A robust transmission method for cognitive backscatter communication based on NOMA

Abstract

The present invention belongs to the technical field of wireless power supply communication, and specifically relates to a NOMA-based cognitive backscatter communication robust transmission method; the method comprises: constructing a cognitive backscatter communication network; constructing a resource allocation problem for maximizing the throughput of a secondary system according to the cognitive backscatter communication network; constructing a Gaussian channel uncertainty model according to the fact that the channel estimation error obeys the Gaussian distribution; constructing a robust resource allocation problem according to the resource allocation problem for maximizing the throughput of the secondary system and the Gaussian channel uncertainty model; solving the robust resource allocation problem to obtain a transmission scheme; a user transmits information according to the transmission scheme; the present invention improves the robustness and throughput of a cognitive wireless power supply backscatter communication network system, and has high practicality.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless power supply communication, and in particular relates to a NOMA-based cognitive backscatter communication robust transmission method. Background Art

[0002] With the rapid development of digitalization and informatization, wireless devices are increasingly required to undertake a large number of computing and communication tasks in today's society. However, the problems of energy shortage and limited spectrum have seriously affected the application of the Internet of Things. Cognitive radio and non-orthogonal multiple access technology (NOMA) are two main technologies to improve spectrum utilization. The secondary transmitter collects energy from the primary signal based on RF energy harvesting technology, and uses the collected energy to execute data information when causing tolerable interference to the primary user. The RF-based cognitive backscattering user network can improve spectrum efficiency. NOMA can improve the spectrum efficiency of the communication system by allowing more users to share the same subchannel. At the same time, the energy consumption problem of the communication network has attracted everyone's attention. It has a serious impact on both economic and ecological costs. Environmental backscattering utilizes the surrounding environmental RF sources without the need to deploy dedicated RF nodes, so it can save costs and reduce energy consumption. It does not require the use of additional spectrum, but only uses existing RF signals, which improves spectrum utilization efficiency.

[0003] Many existing methods have studied the cognitive backscatter communication network based on NOMA, but only considered the system performance optimization problem under ideal channel conditions. Due to the complexity of actual communication scenarios, it is not feasible to assume perfect channel state information. Imperfect channel state information may lead to performance degradation of resource allocation methods. Therefore, in order to facilitate practical engineering applications, a robust transmission method for cognitive backscatter communication based on NOMA is urgently needed to improve system throughput. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a NOMA-based cognitive backscatter communication robust transmission method, which includes:

[0005] S1: Building a cognitive backscatter communication network;

[0006] S2: Resource allocation problem for maximizing the throughput of secondary systems based on cognitive backscatter communication networks;

[0007] S3: Construct a Gaussian channel uncertainty model based on the fact that the channel estimation error follows a Gaussian distribution;

[0008] S4: The robust resource allocation problem is constructed based on the resource allocation problem of maximizing the secondary system throughput and the Gaussian channel uncertainty model;

[0009] S5: Solve the robust resource allocation problem and obtain a transmission plan; the user transmits information according to the transmission plan.

[0010] Preferably, the cognitive backscatter communication network comprises: a primary base station, M primary receivers, K cognitive backscatter users, and a cognitive receiver;

[0011] Phase 1: The master base station transmits signals to all cognitive backscatter users. The cognitive backscatter users take turns to transmit signals to the cognitive receiver using time division multiple access. All cognitive backscatter users collect energy from the received signals.

[0012] Phase 2: Cognitive backscattering users use the energy collected in the first phase to actively transmit data to cognitive receivers through uplink NOMA.

[0013] Preferably, the resource allocation problem for maximizing the secondary system throughput is expressed as:

[0014]

[0015] Among them, R k represents the system throughput, represents the total throughput of the kth cognitive backscattering user in the first phase, represents the minimum total throughput threshold of the kth cognitive backscattering user in the backscattering phase, represents the total throughput of the kth cognitive backscattering user in the second phase, represents the minimum total throughput threshold of the kth cognitive backscattering user in the second phase, represents the minimum energy consumption threshold of the kth cognitive backscattering user, E k represents the total energy collected by the kth cognitive backscattering user, T represents the total transmission time, Reflection time slot, β k represents the reflection coefficient of the kth cognitive backscattering user, P0 represents the total transmission power of the main transmitter, and f k represents the channel gain from the primary base station to the kth cognitive backscattering user, h k,m represents the channel gain from the kth cognitive backscattering user to the mth primary receiver, t a represents the active transmission time, K represents the number of cognitive backscattering users, and p k represents the transmission power of the kth cognitive backscattering user during active transmission, Indicates the maximum interference power that the mth primary receiver can accept.

