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Performance analysis of superimposed training-based cooperative spectrum sensing
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Orientador(es)
Resumo(s)
Superimposed training (ST) technique can be used
at primary users’ transmitters to improve parameter estimation
tasks (e.g. channel estimation) at primary users’ receivers. Since
ST adds the training sequence to the data sequence the total available
bandwidth is used for data transmission. The exploitation
of the ST sequence in the context of cognitive radio networks
leads to a significant increase in the detection performance of
secondary users operating in the very low signal-to-noise ratio
region. Hence, a considerably smaller number of samples are
required for sensing. In this paper, the performance of STbased
spectrum sensing in a cooperative centralized cognitive
radio network with soft-decision fusion is studied. Furthermore,
a throughput analysis is carried out to quantify the benefits of
using ST in the co
Descrição
Palavras-chave
Spectrum sensing Cooperative Cognitive radio Superimposed training
Contexto Educativo
Citação
Editora
Institute of Electrical and Electronics Engineers