing an RIS-assisted MIMO system; the receiving end channel of the MIMO system comprises 3 sub-channels adopting Saleh-Vallenzuela channel model based on angle expansion, which are respectively: base station-RIS channel, RIS-user channel, and base station-user channel;
specifically, each passive reflection unit on the RIS in the RIS-assisted MIMO system can realize real-time phase adjustment and beam forming on incident electromagnetic wave signals through an intelligent controller, and the phase shift matrix is
Figure BDA0003694524640000071
Wherein phi i ∈Γ＝{0,Δφ,...,Δφ(2 b -1) }, wherein i ═ 1, 2.., M, Δ Φ ═ 2 pi/2 b B is the number of quantization bits, and in the present invention, b is preferably 1 and Γ is {0, pi }. The channels of the transmitting and receiving ends of the RIS-assisted MIMO system are divided into a base station-RIS channel G, RIS-user channel H r And base station-user channel H d And each subchannel adopts a Saleh-Vallenzuela channel model based on angle expansion.
The structure of the RIS-assisted MIMO system is shown in fig. 2.
Step 102: according to the base station-RIS channel, the RIS-user channel and the base station-user channel, the optimization problem of discrete phase shift matrix design is constructed by taking the frequency spectrum efficiency maximization of the MIMO system as a target, and an objective function of an optimization algorithm is obtained.
Specifically, the discrete phase shift matrix design problem of the RIS-assisted MIMO system is converted into an optimization problem with the goal of maximizing the spectral efficiency of the MIMO system.
The objective function of the optimization problem is that SE takes the maximum value, and the objective function f (x) of the optimization algorithm is obtained based on the objective function, where f (x) is 1/SE. Because the satin blue gardener algorithm searches the minimum value, the purpose of obtaining the maximum value of SE can be achieved by searching the minimum value of 1/SE in the method.
Step 104: generating nPop pavilion vectors serving as an initial population based on Logistic chaotic mapping, wherein the nPop is an integer larger than 1; setting the initial value of the iteration times to be 0, calculating an objective function value, and taking the pavilion vector with the minimum objective function value as a historical optimal pavilion vector.
Step 106: and calculating the fitness value and the selection probability value of each pavilion vector in the initial population according to the objective function.
Specifically, the selection probability value of the pavilion vector is the proportion of the fitness value of the pavilion vector in the population fitness value sum.
Step 108: and selecting the current best pavilion vector by adopting a roulette method according to the selection probability value.
Specifically, the current best pavilion vector is obtained by roulette and is set up for location update.
The purpose of selecting the current best pavilion vector is to provide better directions for other males to set up the pavilion.
Step 110: and carrying out position updating and population variation according to the current optimal pavilion vector and the historical optimal pavilion vector to generate a new pavilion population.
Step 112: and evaluating a population combined by the initial population and the new wayside pavilion population according to the objective function, updating the historical best wayside pavilion vector into a wayside pavilion vector with the minimum objective function value, and taking the nPop wayside pavilion vectors with smaller objective function values as the initial population of the next iteration.
Specifically, the old population and the new population are combined, the combined population is evaluated according to the objective function value, the historical best wayside pavilion vector is updated to be the wayside pavilion vector with the minimum function value, and the nPop wayside pavilion vectors with smaller function values are used as the initial population of the next iteration.
The historical optimal pavilion vector is the optimal vector calculated through a plurality of historical iterations, namely the optimal individual which is finally reserved.
Step 114: and setting the iteration times to be self-added by 1, and generating an optimal discrete phase shift matrix according to the historical optimal pavilion vector when the iteration times are larger than the preset maximum iteration times.
In the above method of discrete phase shift design for an RIS-assisted MIMO system, the method comprises establishing an RIS-assisted MIMO communication system; deducing a discrete phase shift matrix design optimization problem by taking the maximization of the frequency spectrum efficiency of the MIMO communication system as a target, and acquiring a target function of an optimization algorithm; searching an optimal discrete phase shift matrix through a chaotic discrete satin blue gardener optimization algorithm; the invention chaotizes the population of the classic satin blue gardener optimization algorithm, reduces the probability of the algorithm falling into the local optimal solution, accelerates the convergence speed and expands the scene of searching the target discrete domain. Compared with a random RIS phase shift scheme and an LS-based discrete phase shift scheme, the RIS reflecting unit discrete phase shift design method provided by the invention can effectively improve the frequency efficiency performance of a system, and greatly reduces the calculation complexity compared with an exhaustive search method.
