A routing method for multi-reconfigurable intelligent reflective surface-assisted mobile cognitive networks

Abstract

This invention discloses a routing method for a multi-reconfigurable smart reflector-assisted mobile cognitive network. Addressing the link instability and interference between primary and secondary users caused by secondary user mobility in mobile cognitive networks, this invention first optimizes the transmit beamforming vector of the secondary base station and the phase shift matrix of the i-th reconfigurable smart reflector for the i-th communication path via the i-th reconfigurable smart reflector. Under the conditions of satisfying transmit power constraints and interference thresholds for the primary user, the maximum end-to-end transmission rate of the i-th communication path is determined. Then, the available time of the link between the i-th reconfigurable smart reflector and the mobile secondary user receiver is predicted, and the end-to-end data transmission volume of the i-th communication path is determined based on the product of the maximum end-to-end transmission rate and the available time. Finally, the communication path with the largest end-to-end data transmission volume is selected as the transmission route.

Description

Technical Field

[0001] This invention relates to a routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network. Background Technology

[0002] With the rapid development of wireless communication technology, spectrum resources are becoming increasingly scarce. Cognitive Radio (CR) technology, as an important means to improve spectrum utilization, allows secondary users (SUs) to dynamically access the primary user's licensed spectrum while ensuring the primary user's (PU) normal communication, thereby achieving efficient spectrum reuse. In cognitive networks, secondary users need to be aware of the primary user's activity status in real time and communicate under the condition of satisfying the primary user's interference constraints, which places higher demands on the reliability and stability of the communication link.

[0003] Reconfigurable Intelligent Surfaces (RIS), as an emerging reconfigurable electromagnetic metasurface technology, have attracted widespread attention in recent years. RIS consists of a large number of low-power passive reflective elements. By digitally controlling the phase and amplitude of each element in response to incident electromagnetic waves, RIS can actively reconfigure the wireless propagation environment, achieving directional enhancement or suppression of multipath signals. Compared with traditional repeater technologies, RIS can improve link quality and extend coverage without adding RF links and power amplifiers, and is considered an effective means to improve the spectral and energy efficiency of wireless networks.

[0004] Existing research on RIS-assisted communication mainly focuses on the following aspects: first, the analysis of rate, signal-to-noise ratio (SNR), and coverage performance of single RIS-assisted systems; and second, the research on routing optimization and multi-hop transmission performance in multi-RIS scenarios. However, existing technologies still have the following limitations: on the one hand, research on single RIS-assisted cognitive networks mostly focuses on static scenarios, assuming fixed transceiver locations and failing to fully consider the impact of network node mobility on link stability; on the other hand, routing optimization research related to multi-RIS collaboration mainly targets ordinary wireless network scenarios, without considering interference constraints and spectrum sharing characteristics between primary and secondary users in cognitive networks.

[0005] In practical applications, especially in scenarios such as Industrial Internet of Things (IIoT) and Vehicle-to-Everything (V2X), secondary user receivers are often in motion, causing the channel state between them and the RIS (Relational Information System) to change dynamically over time, resulting in unstable and easily interrupted links. In such cases, if routing is based solely on instantaneous transmission rate or signal-to-noise ratio (SNR) without considering link duration, communication interruptions and data loss will occur. Conversely, if only link stability is considered while ignoring transmission rate, spectrum resources will be wasted. Furthermore, in cognitive network scenarios, particularly in underlay mode, secondary users must strictly control interference to primary users, further increasing the complexity of routing decisions. Summary of the Invention

[0006] This invention provides a routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network to address the problems existing in the prior art. This invention comprehensively considers end-to-end transmission rate and link stability, providing a reliable and efficient data transmission path for mobile secondary users while ensuring the communication quality of primary users.

