CN114422005A - Symbol-level precoding method of intelligent reflector assisted cognitive radio system - Google Patents

Symbol-level precoding method of intelligent reflector assisted cognitive radio system Download PDF

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CN114422005A
CN114422005A CN202210081393.8A CN202210081393A CN114422005A CN 114422005 A CN114422005 A CN 114422005A CN 202210081393 A CN202210081393 A CN 202210081393A CN 114422005 A CN114422005 A CN 114422005A
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CN114422005B (en
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史大帅
蔡曙
朱洪波
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • 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
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H04B7/046—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting taking physical layer constraints into account
    • H04B7/0465—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting taking physical layer constraints into account taking power constraints at power amplifier or emission constraints, e.g. constant modulus, into account
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • 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
    • H04B7/0619—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 using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
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Abstract

The invention relates to a symbol-level precoding method of an intelligent reflector assisted cognitive radio system, which comprises the steps of firstly establishing a received signal model of a cognitive user and a main user, secondly establishing a related symbol-level precoding problem, and finally solving the established system transmitting power minimization design problem on the basis of cognitive user side symbol error probability constraint, interference temperature constraint, constant modulus constraint of an intelligent reflector and known user channel state information of a base station. Based on a symbol-level precoding scheme, the intelligent reflector is used for assisting the cognitive radio system, and a method for minimizing the transmitting power of the system is designed under the constraint conditions of symbol error probability, interference temperature and the like of a cognitive user side, so that the transmitting power of the system is saved, and the construction of a future green wireless communication network is facilitated.

Description

Symbol-level precoding method of intelligent reflector assisted cognitive radio system
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a symbol-level precoding method of an intelligent reflector assisted cognitive radio system.
Background
With the rapid development of wireless communication technology, a large number of mobile terminals need to be accessed into a wireless communication network, so that the contradiction between huge spectrum requirements and limited spectrum resources is increasingly sharp, and the cognitive radio technology can realize the reutilization of the idle spectrum resources, so that the problems of scarce spectrum resources and low utilization rate are solved, and the development of the future wireless communication technology can be better conformed.
An Intelligent Reflection Surface (IRS) can dynamically configure a wireless communication environment in real time, and has been widely used in the aspects of improving communication reliability, reducing power consumption, ensuring physical layer security, and the like. The IRS realizes a specific communication function by adjusting the phase or amplitude of an incident signal, and can more conveniently and flexibly realize communication between a base station and a user. In addition, the signal reflected by the IRS may be superimposed with the signals of other paths to increase the receiving end signal power or reduce co-channel interference. For example, in a document [ j.yuan, y.liang, j.joint, g.feng and e.g.larsson, "Intelligent Reflecting Surface (IRS) -Enhanced coherent Radio System," ICC 2020 International Conference on Communications (ICC),2020, pp.1-6 ] under a linear precoding scheme, considering a scene with an Intelligent reflector assisted Cognitive Radio, the reachable rates of Cognitive users under different conditions are compared; however, the conventional precoding scheme is relatively simple to implement, but requires high power consumption to ensure signal transmission from the base station to the user.
The SLP scheme extracts transmission symbols from a constellation diagram under Quadrature Amplitude Modulation (QAM) or M-ary phase shift keying (MPSK) modulation, and converts interference signals into a beneficial factor, and the interference signals are utilized to convert the interference signals into useful signal power when processing transmission data frames at the Symbol Level, so that the base station can complete communication with smaller transmission power. Compared with the traditional precoding scheme, the SLP scheme has obvious advantages in the aspects of reducing the symbol error probability of the user side and improving the power consumption of the system during communication. For example, under the assistance of an Intelligent Reflecting Surface, an SLP scheme is considered for a conventional Wireless communication environment, and the SLP scheme is emphasized to have more advantages than a conventional Zero Forcing (ZF) Precoding scheme in terms of reducing a user Error rate, but only the conventional Wireless communication scenario is considered, the SLP scheme is not applied to a cognitive radio scenario, and only the research on the bit Error rate at the user side is emphasized, and the related problem of the system transmission power is not researched.
