Quantum foxbat optimization mechanism relay resource allocation method and system

Abstract

This invention discloses a relay resource allocation method and system based on a quantum flying fox optimization mechanism, relating to the field of wireless communication technology. It aims to address the problems of uneven energy consumption, short network lifetime, and poor usability in existing cooperative multi-user relay networks under high-dimensional relay nodes. The key technical points of this invention include: Step 1: Establishing a wireless sensor network system model with the goal of maximizing network throughput; Step 2: Setting the operating parameters of the cooperative multi-user relay network and initializing the network state; Step 3: Initializing the quantum flying fox population, constructing an fitness function based on the objective function, and determining the globally optimal and worst quantum positions; Step 4: Updating the quantum rotation angle of each quantum flying fox using hopping and exploration strategies to generate the quantum positions of the quantum flying foxes; Step 5: Updating the next generation of quantum positions, the globally optimal quantum position, and the worst quantum position using a greedy selection mechanism; and finally, solving for the relay resource allocation result.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to a relay resource allocation method and system based on a quantum flying fox optimization mechanism. Background Technology

[0002] With the rapid development of 5G / 6G mobile communication technology and the Industrial Internet of Things (IIoT), wireless networks are playing an increasingly important role in today's network field, especially cooperative relay networks. Currently, cooperative relay networks face various limitations in performance improvement, such as path loss, multipath fading, limited communication distance, and limited resources. To efficiently utilize and balance network energy consumption, relay selection design tends towards high-dimensional, adaptive, coordinated, and balanced approaches.

[0003] A review of existing literature reveals that Yindi Jing et al., in "Single and Multiple Relay Selection Schemes and their Achievable Diversity Orders," proposed a parallel relay network based on an amplification-forwarding protocol, which is also a typical cooperative communication network architecture. This approach assumes there is no direct communication path between the transmitter and receiver, requiring relays for each communication step, and all relays use the amplification-forwarding protocol. While it assumes all relays can synchronize time slots and carriers in the second transmission phase, achieving synchronization in real-world environments is difficult, and feedback delays also exist. Aggelos Bletsas et al., in "A Simple Cooperative Diversity Method Based on Network Path Selection," proposed a distributed, selection-based parallel relay network architecture. In the second phase, they select an "optimal" relay from multiple relay nodes for cooperative communication. However, this method only maximizes gain and reliability, without considering the system's total power consumption. Farrokh Etezadi et al., in "Decentralized Relay Selection Schemes in Uniformly Distributed Wireless Sensor Networks," proposed three relay selection schemes suitable for uniformly distributed wireless networks: first, selecting the relay node that maximizes the destination signal-to-noise ratio; second, selecting the node closest to the source node; and third, randomly selecting a node from a specific neighborhood of the source node. While these schemes can address the balance between fairness, energy efficiency, and performance in relay selection within uniformly distributed wireless sensor networks, the geometry-based selection scheme, choosing the node closest to the source node, leads to high power consumption and reduces network lifetime due to its proximity to the source node. All of the above protocols involve distributed networks and relay selection based on a specific cooperative relay network protocol. Although these protocols cooperate in terms of optimal relay selection and resource allocation, they do not substantially enhance scalability. Therefore, while ensuring increased throughput in single-user cooperative relay network systems, a cooperative multi-user relay network model is introduced to improve system throughput and optimize energy efficiency while maintaining fairness among users. (Ma Zhanying) [4]This paper proposes two methods for relay selection and power allocation: one based on optimal and suboptimal channel states, and the other based on optimal channel and maximum remaining energy. The first method first calculates the two-hop channel function for each candidate node, then sorts the channel function values ​​from largest to smallest, selecting the node with the highest value as the "optimal channel relay" and the node with the second highest value as the "suboptimal channel relay." The second method first selects the node with the largest channel function as the "optimal channel relay," disregarding the channel state of the remaining candidate nodes and only selecting the node with the largest remaining energy as the second relay. However, this scheme only studies the "dual-relay" scenario and does not consider cases where the number of relays is greater than two. Therefore, as the number of relays increases, relay selection requires traversing the channels of more nodes, significantly increasing computational complexity and power consumption, making it difficult to guarantee fairness among multiple users. Existing research shows that problems such as multi-user cooperation, uneven energy consumption, short network lifetime, and poor usability affect the design of existing cooperative relay networks, lacking a simple, efficient, low-complexity relay resource allocation method with high system throughput and guaranteed user fairness. Summary of the Invention

[0004] The technical problem to be solved by this invention is:

[0005] Existing collaborative multi-user relay networks suffer from uneven energy consumption, short network lifetime, and poor usability under high-dimensional relay nodes.