In one embodiment, the objective function of the optimization algorithm in step 102 is:
Figure BDA0003694524640000091
wherein: h eff ＝H r ΦG+H d Is an equivalent channel matrix, H r Is RIS-user channel, G is base station-RIS channel, H d Is a base station-user channel, phi is a discrete phase shift matrix, N s 、N r Respectively the number of data streams and the number of user antennas; sigma 2 Is the variance of the noise complex signal; p is the transmitted signal power; SE is spectral efficiency.
In one embodiment, the optimization problem of the discrete phase shift matrix design in step 102 is:
Figure BDA0003694524640000092
wherein: h eff ＝H r ΦG+H d Is an equivalent channel matrix, H r Is RIS-user channel, G is base station-RIS channel, H d Is a base station-user channel, phi is a discrete phase shift matrix, N s 、N r M is the number of data streams, the number of user antennas and the number of RIS reflecting unit antennas respectively; sigma 2 Is the variance of the noise complex signal; p is the transmitted signal power, and Γ isThe discrete phase shift matrix may take a set of elements.
In one embodiment, the expression of the pavilion vector is θ ═ θ 1 ,θ 2 ,...,θ M ](ii) a Step 104 comprises: according to the concept of the chaotic sequence, generating nPop pavilion vectors randomly by adopting a Logistic chaotic mapping expression; the Logistic chaotic mapping expression is as follows:
x(t+1)＝ux(t)(1-x(t)) (3)
wherein u belongs to (2.6, 4) as a chaos parameter, t represents the iteration times of the chaos sequence, t belongs to [1, nPop ], and x (t) is a RIS reflecting unit phase shift vector generated by the iteration of the t-th chaos sequence.
Specifically, a general expression for defining a pavilion vector is θ ═ θ 1 ,θ 2 ,...,θ M ]According to the concept of chaos sequence, the following Logistic chaos mapping expression is adopted to randomly generate nPop pavilion populations and quantize the nPop pavilion populations to a discrete set [0,1]]In the method, the diversity and uniformity of the population are improved, so that the algorithm can be quickly converged and approaches to an optimal solution. Wherein u e (2.6, 4)]Is chaotic parameter, t represents chaotic sequence iteration times, x (t) is RIS reflecting unit phase shift vector generated by the t iteration.
In one embodiment, the fitness value of the kiosk vector in step 106 is calculated by the expression:
Figure BDA0003694524640000101
where fit is the fitness value and f (x) is the objective function.
The calculation expression of the selection probability value of the pavilion vector is as follows:
Figure BDA0003694524640000102
where prob is the selection probability value of the pavilion vector.
In one embodiment, the update expression used in the location update in step 110 is:
Figure BDA0003694524640000103
wherein the content of the first and second substances,
Figure BDA0003694524640000104
α is the maximum step size, and preferably, α is set to 0.94; round (·) denotes round;
Figure BDA0003694524640000105
and
Figure BDA0003694524640000106
the ith element of the ith pavilion vector in the last iteration and the current iteration respectively,
Figure BDA0003694524640000107
and theta elite,l Respectively representing the ith elements of the historical best vector and the current best vector.
The variant expressions used in population variation are:
Figure BDA0003694524640000108
wherein, var max And var min Respectively, an upper limit and a lower limit of each element in the vector theta, z is a preset constant, and N (0,1) represents a random number that follows a normal distribution.
In one embodiment, the communication system employed in the simulation is a RIS assisted point-to-point MIMO system, and the initial parameters include: the number of base station transmitting antennas is 16, the number M of RIS reflecting units is 36, the number of user antennas is 4, the number of scattering clusters is N cl To 4, the number of paths N in each cluster ray For 3, for convenience of simulation, the power of each cluster is assumed to be equal, the departure angle and the arrival angle of the corresponding path are the same, the angle spread is 10 degrees, the sample number of the simulation channel at each time is 1000, the population nPop based on the chaotic DSBO algorithm is 200, the maximum iteration number is 7, and a system block diagram is shown in fig. 2.