Disclosure of Invention
In order to solve the problem of overhigh transmission power loss of the system, on the basis of an SLP scheme, the cognitive radio technology is firstly used for solving the problem of low frequency spectrum utilization rate, then an intelligent reflecting surface is used for widening paths for signal transmission and user receiving, the power loss caused by various factors in the signal transmission process is made up, and finally the design problem of minimizing the system transmission power as a target is put forward and solved under the cognitive radio communication system assisted by the intelligent reflecting surface. Therefore, the transmitting power of the system is saved, and the construction of a green wireless communication network in the future is facilitated.
The invention relates to a symbol-level precoding method of an intelligent reflector assisted cognitive radio system, which comprises the following steps:
s1, establishing a signal receiving model of the cognitive user and the master user according to parameter setting, channel state information and intelligent reflecting surface parameters of the cognitive radio communication system;
s2, establishing a related symbol-level precoding problem aiming at an intelligent reflector-assisted cognitive radio communication scene, and providing a design problem aiming at minimizing system transmitting power on the basis of cognitive user side symbol error probability constraint, interference temperature constraint, intelligent reflector constant modulus constraint and user channel state information known by a base station;
s3, aiming at the established system transmitting power minimization design problem, firstly, a block coordinate reduction algorithm based on double-layer iteration is used, the established design problem is decomposed into two sub-problems, and iteration updating and solving are respectively carried out; secondly, in the layered iteration process, a semi-positive definite relaxation algorithm and a Gaussian randomization method are used for solving the constant modulus constraint of the intelligent reflecting surface; and finally solving the minimum system transmitting power under a symbol-level precoding scheme, and designing and using a dichotomy algorithm for reducing the iteration times during solving.
Further, the specific step of S1 is:
for a light source with the aid of an intelligent reflector and provided with NTA Cognitive Base Station (CBS) of a root antenna serving a cognitive radio communication system model of K single-antenna Cognitive Users (CU) and L single-antenna Primary Users (PU); the Intelligent Reflector (IRS) is provided with NRA reflection unit having a relative reflection coefficient
Figure BDA0003486046330000021
Can be expressed as
Figure BDA0003486046330000022
HrRepresenting a transmission channel from CBS to IRS in the cognitive wireless communication system; gr,l、hr,kInterference channels from the IRS to the l-th PU and transmission channels from the IRS to the k-th CU are respectively arranged; gl、hkRespectively representing a direct interference channel generated by the CBS to the ith PU and a direct transmission channel from the CBS to the kth CU; the signal received by the kth CU is:
Figure BDA0003486046330000031
the interference signal received by the ith PU is:
Figure BDA0003486046330000032
wherein, yk(t) is the signal received by the kth CU during symbol time t; i.e. il(t) is the interference signal received by the l-th PU within symbol time t; t is the channel coherence time; Φ ═ Diag (θ) is the reflection matrix of the IRS;
Figure BDA0003486046330000033
is the transmit signal of the CBS;
Figure BDA0003486046330000034
Figure BDA0003486046330000035
and
Figure BDA0003486046330000036
is a circular complex gaussian noise; the channel state information known by the cognitive base station comprises an interference channel gr,l、glAnd a transmission channel Hr、hr,k、hk。
Further, the specific step of S2 is:
under the cognitive radio communication scene assisted by the intelligent reflecting surface, symbol error probability constraint, interference temperature constraint and constant modulus constraint of the intelligent reflecting surface at the cognitive user side are simultaneously met, and the design problem with the minimization of system transmitting power as the target is provided; the objective function and constraints are expressed as:
Figure BDA0003486046330000037
wherein "min" represents a minimization operation; "s.t." means a constraint;
Figure BDA0003486046330000038
is the real part of the signal,
Figure BDA0003486046330000039
as the imaginary part of the signal, the imaginary part,
Figure BDA00034860463300000310
is a cognitive user side symbol error probability constraint;
Figure BDA00034860463300000311
is a disturbance of the temperature constraint, IthIs the maximum interference that the system can tolerateA temperature threshold; [ theta ]i1 is the constant modulus constraint of the intelligent reflecting surface; under QPSK modulation, to solve the problem conveniently
Figure BDA0003486046330000041
By using
Figure BDA0003486046330000042
Alternatively, equation (11) may be written as:
Figure BDA0003486046330000043
further, the specific step of S3 is:
s3-1, because the constraint conditions include variable coupling and are not suitable for direct solution, the constraint condition u (t) in equation (12) is set to (H)kθ+hk)Hx(t)、v(t)＝(Glθ+gl)Hx (t) exchanging positions with the objective function, resulting in a problem to be handled:
Figure BDA0003486046330000044
wherein, PTIs the minimum transmit power value that needs to be solved for.