[0016] Furthermore, the total throughput of the kth cognitive backscattering user in the first phase is:

[0017]

[0018] Where B represents the channel bandwidth, h k represents the channel gain from the kth cognitive backscattering user to the cognitive receiver, f PT-CR represents the channel gain from the master base station to the cognitive receiver, σ 2 represents the background noise power of the cognitive receiver.

[0019] Furthermore, the total throughput of the kth cognitive backscattering user in the second phase is:

[0020]

[0021] Where B represents the channel bandwidth, γ k represents the signal-to-noise ratio of the kth cognitive backscattering user in the active transmission phase.

[0022] Furthermore, the total energy collected by the kth cognitive backscattering user is:

[0023]

[0024] in, represents the energy collected by the kth cognitive backscattering user in the first energy harvesting stage, represents the energy collected by the kth cognitive backscattering user in the second energy harvesting stage.

[0025] Furthermore, the minimum energy consumption threshold of the kth cognitive backscattering user is:

[0026]

[0027] in, and They represent the constant circuit power consumption of the kth cognitive backscattering user during backscattering communication and active communication, respectively.

[0028] Preferably, the Gaussian channel uncertainty model is expressed as:

[0029]

[0030]

[0031]

[0032] in, and represent the first, second and third uncertainty sets respectively; f k , and Δf kdenote the channel gain, estimated channel gain and estimated channel gain error from the primary base station to the kth cognitive backscattering user, respectively, and h k , and Δh k denote the channel gain, estimated channel gain and estimated channel gain error from the kth cognitive backscattering user to the cognitive receiver, respectively, and h k,m , and Δh k,m denote the channel gain, estimated channel gain and estimated channel gain error from the kth cognitive backscattering user to the mth primary receiver, respectively. and represent the first, second, and third differences respectively.

[0033] Preferably, the robust resource allocation problem is expressed as:

[0034]

[0035] Among them, R k represents the system throughput, K represents the number of cognitive backscattering users, represents the total throughput of the kth cognitive backscattering user in the first phase, represents the minimum total throughput threshold of the kth cognitive backscattering user in the backscattering phase, represents the total throughput of the kth cognitive backscattering user in the second phase, represents the minimum total throughput threshold of the kth cognitive backscattering user in the second phase, represents the minimum energy consumption threshold of the kth cognitive backscattering user, E k represents the total energy collected by the kth cognitive backscattering user, Pr{·} represents the interruption probability; ε k , k , k and θ k represent the first, second, third and fourth interruption probability thresholds respectively, T represents the total transmission time, Reflection time slot, β k represents the reflection coefficient of the kth cognitive backscattering user, P0 represents the total transmission power of the main transmitter, and f k represents the channel gain from the primary base station to the kth cognitive backscattering user, h k,m represents the channel gain from the kth cognitive backscattering user to the mth primary receiver, t a represents the active transmission time, p k represents the transmission power of the kth cognitive backscattering user during active transmission, represents the maximum interference power that the mth primary receiver can accept; and denote the first, second and third uncertainty sets, Δf k represents the estimated channel gain error from the primary base station to the kth cognitive backscattering user, Δh k represents the estimated channel gain error from the kth cognitive backscattering user to the cognitive receiver, Δh k,m represents the estimated channel gain error from the kth cognitive backscattering user to the mth primary receiver.

