CN116321186A - IRS (inter-range request System) auxiliary cognition SWIPT (SWIPT) system maximum and rate resource optimization method - Google Patents

IRS (inter-range request System) auxiliary cognition SWIPT (SWIPT) system maximum and rate resource optimization method Download PDF

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CN116321186A
CN116321186A CN202310337625.6A CN202310337625A CN116321186A CN 116321186 A CN116321186 A CN 116321186A CN 202310337625 A CN202310337625 A CN 202310337625A CN 116321186 A CN116321186 A CN 116321186A
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许晓荣
张宸宇
包建荣
刘兆霆
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Hangzhou Dianzi University
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention discloses an IRS-assisted cognitive SWIPT system maximum and rate resource optimization method. With the end-to-end and rate of the maximized IRS-assisted cognitive SWIPT system as the optimization targets, the cognitive SWIPT receiver employs nonlinear energy harvesting. The multi-resource allocation problem is modeled as a nonlinear non-convex optimization problem, and is mutually coupled with subcarriers, power and IRS reflection phase shift vectors, so that the IRS phase shift vectors are fixed by adopting an alternating optimization method, and an optimal power and subcarrier set for information decoding and nonlinear energy collection is obtained by adopting a Lagrangian dual conversion and a secondary gradient method. And then, obtaining the IRS reflection phase shift vector by a continuous convex approximation method, thereby realizing end-to-end and rate maximization of the IRS-assisted cognitive SWIPT system. The invention effectively distributes various resources such as sub-carriers, power and IRS reflection phase shift vectors and the like for information decoding and nonlinear energy collection in the cognitive SWIPT system, and maximizes the end-to-end sum rate under the actual SWIPT nonlinear energy collection model.

Description

IRS (inter-range request System) auxiliary cognition SWIPT (SWIPT) system maximum and rate resource optimization method
Technical Field
The invention belongs to the technical field of information and communication engineering, and provides a multi-resource joint optimization method for maximizing sum rate in an intelligent reflection surface (Intelligent Reflecting Surface, IRS) -assisted cognitive wireless energy-carrying communication (Simultaneous Wireless Information and Power Transfer, SWIPT) system.
Background
With the application of 5G in the ground, the rapid development of intelligent mobile multimedia terminals and the global Internet of things industry is brought, and the energy consumption requirement of equipment is increased due to explosive communication traffic demand. The increasing density of nodes in communication networks and the expanding network coverage have led to increasing concerns about the energy consumption of communication networks, and "greening" has become one of the directions of research in future wireless networks. The Energy-efficient (EE) green communication and ubiquitous intelligent (Pervasive-AI) technology is one of the leading edge technologies of the 6G mobile communication system in the future, and is also a key technology for Energy conservation, emission reduction and environmental protection of the green mobile communication network in the future. Intelligent Reflection Surface (IRS) -assisted wireless power-carrying communication (swit), which is one of the green communication technologies with high energy efficiency and low power consumption, has been proposed to solve the energy efficiency problem of the future 6G mobile communication system and improve the communication quality, and has been receiving extensive attention from the industry.
Cognitive Radio (CR) provides a new solution for the contradiction between spectrum resource scarcity and spectrum utilization rate inefficiency. Swits greatly improve the energy efficiency of the network by collecting energy (Energy Harvesting, EH) from the radio frequency signals. As the combination of the two, the cognitive SWIPT can fully utilize the characteristic that the CR opportunity utilizes the spectrum resource and the SWIPT information energy to be transmitted simultaneously, and improve the spectrum efficiency and the energy efficiency of the wireless network. The objective of SWIPT multi-user multi-resource joint optimization is to realize the optimization of system resources through joint allocation and scheduling of multiple resources among different users. Compared with the multi-user scheduling of the conventional wireless network, the multi-user multi-resource joint allocation and scheduling in SWIPT requires a compromise of information and energy. Currently, the resources that can be allocated in SWIPT include time slots, power, bits, subcarriers, etc. Technical indexes of SWIPT network resource allocation and optimization mainly comprise sum rate (spectrum effectiveness), energy effectiveness, outage probability and the like.
The Intelligent Reflection Surface (IRS) is a planar array formed by a plurality of reconstructed passive reflection elements, and the working modes of the IRS are coordinated by a software controller, wherein each element can control the reflection angle and the reflection intensity of an incident electromagnetic wave, so that the phase and the amplitude of a reflection signal are controlled, the reflected electromagnetic wave is enabled to generate phase shift independently, and a three-dimensional passive beam meeting the requirement of differential communication is formed. The IRS element units are connected to a controller and are uniformly controlled by the controller, so that the intelligent reflecting surface can reconfigure the wireless propagation environment, and the degree of freedom of the wireless communication network performance is improved. The IRS adjusts the phase shift of each element in real time through the controller to reflect signals, and the transmitting signals can enhance the receiving power of a receiving end and weaken the receiving power of an eavesdropper at the same time, so that the safety of the system is improved. For example, in a wireless communication network, fading and interference problems of wireless channels are addressed by deploying IRSs to intelligently coordinate their reflections. For combined uplink and downlink communications, passive IRSs tend to achieve higher weighted sum rates than active IRSs, optimizing IRS placement separately, because optimal active IRS placement requires balancing the rate performance of the uplink with the downlink, while deploying passive IRSs near the transmitter or receiver is optimal for both the uplink and the downlink.
