Channel Allocation Based on Kalman Filter Prediction for Downlink OFDMA Systems

In this paper, a novel channel allocation method is proposed to reduce the time-varying effects in downlink OFDMA systems. This method makes use of a Kalman filter in order to predict the user SINR and rate in the next time slot. Based on this estimated rate, the available subcarriers are assigned according to a rate greeding algorithm. Compared with the rate-craving greedy (RCG) algorithm and the rate allocation with fixed increase (RAFI) algorithm, the numerical results show that the proposed algorithm can effectively improve the throughput for a single user, averagely 3 to 7 % more than the RCG algorithm and 1 to 3 % more than the RAFI algorithm. Moreover, the total outage probability is also reduced as the number of users increases, averagely 8 to 11% more than the RCG algorithm and 3 to 7% more than the RAFI algorithm.