S3-2, for solving the system transmission power minimization design problem, the invention designs and uses a block coordinate descent algorithm based on double-layer iteration. Due to the variable coupling and the non-convex constraint in the formula (13), the direct solution of the design problem becomes very difficult; through analysis and verification, T time slots of the target function are merged, the design problem is divided into two subproblems of (P0) and (P1) by using a block coordinate descent algorithm, alternate iterative updating is carried out, the optimal u, v, X and theta values are solved, and the minimum transmitting power P is solvedTA value;
updating u, v, X
Fixing the value of theta and then processing u, v and X to obtain a subproblem (P0):
Figure BDA0003486046330000051
② updating theta
When the values of u, v, and X are known, the sub-problem (P1) is obtained by updating θ by substituting the following equation:
Figure BDA0003486046330000052
due to constraint | θi|＝1,i＝1,...,NRThe constraint of (2) cannot be solved directly, and the above equation needs to be processed into the following form by using a semi-definite relaxation algorithm:
Figure BDA0003486046330000053
by the expression (16), X can be directly obtained and an optimum θ value can be obtained by the gaussian randomization method.
The block coordinate descent algorithm specifically comprises the following steps:
1) first, given Ith、PTAn initial value of (1);
2) secondly, giving an initial value of theta, solving a subproblem (P0), and obtaining values of u, v and X;
3) the sub-problem is solved (P1) using the obtained u, v, and X values as initial values, and a value of θ is obtained. The design problem processed by using a semi-positive definite relaxation method cannot be solved directly, and the optimal theta is solved by means of a Gaussian randomization method;
4) satisfy the maximum number of iterations n or
Figure BDA0003486046330000054
When the iteration is finished, exiting the iteration;
5) obtaining values of u, v, X and theta, and calculating whether the objective function in the formula (13) is 0 or not;
6) the flow ends.
S3-3, for ultimate purposeThe criterion is to solve for the smallest PTA value; the method adopted by the invention comprises the following steps: first, an initial P is givenTOn the basis of the values, solving the values of u, v, X and theta in the target function; at this time, the values of u, v, X, and θ obtained by the solution are substituted into the objective function of expression (13), and if the objective function is 0, the solution showing the problem optimum is in the interval [0, P ]T]In the interior, the length of the interval needs to be shortened; otherwise, the optimal solution for the illustrative problem is not in the interval [0, P ]T]The length of the interval needs to be expanded; the invention designs and uses a binary solution algorithm to shorten and expand the interval so as to reduce the iteration times.