Compared with the traditional relay base station, the intelligent reflecting surface is a passive device, and has the remarkable characteristics of low power consumption, and the user receiving signal-to-noise ratio is in direct proportion to the square of the IRS reflecting array source number. The intelligent reflecting surface reflects signals, so that the method of increasing the energy consumption of the system and improving the data rate is avoided, and the energy efficiency of the system is improved. In addition, the IRS works in a full duplex mode, does not have any antenna noise amplification and self-interference, and enhances the effectiveness of the communication system. Because the IRS is light in weight and convenient to deploy, a special power supply room is not needed for supplying power to the IRS, and the IRS can be easily installed and deployed in a required environment.
Disclosure of Invention
The invention designs a maximum and rate resource optimization method of an IRS-assisted cognitive SWIPT system. The method takes the maximized end-to-end and speed of the cognitive network as optimization targets, and constructs the multivariable coupling non-convex nonlinear optimization problem under the conditions of power control of a cognitive user transmitter, nonlinear energy collection of a cognitive SWIPT user, IRS reflection coefficient vector mode constraint and the like. The optimization problem is solved using an Alternating Optimization (AO) method. Firstly, fixing IRS phase shift vector, converting a split objective function into a reduced one by using Dinkelbach method, and obtaining optimal sub-carrier and power by using Lagrangian dual method. Then, an IRS phase shift vector is obtained by adopting a successive approximation (SCA) method. Simulation shows that the method effectively distributes various resources such as subcarriers, power, IRS phase shift vectors and the like, and achieves system and rate maximization under the condition of actual nonlinear energy collection.
The technical scheme of the invention comprises the following steps:
step 1, scene assumption and modeling of IRS auxiliary cognition SWIPT system maximum and rate resource optimization method:
in order not to lose generality, the following assumptions are made before describing the design strategy in detail:
(1) Ignoring the power of the reflected signal of more than two times of the IRS, and ensuring that the maximum reflection on the IRS is free of loss;
(2) The channel gain of the cascade channel of the cognitive user transmitter-IRS-cognitive user receiver obeys quasi-static block fading, and the cognitive user transmitter (base station) can acquire the channel state information of all the receivers;
(3) In the main network, the main user transmitter does not interfere with the cognitive user receiver;
in an IRS-assisted cognitive SWIPT system, an underley spectrum sharing model is adopted by a main network and a cognitive network. While the primary user receiver receives signals from the primary user transmitter, the cognitive user transmitter (cognitive base station) transmits signals to the cognitive user receiver (cognitive SWIPT user) through a direct link and an IRS forwarding link. Cognitive SWIPT adopts a power division (PS) structure, and uses a received signal for Information Decoding (ID) and nonlinear energy collection (EH);
assuming that the IRS-assisted cognitive SWIPT network comprises a cognitive transmitter and a cognitive receiver, adopting orthogonal frequency division multiplexing multiple access (OFDMA) with the number of subcarriers being N; system subcarrier set s=s I ∪S E ＝{1,2,…,N}，
Figure BDA0004156969230000031
wherein SI Decoding a set of subcarriers for information, S E A set of subcarriers is collected for nonlinear energy. Each subcarrier is used for an ID or nonlinear EH; the IRS assisted cognitive SWIPT is assumed to acquire statistical Channel State Information (CSI) by adopting channel estimation, and the IRS with the number of reflection array sources being M is used as an intelligent passive relay;
let the direct link channel between the cognitive transmitter and the cognitive receiver be denoted as h tr ∈C N×1 The IRS phase shift diagonal matrix is expressed as
Figure BDA0004156969230000032
IRS reflection phase shift vector is denoted as +.>
Figure BDA0004156969230000033
wherein
Figure BDA0004156969230000034
Representing the reflection coefficient, theta, of the mth IRS reflection array source m ∈[0,2π]The reflection phase shift angle of the mth IRS reflection array source. The channel matrix of cognitive transmitter to IRS is denoted as H SI ∈C M×N The IRS to cognitive receiver channel matrix is denoted as H IR ∈C M×N The composite channel gain of the direct link of the cognitive transmitter to the cognitive receiver and the cascade channel is denoted +.>
Figure BDA0004156969230000035
wherein hcom ∈C N×1 ；
The received signal-to-noise ratio (SNR) for information decoding for a cognitive swift user is:
Figure BDA0004156969230000036
wherein P is the transmission power of the cognitive transmitter, S is the subcarrier set,
Figure BDA0004156969230000037
reflecting the phase shift vector for the IRS; p is p n Decoding the transmit power, h, of the sub-carrier for the nth information for the cognitive transmitter n Representing the composite gain of the direct link and the cascade channel of the cognitive transmitter to the cognitive receiver on the nth subcarrier,/for>