As shown in fig. 3, there is provided a method for designing discrete phase shift of RIS assisted MIMO system based on chaotic DSBO algorithm, the method comprising the steps of:
step 1: and establishing a RIS-assisted point-to-point MIMO communication system model.
Millimeter wave point-to-point MIMO communication system with RIS assistance considered, base station equipped with N t Root antenna, transmitting N s A data stream (N) s ≤N t ) The RIS is equipped with M reflecting units on the surrounding wall, wherein the RIS dynamically adjusts the reflection coefficient through its controller. We assume that each element at the RIS is able to re-scatter the signal independently (no cross-interference), and that signals that reflect twice and more at the RIS are ignored. In the communication process, the BS transmits signals
Figure BDA0003694524640000111
And satisfy
Figure BDA0003694524640000112
The IRS's received signal passes through a diagonal reflection matrix (phase shift matrix) through a linear precoder F to a reflection unit of the IRS
Figure BDA0003694524640000113
Then arrives at the user, where i E Γ ═ {0, pi }, i ═ 1,2,. and M, then RIS will N s Data stream transmission equipment N r Users of each antenna. The overall received signal, which contains the BS-UE direct link and the BS-RIS-UE cascaded link, is thus represented as:
Figure BDA0003694524640000114
wherein P is the transmission signal power, F is the precoding matrix, and the requirements are satisfied
Figure BDA0003694524640000115
And (4) restraining. n represents a complex Gaussian noise vector with a mean sum of 0
Figure BDA0003694524640000116
Covariance, σ 2 Representing the average noise power.
H r 、G、H d The Saleh-Valenzuela channel based on angle expansion is suitable for millimeter wave scenes, and antennas at a BS are arranged in a Uniform Linear Array (ULA) mode, and antennas at an IRS are arranged in a Uniform Plane Array (UPA) mode. The channel between the base station to the IRS can be expressed as:
Figure BDA0003694524640000117
wherein N is cl Is the number of scattering clusters, N ray Is the number of paths in each cluster, α il Is the complex gain of the i-th path in the i-th scattering cluster and satisfies the complex gaussian distribution, usually assumed to be 1. a is r (φ r,l ,ψ r,l ) And a t (θ t,l ) Respectively representing the corresponding antenna array response vectors of the receiver and the transmitter, where r,l 、ψ r,l And theta t,l Are azimuth and elevation departure (AoD, ZoD) and azimuth arrival (AoA). The specific expression is as follows:
Figure BDA0003694524640000121
Figure BDA0003694524640000122
where λ is the wavelength and d represents the antenna spacing. The IRS-user channel and the BS-user channel are modeled similarly to the BS-IRS.
Step 2: and establishing a frequency effect optimization problem and obtaining an objective function of an optimization algorithm.
The spectral efficiency expression for the RIS-assisted MIMO system of the present invention can be represented by the following formula:
Figure BDA0003694524640000123
to obtain the ideal phase shift matrix and precoding matrix, we let the equivalent matrix perform SVD decomposition H eff H ＝UΛV H Then, when the phase shift matrix Φ is given, the ideal precoding matrix is obtained by the following equation:
Figure BDA0003694524640000124
wherein Q is a power distribution matrix, and the invention adopts a water filling algorithm to carry out power distribution. F is to be opt Substituting the frequency efficiency expression can convert the spectrum efficiency expression into:
Figure BDA0003694524640000125
then, the following frequency-effect optimization problem is established for the DSBO algorithm, and the specific expression is shown in formula (2). Based on the objective function of the spectrum efficiency optimization problem, the objective function of the optimization algorithm is expressed as:
Figure BDA0003694524640000131
and step 3: and initializing the pavilion population of the DSBO algorithm based on Logistic chaotic mapping.
Generating nPop pavilion vectors (phase shift vectors) according to the Logistic chaos mapping sequence x (t +1) ═ ux (t) (1-x (t)), u ═ 4, and quantizing each element in the vectors to a discrete set [0,1]The general expression of each pavilion vector is [ theta ═ theta [ ] 1 ,θ 2 ,...,θ M ]。
And 4, step 4: setting the initial value of the iteration times as 0, calculating an objective function value of a pavilion vector, and taking the pavilion vector with the minimum objective function value as a history optimal pavilion vector;
the calculation expression of the objective function value is shown in formula (1).