The dichotomy solving algorithm comprises the following specific steps:
1) substituting the u, v, X and theta values of which the updating iteration is finished into the objective function of the formula (13);
2) verifying whether the target function is 0;
(2.1) if 0, then P will be givenTInitial value becomes PT(ii)/2, looping until the objective function is not 0;
then executing:
(a) set the lower bound to LB＝PT/2, the upper bound is set to UB＝PT；
(b)PT＝LB+(UB-LB)/2；
(c) Will PTSubstituting the target function to verify whether the target function is 0; if 0, UB＝PTOtherwise
LB＝PT；
(d) Circularly updating until UB-LB＜10-6To obtain LB＝UB＝PT；
(2.2) if not 0, then P will be givenTInitial value becomes 2PTLooping until the objective function is 0;
then executing:
(a) set the lower bound to LB＝PTThe upper bound is set to UB＝2PT；
(b)PT＝LB+(UB-LB)/2；
(c) Will PTSubstituting the target function to verify whether the target function is 0; if 0, UB＝PTOtherwise
LB＝PT；
(d) Circularly updating until UB-LB＜10-6To obtain LB＝UB＝PT；
3) Obtaining the minimum PTAnd the flow ends.
The invention has the beneficial effects that: based on a symbol-level precoding scheme, the intelligent reflector is used for assisting the cognitive radio system, so that the utilization rate of frequency spectrum resources is improved, and paths for signal transmission and user receiving are widened; and under the constraint conditions of symbol error probability, interference temperature and the like of a cognitive user side, the design problem aiming at minimizing the system transmitting power is provided and solved, so that the transmitting power of the system is saved, and the construction of a future green wireless communication network is facilitated.
Drawings
In order that the present invention may be more readily and clearly understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
FIG. 1 is a diagram of a system model of the present invention.
Fig. 2 is a graph of transmit power versus transmit power for various aspects of the invention.
Fig. 3 is a graph of the transmission power comparison for different CU numbers according to the present invention.
FIG. 4 is a graph of interference temperature versus transmit power variation for the present invention;
fig. 5 is a graph of the number of transmit units versus the change in transmit power for the present invention.
Detailed Description
As shown in FIG. 1, the present embodiment contemplates an IRS-assisted multiple-input-single-output (MISO) downlink CR communication system comprising an N-deviceTA cognitive base station of root antenna, a CU serving K single antennas and a PU serving L single antennas, and an intelligent reflector equipped with NRA reflection unit, its associated reflectionCoefficient of performance
Figure BDA0003486046330000071
Can be expressed as
Figure BDA0003486046330000072
HrRepresenting a transmission channel from the CBS to the IRS in the CR communication system; gr,l、hr,kInterference channels from the IRS to the l-th PU and transmission channels from the IRS to the k-th CU are respectively arranged; gl、hkRespectively representing a direct interference channel generated by the CBS to the ith PU and a direct transmission channel from the CBS to the kth CU; the signal received by the kth CU is:
Figure BDA0003486046330000073
the interference signal received by the ith PU is:
Figure BDA0003486046330000074
wherein, yk(t) is the signal received by the kth CU during symbol time t; i.e. il(t) is the interference signal received by the l-th PU within symbol time t; t is the channel coherence time; Φ ═ Diag (θ) is the reflection matrix of the IRS;
Figure BDA0003486046330000075
is the transmit signal of the CBS;
Figure BDA0003486046330000076
Figure BDA0003486046330000077
and
Figure BDA0003486046330000078
is a circular complex gaussian noise; the channel state information known by the cognitive base station comprises an interference channel gr,l、glAnd a transmission channel Hr、hr,k、hk。
Under the cognitive radio communication scene assisted by the intelligent reflecting surface, the realization method for minimizing the system transmitting power is designed under the conditions of simultaneously meeting the symbol error probability of the cognitive user side, the interference temperature and the constant modulus constraint condition of the intelligent reflecting surface. The objective function and constraints can be expressed as:
Figure BDA0003486046330000081
under QPSK modulation, the design problem can be written as:
Figure BDA0003486046330000082
by observing and analyzing the formula (4), the method can be obtained that the constraint conditions have variable coupling and are not suitable for direct solution. The constraint of u (t) ═ Hkθ+hk)Hx (t) and v (t) ═ Glθ+gl)Hx (t) exchanges positions with the objective function, and the problem needing to be processed is obtained after processing:
Figure BDA0003486046330000083
due to the non-convex constraint, it becomes very difficult to directly solve the optimization problem. T time slots of the objective function can be merged, the optimization problem is divided into two sub-problems of (P0) and (P1) by using a block coordinate descent algorithm, and alternate updating iteration is carried out.