Figure BDA0004156969230000041
Is an Additive White Gaussian Noise (AWGN) channel noise power; the IRS-assisted cognitive SWIPT system end-to-end and rate R are expressed as:
Figure BDA0004156969230000042
wherein B is the system bandwidth;
if linear energy harvesting is employed, the energy harvested by the cognitive SWIPT user is modeled as:
Figure BDA0004156969230000043
if nonlinear energy harvesting is employed, the energy harvested by the cognitive SWIPT user is modeled as:
Figure BDA0004156969230000044
wherein ,
Figure BDA0004156969230000045
omega is a constant that ensures zero input zero output response; p (P) sat Is the most saturated EH circuitLarge collection power; parameters a and b are constants related to the circuit specification and are positive numbers such as resistance value, capacitance value, and diode turn-on voltage value; in effect, parameter a reflects the non-linear charge rate with respect to the input power, while parameter b is related to the EH circuit on threshold; the parameter P may be determined by curve fitting when the energy harvesting circuit is given sat A and b; without loss of generality, use ψ n Representing the collected energy;
the end-to-end and rate maximization of the cognitive SWIPT network is used as an optimization target, meanwhile, a plurality of constraint conditions such as the control of the transmitting power of a cognitive user transmitter, the nonlinear energy collection constraint of a cognitive SWIPT receiver, the constraint of IRS reflection phase shift vectors and the like are met, and a constructed mathematical optimization model is expressed as follows:
Figure BDA0004156969230000046
wherein ,
Figure BDA0004156969230000051
representing the received signal-to-noise ratio of a cognitive SWIPT user on an information decoding subcarrier, R representing the end-to-end and rate of the cognitive SWIPT network, < >>
Figure BDA0004156969230000052
Representing cognitive SWIPT user nonlinear energy harvesting; />
Figure BDA0004156969230000053
Representing the reflection coefficient of the mth IRS reflection array source; p is p n ,n∈S I Representing cognitive transmitter transmit power, p, for information decoding n ,n∈S E Representing cognitive transmitter transmit power for energy harvesting, h n Representing the composite gain of the direct link and the cascade channel of the cognitive transmitter to the cognitive receiver on the nth subcarrier,/for>
Figure BDA0004156969230000054
Representing the additive Gaussian white noise power, P s Representing cognitionTransmitter transmit power threshold, Q min A minimum collection energy threshold representing a non-linear energy collection,/->
Figure BDA0004156969230000055
Representing the reflection coefficient modulus value of the mth IRS reflection array source.
Step 2, optimizing a nonlinear energy collection model in an IRS (inter-range hood) auxiliary cognitive SWIPT system maximum and rate resource optimization method:
after the nonlinear energy collection model reaches a saturated state, the nonlinear energy collection model is not increased along with the increase of distributed power in energy collection subcarriers, E is an energy collection threshold value reaching the saturated state, and constraint conditions of optimization problem are introduced
Figure BDA0004156969230000056
The optimization problem formula (5) becomes:
Figure BDA0004156969230000057
step 3, information decoding and nonlinear energy collection optimal power distribution in the IRS auxiliary cognition SWIPT system maximum and rate resource optimization method:
firstly, fixing IRS reflection phase shift vector, solving formula (6) optimization problem by Lagrange dual method because the power and subcarrier in objective function and constraint condition are dual, normalizing system bandwidth and energy conversion efficiency without losing generality, and obtaining system reachable sum rate under unit frequency band as spectrum efficiency, which is expressed as
Figure BDA0004156969230000061
The lagrangian dual function of the optimization problem (6) is:
Figure BDA0004156969230000062
wherein α= (α) 1 ,α 2 ,α 3 ) Is a non-negative Lagrangian dual variable, thenThe problem-solving formula (6) can be converted into the following dual problem:
Figure BDA0004156969230000063
wherein ,
Figure BDA0004156969230000064
solving the dual variable by adopting a sub-gradient method>
Figure BDA0004156969230000065
Reconstructing the lagrangian dual function as:
Figure BDA0004156969230000066
wherein ,
Figure BDA0004156969230000067
it is related to the transmit power of the cognitive user transmitter in each subcarrier; set of given subcarriers s=s I ∪S E = {1,2, …, N }, in ID subcarrier set S I Nonlinear EH subcarrier set S E Inner pair p n Obtaining the partial derivative:
Figure BDA0004156969230000068
Figure BDA0004156969230000069
let the partial derivative values of the formula (10) and the formula (11) be zero, and respectively obtain the optimal power of information decoding and energy collection as follows:
Figure BDA00041569692300000610
Figure BDA00041569692300000611
wherein ,(x)+ =max (x, 0), the power allocated to EH subcarriers is related to channel gain and dual variable; p is p max And p is as follows min Representing the maximum and minimum power constraint values within each subcarrier, respectively.