Step 5, calculating the fitness value of each pavilion vector according to the target function expression and the proportion of the fitness value in the population fitness value sum (namely the probability of the individual being selected in the selection process)
The expression of fitness is shown as formula (4), and the expression of probability is shown as formula (5).
Step 6: selecting the current best vector of the pavilions by a roulette method according to the calculation result of the step 4, and recording the vector as theta * Corresponding probability is prob * And a better direction is provided for other males to establish a pavilion vector.
The basic idea of the roulette algorithm is as follows: the cumulative probability is first calculated according to:
Figure BDA0003694524640000132
where prob (j) represents the fitness of the jth pavilion vector. q (i) represents the cumulative probability of the ith pavilion vector. The cumulative probability represents the probability of each pavilion vector by small segments of different lengths (the combined segment length of these small segments is 1).
Then randomly generating a random number between [0,1], judging which section of the line segment the number falls in, and selecting the pavilion vector corresponding to the section.
And 7: individuals in the pavilion population generate location updates.
Figure BDA0003694524640000133
Wherein
Figure BDA0003694524640000141
α is the maximum step size and is set to 0.94.
And 8: and (5) carrying out variation on the pavilion population according to the probability pMutation.
Figure BDA0003694524640000142
Wherein var max And var min Respectively indicate toThe upper and lower limits of each element in the quantity θ are set to 1 and 0 in the present invention, z is a preset constant, as preferable z is 0.02, N (0,1) represents a random number following a normal distribution, and pMutation is set to 0.2.
And step 9: combining the old population and the new population, evaluating the combined population according to the objective function value, updating the historical best wayside pavilion vector into the wayside pavilion vector with the minimum function value, and recording the wayside pavilion vector as theta elite And taking the nPop pavilion vectors with smaller function values as the initial population of the next iteration. Wherein, the old population refers to the initial population in each iteration;
and (5) when the iteration number k is k +1, returning to the step 5 when the iteration number is less than Ite, otherwise, entering the step 10. And Ite is a preset maximum iteration number.
Step 10: selecting theta elite As the optimal phase shift vector, and output
Figure BDA0003694524640000143
As the optimal discrete phase shift matrix.
Fig. 4 is a comparison graph of simulation curves of Spectral Efficiency (Spectral Efficiency) with respect to variation of signal-to-noise ratio (SNR) of the RIS-assisted MIMO system provided by the present invention, the random-phase RIS-assisted millimeter wave MIMO communication system, and the LS algorithm-based RIS-assisted millimeter wave MIMO communication system. It can be seen from fig. 4 that as the signal-to-noise ratio increases, the spectral efficiency of the three schemes increases, and under the same signal-to-noise ratio, the frequency efficiency of the scheme proposed by the present invention increases by about 28% compared with the random phase RIS-assisted scheme, and increases by more than 5% compared with the conventional scheme of RIS phase based on LS algorithm.
Fig. 5 is a comparison graph of simulation curves of Spectral Efficiency (Spectral Efficiency) of the RIS-assisted MIMO communication system and the RIS reflection unit number (M) change of the random-phase RIS-assisted millimeter wave MIMO communication system and the LS algorithm-based RIS-assisted millimeter wave MIMO communication system according to the discrete phase shift design method of the RIS-assisted MIMO system provided by the present invention. As can be seen from fig. 5, under the same number of reflection units, the proposed scheme of the present invention is superior to the conventional schemes of random phase RIS and RIS based on LS algorithm, and the spectral efficiency of the two schemes is improved more slowly when the number of reflection units is increased.
The chaotic DSBO algorithm is used for solving the RIS discrete phase shift optimization problem, and simulation results show that the beam forming algorithm provided by the invention is superior to a scheme of random phase RIS and a traditional RIS scheme based on LS under the condition of the same signal to noise ratio, so that the spectrum efficiency is effectively improved.
It can be seen from the specific examples described in the invention that the method provided by the invention converts the discrete phase shift design problem of the RIS-assisted MIMO system into the phase shift search problem, provides a chaos DSBO algorithm to search for the optimal discrete phase shift, simultaneously uses 1/SE as the objective function of the algorithm, each pavilion vector corresponds to a phase shift vector, and finally searches for the optimal vector to obtain the optimal discrete phase shift matrix. Compared with the existing random phase RIS scheme and LS-based scheme, the invention can effectively improve the spectrum efficiency of the system.