[0006] Therefore, this invention provides a relay resource allocation method and system based on the quantum flying fox optimization mechanism.

[0007] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0008] This invention provides a cooperative multi-user relay resource allocation method that combines the advantages of foxbat optimization and quantum computing. This method improves the performance of the cooperative multi-user relay network in terms of both system throughput and convergence performance. The design of this invention includes: establishing a wireless sensor network system model; using the objective function as the fitness function; determining the globally optimal quantum position by calculating the fitness function value of the quantum foxbat; introducing hopping and exploration strategies to update the quantum rotation angle; generating the quantum position of the quantum foxbat based on the quantum rotation gate; and determining the globally optimal quantum position by calculating the fitness value of the evolved quantum foxbat's new position and employing a greedy selection mechanism. This invention can balance network energy efficiency, improve system throughput, and achieve energy efficiency optimization while ensuring fairness among users, even in high-dimensional relay node scenarios.

[0009] This invention provides a relay resource allocation method for the quantum flying fox optimization mechanism, comprising the following steps:

[0010] Step 1: Establish a wireless sensor network system model. The objective function of the model is to maximize the network throughput equation.

[0011] Step 2: Set the operating parameters of the collaborative multi-user relay network and initialize the network status;

[0012] Step 3: Initialize the quantum flying fox population. Construct an fitness function based on the objective function of the wireless sensor network system model to calculate the fitness value of each quantum flying fox and determine the globally optimal and worst quantum positions.

[0013] Step 4: Update the quantum rotation angle of each quantum flying fox using the jumping strategy and the exploration strategy, generate the quantum position of the quantum flying fox according to the quantum rotation gate, and obtain the corresponding quantum flying fox position;

[0014] Step 5: Calculate the fitness value of the updated quantum flying bat position, and use a greedy selection mechanism to update the next generation of quantum positions, the globally optimal quantum position, and the worst quantum position;

[0015] Step Six: Repeat steps four and five until the maximum number of iterations is reached, output the globally optimal quantum position, and obtain the relay resource allocation result after transformation.

[0016] Furthermore, step one includes the following steps:

[0017] Let the first time slot start from the first Source node SN to the first The channel state information of each relay node RN is represented as follows: From the first Source node SN to the first The channel state information of each destination node (DN) is represented as follows: In the second time slot, from the first The relay node RN to the first The channel state information of each destination node (DN) is represented as follows: ;

[0018] In the first time slot, the Source node SN sends , No. The signals received at each destination node DN are: ,in Representing the Source node SN to the first Channel gain of each destination node DN Representing the Source node SN to the first Channel gain of each destination node DN Representing the The transmit power of each source node SN Representing the The information symbols sent by the source node SN, if Satisfy normalization conditions Then the first The average power used by each source node SN is , For the first The transmit power of each source node SN For the first Information symbols sent by the source node SN This represents the number of transmission pairs from the source node SN to the destination node DN. The power is The additive white Gaussian noise; therefore, the signal-to-interference-plus-noise ratio (SINR) of the SN-DN link is... In the At each relay node RN, the signal received in the first time slot is ,in Representing the Source node SN to the first Channel gain of each relay node RN Representing the Source node SN to the first The channel gain of each relay node RN, therefore, the signal-to-interference-plus-noise ratio (SINR) of the source node-relay node link is... ;

[0019] In the second time slot, the relay node RN receives and decodes the information, re-encodes the decoded information, and sends the re-encoded information to the destination node DN; The signal received at each destination node DN is ,in Representing the The relay node RN to the first Channel gain of each destination node DN Representing the The relay node RN to the first Channel gain of each destination node DN Yes The recoding, i.e., the first The information symbols sent by the relay node RN For the first The transmit power of each relay node RN For the first The transmit power of each relay node RN Yes The recoding process, at which point the signal-to-interference-plus-noise ratio (SINR) of the RN-DN link is... ;

[0020] No. A dual-timeslot structure with one SN-DN pair can achieve a data rate of , ,in It is the bandwidth of the available channel. It is the first The end-to-end signal-to-interference-plus-noise ratio (SINR) of each SN-DN pair;

[0021] The objective function is to maximize network throughput. The equation is as follows: ,in To select a relay scheme, Representing the Relay selection for individual users , , , It represents the maximization function and specifies that each relay node (RN) can assist at most one SN-DN transmission pair.

