A superimposed training (ST) based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing (OFDM) channel while improving bandwidth transmission efficiency. A periodic low power training sequence is superimposed on the information sequence at the transmitter. The channel parameters can be estimated without consuming any extra system bandwidth, but an unknown information sequence can interfere with the ST channel estimation method, so in this paper, an iterative method was adopted to improve estimation performance. An underwater acoustic channel's properties include large channel dimensions and a sparse structure, so a matching pursuit (MP) algorithm was used to estimate the nonzero taps, allowing the performance loss caused by additive white Gaussian noise (AWGN) to be reduced. The results of computer simulations show that the proposed method has good channel estimation performance and can reduce the peak-to-average ratio of the OFDM channel as well.
Efficiency and fairness are two crucial issues to be considered for resource allocation in multi-user wireless networks. Based on the joint optimization of physical layer and data link layer, an optimization model is derived to achieve efficient and fair downlink data scheduling in multi-user OFDM wireless networks by maximizing the total utility function with respect to the average waiting time of user queue. A dynamic sub-carrier allocation algorithm (DSAA) based on the optimization model is proposed in order to obtain the maximization of the total scheduling utility. Efficiency is improved by combining DSAA with time scale interference predictor (TSIP) which at large time scales predict ON/OFF period of user data with temporal correlation structure across multiple time scales in multi-user interference environment. Simulation results verify the efficiency and fairness of the scheme.
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