It should be understood that although the steps in the flowcharts of fig. 1 and 3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1 and 3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 6, there is provided a discrete phase shift design apparatus of an RIS-assisted MIMO system, including: the system comprises an RIS auxiliary MIMO system establishing module, a discrete phase shift matrix design optimization problem establishing module and a module for solving the discrete phase shift matrix design optimization problem based on a chaos DSBO algorithm, wherein:
the RIS auxiliary MIMO system establishing module is used for establishing the RIS auxiliary MIMO system; the receiving end channel of the MIMO system comprises 3 sub-channels adopting Saleh-Vallenzuela channel model based on angle expansion, which are respectively: base station-RIS channel, RIS-user channel, and base station-user channel.
And the discrete phase shift matrix design optimization problem construction module is used for constructing a discrete phase shift matrix design optimization problem by taking the frequency spectrum efficiency maximization of the MIMO system as a target according to the base station-RIS channel, the RIS-user channel and the base station-user channel, and acquiring an objective function of an optimization algorithm.
Solving a discrete phase shift matrix design optimization problem module based on a chaos DSBO algorithm, and generating nPop pavilion vectors serving as an initial population based on Logistic chaotic mapping, wherein nPop is an integer greater than 1; setting the initial value of the iteration times as 0, calculating an objective function value of a pavilion vector, and taking the pavilion vector with the minimum objective function value as a history optimal pavilion vector; calculating the fitness value and the selection probability value of each pavilion vector in the initial population according to the target function; selecting the current best pavilion vector by adopting a roulette method according to the selection probability value; performing position updating and population variation according to the current optimal pavilion vector and the historical optimal pavilion vector to generate a new pavilion population; evaluating a population combined by the initial population and the new wayside pavilion population according to an objective function, updating the historical best wayside pavilion vector into a wayside pavilion vector with the minimum objective function value, and taking nPop wayside pavilion vectors with smaller objective function values as the initial population of the next iteration; setting the iteration times to be self-adding 1, and continuing to perform the next iteration; and generating an optimal discrete phase shift matrix according to the historical optimal pavilion vector until the iteration times are greater than the preset maximum iteration times.
In one embodiment, the expression of the objective function in the discrete phase shift matrix design optimization problem building block is shown in formula (1).
In one embodiment, the expression of the discrete phase shift matrix design optimization problem in the discrete phase shift matrix design optimization problem building block is shown in formula (2).
In one embodiment, the expression for the pavilion vector is θ ═ θ 1 ,θ 2 ,...,θ M ](ii) a The module is used for solving the optimization problem of the discrete phase shift matrix design based on the chaos DSBO algorithm and is also used for randomly generating nPop pavilion vectors by adopting a Logistic chaos mapping expression according to the idea of a chaos sequence; the Logistic chaotic mapping expression is shown in formula (3).
In one embodiment, the calculation expression for solving the fitness value of the pavilion vector in the discrete phase shift matrix design optimization problem module based on the chaos DSBO algorithm is shown as formula (4). The calculation expression of the selection probability value of the pavilion vector is shown in the formula (5).
In one embodiment, the update expression used in the location update in the module for solving the optimization problem of the discrete phase shift matrix design based on the chaotic DSBO algorithm is shown as formula (6). The variant expression adopted in the population variation is shown as the formula (7).