[0007] The technical solutions adopted in this invention are as follows:

[0008] A routing method for a mobile cognitive network assisted by multiple reconfigurable smart reflectors, the mobile cognitive network including a primary base station, a primary user receiver, a secondary base station, a mobile secondary user receiver, and multiple reconfigurable smart reflectors, wherein the secondary base station transmits signals to the mobile secondary user receiver via the reconfigurable smart reflectors, comprising the following steps:

[0009] (1) Regarding the communication between the secondary base station and the mobile secondary user receiver in the mobile cognitive network via a reconfigurable smart reflector, the communication will be transmitted via the first... The communication path of the reconfigurable smart reflector is used as the first A communication path is established, and under the conditions of satisfying the transmit power constraint of the secondary base station and the interference constraint on the primary user receiver, the transmit beamforming vector of the secondary base station and the primary user receiver are jointly optimized. The phase shift matrix of the reconfigurable smart reflector determines the first... The maximum end-to-end transmission rate of the communication path;

[0010] (2) Predict the first Available time for the link between a reconfigurable smart reflector and a mobile secondary user receiver;

[0011] (3) Determine the first [time period] based on the product of the maximum end-to-end transmission rate and the available time. The amount of data transmitted end-to-end along a communication path;

[0012] (4) Select the communication path with the largest end-to-end data transmission volume as the mobile cognitive network transmission route.

[0013] Further, in step (1), the joint optimization of the maximum end-to-end transmission rate is achieved with the goal of maximizing the signal-to-noise ratio at the mobile secondary user receiver. The signal-to-noise ratio calculation formula is as follows:

[0014] ,

[0015] in, For the first Channel fading between a reconfigurable smart reflector and a mobile secondary user receiver For the first A phase shift matrix for a reconfigurable smart reflector. For the secondary base station and the first Channel fading between reconfigurable smart reflectors This is the transmit beamforming vector of the secondary base station. The main base station's transmit power. Main base station and the Channel fading between reconfigurable smart reflectors Channel fading between the primary base station and the mobile secondary user receiver This represents the noise variance at the mobile secondary user receiver.

[0016] Furthermore, in step (1), the maximum end-to-end transmission rate is calculated by combining the optimized maximum signal-to-noise ratio with the Shannon formula, which is:

[0017] ,

[0018] in, For the first The end-to-end transmission rate of the communication path. This represents the maximum signal-to-noise ratio after joint optimization at the mobile secondary user receiver.

[0019] Furthermore, in step (1), the transmit power constraint and the primary user receiver interference threshold constraint are specifically as follows:

[0020] Transmit power constraints: , This is the maximum transmission power of the secondary base station. This is the transmit beamforming vector for the secondary base station;

[0021] Disturbance threshold constraint: , This is due to channel fading between the secondary base station and the primary user receiver. For the first Channel fading between a reconfigurable smart reflector and the primary user receiver. The interference threshold allowed by the primary user receiver.

[0022] Furthermore, in step (2), the available time of the link is... , The future availability time of the link when the mobile secondary user receiver remains in motion. When the motion state of the mobile secondary user receiver changes, The probability of maintaining availability.

[0023] Furthermore, in step (3), the formula for calculating the end-to-end data transmission volume is:

[0024] ,

[0025] in, For the first The amount of data transmitted along each communication path. For the maximum end-to-end transmission rate, For the first The available time of the link between a reconfigurable smart reflector and a mobile secondary user receiver.

[0026] Furthermore, the joint optimization is achieved by combining an alternating iterative optimization algorithm with a semidefinite relaxation technique, which optimizes one variable by alternately fixing one variable.

[0027] Furthermore, the specific process of the alternating iterative optimization algorithm is as follows:

[0028] Initialize the transmit beamforming vector initial values , No. Initial values ​​of the phase shift matrix of a reconfigurable smart reflector Iterative index Signal-to-noise ratio incremental accuracy Determine the initial transmission rate ;

[0029] Increment by 1, fixed. Optimized Then fix Optimized ;

[0030] Calculate the current transmission rate ,like The iteration then terminates.

[0031] Furthermore, when optimizing another variable while keeping the variable fixed, after obtaining the solution to the semidefinite programming problem using the positive semidefinite relaxation technique, the random variable method is combined to ensure that the rank of the solution is 1, thus obtaining a feasible solution that satisfies the constraints.

[0032] Furthermore, in step (4), the formula for determining the optimal communication path is:

[0033] ,

[0034] in, Number the optimal communication path. The number of reconfigurable smart reflective surfaces, For the first The amount of data transmitted end-to-end along a communication path.

[0035] The present invention has the following beneficial effects:

[0036] (1) By combining the end-to-end transmission rate with the link availability time and using their product as a metric for routing selection, compared to schemes that simply pursue transmission rate or link duration, it helps to transmit more effective data before the mobile secondary user receiver leaves the coverage area and reduces data transmission failures caused by link interruption.