[0022] Furthermore, step two includes the following steps:

[0023] Setting up a collaborative multi-user relay network The set of properties during wheel runtime ,in, For the first The achievable data rate for source-to-destination node transmission to the selected relay node;

[0024] This refers to the relay node's transmit power. Initialize the number of rounds of operation for the cooperative multi-user relay network, based on the source node's transmit power. The network started running.

[0025] Furthermore, step three includes the following steps:

[0026] The population size of the quantum flying fox swarm is set to The maximum number of iterations is ; in the The second iteration The quantum position of a quantum flying fox is represented as ,in for Random numbers between , , The maximum dimension representing the quantum position vector, a value that matches the maximum number of repeaters; the... The middle generation The position of a quantum flying fox is represented as The quantum position and position mapping relationship of the quantum flying fox are as follows: ,in and The search space is respectively The maximum and minimum values ​​of the dimensional position;

[0027] No. The second iteration The fitness function value of the quantum flying fox position is The quantum position of the quantum flying fox corresponding to the maximum fitness value is taken as the global optimal quantum position, denoted as . The quantum position of the quantum flying fox corresponding to the minimum fitness value is taken as the global worst quantum position, denoted as... .

[0028] Furthermore, step four includes the following steps:

[0029] No. The next iteration generates one random numbers within the interval ,like , Indicates the exploration factor, the first Vidi sequence The quantum flying fox evolves using a hopping strategy, evolving through the globally optimal and worst quantum positions. The first quantum flying fox The quantum rotation angle has been updated as follows: , The jump height is a very small positive number. ,in For the number of iterations, Let gravitational acceleration be the acceleration due to gravity; if The quantum flying fox evolves through an exploratory strategy. The second iteration Quantum Fox Bat The quantum rotation angle has been updated as follows: Adaptive exploration intensity , for Random numbers within the interval;

[0030] The quantum position of the quantum flying fox was then updated using a quantum rotation gate at the [number]th [time]. In the nth iteration, the 1st Quantum Fox Bat The quantum position of dimension is updated to .

[0031] Furthermore, step five includes the following steps:

[0032] The updated number The quantum position of a quantum flying fox Mapped to position Calculate the first The second iteration The fitness value of the quantum flying fox's position. Select the first through a greedy selection mechanism The quantum position of the quantum flying fox, where the first The quantum position of a quantum flying fox is denoted as ,in, express Adaptability, express Fitness; updating the global optimal quantum position and worst quantum position .

[0033] This invention provides a relay resource allocation system based on the quantum flying fox optimization mechanism. The system has a program module corresponding to the steps of the method described in any of the above technical solutions, and executes the steps in the above-described relay resource allocation method based on the quantum flying fox optimization mechanism when running.

[0034] The present invention provides a computer-readable storage medium storing a computer program configured to implement, when invoked by a processor, the steps of the relay resource allocation method of the quantum foxbat optimization mechanism described in any of the above technical solutions.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] This invention proposes a quantum flying fox search mechanism for relay resource allocation. In high-dimensional relay node scenarios, this method balances energy consumption, improves system throughput, and optimizes energy efficiency while ensuring fairness among users. The cooperative relay network uses the designed quantum flying fox search mechanism to perform a global search through hopping and search strategies, thereby improving system throughput and enhancing convergence. Simulation experiments demonstrate that the cooperative multi-user relay network applying the quantum flying fox search mechanism in high-dimensional relay node scenarios exhibits high throughput and high energy efficiency, making it suitable for the resource allocation needs of high-power, large-scale relay networks. Attached Figure Description

[0037] Figure 1 This is a flowchart of the relay resource allocation method of the quantum flying fox optimization mechanism in this embodiment of the invention;

[0038] Figure 2The number of source nodes in this embodiment of the invention Number of relay nodes Source node power Under these circumstances, the system throughput changes as the relay node power increases.

[0039] Figure 3 When in the embodiments of the present invention , Source node power Relay node power In this case, the system throughput changes with the number of iterations.