[0036] Preferably, the process of solving the robust resource allocation problem includes:

[0037] S51: The outage probability constraint is transformed into a deterministic constraint using the Q function, and the rewritten robust resource allocation problem is obtained;

[0038] S52: perform equivalent replacement on the rewritten robust resource allocation problem; use an alternating iterative algorithm to decompose the robust resource allocation problem after equivalent replacement into two sub-problems;

[0039] S53: Use convex optimization tools to solve two sub-problems and obtain the transmission power, reflection coefficient, active transmission time and backscattering transmission time of the cognitive backscattering user, i.e., the transmission scheme

[0040] The beneficial effects of the present invention are as follows: the backscattering of the network system of the present invention greatly reduces the power consumption of communication nodes, breaks through the problems of limited energy, high power consumption and short life of traditional Internet of Things, and alleviates the problem of over-reliance on battery supply. NOMA improves the spectrum efficiency of the communication system by allowing more users to share the same subchannel. The present invention takes into account the imperfect channel state information and avoids the performance degradation of the resource allocation method. And by adjusting the time allocation, backscatter coefficient and active transmission power of the system user transmission signal, the robustness and throughput of the cognitive wireless power supply backscattering communication network system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of the robust transmission method of cognitive backscatter communication based on NOMA in the present invention;

[0042] Figure 2 A model diagram of a cognitive backscatter communication network system in the present invention;

[0043] Figure 3 is a throughput convergence diagram of the present invention;

[0044] Figure 4 This is a relationship diagram between the user interruption probability and the channel error of the present invention and the comparative method. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The present invention proposes a robust transmission method for cognitive backscatter communication based on NOMA, such as Figure 1 As shown, the method includes the following contents:

[0047] S1: Building a cognitive backscatter communication network.

[0048] like Figure 2 As shown in FIG, a cognitive backscatter communication network scenario based on NOMA is constructed; the network scenario consists of a primary network (including a primary base station and a primary receiver) and a secondary network (including cognitive backscatter users and cognitive receivers). Specifically, this network has a primary base station and M primary receivers, which can be expressed as K cognitive backscattering users, which can be expressed as and a cognitive receiver, with single antennas on both the transmitting and receiving ends of the device.

[0049] Assume that the channel gains from the main base station to the cognitive backscattering user, cognitive receiver, and main receiver are: k 、f PT-CR 、f P,m ; The channel gain from the cognitive backscatter user to the primary receiver and cognitive receiver is: h k 、h k,m In order to realize backscatter communication and active transmission in the transmission phase, each cognitive backscatter user is equipped with an energy harvesting module, a backscatter circuit, and an active transmission circuit, and can transmit data to the cognitive receiver via backscatter or active transmission.

[0050] The main base station has been sending signals to the main receiver, cognitive backscatter users and the main receiver. The entire time block is defined as T, which is divided into the first stage and the second stage. In the first stage, i.e., the backscatter transmission stage, the main base station transmits carrier signals to all cognitive backscatter users. Cognitive backscatter users use time division multiple access to transmit data to cognitive receivers in turn, i.e., backscatter transmission. The transmission time, i.e., the reflection time slot, is All cognitive backscatter users collect energy from the received signals. The process of cognitive backscatter users collecting energy is divided into the first energy collection stage and the second energy collection stage. In the first energy collection stage, cognitive backscatter users collect energy before backscattering, and in the second energy collection stage, cognitive backscatter users collect energy when backscattering. In the second stage, i.e., the active transmission stage, cognitive backscatter users use the energy collected in the first stage to actively transmit data to cognitive receivers through uplink NOMA. The transmission time is t a .

[0051] S2: Constructing the resource allocation problem of maximizing the throughput of the secondary system based on cognitive backscatter communication networks.

[0052] In the first phase, i.e., the backscatter transmission phase, cognitive backscatter users use time division multiple access to reflect information to cognitive receivers; The throughput from cognitive backscatter users to cognitive receivers, i.e., the total throughput of cognitive backscatter users in the first phase, is expressed as:

[0053]

[0054] in, represents the total throughput of the kth cognitive backscattering user in the first phase, B represents the channel bandwidth, β k represents the reflection coefficient of the kth cognitive backscattering user, P0 represents the total transmission power of the main transmitter, and f k represents the channel gain from the primary base station to the kth cognitive backscattering user, h k represents the channel gain from the kth cognitive backscattering user to the cognitive receiver, f PT-CR represents the channel gain from the master base station to the cognitive receiver, σ 2 represents the background noise power of the cognitive receiver.