Step 4, information decoding and nonlinear energy collection optimal subcarrier allocation in the IRS auxiliary cognition SWIPT system maximum and rate resource optimization method:
then, formula (12) and formula (13) are substituted into L (P), to obtain:
Figure BDA0004156969230000071
wherein ,
Figure BDA0004156969230000072
nonlinear energy harvesting subcarrier set S E In relation to U, selecting the subcarrier set with the most collected energy as the optimal nonlinear energy collection subcarrier set, namely:
Figure BDA0004156969230000073
the remaining subcarriers are used for information decoding, i.e.:
Figure BDA0004156969230000074
further, in the step 4, the optimal power and subcarrier allocation steps of information decoding and nonlinear energy collection are carried out by adopting a secondary gradient method:
4-1 initialization: randomly giving a group of alpha initial values, wherein the step length is delta, and the maximum iteration number is I max And an iteration index i=1, and the iteration error is epsilon;
4-2, calculating the secondary gradient, if the iteration times are lower than the maximum iteration times, and if the updated function value and the original function value are higher than the iteration errors, executing the following loops:
(a) Updating the secondary gradient function alpha i+1 ＝α i +δ i Δα and lagrangian dual function L (p, S, α);
(b) Recalculating the secondary gradient;
4-3 cycles are finished, and the optimal variable alpha is output * ,S * ,P * 。
Step 5, optimizing IRS reflection phase shift vectors in the IRS auxiliary cognition SWIPT system maximum sum rate resource optimization method:
finally, after obtaining optimal power (formula (12) and formula (13)) and optimal subcarrier sets (formula (15) and formula (16)) of information decoding and nonlinear energy collection, solving IRS reflection phase shift vectors by adopting a successive approximation (SCA) method; the construction optimization problem is as follows:
Figure BDA0004156969230000081
gain for composite channel
Figure BDA0004156969230000082
Performing N-point Discrete Fourier Transform (DFT) to obtain frequency response vector y E C of composite channel gain N×1 The method comprises the following steps:
Figure BDA0004156969230000083
wherein ,
Figure BDA0004156969230000084
is an element of a frequency response vector, f n ∈C N×1 Is a discrete Fourier matrix F N ∈C N×N Line n vector of V.epsilon.C M×N Is a cascading channel gain;
substituting the formula (2) and the formula (6) into the optimization problem formula (17), the optimization problem formula (17) is equivalent to:
Figure BDA0004156969230000085
order the
Figure BDA0004156969230000086
Definition of the definition
Figure BDA0004156969230000087
It is a n and bn Convex micro-functions of (a); given->
Figure BDA0004156969230000088
and />
Figure BDA0004156969230000089
At the point->
Figure BDA00041569692300000810
Can be the lower bound of the original function, namely:
Figure BDA00041569692300000811
if and only if
Figure BDA00041569692300000812
and />
Figure BDA00041569692300000813
The time equation holds; f (f) n (a n ,b n ) Is a n And b n So that it is at point (a n ,b n ) Has an AND function->
Figure BDA00041569692300000814
At the point->
Figure BDA00041569692300000815
The same gradient; formula (19) can be written as:
Figure BDA00041569692300000816
the optimization problem is a convex optimization problem, which is solved by adopting a continuous convex approximation method (SCA) and MATLAB CVX convex optimization tool box, and the function can be updated
Figure BDA0004156969230000091
At the point->
Figure BDA0004156969230000092
Is obtained by approximate solution of (2);
further, the specific steps of solving the IRS reflection phase shift vector by adopting alternate optimization, decoding information, and collecting optimal power and subcarrier allocation of nonlinear energy in the step 5 are as follows:
5-1 fixed IRS reflection phase shift vector
Figure BDA0004156969230000093
Obtaining optimal power and subcarrier allocation of information decoding and nonlinear energy collection through formulas (12), (13), (15) and (16);
5-2 fixed optimum power allocation vector P, subcarrier allocation vector S, initialization IRS reflection phase shift vector
Figure BDA0004156969230000094
Updating the IRS reflection phase shift vector through SCA, and solving by adopting MATLAB CVX convex optimization toolbox;
5-3 up to P, S and
Figure BDA0004156969230000095
the objective function is converged to obtain the optimal P * ,S * and />
Figure BDA0004156969230000096
The invention has the following beneficial effects:
the invention discloses an IRS-assisted cognitive SWIPT system maximum and rate resource optimization method. With the end-to-end and rate of the maximized IRS-assisted cognitive SWIPT system as the optimization targets, the cognitive SWIPT receiver employs nonlinear energy harvesting. The multi-resource allocation problem is modeled as a nonlinear non-convex optimization problem, and is mutually coupled with subcarriers, power and IRS reflection phase shift vectors, so that the IRS phase shift vectors are fixed by adopting an alternating optimization method, and an optimal power and subcarrier set for information decoding and nonlinear energy collection is obtained by adopting a Lagrangian dual conversion and a secondary gradient method. And then, obtaining the IRS reflection phase shift vector by a continuous convex approximation method, thereby realizing end-to-end and rate maximization of the IRS-assisted cognitive SWIPT system. Research shows that compared with other multi-resource optimization strategies, the method effectively allocates various resources such as subcarriers for information decoding and nonlinear energy collection, power and IRS reflection phase shift vectors and the like in the cognitive SWIPT system, and maximizes the end-to-end sum rate under an actual SWIPT nonlinear energy collection model.