[0037] (2) By predicting the available time of the link between the reconfigurable smart reflector and the mobile secondary user receiver, and making routing decisions accordingly, the choice of communication path can adapt to the mobile state of the secondary user, thereby reducing the probability of communication interruption caused by node movement to a certain extent and improving the continuity of communication.

[0038] (3) In the underlay cognitive network mode, by jointly optimizing the transmit beamforming vector of the secondary base station and the phase shift matrix of the reconfigurable smart reflector, under the condition of satisfying the interference threshold constraint of the primary user, it helps to improve the end-to-end transmission rate of the secondary user, thereby improving the reuse efficiency of the licensed spectrum while ensuring the normal communication of the primary user.

[0039] (4) By introducing reconfigurable smart reflector-assisted communication, a communication connection can be established using the reflection path when there is no line-of-sight link between the secondary base station and the secondary user receiver or when the link is blocked by obstacles. This helps to expand the network coverage and improve the signal attenuation problem caused by physical obstruction.

[0040] (5) The alternating iterative optimization algorithm is used to solve the transmit beamforming vector and phase shift matrix respectively, which decomposes the complex joint non-convex optimization problem into a solvable convex optimization subproblem, which helps to reduce the computational complexity and facilitates implementation in actual systems. Attached Figure Description

[0041] Figure 1 This is a model diagram of a cognitive network system with multi-RIS assistance.

[0042] Figure 2 This is a graph showing the relationship between channel capacity and the number of RIS units.

[0043] Figure 3This is a graph showing the relationship between the amount of data transmitted and the number of RIS units. Detailed Implementation

[0044] The invention will now be further described with reference to the accompanying drawings.

[0045] This invention is applicable to downlink communication scenarios in mobile cognitive networks assisted by multiple reconfigurable intelligent reflectors (RIS) in underlay mode. It aims to solve the technical problems of link instability and mutual interference between primary and secondary users caused by secondary user mobility in mobile cognitive networks. By jointly optimizing the beamforming vector transmitted by the secondary base station and the RIS phase shift matrix, the end-to-end transmission rate is maximized. Link stability is quantified by combining link availability time prediction. Finally, the optimal route is selected with the product of transmission rate and link availability time as the core indicator, thereby achieving a dual improvement in secondary user communication reliability and data transmission efficiency.

[0046] like Figure 1 The mobile cognitive network system of the present invention includes a primary user system, a secondary user system, and L reconfigurable intelligent reflectors. The primary user system consists of a primary base station (PBS) and a primary user receiver (PU). D The secondary user system consists of a secondary base station (CBS, secondary user transmitter) and a mobile secondary user receiver (SU). R The system consists of a primary base station and a secondary user receiver. The secondary base station is equipped with M antennas, the secondary user receiver is equipped with a single antenna, and both the primary base station and the primary user receiver are equipped with a single antenna. Each RIS consists of N passive reflective elements, and each reflective element can independently adjust the phase and amplitude of the incident electromagnetic wave.

[0047] Due to the long spatial distance, the secondary base station and the mobile secondary user receiver cannot communicate directly. A single RIS (Receiving Irregular Receiver) is needed to complete a two-hop signal transmission from the secondary base station to the RIS and then to the mobile secondary user receiver. Unselected RISs undergo diffuse reflection processing or are disabled by the secondary base station. This invention considers the mutual interference between the primary and secondary user systems, namely, interference from the primary base station to the mobile secondary user receiver and interference from the secondary base station to the primary user receiver. It achieves efficient end-to-end communication for the secondary user without affecting the normal communication of the primary user.

[0048] The channel fading between nodes in the system is defined as follows:

[0049] Channel fading between the primary base station and the primary user receiver;

[0050] Main base station and the Channel fading between RIS;

[0051] Channel fading between the primary base station and the mobile secondary user receiver;

[0052] This refers to channel fading between the secondary base station and the primary user receiver.

[0053] For the secondary base station and the first Channel fading between RIS;

[0054] For the first Channel fading between the RIS and the main user receiver;

[0055] For the first Channel fading between the RIS and the mobile sub-user receiver.