[0040] Figure 4 When in the embodiments of the present invention Number of relay nodes From 25 to 35, source node power Relay node power Under these circumstances, the system throughput varies with the number of relay nodes. Detailed Implementation

[0041] To enable those skilled in the art to better understand the present invention, exemplary embodiments or examples of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described embodiments or examples are merely some, not all, of the embodiments or examples of the present invention. All other embodiments or examples obtained by those skilled in the art based on the embodiments or examples of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] Combination Figure 1 As shown, this invention provides a relay resource allocation method based on the quantum flying fox optimization mechanism, comprising the following steps:

[0044] Step 1: Establish a wireless sensor network system model. The objective function of the model is to maximize the network throughput equation.

[0045] This invention establishes a cooperative multi-user relay system model. This model consists of multiple source and destination nodes using SN-DN pairs for transmission and reception, and a certain number of idle nodes acting as potential relays for collaborative communication. When a source node has information to transmit to various destination nodes, a process is formed. One SN-DN transmission pair, A potential relay node, typically Greater than Each SN-DN can select a relay node to assist in transmission. Since only one channel is available, a two-step decode-forward protocol is used to send information. The source node transmits in the first time slot, and the relay node transmits in the second time slot. The relay node can receive signals in the first time slot, while the destination node can combine the signals received from the source node and the relay node in the second time slot, using maximum ratio combining. The transmissions of the source node and the relay node are divided into two time slots, thus avoiding interference between them. It is assumed that the source node's transmit power is fixed.

[0046] From the first time slot from the... Source node SN to the first The channel state information of each relay node RN is represented as follows: From the first Source node SN to the first The channel state information of each destination node (DN) is represented as follows: In the second time slot, from the first... The relay node RN to the first The channel state information of each destination node (DN) is represented as follows: .

[0047] In the first time slot, the Source node SN sends , No. The signals received at each destination node DN are: ,in Representing the Source node SN to the first Channel gain of each destination node DN Representing the Source node SN to the first Channel gain of each destination node DN Representing the The transmit power of each source node SN Representing the Information symbols sent by the source node SN Representing the The modulated signal transmitted by each source node SN, if Satisfy normalization conditions Then the first The average power used by each source node SN is , For the first The transmit power of each source node SN For the first Information symbols sent by the source node SN Representing the Modulated signals transmitted by each source node SN This represents the number of transmission pairs from the source node SN to the destination node DN. The power is The additive white Gaussian noise; therefore, the signal-to-interference-plus-noise ratio (SINR) of the SN-DN link is... In the At each relay node RN, the signal received in the first time slot is ,in Representing the Source node SN to the first Channel gain of each relay node RN Representing the Source node SN to the first The channel gain of each relay node RN, therefore, the signal-to-interference-plus-noise ratio (SINR) of the source node-relay node link is... .

[0048] In the second time slot, the relay node RN receives and decodes the information, re-encodes the decoded information, and sends the re-encoded information to the destination node DN; The signal received at each destination node DN is ,in Representing the The relay node RN to the first Channel gain of each destination node DN Representing the The relay node RN to the first Channel gain of each destination node DN Yes The recoding, i.e., the first The information symbols sent by the relay node RN For the first The transmit power of each relay node RN For the first The transmit power of each relay node RN Yes The recoding, i.e., the first The information symbols sent by the relay node RN Representing the The modulated signal transmitted by each relay node RN is such that the signal-to-interference-plus-noise ratio (SINR) of the RN-DN link is... .

[0049] No. A dual-timeslot structure with one SN-DN pair can achieve a data rate of , ,in It is the bandwidth of the available channel. It is the first The end-to-end signal-to-interference-plus-noise ratio (SINR) of each SN-DN pair.

[0050] The objective function is to maximize network throughput. The equation is as follows: ,in To select a relay scheme, Representing the Relay selection for individual users , , , It represents the maximization function and specifies that each relay node (RN) can assist at most one SN-DN transmission pair.

[0051] Step 2: Set the operating parameters of the collaborative multi-user relay network and initialize the network status.

[0052] Setting up a collaborative multi-user relay network The set of properties during wheel runtime ,in, For the first The achievable data transmission rate between source and destination nodes for selected relay nodes. This represents the number of transmission pairs from the source node SN to the destination node DN. For from the first Source node SN to the first Channel state information of each destination node DN. For from the first Source node SN to the first Channel state information of each relay node (RN) For from the first The relay node RN to the first Channel state information of each destination node DN This refers to the relay node's transmit power. Initialize the number of rounds of operation for the cooperative multi-user relay network, based on the source node's transmit power. The network started running.

