Adaptive Lp-norm diversity combining in non-gaussian noise and interference

In this paper, we introduce an adaptive L_p-norm metric for robust coherent, differential, and noncoherent diversity combining in non-Gaussian noise and interference. We consider the general case where all diversity branches may use different combining weights and different L_p-norms. We derive a general closed-form expression for the asymptotic bit error rate (BER) for L_p-norm combining in independent non-identically distributed Ricean fading and non-Gaussian noise and interference with finite moments. The asymptotic BER expression reveals that the diversity gain of L_p-norm combining is independent of the type of noise and the metric parameters. In contrast, the combining gain depends on both the type of noise and the metric parameters. Thus, the asymptotic BER can be minimized by optimizing the L_p-norm metric parameters for the underlying type of noise. For this purpose finite difference stochastic approximation (FDSA) and localized random search (LRS) algorithms are developed. Both adaptive algorithms do not require any a priori knowledge about the underlying noise and are able to track changes in the noise statistics. Simulation results confirm the validity of the derived asymptotic BER expressions, the effectiveness of the proposed adaptive algorithms, and the excellent performance of the proposed adaptive L_p-norm metric compared to other popular metrics.