[0056] Each channel is independent of the others. , , , , , Obeying large-scale decay and Rayleigh decay, It conforms to large-scale fading and Ricean fading; the secondary base station can obtain the status information of each channel through channel acquisition and data transmission.

[0057] This invention sets the core parameters of the system: the interference threshold allowed by the main user receiver is [value missing]. The maximum transmit power of the secondary base station CBS is The main base station's transmission power is The maximum communication range of each RIS is The noise variance at the mobile secondary user receiver is .

[0058] The routing method of this invention aims to maximize the amount of data transmitted end-to-end to the secondary user, specifically targeting the data transmitted via the third... The communication path of the RIS (denoted as the first RIS) One communication path, The process involves sequentially determining the maximum end-to-end transmission rate, predicting link availability time, and calculating end-to-end data transmission volume. Finally, the communication path with the largest data transmission volume is selected as the optimal route. The specific implementation steps are as follows:

[0059] Step 1: Determine the first The maximum end-to-end transmission rate of the communication path.

[0060] With the first Taking the communication path as the research object, the transmit beamforming vector of the secondary base station is jointly optimized. With the Phase shift matrix of RIS Under the premise of satisfying the secondary base station transmit power constraint and the primary user interference threshold constraint, the maximum end-to-end transmission rate of the path is obtained by maximizing the signal-to-noise ratio at the mobile secondary user receiver.

[0061] (1) Signal model and signal-to-noise ratio calculation.

[0062] Let the first The phase offset vector of each RIS is ,in , , Then the first Phase shift matrix of RIS .

[0063] The transmission signal of the secondary base station is The main base station's transmission signal is ,and , The signal received by the mobile secondary user receiver is: ,in For noise at the mobile secondary user receiver end, .

[0064] Based on the above signal model, the signal-to-noise ratio at the mobile secondary user receiver is obtained. for:

[0065] .

[0066] (2) Optimize problem construction.

[0067] To maximize end-to-end transmission rate, the signal-to-noise ratio must be maximized first. Construct a joint optimized transmit beamforming vector With phase offset vector optimization problem :

[0068] .

[0069] Constraints:

[0070] Secondary base station transmit power constraints: ;

[0071] Main user interference threshold constraint: ;

[0072] RIS phase constraint: , .

[0073] (3) Solve using the alternating iterative optimization algorithm.

[0074] because and For coupled variables, optimization problem As this is a non-convex optimization problem, this invention employs an alternating iterative optimization algorithm combined with semidefinite relaxation (SDR) techniques to solve it. Split into fixed optimization ,fixed optimization The two subproblems are iterated alternately until the transmission rate converges, as follows:

[0075] Given ,optimization Transform problem P1 into problem P2

[0076] ,

[0077] ,

[0078] ,

[0079] Obviously, ,

[0080] ,

[0081] Other: ,

[0082] ,

[0083] Problem P2 is transformed into the form of problem P2.1:

[0084] ,

[0085] ,

[0086] ,

[0087] Other ,but It is a positive semi-definite matrix, and Using the semidefinite relaxation (SDR) technique, problem P2.1 is transformed into problem P2.2:

[0088] ,

[0089] ,

[0090] ,

[0091] Problem P2.2 is a convex positive semidefinite programming problem, which can be solved using convex optimization tools such as CVX.

[0092] It is worth noting that the optimization process overlooked... Given the constraints, a feasible solution is found using the random variable method. Thus, to find .

[0093] Given ,optimization Transform problem P1 into problem P3:

[0094] ,

[0095] ,

[0096] .

[0097] To solve problem P3, also:

[0098] , ,

[0099] , ,get:

[0100] , ,

[0101] ,

[0102] in:

[0103] ,

[0104] ,

[0105] ,

[0106] Problem P3 is transformed into the form of problem P3.1:

[0107] ,

[0108] , ,

[0109] ,

[0110] express The The element is to ensure the constraints. This is correct. Problem P3.1 is still non-convex; similarly, this problem can be solved using SDR techniques. Additionally... ,neglect Due to limitations, question P3.1 can be written in the form of P3.2.

[0111] ,

[0112] ,

[0113] ,

[0114] ,

[0115] Using the Charnes-Cooper transformation, the fractional linear programming problem on page 3.2 is transformed into a linear programming problem. Let... , Problem P3.2 is transformed into problem P3.3.