Drawings
Fig. 1 is a 3D model scene diagram of an IRS-assisted cognitive swit system.
Fig. 2 is a graph of system and rate (spectral efficiency) versus iteration number for different IRS strategies.
FIG. 3 is a graph of system and rate (spectral efficiency) versus IRS reflection array source number for different IRS strategies.
Fig. 4 is a graph of system and rate (spectral efficiency) versus transmit power for optimal power allocation in different IRS reflective array sources versus information decoding/nonlinear energy harvesting subcarriers.
Detailed Description
The invention is further described below with reference to the drawings and examples.
Fig. 1 shows a 3D model scene diagram of an IRS-assisted cognitive swit system. In an IRS-assisted cognitive SWIPT system, an underley spectrum sharing model is adopted by a main network and a cognitive network. While the primary user receiver receives signals from the primary user transmitter, the cognitive user transmitter (cognitive base station) transmits signals to the cognitive user receiver (cognitive SWIPT user) through a direct link and an IRS forwarding link. Cognitive SWIPT employs a Power Split (PS) architecture to receiveThe signal is used for Information Decoding (ID) and non-linear Energy Harvesting (EH). Assuming that the IRS-assisted cognitive SWIPT network comprises a cognitive base station and cognitive SWIPT users, orthogonal frequency division multiplexing multiple access (OFDMA) with the number of subcarriers of N is adopted. System subcarrier set s=s I ∪S E ＝{1,2,…,N}，
Figure BDA0004156969230000101
wherein SI Is an ID subcarrier set, S E Is a nonlinear EH subcarrier set. Each subcarrier is used for an ID or nonlinear EH. It is assumed that IRS-assisted cognitive SWIPT obtains statistical Channel State Information (CSI) by adopting channel estimation, and IRS with the number of reflection array sources being M is used as intelligent passive relay.
Fig. 2 shows a graph of system and rate (spectral efficiency) versus iteration number for different IRS strategies. The proposed method is known by comparing with a random IRS reflection phase shift vector strategy, an optimal power allocation strategy without SWIPT, and the sum rate increases monotonically with the increase of the iteration times and converges to a specific value rapidly. Compared with the other two strategies, the method has the advantages of high convergence speed and high reachable sum rate (spectrum efficiency). In addition, the achievable sum rate convergence value of the proposed method is 4bps/Hz, the achievable sum rate convergence value of the optimal power allocation strategy without SWIPT is 3.5bps/Hz, and the achievable sum rate convergence value of the random IRS reflection phase shift vector strategy is 2.5bps/Hz.
FIG. 3 shows the relationship between the system and rate (spectral efficiency) and the IRS reflection array source number under different IRS strategies. From the figure, the system and rate performance of all strategies improves as the IRS reflective array source number increases. Comparing with the random IRS reflection phase shift vector strategy, the optimal power distribution strategy without SWIPT and the optimal power distribution strategy without IRS, the system and rate performance of the method is superior to the other three strategies. As can be seen, the proposed method allocates optimal power for the sub-carriers of information decoding and nonlinear energy harvesting, thus it achieves a sum rate gain of 0.5bps/Hz compared to the optimal power allocation strategy without swit. However, in contrast to other strategies, the sum rate performance of the proposed method converges to a particular value due to the constraint of the nonlinear energy harvesting threshold.
Fig. 4 shows a graph of system and rate (spectral efficiency) versus transmit power for optimal power allocation in different IRS reflective array sources versus information decoding/nonlinear energy harvesting subcarriers. From the graph, the sum rate of various strategies is improved along with the increase of the transmitting power and the IRS reflective array source number. Under the condition of the same IRS reflective array source number, the optimal power of the nonlinear energy collection subcarrier is better than that of the information decoding subcarrier. However, when the transmission power is 36dB, the two overlap. For this reason, the optimal power of the nonlinear energy collection subcarrier will not increase with increasing transmit power after reaching its saturation threshold.