[0055] In the first stage, i.e., the backscatter transmission stage, the total energy E collected by the kth cognitive backscatter user is k for:

[0056]

[0057]

[0058]

[0059] in, represents the energy collected by the kth cognitive backscattering user in the first energy harvesting stage, represents the energy collected by the kth cognitive backscattering user in the second energy harvesting stage; η∈(0,1) represents the energy conversion efficiency factor, t a is the active transmission time, and T is the total transmission time.

[0060] In the second stage, i.e., the active transmission stage, the cognitive backscattering user actively transmits data to the cognitive receiver through uplink NOMA. The total throughput of the kth cognitive backscattering user in the second stage is:

[0061]

[0062]

[0063] in, represents the total throughput of the kth cognitive backscattering user in the second phase, γ k represents the signal-to-noise ratio of the kth cognitive backscattering user during active transmission, p k represents the transmission power of the kth cognitive backscattering user during active transmission; if k = K, then

[0064] Both the first and second phase cognitive backscatter users interfere with the primary receiver. In order to ensure that the normal communication of the primary user is not affected during resource sharing, it is necessary to ensure that the interference caused by all cognitive backscatter users in the network to the primary receiver meets the following constraints:

[0065]

[0066] Among them, h k,m represents the channel gain from the kth cognitive backscattering user to the mth primary receiver, Indicates the maximum interference power that the mth primary receiver can accept.

[0067] The minimum energy consumption of the kth cognitive backscattering user is expressed as:

[0068]

[0069] in, and They represent the constant circuit power consumption of cognitive backscattering user k during backscattering communication and active communication, respectively.

[0070] The resource allocation problem for maximizing the secondary system throughput is expressed as:

[0071]

[0072] Among them, R k represents the system throughput, represents the minimum total throughput threshold of the kth cognitive backscattering user in the backscattering phase, represents the minimum total throughput threshold of the kth cognitive backscattering user in the second stage; C1 represents the minimum throughput constraint of the cognitive backscattering user in the backscattering stage, C2 represents the minimum throughput constraint of the cognitive backscattering user in the active transmission stage; C3 represents the energy collected in the first stage must be greater than the sum of the constant circuit power consumption of the circuits in the first stage and the second stage, C4 represents the interference power received by the mth main receiver is less than the maximum interference power, C5 is the time allocation constraint, and C6 represents the range of the reflection coefficient.

[0073] S3: Construct a Gaussian channel uncertainty model based on the fact that the channel estimation error follows a Gaussian distribution.

[0074] Considering the additive model of uncertainty parameters and assuming that the channel estimation error obeys Gaussian distribution, a Gaussian channel uncertainty model is constructed, which is expressed as:

[0075]

[0076]

[0077]

[0078] in, and represent the first, second and third uncertainty sets respectively; f k , and Δf k denote the channel gain from the primary base station to the kth cognitive backscatter user, the estimated channel gain from the primary base station to the kth cognitive backscatter user, and the estimated channel gain error from the primary base station to the kth cognitive backscatter user, respectively. Denotes Δf k It has a mean of zero and a variance of Gaussian distribution of h k , and Δh k denote the channel gain from the kth cognitive backscattering user to the cognitive receiver, the estimated channel gain from the kth cognitive backscattering user to the cognitive receiver, and the estimated channel gain error from the kth cognitive backscattering user to the cognitive receiver, respectively. Denotes Δh k It has a mean of zero and a variance of Gaussian distribution of h k,m , and Δh k,mdenote the channel gain from the kth cognitive backscattering user to the mth primary receiver, the estimated channel gain from the kth cognitive backscattering user to the mth primary receiver, and the estimated channel gain error from the kth cognitive backscattering user to the mth primary receiver, respectively. Denotes Δh k,m It has a mean of zero and a variance of Gaussian distribution of and represent the first, second, and third differences respectively.

[0079] S4: The robust resource allocation problem is constructed based on the resource allocation problem of maximizing the secondary system throughput and the Gaussian channel uncertainty model.

[0080] The resource allocation problem P1 for maximizing the secondary system throughput can be expressed as a robust resource allocation problem:

[0081]

[0082] Where Pr{·} represents the interruption probability; ε k ∈[0,1],ζ k ∈[0,1],ρ k ∈[0,1] and θ k ∈[0,1] represents the first, second, third and fourth interruption probability thresholds respectively; represents the interruption probability constraint of the throughput of the kth cognitive backscattering user in the backscattering phase, represents the interruption probability constraint of the throughput of the kth cognitive backscattering user during the active transmission phase, represents the outage probability constraint for energy harvesting of the kth cognitive backscattering user, represents the interruption probability constraint of the interference suffered by the mth primary receiver, and C7 is the constraint on the channel estimation error.