(1) Updating u, v, X
Fixing θ first, then processing u, v, and X, a subproblem can be obtained (P0):
Figure BDA0003486046330000091
(2) updating theta
When the values of u, v, and X are known, the sub-problem (P1) is obtained by updating θ by substituting the following equation:
Figure BDA0003486046330000092
due to constraint | θi|＝1,i＝1,...,NRThe limitation of (2) cannot be solved directly, and the limitation of (2) needs to be transformed and solved by using a semi-positive definite relaxation algorithm. The specific treatment process is as follows:
expanding the target function, and performing conjugate transpose on the target function to obtain:
Figure BDA0003486046330000093
let ak＝uk-XHhk、bk＝-XHHkAnd aK+l＝vl-XHgl、bK+l＝-XHGlThen the objective function can be expressed as:
Figure BDA0003486046330000094
and integrate the above formula into
Figure BDA0003486046330000097
Order to
Figure BDA0003486046330000095
Then | | a + b θ | | non-woven phosphor2Can be converted into | [ b, a | ]]x||2. Then let R ═ b, a]H[b,a]Then | | [ b, a | ]]x||2Can be rewritten as xHRx; to give the following formula:
Figure BDA0003486046330000096
let X be xxHAnd obtaining the final optimization problem needing to be solved:
Figure BDA0003486046330000101
by the expression (27), X can be directly obtained and the optimal θ value can be obtained by the gaussian randomization algorithm.
The method of the present invention is described below with reference to an example, which is shown in fig. 2 to 5, and this embodiment simulates the above scenario using MATLAB R2020 a. Setting the number of Cognitive Base Stations (CBS) as 1 and the number of antennas N of the base stationsT12, number of reflection units of the intelligent reflection surface NR32, the cognitive user number K is 10, the primary user number L is 2, the cognitive user and the primary user are both single-antenna users, the time slot length T is 10, and the number Num of iterative channels is 100. The path loss of all channels is given by the formula L (d) ═ C0(d/D0)-αGiven, and the path loss coefficients of CBS to IRS, IRS to CU, CBS to CU, IRS to PU, CBS to PU are respectively alphaBI＝2.2、αIC＝αBC＝αIP＝αCP2.8. Channel H between CBS and IRSrBy the formula
Figure BDA0003486046330000102
Given, and βBI＝0.7、βIC＝βBC＝βIP＝β CP0. The coordinates of the base station are (0,0), the coordinates of the intelligent reflecting surface are (20,0), the coordinates of the cognitive user are (18,2i), i belongs to [1,10 ]]The coordinate of the master user is (17, -1+2i), i belongs to [1,2 ]]. Maximum threshold value I in the system imposed disturbance temperature constraintth0dB, the initial value of the transmission power is PT＝10dB。
Figure 2 shows a graph of the transmitted power versus the different schemes. Firstly, setting the communication system to meet the requirement that the target value of the SEP at the user side is [0.05,0.01,0.005,0.001,0.0005,0.0001], and respectively corresponding to six mark points in the graph; secondly, by comparing the linear precoding ZF scheme under QPSK modulation with the SLP scheme under the conventional communication scenario, it can be seen that the SLP scheme is better than the ZF scheme under the same SEP requirement, and the transmission power is reduced by about 2 dB. Compared with the two schemes, the SLP scheme used in the present embodiment under the CR communication scenario with IRS assistance reduces the transmission power by 13-15 dB, which effectively proves that the CR communication model with IRS assistance can complete communication with smaller transmission power on the basis of the SEP requirement of the user side.