[0053] Step 3: Initialize the quantum flying fox population. Construct an fitness function based on the objective function of the wireless sensor network system model to calculate the fitness value of each quantum flying fox and determine the globally optimal and worst quantum positions.

[0054] The population size of the quantum flying fox swarm is set to The maximum number of iterations is In the first In the nth iteration, the 1st The quantum position of a quantum flying fox is represented as ,in for Random numbers between, where The maximum dimension representing the quantum position vector, a value that matches the maximum number of repeaters. , . No. The position of a quantum flying fox is represented as The quantum position and position mapping relationship of the quantum flying fox are as follows: ,in and The search space is respectively The maximum and minimum values ​​of the dimensional position.

[0055] No. The second iteration The fitness function value of the quantum flying fox position is Find the quantum position of the quantum flying fox that corresponds to the maximum fitness value as the globally optimal quantum position, denoted as . The quantum position of the quantum flying fox corresponding to the minimum fitness value is taken as the global worst quantum position, denoted as... .

[0056] Step 4: Update the quantum rotation angle of each quantum flying fox using the jumping strategy and the exploration strategy, generate the quantum position of the quantum flying fox according to the quantum rotation gate, and obtain the corresponding quantum flying fox position.

[0057] The quantum flying fox population evolves through two strategies: a jumping strategy and an exploratory strategy. The next iteration generates one random numbers within the interval .like , Indicates the exploration factor, the first Vidi sequence The quantum flying fox evolves using a hopping strategy, evolving through the globally optimal and worst quantum positions. The first quantum flying fox The quantum rotation angle has been updated as follows: , The jump height is a very small positive number. ,in For the number of iterations, Let gravitational acceleration be the acceleration due to gravity; if The quantum flying fox, through its exploration strategy, the first The second iteration Quantum Fox Bat The quantum rotation angle has been updated as follows: Adaptive exploration intensity , for Random numbers within the interval.

[0058] The quantum position of the quantum flying fox was then updated using a quantum rotation gate at the [number]th [time]. In the nth iteration, the 1st Quantum Fox Bat The quantum position update formula for dimensionality is: .

[0059] Step 5: Calculate the fitness value of the updated quantum flying bat position, and use a greedy selection mechanism to update the next generation of quantum positions, the globally optimal quantum position, and the worst quantum position.

[0060] The updated number The quantum position of a quantum flying fox Mapped to position Calculate the first The second iteration Fitness value of quantum flying fox position The quantum positions of the quantum flying foxes are sorted in descending order of their fitness values, and a greedy selection mechanism is used to select the first... The quantum position of the quantum flying fox, where the first The quantum position of a quantum flying fox is denoted as

[0061]

[0062] in, express Adaptability, express The fitness of the quantum. Update the global optimal quantum position. and worst quantum position .

[0063] Step 6: Determine if the maximum number of iterations has been reached. If the maximum number of iterations has been reached, output the globally optimal quantum position after mapping and convert it into a relay resource allocation result; otherwise, return to Step 4.

[0064] The relay resource allocation method (algorithm) of the quantum flying fox optimization mechanism proposed in this invention is the underlying technical core of this invention, and various products can be derived based on the algorithm.

[0065] Based on the method proposed in this invention, a relay resource allocation system based on the quantum flying fox optimization mechanism is developed using a programming language. This system has program modules corresponding to the steps of the above-mentioned technical solution, and executes the steps in the above-mentioned relay resource allocation method based on the quantum flying fox optimization mechanism when running.

[0066] The developed system (software) computer program is stored on a computer-readable storage medium, and the computer program is configured to implement the relay resource allocation method of the quantum foxbat optimization mechanism described above when called by a processor. In other words, the invention is materialized on a carrier, becoming a computer program product.

[0067] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0068] The computational programs (also referred to as programs, software, software applications, or code) of this invention include machine instructions of a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device PLD) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0069] The beneficial effects of the present invention will be described below with reference to specific embodiments.

[0070] Example 1

[0071] For ease of description, the resource allocation method of the quantum flying fox mechanism proposed in this invention is abbreviated as QFFO, and the search mechanism used for comparison is the artificial bee colony mechanism, abbreviated as ABC. [1] The quantum bee colony mechanism is abbreviated as QBCO. [2]Gale-Shapley-Min mechanism and Gale-Shapley-Harmonic mechanism [3] .