[0083] S5: Solve the robust resource allocation problem and obtain a transmission plan; the user transmits information according to the transmission plan.

[0084] The process of solving the robust resource allocation problem includes:

[0085] S51: The outage probability constraint is transformed into a deterministic constraint using the Q function, and the rewritten robust resource allocation problem is obtained.

[0086] definition based on You can get:

[0087]

[0088] because and Based on probability theory, we can get:

[0089]

[0090] in, So the original probability constraint Transformed into a deterministic constraint

[0091] Using a transformation method similar to formula (2), and Convert to a deterministic constraint:

[0092]

[0093]

[0094]

[0095] in,

[0096] According to equations (3) to (6), P2 can be re-expressed as:

[0097]

[0098] S52: Perform equivalent replacement on the rewritten robust resource allocation problem; use an alternating iterative algorithm to decompose the robust resource allocation problem after equivalent replacement into two sub-problems.

[0099] Due to the constraints of coupling variables, And the objective function is non-convex, we have:

[0100]

[0101] In addition, due to The coupling of , also needs to be transformed and rewritten as:

[0102]

[0103] The iterative algorithm of alternating iterative optimization is used to solve the problem. k and P k In the case of and t a Sub-problem of ; expressed as:

[0104]

[0105] In a given and t a , we can solve for β k and pk The sub-problem is expressed as:

[0106]

[0107] S53: The two sub-problems are solved using a convex optimization tool to obtain the transmission power, reflection coefficient, active transmission time and backscattering transmission time of the cognitive backscattering user, i.e., the transmission scheme.

[0108] P3 and P4 are both convex optimization problems, so the present invention directly uses convex optimization tools to solve these two sub-problems to obtain the transmission power, reflection coefficient, active transmission time and backscattering transmission time of the cognitive backscattering user, that is, the transmission plan; users in the cognitive backscattering communication network system can transmit information according to the transmission plan.

[0109] Evaluation of the present invention:

[0110] The application effect of the present invention is described in detail in combination with simulation:

[0111] 1) Simulation conditions

[0112] Assume that there is one primary transmitter and two primary receivers in the primary network, and two cognitive backscatter users and one cognitive receiver in the secondary network. Assume that all devices are randomly distributed in a circle with a radius of 10 meters. The channel model is Where d is the distance between the transmitter and the receiver, χ=3 is the path loss exponent, represents the fading coefficient; other parameters are shown in Table 1.

[0113] Table 1 Simulation parameters

[0114]

[0115] 2) Simulation results

[0116] Figure 3 The present invention is different The convergence under the condition Figure 3 It can be seen that the total throughput of cognitive backscatter users can converge to a stable point within 4 iterations under the present invention. Therefore, the present invention has good convergence. In addition, the total throughput of cognitive backscatter users increases with increases with the increase of The larger the value is, the more backscattering rate or transmission time the cognitive backscattering users need to allocate to ensure the basic quality of service requirements, thereby increasing the total throughput. Figure 4 The actual outage probability and channel estimation error σ of the present invention and the comparative method are described k,m relationship; from Figure 4It can be seen that under different algorithms, the actual outage probability of the primary receiver is related to the channel estimation error from the cognitive backscattering user to the primary receiver. k,m The actual interruption probability of the present invention is lower than that of the non-robust algorithm and does not exceed the interruption probability threshold. Since the non-robust algorithm ignores the channel estimation error, the actual interruption probability will be higher than that of the algorithm of the present invention. Therefore, the present invention has better robustness.

