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Classification-based swarm intelligence spectrum handoff method

A spectrum switching and swarm intelligence technology, applied in transmission monitoring, electrical components, wireless communication, etc., can solve the problems of difficult to simulate complex and changeable CRN environment, high probability of switching failure, and large protocol overhead, so as to reduce spectrum switching. Failure probability, saving protocol overhead, and the effect of small protocol overhead

Active Publication Date: 2018-11-02
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

[0003] Existing spectrum switching methods mostly use probability theory, Markov process and queuing theory to model, these models are too idealized, and it is difficult to simulate the complex and changeable CRN environment
At the same time, there are disadvantages such as excessive protocol overhead, long delay, and high probability of handover failure.

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  • Classification-based swarm intelligence spectrum handoff method
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Embodiment Construction

[0040] The implementation steps of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0041] Such as figure 1 As shown, a classification-based swarm intelligence spectrum switching method specifically includes the following steps:

[0042] Step 1. Establish a CRN system model, as follows:

[0043] Assuming that the CRN network structure is ad hoc, consisting of M primary users PU and N cognitive users SU, the frequency spectrum is divided into P non-overlapping and mutually orthogonal channels. When the primary user is not communicating, its authorized channel can be used by the cognitive user. The i-th cognitive user SU i Have a set of target channels Chn i . Let SU i There are two antennas for receiving and sending, and it has the functions of listening and sending at the same time. Considering that SU communication is affected by noise and channel fading and for the convenience of modeling, cognitive users...

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Abstract

The invention discloses a classification-based swarm intelligence spectrum handoff method. The method includes the following steps: step 1, establishing a CRN system model; step 2, identifying a neighbor node of each cognitive user by using a neighbor discovery process; step 3, designing cognitive node classification rules, and dividing the cognitive users into two categories that are an advancednode and an ordinary node; step 4, carrying out voting networking, when the nodes are classified completely, enabling all the nodes to perform statistical analysis on available channel sets of themselves and the available channel sets of neighbors, and determining a communication channel; and step 5, switching and reconstructing a CRN network, when some cognitive users are disturbed, switching toother channels according to certain rules, and maintaining the integrity of the entire CRN network. Simulation results of the invention show that the proposed classification-based swarm intelligence spectrum handoff algorithm has the advantages of small protocol overhead, low resource consumption, simple structure, strong anti-destruction ability and the like.

Description

technical field [0001] The invention belongs to the field of cognitive radio in wireless communication, in particular to a spectrum switching method based on swarm intelligence. Background technique [0002] Cognitive Radio (CR) is regarded as a key technology that can alleviate the spectrum scarcity caused by the growth of wireless devices. In order to improve spectrum utilization, in a Cognitive Radio Network (CRN), cognitive users (Secondary User, SU) opportunistically access licensed idle frequency bands that are not used by Primary Users (Primary User, PU). When a PU with high priority appears in the channel used by the SU or the communication quality of the channel deteriorates, the SU must leave the current channel and find a new idle channel to ensure the continuity of communication. This process is spectrum switching. [0003] Existing spectrum switching methods mostly use probability theory, Markov process and queuing theory to model, and these models are too idea...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B17/382H04W72/08H04W72/54
CPCH04B17/382H04W72/541H04W72/542
Inventor 张煜培赵知劲王歌鲁华超杨安锋
Owner HANGZHOU DIANZI UNIV
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