The specific definition of the discrete phase shift design apparatus of the RIS-assisted MIMO system can be referred to the definition of the discrete phase shift design method of the RIS-assisted MIMO system, which is not described herein again. The various modules in the discrete phase shift design means of the RIS-assisted MIMO system described above can be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method of discrete phase shift design for a RIS-assisted MIMO system, the method comprising:
constructing an RIS-assisted MIMO system; the receiving end channel of the MIMO system comprises 3 sub-channels adopting Saleh-Vallenzuela channel models based on angle expansion, which are respectively as follows: base station-RIS channel, RIS-user channel, and base station-user channel;
according to the base station-RIS channel, the RIS-user channel and the base station-user channel, constructing a discrete phase shift matrix design optimization problem by taking the frequency spectrum efficiency maximization of the MIMO system as a target, and acquiring an objective function of an optimization algorithm;
generating nPop pavilion vectors serving as an initial population based on Logistic chaotic mapping, wherein the nPop is an integer larger than 1;
setting the initial value of the iteration times as 0, calculating an objective function value of a pavilion vector, and taking the pavilion vector with the minimum objective function value as a history optimal pavilion vector;
calculating the fitness value and the selection probability value of each pavilion vector in the initial population according to the target function;
selecting the current best pavilion vector by adopting a roulette method according to the selection probability value;
performing position updating and population variation according to the current optimal pavilion vector and the historical optimal pavilion vector to generate a new pavilion population;
evaluating a population combined by the initial population and the new wayside pavilion population according to the objective function, updating a historical optimal wayside pavilion vector into a wayside pavilion vector with the minimum objective function value, and taking nPop wayside pavilion vectors with smaller objective function values as initial populations of next iteration;
and setting the iteration number self-adding 1 until the iteration number is larger than the preset maximum iteration number, and generating an optimal discrete phase shift matrix according to the historical optimal pavilion vector.
2. The method according to claim 1, wherein a discrete phase shift matrix design optimization problem is constructed with the goal of maximizing the spectral efficiency of the MIMO system based on the base station-RIS channel, the RIS-user channel, and the base station-user channel, and an objective function of an optimization algorithm is obtained, wherein the objective function of the optimization algorithm in the step is:
Figure FDA0003694524630000011
wherein: h eff ＝H r ΦG+H d Is an equivalent channel matrix, H r Is RIS-user channel, G is base station-RIS channel, H d Is a base station-user channel, phi is a discrete phase shift matrix, N s 、N r Respectively the number of data streams and the number of user antennas; sigma 2 Is the variance of the noise complex signal; p is the transmit signal power.
3. The method according to claim 1, wherein a discrete phase shift matrix design optimization problem is constructed with the goal of maximizing the spectral efficiency of the MIMO system based on the base station-RIS channel, the RIS-user channel, and the base station-user channel, wherein the discrete phase shift matrix design optimization problem is:
Figure FDA0003694524630000021
Figure FDA0003694524630000022
φ m ∈Γ,m＝1,2,...,M.
wherein: h eff ＝H r ΦG+H d Is an equivalent channel matrix, H r Is RIS-user channel, G is base station-RIS channel, H d Is a base station-user channel, phi is a discrete phase shift matrix, N s 、N r M is divided intoThe number of data streams, the number of user antennas and the number of RIS reflecting unit antennas are respectively; sigma 2 Is the variance of the noise complex signal; p is the transmit signal power and Γ is the set of desirable elements of the discrete phase shift matrix.
4. The method of claim 1, wherein the pavilion vector is expressed by θ ═ θ 1 ,θ 2 ,...,θ M ]；
Generating nPop pavilion vectors as initial populations based on Logistic chaotic mapping, wherein the nPop pavilion vectors comprise:
according to the concept of the chaotic sequence, nPop pavilion vectors are randomly generated by adopting a Logistic chaotic mapping expression; the Logistic chaotic mapping expression is as follows:
x(t+1)＝ux(t)(1-x(t))
wherein u belongs to (2.6, 4) as a chaos parameter, t represents the iteration times of the chaos sequence, t belongs to [1, nPop ], and x (t) is a RIS reflecting unit phase shift vector generated by the iteration of the t-th chaos sequence.
5. The method of claim 1, wherein the fitness value of each of the pavilion vectors in the initial population is calculated according to an objective function and the selection probability value is calculated according to the following expression:
Figure FDA0003694524630000023
where fit is the fitness value and f (x) is the objective function;
the calculation expression of the selection probability value of the pavilion vector is as follows:
Figure FDA0003694524630000031
where prob is the selection probability value of the pavilion vector.
6. The method according to claim 1, wherein a new pavilion population is generated by performing location update and population variation according to the current optimal pavilion vector and the historical optimal pavilion vector, and the update expression adopted in the location update in the step is as follows:
Figure FDA0003694524630000032
wherein the content of the first and second substances,
Figure FDA0003694524630000033
alpha is the maximum step length; round (·) denotes round;
Figure FDA0003694524630000034
and
Figure FDA0003694524630000035
the ith element of the ith pavilion vector in the last iteration and the current iteration respectively,
Figure FDA0003694524630000036
and theta elite,l Respectively representing the ith elements of the historical best vector and the current best vector;
the variant expressions used in population variation are:
Figure FDA0003694524630000037
wherein, var max And var min Respectively, an upper limit and a lower limit of each element in the vector theta, z is a preset constant, and N (0,1) represents a random number that follows a normal distribution.