It will be appreciated by persons skilled in the art that the above embodiments are provided for illustration of the invention and are not intended to be limiting, and that changes and modifications to the above embodiments are intended to fall within the scope of the invention.

Claims (8)

  1. The IRS assisted cognitive SWIPT system maximum and rate resource optimization method is characterized by comprising the following steps of:
    step 1, scene assumption and modeling of an IRS auxiliary cognition SWIPT system maximum and rate resource optimization method;
    step 2, optimizing a nonlinear energy collection model in an IRS auxiliary cognition SWIPT system maximum and rate resource optimization method;
    step 3, information decoding and nonlinear energy collection optimal power distribution in an IRS auxiliary cognition SWIPT system maximum and rate resource optimization method;
    step 4, information decoding and nonlinear energy collection optimal subcarrier allocation in an IRS auxiliary cognition SWIPT system maximum and rate resource optimization method;
    and 5, optimizing the IRS reflection phase shift vector in the maximum sum rate resource optimization method of the IRS auxiliary cognitive SWIPT system.
  2. 2. The IRS-assisted cognitive SWIPT system maximum and rate resource optimization method as claimed in claim 1, wherein the scene assumption and modeling of the IRS-assisted cognitive SWIPT system maximum and rate resource optimization method in step 1 is as follows:
    in order not to lose generality, the following assumptions are made before describing the design strategy in detail:
    (1) Ignoring the power of the reflected signal of more than two times of the IRS, and ensuring that the maximum reflection on the IRS is free of loss;
    (2) The channel gain of a cascade channel of the cognitive user transmitter-IRS-cognitive user receiver obeys quasi-static block fading, and the cognitive user transmitter can acquire the channel state information of all the receivers;
    (3) In the main network, the main user transmitter does not interfere with the cognitive user receiver;
    in an IRS-assisted cognitive SWIPT system, an underley spectrum sharing model is adopted by a main network and a cognitive network; the method comprises the steps that when a main user receiver receives signals from a main user transmitter, a cognitive user transmitter transmits signals to the cognitive user receiver through a direct link and an IRS forwarding link; the cognitive SWIPT adopts a power division structure, and a received signal is used for information decoding and nonlinear energy collection;
    assuming that the IRS-assisted cognitive SWIPT network comprises a cognitive transmitter and a cognitive receiver, and adopting orthogonal frequency division multiplexing multiple access with the number of subcarriers of N; system subcarrier set s=s I ∪S E ＝{1,2,…,N}，
    Figure FDA0004156969210000011
    wherein SI Is an ID subcarrier set, S E Is a nonlinear EH subcarrier set; each subcarrier is used for an ID or nonlinear EH; the IRS assisted cognitive SWIPT is assumed to acquire statistical channel state information by adopting channel estimation, and the IRS with the number of reflection array sources being M is used as an intelligent passive relay;
    let the direct link channel between the cognitive transmitter and the cognitive receiver be denoted as h tr ∈C N×1 The IRS phase shift diagonal matrix is expressed as
    Figure FDA0004156969210000021
    IRS reflection phase shift vector is denoted as +.>
    Figure FDA0004156969210000022
    wherein />
    Figure FDA0004156969210000023
    Representing the reflection coefficient, theta, of the mth IRS reflection array source m ∈[0,2π]The reflection phase shift angle is the m-th IRS reflection array source; the channel matrix of cognitive transmitter to IRS is denoted as H SI ∈C M×N The channel matrix of the IRS to the cognitive SWIPT receiver is denoted as H IR ∈C M×N The composite channel gain of the direct link of the cognitive transmitter to the cognitive receiver and the cascade channel is denoted +.>
    Figure FDA0004156969210000024
    wherein hcom ∈C N×1 ；
    The received signal-to-noise ratio of the cognitive SWIPT user for information decoding is as follows:
    Figure FDA0004156969210000025
    wherein P is the transmission power of the cognitive transmitter, S is the subcarrier set,
    Figure FDA0004156969210000026
    reflecting the phase shift vector for the IRS; p is p n Decoding the transmit power, h, of the sub-carrier for the nth information for the cognitive transmitter n Representing the composite gain of the direct link and the cascade channel of the cognitive transmitter to the cognitive receiver on the nth subcarrier,/for>
    Figure FDA0004156969210000027
    Is the noise power of the additive white Gaussian noise channel; the IRS-assisted cognitive SWIPT system end-to-end and rate are expressed as:
    Figure FDA0004156969210000028
    wherein B is the system bandwidth;
    if linear energy harvesting is employed, the energy harvested by the cognitive SWIPT user is modeled as:
    Figure FDA0004156969210000029