[0072] To comprehensively compare the performance of the five methods, the same initialization was performed on Gale-Shapley-Min, Gale-Shapley-Harmonic, ABC, and QBCO, setting the cooperative multi-user relay network system model parameters: the bandwidth of the available channels was set to... Path gain between two nodes ,in The distance between the two nodes is the wireless link, and the nodes are evenly distributed. Within the square area, The distance between the source node and the destination node is evenly distributed. Within the range, of which , The additive white Gaussian noise power at all nodes is ,Right now The upper limit of the search space is the number of relay nodes. lower limit .

[0073] Setting the exploration factor in QFFO Gravitational acceleration Minimal positive number The five mechanisms all have the same population size and maximum number of iterations, which are respectively and The fitness curve is plotted by taking the average fitness value of 100 runs.

[0074] like Figure 2 As shown, when the number of source nodes Number of relay nodes Source node power In the case where the relay transmit power increases from 3 to 18, the system throughput of all mechanisms increases accordingly, but the rate of increase varies. When the relay transmit power is 18, QFFO reaches its highest throughput, indicating that in high-power communication scenarios, the QFFO mechanism can utilize resources more effectively and improve system performance. Figure 3 It can be seen from this that when , Source node power relay node power In the initial stages, the QFFO mechanism converges quickly, and the system throughput increases with the number of iterations, reaching its maximum at 500 iterations. Figure 4 It can be seen from this that when Number of relay nodes From 25 to 35, source node power relay node power In all cases, the throughput of all mechanisms increases with the number of relays, with the QFFO mechanism consistently maintaining the best performance. This indicates that in high-relay scenarios, the QFFO mechanism can more effectively solve the resource allocation problem and achieve higher system throughput. In summary, QFFO's overall performance is superior to other mechanisms regardless of the number of relays. It can find an economical and efficient resource allocation method under the constraints of the source node's transmit power and the relay node's transmit power, and it has significant advantages in relay cooperation scenarios. It is particularly suitable for the resource allocation needs of high-power, large-scale relay networks, balancing network energy efficiency, improving system throughput, and optimizing energy efficiency while ensuring fairness among users.

[0075] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

[0076] [1] Karaboga, D et al. On the performance of artificial bee colony (ABC) algorithm. "APPLIED SOFT COMPUTING".

[0077] [2]Ji Qiang et al. Quantum Bee Colony Optimization and Non-dominatedSorting Quantum Bee Colony Optimization Based Multi-relay SelectionScheme.KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS.

[0078] [3]Jie Xu et al. Interference-aware relay selection for multiple source-destination cooperative networks.

[0079] [4] Ma Zhanying. Multi-relay selection power allocation algorithm in wireless sensor networks. Journal of Guilin University of Electronic Technology, (2019, 39(03): 202-205.

Claims

1. A relay resource allocation method for a quantum flying fox optimization mechanism, characterized in that, Includes the following steps: Step 1: Establish a wireless sensor network system model. The objective function of the model is to maximize the network throughput equation. Step 2: Set the operating parameters of the collaborative multi-user relay network and initialize the network status; Step 3: Initialize the quantum flying fox population. Construct an fitness function based on the objective function of the wireless sensor network system model to calculate the fitness value of each quantum flying fox and determine the globally optimal and worst quantum positions. Step 4: Update the quantum rotation angle of each quantum flying fox using the jumping strategy and the exploration strategy, generate the quantum position of the quantum flying fox according to the quantum rotation gate, and obtain the corresponding quantum flying fox position; Step 5: Calculate the fitness value of the updated quantum flying bat position, and use a greedy selection mechanism to update the next generation of quantum positions, the globally optimal quantum position, and the worst quantum position; Step Six: Repeat steps four and five until the maximum number of iterations is reached, output the globally optimal quantum position, and obtain the relay resource allocation result after transformation.