7. An apparatus for discrete phase shift design for a RIS-assisted MIMO system, the apparatus comprising:
the system comprises an RIS auxiliary MIMO system establishing module, a RIS auxiliary MIMO system establishing module and a RIS auxiliary MIMO system establishing module, wherein the RIS auxiliary MIMO system establishing module is used for establishing an RIS auxiliary MIMO system and acquiring a target function of the MIMO system; the receiving end channel of the MIMO system comprises 3 sub-channels adopting Saleh-Vallenzuela channel models based on angle expansion, which are respectively as follows: base station-RIS channel, RIS-user channel, and base station-user channel;
a discrete phase shift matrix design optimization problem construction module, configured to construct a discrete phase shift matrix design optimization problem with a goal of maximizing spectral efficiency of the MIMO system according to the base station-RIS channel, the RIS-user channel, and the base station-user channel, and obtain an objective function of an optimization algorithm according to the optimization problem;
solving a discrete phase shift matrix design optimization problem module based on a chaos DSBO algorithm, and generating nPop pavilion vectors serving as an initial population based on Logistic chaotic mapping, wherein nPop is an integer greater than 1; setting the initial value of the iteration times as 0, calculating an objective function value of a pavilion vector, and taking the pavilion vector with the minimum objective function value as a history optimal pavilion vector; calculating the fitness value and the selection probability value of each pavilion vector in the initial population according to the target function; selecting the current best pavilion vector by adopting a roulette method according to the selection probability value; performing position updating and population variation according to the current optimal pavilion vector and the historical optimal pavilion vector to generate a new pavilion population; evaluating a population combined by the initial population and the new wayside pavilion population according to the objective function, updating a historical optimal wayside pavilion vector into a wayside pavilion vector with the minimum objective function value, and taking nPop wayside pavilion vectors with smaller objective function values as initial populations of next iteration; and setting the iteration times to be self-added by 1, and generating an optimal discrete phase shift matrix according to the historical optimal pavilion vector when the iteration times are larger than the preset maximum iteration times.
8. The apparatus of claim 7, wherein the objective function of the optimization algorithm in the discrete phase shift matrix design optimization problem construction module is:
Figure FDA0003694524630000041
wherein: h eff ＝H r ΦG+H d Is an equivalent channel matrix, H r Is RIS-user channel, G is base station-RIS channel, H d Is a base station-user channel, phi is a discrete phase shift matrix, N s 、N r Respectively the number of data streams and the number of user antennas; sigma 2 Is the variance of the noise complex signal; p is the transmit signal power.
9. The apparatus of claim 7, wherein the discrete phase shift matrix design optimization problem in the discrete phase shift matrix design optimization problem building block is:
Figure FDA0003694524630000042
Figure FDA0003694524630000043
φ m ∈Γ,m＝1,2,...,M.
wherein: h eff ＝H r ΦG+H d Is an equivalent channel matrix, H r Is RIS-user channel, G is base station-RIS channel, H d Is a base station-user channel, phi is a discrete phase shift matrix, N s 、N r M is the number of data streams, the number of user antennas and the number of RIS reflecting unit antennas respectively; sigma 2 Is the variance of the noise complex signal; p is the transmit signal power and Γ is the set of desirable elements of the discrete phase shift matrix.
10. The apparatus of claim 7, wherein the pavilion vector is expressed by θ ═ θ 1 ,θ 2 ,...,θ M ](ii) a The module for solving the optimization problem of the discrete phase shift matrix design based on the chaos DSBO algorithm is also used for randomly generating nPop pavilion vectors by adopting a Logistic chaos mapping expression according to the idea of a chaos sequence; the Logistic chaotic mapping expression is as follows:
x(t+1)＝ux(t)(1-x(t))
wherein u belongs to (2.6, 4) as a chaos parameter, t represents the iteration times of the chaos sequence, t belongs to [1, nPop ], and x (t) is a RIS reflecting unit phase shift vector generated by the iteration of the t-th chaos sequence.
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