Wireless communication systems often operate in environments with severe fading due to multipath propagation. To mitigate the effects of fading, which limits system performance [1], diversity techniques using multiple antennas have been studied and implemented for signals received in Gaussian noise. Significant performance improvements are obtained with these schemes for channels with additive Gaussian noise and slow Rayleigh fading [2]. Impulsive noise occurs in indoor and outdoor environments due to a variety of sources [3, 4]. In general, optimum reception schemes designed for Gaussian noise environments perform very poorly when impulsive noise is present [5]. In particular, it has been demonstrated in [6] that the standard diversity combining methods (maximal ratio combining, equal gain combining, selection diversity) are not effective in impulsive noise environments.
As an alternative to using schemes that are optimum only for Gaussian noise cases, we study diversity schemes that adapt to the actual noise distribution encountered. The adaptive diversity receiver we have developed is a generalized likelihood ratio detector that is based on modeling the noise as a mixture of Gaussian distributions. Maximum likelihood estimates of the model parameters are obtained using the expectation-maximization (EM) algorithm. The Gaussian mixture distribution noise model and EM algorithm for parameter estimation fit together well in this application. Middleton and others [7, 8] have shown that the Gaussian mixture distribution model is accurate for many impulsive noise environments, while Redner and Walker [9] and others [10] have shown that the EM algorithm has attractive computational features when applied to Gaussian mixture distributions. Thus the Gaussian mixture distribution and EM algorithm combine to provide accurate noise modeling with relatively simple and reliable computations. The adaptive receiver achieves significant improvements over the more common linear diversity approaches, and it appears to be relatively insensitive to mismatch between the actual noise distribution and the Gaussian mixture distribution model.
As an example of the bit error rate (BER) performance of a maximal ratio combiner versus our new receiver, a channel with flat, Rayleigh fading and impulsive noise was simulated. The details of the signal and noise environment are explained in [6], but the key result is that the BER of the maximal ratio combiner does not improve, and thus remains poor, as more antennas are added, while the new receiver performs better with each new antenna that is added. Typical numerical BER values with four antennas are 0.1 for the maximal ratio combiner and 0.0025 for the new receiver at a signal to noise ratio of -14 dB.