A Hierarchical-Based Swarm Intelligence Spectrum Switching Method

Abstract

The invention discloses a group intelligent spectrum switching method based on classification. The present invention comprises the following steps: step 1, establishing a CRN system model; step 2, identifying neighbor nodes of each cognitive user through a neighbor discovery process; step 3, designing cognitive node classification rules, and dividing cognitive users into high-level nodes and The second type of ordinary nodes; step 4, voting networking, when the node classification is completed, all nodes count the available channel sets of themselves and their neighbors to determine the communication channel; step 5, switch and reconstruct the CRN network, when some cognitive users are affected In case of interference, switch to other channels according to certain rules and maintain the integrity of the entire CRN network. The simulation results of the present invention show that the proposed hierarchical group intelligent spectrum switching algorithm has the advantages of small protocol overhead, less resource consumption, simple structure, strong anti-destroy 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...

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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