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This work aims at improving the power amplifier (PA) efficiency in uplink OFDM-based cognitive radio (CR) communications. Unlike the traditional approaches, we suggest transmitting a non-linearily ampliied signal without any il-tering and addressing the OFDM sample estimation from the distorted signal at the receiver. The proposed post-distortion and detection technique is based on a Volterra model for the PA and the channel. As the transmission can switch from one sub-band to another, the CR-PA behavior varies over time and the Volterra kernels can be constant or suddenly change. Therefore, an interactive multiple model (IMM) combining extended Kalman filters is considered. The transition probability matrix, which plays a key role in the IMM, is also sequentially estimated. The resulting uplink system has various advantages: it learns from the observations and a part of the computational load is exported to the receiver, which is not battery driven unlike the mobile terminal.

Dirichlet process (DP) mixtures were recently introduced to deal with switching linear dynamical models (SLDM). They assume the system can switch between an a priori infinite number of state-space representations (SSR) whose parameters are on-line inferred. The estimation problem can thus be of high dimension when the SSR matrices are unknown. Nevertheless, in many applications, the SSRs can be categorized in different classes. In each class, the SSRs are characterized by a known functional form but differ by a reduced set of unknown hyperparameters. To use this information, we thus propose a new hierarchical model for the SLDM wherein a discrete variable indicates the SSR class. Conditionally to this class, the distributions of the hyperparameters are modeled by DPs. The estimation problem is solved by using a Rao-Blackwellized particle filter. Simulation results show that our model outperforms existing methods in the field of target tracking.