Fig. 3 shows a comparison of the required transmission power for different CU numbers, which shows the required transmission power for CU 5, CU 10 and CU 15. As can be seen from the figure, the required transmit power gradually increases with the number of CUs under the same user-side SEP requirement. This is because when the number of users increases, the base station needs to use higher transmission power to ensure the communication demand and service quality of each cognitive user, which is in line with the expectation.
Fig. 4 shows interference temperature versus transmit power variation graphs, which show the variation of transmit power with increasing interference temperature at fixed SEP target values of 0.0001, 0.001 and 0.05, respectively. From the vertical view of the figure, the required transmit power is becoming larger as the SEP is lower. In a transverse view, as the interference temperature threshold value applied by the system is continuously larger, the emission power is in a slow descending trend. This is because as the interference temperature threshold is continuously increased, the interference that the PU can tolerate is also continuously increased, and the cognitive base station can achieve the SEP target requirement on the user side with smaller transmission power.
Fig. 5 shows a graph of the number of transmit units versus the change in transmit power. The variation of the transmit power with increasing number of transmit units is shown at fixed SEP target values of 0.0001, 0.001 and 0.05, respectively. From the vertical view of the figure, the required transmit power is becoming larger as the SEP is lower. In a transverse view, as the number of the transmitting units is increased, the transmitting power is approximately maintained at the same value and hardly changed. This is because, given the SEP target value, the value of the transmission power required by the system is already determined and will not change due to the change of the number of reflection units.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and all equivalent variations made by using the contents of the present specification and the drawings are within the protection scope of the present invention.

Claims (6)

1. A symbol-level precoding method of an intelligent reflector assisted cognitive radio system is characterized by comprising the following steps:
s1, establishing a signal receiving model of the cognitive user and the master user according to parameter setting, channel state information and intelligent reflecting surface parameters of the cognitive radio communication system;
s2, establishing a related symbol-level precoding problem aiming at an intelligent reflector-assisted cognitive radio communication scene, and providing a design problem aiming at minimizing system transmitting power on the basis of cognitive user side symbol error probability constraint, interference temperature constraint, intelligent reflector constant modulus constraint and user channel state information known by a base station;
s3, aiming at the established system transmitting power minimization design problem, firstly, a block coordinate reduction algorithm based on double-layer iteration is used, the established design problem is decomposed into two sub-problems, and iteration updating and solving are respectively carried out; secondly, in the layered iteration process, a semi-positive definite relaxation algorithm and a Gaussian randomization method are used for solving the constant modulus constraint of the intelligent reflecting surface; and finally solving the minimum system transmitting power under a symbol-level precoding scheme, and designing and using a dichotomy algorithm for reducing the iteration times during solving.
2. The symbol-level precoding method for the intelligent reflector assisted cognitive radio system according to claim 1, wherein the specific steps of S1 are as follows:
for a light source with the aid of an intelligent reflector and provided with NTA Cognitive Base Station (CBS) of a root antenna serving a cognitive radio communication system model of K single-antenna Cognitive Users (CU) and L single-antenna Primary Users (PU); the Intelligent Reflector (IRS) is provided with NRA reflection unit having a relative reflection coefficient
Figure FDA0003486046320000011
Can be expressed as
Figure FDA0003486046320000012
HrRepresenting a transmission channel from CBS to IRS in the cognitive wireless communication system; gr,l、hr,kInterference channels from the IRS to the l-th PU and transmission channels from the IRS to the k-th CU are respectively arranged; gl、hkRespectively representing a direct interference channel generated by the CBS to the ith PU and a direct transmission channel from the CBS to the kth CU; the signal received by the kth CU is:
Figure FDA0003486046320000013
the interference signal received by the ith PU is:
