CN108471619B - Channel selection method of cognitive wireless sensor network - Google Patents

Channel selection method of cognitive wireless sensor network Download PDF

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CN108471619B
CN108471619B CN201810240868.7A CN201810240868A CN108471619B CN 108471619 B CN108471619 B CN 108471619B CN 201810240868 A CN201810240868 A CN 201810240868A CN 108471619 B CN108471619 B CN 108471619B
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wireless sensor
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邓晓衡
李锋
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Central South University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

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Abstract

The invention discloses a channel selection method of a cognitive wireless sensor network, which comprises the steps of acquiring network parameter information of the cognitive wireless sensor network; selecting whether to access the channel; calculating the channel gain of each channel; selecting a channel as an access channel; updating the system state; and finding out the optimal channel selection combination to complete the channel selection of the cognitive wireless sensor network. The method and the device consider from the perspective of the cognitive user, design a channel gain function as an evaluation index to evaluate the quality of channel selection, guide the secondary user to select a channel with better channel quality to supply the cognitive user to transmit data by continuously interacting and learning with the environment, thereby reducing the channel switching times of the secondary user in the communication process, ensuring the service quality of the cognitive user, reducing the interference on the primary user and improving the communication quality and the spectrum utilization rate.

Description

Channel selection method of cognitive wireless sensor network
Technical Field
The invention particularly relates to a channel selection method of a cognitive wireless sensor network.
Background
With the continuous development of scientific technology, information technology is also changing day by day, and wireless sensor networks are widely applied to the fields of smart homes, smart cities, military affairs, counter terrorism, disaster relief, environmental monitoring and the like. Because the wireless sensor network is composed of a large number of cheap miniature sensor nodes, the communication between the nodes adopts unlicensed spectrum, the number of devices using the unlicensed spectrum is exponentially increased along with the development of wireless communication technology, so that congestion is caused, the reliability of communication is not guaranteed, and the development of the wireless sensor network is greatly limited. The introduction of cognitive radio technology into sensor networks is currently the best approach to solve the above problems, but the introduction of this technology also brings challenges: the problem of the conflict between the randomness of the master user and the slave user channel is how to select the authorized spectrum with better channel quality to transmit the data of the cognitive user, so that the time-averaged network utility of the whole network is maximized, and the method becomes one of the problems to be solved urgently. Therefore, the research on the channel selection problem in the cognitive wireless sensor network has a crucial meaning, and how to find the optimal channel selection scheme has become one of the hot research fields.
The problem of channel selection in the cognitive wireless sensor network is to select an optimal channel scheme to achieve optimal network utility. In the cognitive wireless sensor network, the probability of a return channel of a Primary User (PU) of each channel is different, so that different frequency spectrum utilization rates are achieved. Most of the current work assumes that a cognitive user (SU) can acquire accurate information of the PU and cannot change within a unit time t in a channel selection strategy, but in practice, the SU is difficult to acquire the accurate information of the PU, and meanwhile, the PU can re-occupy a channel within the unit time t.
Disclosure of Invention
The invention aims to provide a channel selection method of a cognitive wireless sensor network, which is scientific and effective in channel selection and high in channel utilization rate.
The channel selection method of the cognitive wireless sensor network provided by the invention comprises the following steps:
s1, acquiring network parameter information of a cognitive wireless sensor network;
s2, aiming at all channels, the node n selects to access or not to access the channel;
s3, according to the selection relation of the step S2, the node n calculates the channel gain of each channel;
s4, according to the channel gains of the channels obtained in the step S3, the node n selects one channel as an access channel;
s5, the node n updates the system state according to the channel selected in the step S4;
s6, repeating the steps S2-S5 until an optimal channel selection combination is found, and finishing channel selection of the cognitive wireless sensor network.
The network parameter information of the cognitive wireless sensor network described in step S1 specifically includes a probability that a channel is idle, a probability that a primary user returns to the channel, and a state of the channel.
Step S3, calculating the channel gain of each channel, specifically, calculating the channel gain by using the following formula:
Figure BDA0001605175470000021
in the formula Dk(t) is the channel gain of node n at time t, channel k, Ank(t) allocating a matrix for the channel, Ank(t)' 1 indicates that at time t node n selects channel k to transmit data, ank(t) ═ 0 shows the probability that the node n does not select the channel k to transmit data at the moment t, phi (t) is the probability that the primary user returns to the channel at the moment t, and Sk(t) the state of channel k at time t, Sk(t) is 1, indicating that at time t, channel k is free, and when S iskA value of 0 (t) indicates that the channel k is busy at time t.
In step S4, a channel is selected as an access channel, specifically, a channel with the largest channel gain is selected as an access channel.
The updating of the system state in step S5 is to enter the next state after the access channel is selected in step S4, and evaluate and select corresponding actions for all possible actions generated in the next state.
The evaluation and selection of all possible actions generated by the next state specifically comprises the following steps:
(1) for all possible actions resulting from the next state, the action value of the action is calculated using the following rules:
Figure BDA0001605175470000031