[0116] ,

[0117] ,

[0118] ,

[0119] ,

[0120] ,

[0121] Problem P3.3 is a convex SDP problem, which can be solved using convex optimization tools. To ensure the rank-1 constraint, a feasible solution is found using the method of random variables. Thus, to find .

[0122] The complete execution flow of alternating iteration is as follows:

[0123] 1) Initialization parameters: set , , , , ,initialization , Iterative index Signal-to-noise ratio incremental accuracy And calculate the initial end-to-end transmission rate. ;

[0124] 2) Iterative update: Increment by 1, fixed. Solving for the results Then fix Solving for the results ;

[0125] 3) Rate calculation: based on and Calculate the current signal-to-noise ratio The current transmission rate is obtained by combining the Shannon formula. ;

[0126] 4) Convergence criterion: Calculate the relative error If the value The iteration terminates when the optimal value is obtained. , If this value If the iteration fails, return to step 2) and continue iterating.

[0127] (4) Calculation of maximum end-to-end transmission rate.

[0128] After the iteration terminates, the optimal signal-to-noise ratio will be obtained. Substituting into Shannon's formula, we can calculate the first... Maximum end-to-end transmission rate of the communication path :

[0129] .

[0130] Step 2: Predict the first Available time of the link between the RIS and the mobile secondary user receiver.

[0131] Since the locations of the secondary base station and the RIS are fixed, the link between them remains stable. The stability of the communication path is determined by the first The present invention determines the link between the RIS and the mobile secondary user receiver by predicting the availability time of the link. Quantify link stability, Predicting time for secondary user receivers with invariant motion state With changes in motion state Actual available probability The product of, i.e. The specific calculation process is as follows:

[0132] (1) Fit the distance-time relationship;

[0133] For the The distance between each RIS and the mobile secondary user receiver is sampled, and the sampling interval is set to [value missing]. Sampling time ( ), measured , , The distances at time points are respectively , , The distance-time relationship between the two nodes was obtained by fitting a quadratic function:

[0134] ,

[0135] in , , The fitting coefficients are calculated from the sampled data:

[0136] ,

[0137] (2) Calculate the available link time when the motion state remains unchanged. ;

[0138] Assuming the mobile secondary user receiver maintains uniform linear motion, the link availability time... For the mobile secondary user receiver from the last sampling time Start, stay in the first The duration within the maximum communication range of each RIS (i.e., satisfying the requirement) The duration); let ,but The calculation formula is:

[0139] ,

[0140] when This indicates that the mobile secondary user receiver will always be within the RIS's communication range. It is infinitely large.

[0141] (3) Calculate the actual usability probability ;

[0142] In real-world scenarios, mobile secondary users may randomly change their speed and / or direction of movement, thus introducing the actual availability probability. Correcting the assumption of uniform linear motion: Assuming the time interval for the mobile secondary user receiver to change its speed follows an exponential distribution, a probability coefficient is introduced. (This represents the probability that the mobile secondary user receiver is near or far from the RIS, and is set to 0.5). If the time interval distribution parameter for changing the speed of the mobile secondary user receiver is then... The calculation formula is:

[0143] .

[0144] (4) Calculate the actual available time of the link. ;

[0145] Link availability time when motion state remains unchanged With actual availability probability Multiply to get the first... The actual available time of the link between the RIS and the mobile secondary user receiver:

[0146] .

[0147] Step 3: Calculate the first... The amount of data transmitted end-to-end along a communication path.

[0148] The first Maximum end-to-end transmission rate of the communication path Link availability time Multiply to obtain the end-to-end data transmission volume of the path. This indicator comprehensively reflects the transmission efficiency and link stability of the communication path, and its calculation formula is as follows:

[0149] .

[0150] Step 4: Select the communication path with the largest end-to-end data transmission volume as the optimal route.

[0151] Following steps 1-3, the mobile cognitive network was calculated respectively. End-to-end data transmission volume of a communication path Following the principle of maximizing end-to-end data transmission, the communication path with the largest data volume is selected as the optimal transmission route from the secondary base station to the mobile secondary user receiver. The optimal path number is... The formula for determining this is:

[0152] ,

[0153] in, This is the RIS number corresponding to the optimal path. The second base station was finally accessed by the first Each RIS transmits signals to the mobile secondary user receiver, completing the routing selection for the multi-RIS assisted mobile cognitive network.