Figure FDA0003486046320000014
wherein, yk(t) is the signal received by the kth CU during symbol time t; i.e. il(t) is the interference signal received by the l-th PU within symbol time t; t is the channel coherence time; Φ ═ Diag (θ) is the reflection matrix of the IRS;
Figure FDA0003486046320000021
is the transmit signal of the CBS;
Figure FDA0003486046320000022
Figure FDA0003486046320000023
and
Figure FDA0003486046320000024
is a circular complex gaussian noise; the channel state information known by the cognitive base station comprises an interference channel gr,l、glAnd a transmission channel Hr、hr,k、hk。
3. The symbol-level precoding method for the intelligent reflector assisted cognitive radio system according to claim 1, wherein the specific steps of S2 are as follows:
under the cognitive radio communication scene assisted by the intelligent reflecting surface, symbol error probability constraint, interference temperature constraint and constant modulus constraint of the intelligent reflecting surface at the cognitive user side are simultaneously met, and the design problem with the minimization of system transmitting power as the target is provided; the objective function and constraints are expressed as:
Figure FDA0003486046320000025
wherein "min" represents a minimization operation; "s.t." means a constraint;
Figure FDA0003486046320000026
is the real part of the signal,
Figure FDA0003486046320000027
as the imaginary part of the signal, the imaginary part,
Figure FDA0003486046320000028
is a cognitive user side symbol error probability constraint;
Figure FDA0003486046320000029
is a disturbance temperature constraint; [ theta ]i1 is the constant modulus constraint of the intelligent reflecting surface; under QPSK modulation, to solve the problem conveniently
Figure FDA00034860463200000210
By using
Figure FDA00034860463200000211
Alternatively, equation (3) may be written as:
Figure FDA0003486046320000031
。
4. the symbol-level precoding method for the intelligent reflector assisted cognitive radio system according to claim 1, wherein the specific steps of S3 are as follows:
s3-1, because the constraint conditions include variable coupling and are not suitable for direct solution, the constraint condition u (t) in equation (4) is set to (H)kθ+hk)Hx(t)、v(t)＝(Glθ+gl)Hx (t) exchanging positions with the objective function, resulting in a problem to be handled:
Figure FDA0003486046320000032
s3-2, solving the system transmission power minimization design problem by using a block coordinate descent algorithm based on double-layer iteration; due to the fact that variable coupling and non-convex constraint exist in the formula (5), direct solving of the design problem is very difficult, T time slots of the objective function are combined through analysis and verification, the design problem is divided into two sub-problems of (P0) and (P1) through a block coordinate descent algorithm, alternate iterative updating is conducted, the optimal u, v, X and theta values are obtained through the combined processing, and then the minimum transmitting power P is obtained through the solutionTThe value is obtained.
S3-3, solving the minimum PTValue, applying a binary solution algorithm: first, an initial P is givenTOn the basis, the optimal u, v, X and theta values in the objective function are solved through iterative updating; at this time, the values of u, v, X, and θ obtained by the solution are substituted into the objective function of expression (5), and if the objective function is 0, the optimal solution for the problem is in the interval [0, P ]T]In the interior, the length of the interval needs to be shortened; otherwise, the optimal solution for the illustrative problem is not in the interval [0, P ]T]In this case, the length of the interval needs to be extended.
5. The symbol-level precoding method of the intelligent reflector assisted cognitive radio system as claimed in claim 4, wherein in S3-2, the sub-problem (P0) is:
updating u, v, X: fixing the value of theta, and then processing u, v and X to obtain a subproblem (P0):
Figure FDA0003486046320000041
。
6. the symbol-level precoding method of the intelligent reflector assisted cognitive radio system as claimed in claim 4, wherein in S3-2, the sub-problem (P1) is:
updating theta: when the values of u, v, and X are known, the sub-problem (P1) is obtained by updating θ by substituting the following equation:
Figure FDA0003486046320000042
due to constraint | θi|＝1,i＝1,...,NRThe constraint of (2) cannot be solved directly, and the above equation needs to be processed into the following form by using a semi-definite relaxation algorithm:
Figure FDA0003486046320000043
by the expression (8), X can be directly obtained, and an optimum θ value can be obtained by using a gaussian randomization method.
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