| Abstract: | Demodulation of data transmitted over time-varying channels with a free running hidden Markov state, like the phase noise channel or the fading channel, requires that the receiver tracks the hidden channel state. The tracking technique adopted in the paper is based on non-data-aided sequential importance sampling, also known as particle filtering.The paper proposes a new particle filtering framework for data communication receivers based on an importance distribution such that each individual particle becomes a decision-directed Kalman filter relying upon its local symbol-by-symbol hard decisions. In this framework, different particles are left free to take different sequences of decisions. This leaves to the receiver the possibility of exploring different sequences of transmitted modulation symbols. The weight of the particle will be high for those particles that took in the past the correct sequence of decisions, while will be low for those particles that took wrong decisions. In the resampling procedure, particles with high weight will survive, while particles with low weight will be terminated, leaving space to the birth of new particles resampled from the surviving ones.The crucial point in importance sampling is the choice of the importance distribution and the main novelty of the paper is the proposal of an importance distribution such that the particles of the particle filter become decision-directed Kalman filters. One important benefit brought by our proposed method is that, being non-data-aided, it does not need pilot symbols, thus allowing to preserve the transmission rate. A significant application example, presented and developed in the paper, is constituted by MIMO systems affected by phase noise, where the channel state vector consists of many parameters. |