2. The method according to claim 1, characterized in that, Step one includes the following steps: Let the first time slot start from the first Source node SN to the first The channel state information of each relay node RN is represented as follows: From the first Source node SN to the first The channel state information of each destination node (DN) is represented as follows: ; In the second time slot, from the first The relay node RN to the first The channel state information of each destination node (DN) is represented as follows: ; In the first time slot, the Source node SN sends , No. The signals received at each destination node DN are: ,in Representing the Source node SN to the first Channel gain of each destination node DN Representing the Source node SN to the first Channel gain of each destination node DN Representing the The transmit power of each source node SN Representing the The information symbols sent by the source node SN, if Satisfy normalization conditions Then the first The average power used by each source node SN is , For the first The transmit power of each source node SN For the first Information symbols sent by the source node SN This represents the number of transmission pairs from the source node SN to the destination node DN. The power is The additive white Gaussian noise; therefore, the signal-to-interference-plus-noise ratio (SINR) of the SN-DN link is... In the At each relay node RN, the signal received in the first time slot is ,in Representing the Source node SN to the first Channel gain of each relay node RN Representing the Source node SN to the first The channel gain of each relay node RN, therefore, the signal-to-interference-plus-noise ratio (SINR) of the source node-relay node link is... ; In the second time slot, the relay node RN receives and decodes the information, re-encodes the decoded information, and sends the re-encoded information to the destination node DN; The signal received at each destination node DN is ,in Representing the The relay node RN to the first Channel gain of each destination node DN Representing the The relay node RN to the first Channel gain of each destination node DN Yes The recoding, i.e., the first The information symbols sent by the relay node RN For the first The transmit power of each relay node RN For the first The transmit power of each relay node RN Yes The recoding process, at which point the signal-to-interference-plus-noise ratio (SINR) of the RN-DN link is... ; No. A dual-timeslot structure with one SN-DN pair can achieve a data rate of , ,in It is the bandwidth of the available channel. It is the first The end-to-end signal-to-interference-plus-noise ratio (SINR) of each SN-DN pair; The objective function is to maximize network throughput. The equation is as follows: ,in To select a relay scheme, Representing the Relay selection for individual users , , , It represents the maximization function and specifies that each relay node (RN) can assist at most one SN-DN transmission pair.

3. The method according to claim 2, characterized in that, Step two includes the following steps: Setting up a collaborative multi-user relay network The set of properties during wheel runtime ,in, For the first The achievable data rate for source-to-destination node transmission to the selected relay node; This refers to the relay node's transmit power. Initialize the number of rounds of operation for the cooperative multi-user relay network, based on the source node's transmit power. The network started running.

4. The method according to claim 3, characterized in that, Step three includes the following steps: The population size of the quantum flying fox swarm is set to The maximum number of iterations is ; in the The second iteration The quantum position of a quantum flying fox is represented as ,in for Random numbers between , , The maximum dimension representing the quantum position vector, a value that matches the maximum number of repeaters; the... The middle generation The position of a quantum flying fox is represented as The quantum position and position mapping relationship of the quantum flying fox are as follows: ,in and The search space is respectively The maximum and minimum values ​​of the dimensional position; No. The second iteration The fitness function value of the quantum flying fox position is The quantum position of the quantum flying fox corresponding to the maximum fitness value is taken as the global optimal quantum position, denoted as . The quantum position of the quantum flying fox corresponding to the minimum fitness value is taken as the global worst quantum position, denoted as... .

5. The method according to claim 4, characterized in that, Step four includes the following steps: No. The next iteration generates one random numbers within the interval ,like , Indicates the exploration factor, the first Vidi sequence The quantum flying fox evolves using a hopping strategy, evolving through the globally optimal and worst quantum positions. The first quantum flying fox The quantum rotation angle has been updated as follows: , The jump height is a very small positive number. ,in For the number of iterations, Let gravitational acceleration be the acceleration due to gravity; if The quantum flying fox evolves through an exploratory strategy. The second iteration Quantum Fox Bat The quantum rotation angle has been updated as follows: Adaptive exploration intensity , for Random numbers within the interval; The quantum position of the quantum flying fox was then updated using a quantum rotation gate at the [number]th [time]. In the nth iteration, the 1st Quantum Fox Bat The quantum position of dimension is updated to .

6. The method according to claim 5, characterized in that, Step five includes the following steps: The updated number The quantum position of a quantum flying fox Mapped to position Calculate the first The second iteration The fitness value of the quantum flying fox's position. Select the first through a greedy selection mechanism The quantum position of the quantum flying fox, where the first The quantum position of a quantum flying fox is denoted as ,in, express Adaptability, express Fitness; updating the global optimal quantum position and worst quantum position .

7. A relay resource allocation system based on a quantum flying fox optimization mechanism, characterized in that, The system has a program module corresponding to the steps of the method described in any one of claims 1 to 6, and executes the steps in the relay resource allocation method of the quantum flying fox optimization mechanism described above when running.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program configured to, when invoked by a processor, implement the steps in the relay resource allocation method of the quantum foxbat optimization mechanism according to any one of claims 1 to 6.

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