[0117] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation modes of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A NOMA-based cognitive backscatter communication robust transmission method, characterized in that: include: S1: Building a cognitive backscatter communication network; S2: Resource allocation problem for maximizing the throughput of secondary systems based on cognitive backscatter communication networks; The resource allocation problem for maximizing the secondary system throughput is expressed as: C6:0≤β k ≤1. Among them, R k represents the system throughput, represents the total throughput of the kth cognitive backscattering user in the first phase, represents the minimum total throughput threshold of the kth cognitive backscattering user in the backscattering phase, represents the total throughput of the kth cognitive backscattering user in the second phase, represents the minimum total throughput threshold of the kth cognitive backscattering user in the second phase, represents the minimum energy consumption threshold of the kth cognitive backscattering user, E k represents the total energy collected by the kth cognitive backscattering user, T represents the total transmission time, Reflection time slot, β k represents the reflection coefficient of the kth cognitive backscattering user, P0 represents the total transmission power of the main transmitter, and f k represents the channel gain from the primary base station to the kth cognitive backscattering user, h k,m represents the channel gain from the kth cognitive backscattering user to the mth primary receiver, t a represents the active transmission time, K represents the number of cognitive backscattering users, and p k represents the transmission power of the kth cognitive backscattering user during active transmission, represents the maximum interference power that the mth primary receiver can accept; The total throughput of the kth cognitive backscattering user in the first phase is: Where B represents the channel bandwidth, h k represents the channel gain from the kth cognitive backscattering user to the cognitive receiver, f PT-CR represents the channel gain from the master base station to the cognitive receiver, σ 2 represents the background noise power of the cognitive receiver; The total throughput of the kth cognitive backscattering user in the second phase is: Among them, γ k represents the signal-to-noise ratio of the kth cognitive backscattering user in the active transmission phase; The total energy collected by the kth cognitive backscattering user is: in, represents the energy collected by the kth cognitive backscattering user in the first energy harvesting stage, represents the energy collected by the kth cognitive backscattering user in the second energy harvesting phase; The minimum energy consumption threshold of the kth cognitive backscattering user is: in, and They represent the constant circuit power consumption of the kth cognitive backscattering user during backscattering communication and active communication, respectively; S3: Construct a Gaussian channel uncertainty model based on the fact that the channel estimation error follows the Gaussian distribution; the Gaussian channel uncertainty model is expressed as: in, and denote the first, second, and third uncertainty sets, respectively; and Δf k denote the estimated channel gain and estimated channel gain error from the primary base station to the kth cognitive backscattering user, respectively, and Δh k denote the estimated channel gain and estimated channel gain error from the kth cognitive backscattering user to the cognitive receiver, respectively, and h k,m , and Δh k,m denote the channel gain, estimated channel gain and estimated channel gain error from the kth cognitive backscattering user to the mth primary receiver, respectively. and denote the first, second, and third differences respectively; S4: Based on the resource allocation problem of maximizing the secondary system throughput and the Gaussian channel uncertainty model, a robust resource allocation problem is constructed; the robust resource allocation problem is expressed as: C6:0≤β k ≤1. Where Pr{·} represents the interruption probability; ε k , k , k and θ k represent the first, second, third and fourth interruption probability thresholds respectively, T represents the total transmission time, Reflection time slot; S5: Solve the robust resource allocation problem and obtain a transmission plan; the user transmits information according to the transmission plan.

2. According to a NOMA-based cognitive backscatter communication robust transmission method according to claim 1, it is characterized in that: The cognitive backscatter communication network includes: a main base station, M main receivers, K cognitive backscatter users, and a cognitive receiver; Phase 1: The master base station transmits signals to all cognitive backscatter users. The cognitive backscatter users take turns to transmit signals to the cognitive receiver using time division multiple access. All cognitive backscatter users collect energy from the received signals. Phase 2: Cognitive backscattering users use the energy collected in the first phase to actively transmit data to cognitive receivers through uplink NOMA.

3. According to a NOMA-based cognitive backscatter communication robust transmission method according to claim 1, it is characterized in that: The process of solving the robust resource allocation problem includes: S51: The outage probability constraint is transformed into a deterministic constraint using the Q function, and the rewritten robust resource allocation problem is obtained; S52: perform equivalent replacement on the rewritten robust resource allocation problem; use an alternating iterative algorithm to decompose the robust resource allocation problem after equivalent replacement into two sub-problems; S53: The two sub-problems are solved using a convex optimization tool to obtain the transmission power, reflection coefficient, active transmission time and backscattering transmission time of the cognitive backscattering user, i.e., the transmission scheme.

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