[0154] To verify the effectiveness and superiority of the routing method of this invention, simulation experiments were conducted using the Matlab+CVX convex optimization tool to analyze the relationship between the maximum transmission rate, the amount of transmitted data, and the number of RIS units. The specific simulation settings and results are as follows.

[0155] The simulation parameters are as follows: , , , , , RIS maximum communication range PBS (PU) S ), PU D CBS (SU) T The position coordinates of RIS1, RIS2, and RIS3 are (0, 150), (150, 150), (0, 0), (90, 20), (100, -40), and (85, 30), respectively. R Movement is within a circle centered at (100, 0) with a radius of 30, measured in meters. All channel vectors... , For large-scale fading at a reference distance of 1 meter, , and The channel matrix represents the Euclidean distance between users x and y, the path loss exponent, and the small-scale Rayleigh fading component, respectively. , , , and For SU T Euclidean distance to the l-th RIS, path loss exponent, and small-scale Ricean fading component, Ricean factor .

[0156] Figure 2 The relationship between the maximum transmission rate C and the number of RIS units N is given. Figure 2 It can be seen that, under the same network parameters, the maximum transmission rate C increases with the increase of the number of RIS units N. The maximum transmission rate under the proposed alternating iterative optimization algorithm is significantly higher than the maximum transmission rate under randomly selected RIS phases. Furthermore, regardless of whether the RIS phase is optimized or randomized, the maximum transmission rate when the number of CBS antennas M=8 is significantly higher than the maximum transmission rate when M=8. This indicates that the alternating iterative optimization algorithm proposed in this invention is effective, and that increasing the number of secondary user transmitter antennas helps to improve the maximum transmission rate.

[0157] Figure 3 The relationship between the amount of data transmitted (Data) and the number of RIS units (N) is given. Figure 3 It can be seen that, under the same network parameters, the amount of data transmitted increases with the increase of the number of RIS units N. Furthermore, Figure 3 The data from CBS to SU is given. TThe information transmission follows two paths, Path 1 and Path 2. These paths consider not only the maximum end-to-end transmission rate but also the available link time, where T1 = 2 seconds and T2 = 1.3 seconds. Path 1 and Path 2 intersect at (80, 11.2). From a network routing perspective, if only the maximum end-to-end transmission rate is considered, Path 2 will be chosen; if only path stability is considered, Path 1 will be chosen. This study considers both the maximum end-to-end transmission rate and the stability of the end-to-end path from the perspective of the amount of data transmitted end-to-end. Therefore, Path 2 will be chosen when the number of RIS units is between 20 and 80, and Path 1 will be chosen when the number of RIS units is between 81 and 200. This multi-objective network routing strategy helps to further improve information transmission capabilities.

[0158] The routing method of the multi-reconfigurable intelligent reflective surface assisted mobile cognitive network of the present invention can be widely applied to communication scenarios of high-speed mobile terminals such as intelligent manufacturing, vehicle networking, drone communication, and indoor mobile communication. It is especially suitable for industrial factory scenarios where there are physical obstructions from large equipment and traditional communication links are prone to signal attenuation or connection interruption.

[0159] In smart manufacturing scenarios, communication between mobile terminals such as mobile robots and intelligent testing equipment and small base stations (secondary base stations) in the factory is easily interrupted due to obstruction by large production equipment. By deploying multiple RIS in reasonable locations in the factory, the routing method of this invention can predict the available time of the link in real time, jointly optimize transmission parameters, and select the optimal communication path. This can reconstruct the wireless propagation environment, achieve communication without dead zones within the factory, and provide technical support for smart production, personnel and equipment collaboration, and large-capacity data transmission of the Industrial Internet of Things. It has important practical significance for promoting the implementation of communication technologies in fields such as smart manufacturing and digital transformation of factories.

[0160] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements without departing from the principle of the present invention, and these improvements should also be considered within the scope of protection of the present invention.

