Adaptive Lp-Norm Metric for Secondary BICM-OFDM Systems

The frequency bands used by secondary systems such as cognitive radio (CR) and ultra-wideband (UWB) systems are subject to various forms of non-Gaussian noise and interference including co-channel interference caused by the primary user and other secondary systems and man-made impulsive noise. For secondary systems employing the popular combination of bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM) it has been recently shown that robustness against the negative effects of non-Gaussian impairments can be achieved by replacing the conventional L-2-norm metric for Viterbi decoding with an L-p-norm metric. However, in order to achieve high performance, the metric parameter p has to be optimized for the underlying type of noise. In this paper, we propose two adaptive algorithms for online optimization of p. The first algorithm is based on a maximum-likelihood parameter estimation framework. The second algorithm is based on the direct minimization of the asymptotic bit error rate of L-p-norm decoding. Simulation results show that both algorithms have excellent performance and that the resulting adaptive L-p-norm metric outperforms other popular metrics in non Gaussian noise channels.