in the formula Q (se)y,ay) To perform action ayThereby entering state seyAction value of Dk(t) is the channel gain, gamma is the discount coefficient and gamma is more than or equal to 0 and less than or equal to 1; SE ═ SE1,se2,...seYIs a set of licensed spectrum white channels, Q (se'y,a'y) Is to execute action a'yThus entering state se'yIs motion value of, and motion a'yIs future motion, state se'yAs a future state.
(2) And selecting the action with the maximum action value as the final action.
The optimal channel selection combination described in step S6 is the channel selection combination with the largest channel gain.
The channel selection method of the cognitive wireless sensor network provided by the invention is considered from the cognitive user perspective, a channel gain function is designed as an evaluation index to evaluate the channel selection quality, and the secondary user is guided to select the channel with better channel quality to supply the cognitive user with transmission data by continuously interacting and learning with the environment, so that the channel switching frequency of the secondary user in the communication process is reduced, the service quality of the cognitive user is ensured, and the communication quality and the frequency spectrum utilization rate are improved while the interference to the primary user is reduced.
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FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a graph comparing network utility generated by the method of the present invention and an optimal resource management and allocation scheme using a greedy algorithm.
Detailed Description
FIG. 1 shows a flow chart of the method of the present invention: the channel selection method of the cognitive wireless sensor network provided by the invention comprises the following steps:
s1, acquiring network parameter information of a cognitive wireless sensor network, wherein the network parameter information comprises channel idle probability, probability of a master user returning to a channel, channel state and the like;
s2, aiming at all channels, the node n selects to access or not to access the channel;
s3, according to the selection relation of the step S2, the node n calculates the channel gain of each channel, specifically, the channel gain is calculated by adopting the following formula:
Figure BDA0001605175470000041
in the formula Dk(t) is the channel gain of node n at time t, channel k, Ank(t) allocating a matrix for the channel, Ank(t)' 1 indicates that at time t node n selects channel k to transmit data, ank(t) ═ 0 shows the probability that the node n does not select the channel k to transmit data at the moment t, phi (t) is the probability that the primary user returns to the channel at the moment t, and Sk(t) the state of channel k at time t, Sk(t) is 1, indicating that at time t, channel k is free, and when S isk(t) a value of 0 indicates that channel k is busy at time t;
s4, according to the channel gains of the channels obtained in the step S3, the node n selects the channel with the largest channel gain as an access channel;
s5, the node n updates the system state according to the channel selected in the step S4; specifically, after the access channel is selected in step S4, the system enters the next state, and simultaneously evaluates all possible actions generated in the next state and selects a corresponding action; specifically, the following steps are adopted for evaluation and selection:
(1) for all possible actions resulting from the next state, the action value of the action is calculated using the following rules:
Figure BDA0001605175470000051
in the formula Q (se)y,ay) To perform action ayThereby entering state seyAction value of Dk(t) is channel gain, gamma is discount coefficient and is more than or equal to 0 and less than or equal to 1, gamma represents the influence strength of future rate of return, namely the importance of future report relative to current return, and the larger gamma represents the more important gamma in the channel selection processPaying attention to future return, the smaller gamma is, the more current return is considered in the channel selection process; SE ═ SE1,se2,...seYIs a set of licensed spectrum white channels, Q (se'y,a'y) Is to execute action a'yThus entering state se'yIs motion value of, and motion a'yIs future motion, state se'yAs a future state.
(2) Selecting the action with the maximum action value as a final action;
s6, repeating the steps S2-S5 until an optimal channel selection combination (the channel selection combination with the maximum channel gain) is found, and finishing the channel selection of the cognitive wireless sensor network.
The advantages of the process according to the invention are further illustrated below with reference to a specific example.
In the embodiment, MATLAB is used for simulating a cognitive wireless sensor network, the cognitive wireless sensor network consists of 6 sensor nodes with cognitive wireless capability, and each time slot of the nodes collects data such as illumination, humidity, temperature and the like from the surrounding environment; the transmission node and the master user share the authorized spectrum K which is 16 orthogonal channels, the channel idle probability is 0.5, and a comparison experiment is designed to analyze the performance of the channel selection method and the random channel selection, so that the correctness of the theoretical analysis method is verified.
This embodiment mainly verifies the performance of the channel selection method proposed in the present invention, and compared with the conventional random selection of a channel (RSC) from idle channels, the network utility of a comparison experiment comparison system is designed to verify the rationality of our channel selection strategy. As can be seen from fig. 2, the channel selection method of the present invention is generally consistent with the network utility variation trend of the conventional RSC, but the network utility of the channel selection method of the present invention is higher than the RSC, mainly because the channel selection method of the present invention considers the return probability of the primary user in selecting the channel, that is, selects a channel with a lower return probability of the primary user to be allocated to the secondary user for data transmission, thereby reducing the network utility loss caused by deferral or channel switching due to the return channel of the primary user, and at the same time, it can be seen that the network utility fluctuation of the RSC is large because a channel frequently used by the primary user PU may be selected during channel selection, which increases the collision rate between PU and SU, thereby affecting the network utility of the system.
From the above experiments, the method comprehensively considers the return probability of the primary user, and the SU is prompted to select the channel with the low PU return probability, namely the channel with the low idle probability to transmit data through continuous learning, so that the method can better accord with the channel selection in the actual cognitive wireless sensor network.