Claims

1. A routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network, the mobile cognitive network comprising a primary base station, a primary user receiver, a secondary base station, a mobile secondary user receiver, and multiple reconfigurable intelligent reflectors, wherein the secondary base station transmits signals to the mobile secondary user receiver via the reconfigurable intelligent reflectors, characterized in that: Includes the following steps: (1) Regarding the communication between the secondary base station and the mobile secondary user receiver in the mobile cognitive network via a reconfigurable smart reflector, the communication will be transmitted via the first... The communication path of the reconfigurable smart reflector is used as the first A communication path is established, and under the conditions of satisfying the transmit power constraint of the secondary base station and the interference constraint on the primary user receiver, the transmit beamforming vector of the secondary base station and the primary user receiver are jointly optimized. The phase shift matrix of the reconfigurable smart reflector determines the first... The maximum end-to-end transmission rate of the communication path; (2) Predict the first Available time for the link between a reconfigurable smart reflector and a mobile secondary user receiver; (3) Determine the first [time period] based on the product of the maximum end-to-end transmission rate and the available time. The amount of data transmitted end-to-end along a communication path; (4) Select the communication path with the largest end-to-end data transmission volume as the mobile cognitive network transmission route.

2. The routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network as described in claim 1, characterized in that: In step (1), the joint optimization of the maximum end-to-end transmission rate is achieved with the goal of maximizing the signal-to-noise ratio at the mobile secondary user receiver. The signal-to-noise ratio calculation formula is as follows: ， in, For the first Channel fading between a reconfigurable smart reflector and a mobile secondary user receiver For the first A phase shift matrix for a reconfigurable smart reflector. For the secondary base station and the first Channel fading between reconfigurable smart reflectors This is the transmit beamforming vector of the secondary base station. The main base station's transmit power. Main base station and the Channel fading between reconfigurable smart reflectors Channel fading between the primary base station and the mobile secondary user receiver This represents the noise variance at the mobile secondary user receiver.

3. The routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network as described in claim 2, characterized in that: In step (1), the maximum end-to-end transmission rate is calculated by combining the optimized maximum signal-to-noise ratio with the Shannon formula, which is: ， in, For the first The end-to-end transmission rate of the communication path. This represents the maximum signal-to-noise ratio after joint optimization at the mobile secondary user receiver.

4. The routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network as described in claim 1, characterized in that: In step (1), the transmit power constraint and the primary user receiver interference threshold constraint are specifically as follows: Transmit power constraints: , This is the maximum transmission power of the secondary base station. This is the transmit beamforming vector for the secondary base station; Disturbance threshold constraint: , This is due to channel fading between the secondary base station and the primary user receiver. For the first Channel fading between a reconfigurable smart reflector and the primary user receiver. The interference threshold allowed by the primary user receiver.

5. The routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network as described in claim 1, characterized in that: In step (2), the available time of the link is , The future availability time of the link when the mobile secondary user receiver remains in motion. When the motion state of the mobile secondary user receiver changes, The probability of maintaining availability.

6. The routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network as described in claim 1, characterized in that: In step (3), the formula for calculating the amount of data transmitted end-to-end is: , in, For the first The amount of data transmitted along each communication path. For the maximum end-to-end transmission rate, For the first The available time of the link between a reconfigurable smart reflector and a mobile secondary user receiver.

7. The routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network as described in claim 1, characterized in that: Joint optimization is achieved by combining an alternating iterative optimization algorithm with a semidefinite relaxation technique, which optimizes one variable by alternately fixing one variable.

8. The routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network as described in claim 7, characterized in that: The specific process of the alternating iterative optimization algorithm is as follows: Initialize the transmit beamforming vector initial values , No. Initial values ​​of the phase shift matrix of a reconfigurable smart reflector Iterative index Signal-to-noise ratio incremental accuracy Determine the initial transmission rate ; Increment by 1, fixed. Optimized Then fix Optimized ; Calculate the current transmission rate ,like The iteration then terminates.

9. The routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network as described in claim 8, characterized in that: When optimizing another variable while keeping the variable fixed, after obtaining the solution to the semidefinite programming problem using the positive semidefinite relaxation technique, the random variable method is combined to ensure that the rank of the solution is 1, thus obtaining a feasible solution that satisfies the constraints.

10. The routing method for a multi-reconfigurable intelligent reflector-assisted mobile cognitive network as described in claim 1, characterized in that: In step (4), the formula for selecting the optimal communication path is: ， in, Number the optimal communication path. The number of reconfigurable smart reflective surfaces, For the first The amount of data transmitted end-to-end along a communication path.

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