Resources allocation for large-scale dynamic spectrum access system using particle filtering

This paper proposes an efficient spectrum and power allocation solution for a large scale dynamic spectrum access (DSA) systems. Unlike conventional methods relying on optimization techniques which need huge computational capabilities and full information exchange, in this paper we rely on particle filtering to allocate the available bands among users in a distributed manner. Particle filter is based on the representation of the searched state, bands allocation per user in our case, by a set of particles. The Particle filter has the advantage, with comparison to Kalman-based filters, of its adaptivity to general scenarios (non-linear models, non-Gaussian noise, multi-modal distributions). Like Kalman-based filters, two model equations are needed for particle filter, (i) A state evolution equation to characterize the time evolution of the state. For our case, we derive a prediction equation of the channel allocation from the previous allocation from the channel fading temporal correlation, (ii) An observation equation which relates the observation, the Quality of Service in our case, to the channel allocation (state). This equation will be useful in the weighting and re-sampling phases of the filtering algorithm. The performances are analyzed in terms of the per user achieved throughput. In addition, comparison with performance when Q-learning is employed to show the efficiency of our approach.