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Robust L_p-Norm Metric for BICM-OFDM Cognitive Radio Systems

机译:BICM-OFDM认知无线电系统的强大L_P-NOM度量

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Cognitive radio (CR) systems make efficient use of the frequency spectrum by opportunistically exploiting unoccupied or under-utilized frequency bands. However, the frequency bands used by CR systems are expected to suffer from various forms of noise and interference with non-Gaussian distributions, such as narrowband and co-channel interference caused by the primary user and other CRs, respectively, and man-made impulsive noise. To mitigate the harmful effects of non-Gaussian noise and interference, we propose a robust L_p-norm metric for CR systems that employ the popular combination of bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM). For the considered CR system we provide an approximate upper bound and an asymptotic closed-form approximation for the bit error rate (BER). Our results show that the proposed L_p-norm metric can achieve large performance gains compared to the conventional L_2-norm metric in non-Gaussian noise and interference.
机译:认知无线电(CR)系统通过机会利用未占用或利用的频带来有效地利用频谱。然而,CR系统使用的频带预计将遭受各种形式的噪声和对非高斯分布的干扰,例如由主要用户和其他CRS引起的窄带和共信道干扰,以及人造脉冲噪音。为了减轻非高斯噪声和干扰的有害影响,我们提出了一种强大的L_P-NOM度量,用于采用比特交织编码调制(BICM)和正交频分复用(OFDM)的流行组合。对于所考虑的CR系统,我们提供了近界的近界和渐近闭合形式近似,用于误码率(BER)。我们的结果表明,与非高斯噪声和干扰的传统L_2-NOM度量相比,所提出的L_P-NOM度量可以实现大的性能增益。

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