    if nonlinear energy harvesting is employed, the energy harvested by the cognitive SWIPT user is modeled as:
    Figure FDA00041569692100000210
    wherein ,
    Figure FDA00041569692100000211
    omega is a constant that ensures zero input zero output response; p (P) sat Maximum harvested power for EH circuit saturation; parameters a and b are constants related to circuit specifications and are positive numbers, and when the energy harvesting circuit is given, the parameter P is determined by curve fitting sat A and b; using ψ n Representing the collected energy;
    the end-to-end and rate maximization of the cognitive SWIPT network is used as an optimization target, meanwhile, a plurality of constraint conditions such as the control of the transmitting power of a cognitive user transmitter, the nonlinear energy collection constraint of a cognitive SWIPT receiver, the constraint of IRS reflection phase shift vectors and the like are met, and a constructed mathematical optimization model is expressed as follows:
    Figure FDA0004156969210000031
    wherein ,
    Figure FDA0004156969210000032
    representing the cognitive SWIPT user in information decoding sub-loadThe received signal-to-noise ratio on the wave, R represents the end-to-end and rate of the cognitive SWIPT network,/->
    Figure FDA0004156969210000033
    Representing cognitive SWIPT user nonlinear energy harvesting; />
    Figure FDA0004156969210000034
    Representing the reflection coefficient of the mth IRS reflection array source; p is p n ,n∈S I Representing cognitive transmitter transmit power, p, for information decoding n ,n∈S E Representing cognitive transmitter transmit power for energy harvesting, h n Representing the composite gain of the direct link and the cascade channel of the cognitive transmitter to the cognitive receiver on the nth subcarrier,/for>
    Figure FDA0004156969210000035
    Representing the additive Gaussian white noise power, P s Representing the threshold of the transmission power of the cognitive transmitter, Q min A minimum collection energy threshold representing a non-linear energy collection,/->
    Figure FDA0004156969210000036
    Representing the reflection coefficient modulus value of the mth IRS reflection array source.
  3. 3. The IRS-assisted cognitive SWIPT system maximum and rate resource optimization method of claim 1, wherein the nonlinear energy collection model optimization in the IRS-assisted cognitive SWIPT system maximum and rate resource optimization method of step 2 is specifically as follows:
    after the nonlinear energy collection model reaches a saturated state, the nonlinear energy collection model is not increased along with the increase of distributed power in energy collection subcarriers, E is an energy collection threshold value reaching the saturated state, and constraint conditions of optimization problem are introduced
    Figure FDA0004156969210000037
    The optimization problem formula (5) becomes:
    Figure FDA0004156969210000041
  4. 4. the IRS-assisted cognitive SWIPT system maximum and rate resource optimization method of claim 1, wherein the information decoding and nonlinear energy collection optimal power allocation in the IRS-assisted cognitive SWIPT system maximum and rate resource optimization method of step 3 is specifically as follows:
    firstly, fixing IRS reflection phase shift vector, solving formula (6) optimization problem by Lagrange dual method because the power and subcarrier in objective function and constraint condition are dual, normalizing system bandwidth and energy conversion efficiency, and obtaining system reachable sum rate under unit frequency band as spectrum efficiency, which is expressed as
    Figure FDA0004156969210000042
    The lagrangian dual function of the optimization problem (6) is:
    Figure FDA0004156969210000043
    wherein α= (α) 1 ,α 2 ,α 3 ) For the non-negative Lagrangian dual variable, then the optimization problem equation (6) translates into the following dual problem:
    Figure FDA0004156969210000044
    wherein ,
    Figure FDA0004156969210000045
    solving the dual variable by adopting a sub-gradient method>
    Figure FDA0004156969210000046
    Reconstructing the lagrangian dual function as:
    Figure FDA0004156969210000047
    wherein ,
    Figure FDA0004156969210000048
    it is related to the transmit power of the cognitive user transmitter in each subcarrier; set of given subcarriers s=s I ∪S E = {1,2, …, N }, in ID subcarrier set S I Nonlinear EH subcarrier set S E Inner pair p n Obtaining the partial derivative:
    Figure FDA0004156969210000051
    Figure FDA0004156969210000052
    let the partial derivative values of the formula (10) and the formula (11) be zero, and the optimal power for information decoding and energy collection is obtained as follows:
    Figure FDA0004156969210000053
    Figure FDA0004156969210000054
    wherein ,(x)+ =max (x, 0), the power allocated to EH subcarriers is related to channel gain and dual variable; furthermore, p max And p is as follows min Representing the maximum and minimum power constraint values within each subcarrier, respectively.