Claims (3)

1. A channel selection method of a cognitive wireless sensor network comprises the following steps:
s1, acquiring network parameter information of a cognitive wireless sensor network;
s2, aiming at all channels, the node n selects to access or not to access the channel;
s3, according to the selection relation of the step S2, the node n calculates the channel gain of each channel; specifically, the channel gain is calculated by adopting the following formula:
Figure FDA0002764513460000011
in the formula Dk(t) is the channel gain of node n at time t, channel k, Ank(t) allocating a matrix for the channel, Ank(t)' 1 indicates that at time t node n selects channel k to transmit data, ank(t) ═ 0 shows the probability that the node n does not select the channel k to transmit data at the moment t, phi (t) is the probability that the primary user returns to the channel at the moment t, and Sk(t) the state of channel k at time t, Sk(t) is 1, indicating that at time t, channel k is free, and when S isk(t) a value of 0 indicates that channel k is busy at time t;
s4, according to the channel gains of the channels obtained in the step S3, the node n selects one channel as an access channel; specifically, a channel with the largest channel gain is selected as an accessed channel;
s5, the node n updates the system state according to the channel selected in the step S4; specifically, after the access channel is selected in step S4, the system enters the next state, and simultaneously evaluates all possible actions generated in the next state and selects a corresponding action; meanwhile, the following steps are adopted for evaluation and selection:
(1) for all possible actions resulting from the next state, the action value of the action is calculated using the following rules:
Figure FDA0002764513460000012
in the formula Q (se)y,ay) To perform action ayThereby entering state seyAction value of Dk(t) is the channel gain, gamma is the discount coefficient and gamma is more than or equal to 0 and less than or equal to 1; SE ═ SE1,se2,...seYIs a set of licensed spectrum white channels, Q (se'y,a'y) Is to execute action a'yThus entering state se'yIs motion value of, and motion a'yIs future motion, state se'yIs a future state;
(2) selecting the action with the maximum action value as a final action;
s6, repeating the steps S2-S5 until an optimal channel selection combination is found, and finishing channel selection of the cognitive wireless sensor network.
2. The method for selecting the channel of the cognitive wireless sensor network according to claim 1, wherein the network parameter information of the cognitive wireless sensor network in the step S1 specifically includes a probability that the channel is idle, a probability that the primary user returns to the channel, and a state of the channel.
3. The method for selecting channels of a cognitive wireless sensor network according to claim 1 or 2, wherein the optimal channel selection combination in step S6 is the channel selection combination with the largest channel gain.
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