  5. 5. The IRS-assisted cognitive SWIPT system maximum and rate resource optimization method of claim 1, wherein the information decoding and nonlinear energy collection optimal subcarrier allocation in the IRS-assisted cognitive SWIPT system maximum and rate resource optimization method of step 4 is specifically as follows:
    then, formula (12) and formula (13) are substituted into L (P), to obtain:
    Figure FDA0004156969210000055
    wherein ,
    Figure FDA0004156969210000056
    nonlinear energy harvesting subcarrier set S E In relation to U, selecting the subcarrier set with the most collected energy as the optimal nonlinear energy collection subcarrier set, namely:
    Figure FDA0004156969210000057
    the remaining subcarriers are used for information decoding, i.e.:
    Figure FDA0004156969210000058
  6. 6. the IRS-assisted cognitive SWIPT system maximum and rate resource optimization method of claim 1, wherein the optimization of the IRS-assisted cognitive SWIPT system maximum and rate resource optimization method in step 5 is specifically as follows:
    finally, after obtaining the optimal power and the optimal subcarrier set for information decoding and nonlinear energy collection, solving an IRS reflection phase shift vector by adopting a continuous convex approximation method; the construction optimization problem is as follows:
    Figure FDA0004156969210000061
    gain for composite channel
    Figure FDA0004156969210000062
    Performing N-point discrete Fourier transform to obtain a frequency response vector y E C of the composite channel gain N×1 The method comprises the following steps:
    Figure FDA0004156969210000063
    wherein ,
    Figure FDA0004156969210000064
    is an element of a frequency response vector, f n ∈C N×1 Is a discrete Fourier matrix F N ∈C N×N Line n vector of V.epsilon.C M×N Is a cascading channel gain;
    substituting the formula (2) and the formula (6) into the optimization problem formula (17), the optimization problem formula (17) is equivalent to:
    Figure FDA0004156969210000065
    order the
    Figure FDA0004156969210000066
    Definition of the definition
    Figure FDA0004156969210000067
    It is a n and bn Convex micro-functions of (a); given->
    Figure FDA0004156969210000068
    and />
    Figure FDA0004156969210000069
    Figure FDA00041569692100000610
    At the point->
    Figure FDA00041569692100000611
    Can be the lower bound of the original function, namely:
    Figure FDA00041569692100000612
    if and only if
    Figure FDA00041569692100000613
    and />
    Figure FDA00041569692100000614
    The time equation holds; f (f) n (a n ,b n ) Is a n And b n So that it is at point (a n ,b n ) Has an AND function->
    Figure FDA00041569692100000615
    At the point->
    Figure FDA00041569692100000616
    The same gradient; formula (19) can be written as:
    Figure FDA0004156969210000071
    the optimization problem is a convex optimization problem, which is solved by adopting a continuous convex approximation method and a MATLAB CVX convex optimization tool box, and the function is updated
    Figure FDA0004156969210000072
    At the point->
    Figure FDA0004156969210000073
    Is obtained by an approximation solution of (a).
  7. 7. The IRS-assisted cognitive SWIPT system maximum sum rate resource optimization method of claim 1, wherein the optimal power and subcarrier allocation steps of performing information decoding and nonlinear energy collection by using a secondary gradient method in step 4 are as follows:
    4-1 initialization: randomly giving a group of alpha initial values, wherein the step length is delta, and the maximum iteration number is I max And an iteration index i=1, and the iteration error is epsilon;
    4-2, calculating the secondary gradient, if the iteration times are lower than the maximum iteration times, and if the updated function value and the original function value are higher than the iteration errors, executing the following loops:
    (a) Updating the secondary gradient function alpha i+1 ＝α i +δ i Δα and lagrangian dual function L (p, S, α);
    (b) Recalculating the secondary gradient;
    4-3 cycles are finished, and the optimal variable alpha is output * 、S * 、P * 。
  8. 8. The IRS-assisted cognitive SWIPT system maximum sum rate resource optimization method of claim 1, wherein the specific steps of solving IRS reflection phase shift vector, information decoding and optimal power and subcarrier allocation for nonlinear energy collection in step 5 are as follows:
    5-1 fixed IRS reflection phase shift vector
    Figure FDA0004156969210000074
    Obtaining optimal power and subcarrier allocation of information decoding and nonlinear energy collection through formulas (12), (13), (15) and (16);
    5-2 fixed optimum power allocation vector P, subcarrier allocation vector S, initialization IRS reflection phase shift vector
    Figure FDA0004156969210000075
    Updating the IRS reflection phase shift vector through SCA, and solving by adopting MATLAB CVX convex optimization toolbox;
    5-3 up to P, S and
    Figure FDA0004156969210000076
    the objective function is converged to obtain the optimal P * ,S * and />
    Figure FDA0004156969210000077
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117042162A (en) * 2023-10-09 2023-11-10 中国移动紫金(江苏)创新研究院有限公司 Communication method, device, reflection plane, computing system, enhancer and repeater
CN117042162B (en) * 2023-10-09 2023-12-26 中国移动紫金(江苏)创新研究院有限公司 Communication method, device, reflection plane, computing system, enhancer and repeater

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