We propose an effective subcarrier allocation scheme for multiuser orthogonal frequency division multiple access (OFDMA) system in the downlink transmission with low computational complexity. In the proposed scheme, by taking multiple attributes of a user's channel, such as carrier gain decrease rate and variation from the mean channel gain of the system, to determine a rank for the user, subcarriers are then allocated depending on the individual user's rank. Different channel characteristics are used to better understand a user's need for subcarriers and hence determine a priority for the user. We also adopt an attribute weighing scheme to enhance the performance of the proposed scheme. The scheme is computationally efficient, since it avoids using iterations for the algorithm convergence and also common water-filling calculations that become more complex with increasing system parameters. Low complexity is achieved by allocating subcarriers to users depending on their determined rank. Our proposed scheme is simulated in comparison with other mathematically efficient subcarrier allocation schemes as well as with a conventional greedy allocation scheme. It is shown that the proposed method demonstrates competitive results with the simulated schemes.

Quantization is an important operation in digital communications systems. It not only introduces quantization noise but also changes the statistical properties of the quantized signal. Furthermore, quantization noise cannot be always considered as an additive source of Gaussian noise as it depends on the input signal probability density function. In orthogonal-frequency-division-multiplexing transmission the signal undergoes different operations which change its statistical properties. In this paper we analyze the statistical transformations of the signal from the transmitter to the receiver and determine how these effect the quantization. The discussed process considers the transceiver parameters and the channel properties to model the quantization noise. Simulation results show that the model agrees well with the simulated transmissions. The effect of system and channel properties on the quantization noise and its effect on bit-error-rate are shown. This enables the design of a quantizer with an optimal resolution for the required performance metrics.

Kalman filters are effective channel estimators but they have the drawback of having heavy calculations when filtering needs to be done in each sample for a large number of subcarriers. In our paper we obtain the steady-state Kalman gain to estimate the channel state by utilizing the characteristics of pilot subcarriers in OFDM, and thus a larger portion of the calculation burden can be eliminated. Steady-state value is calculated by transforming the vector Kalman filtering in to scalar domain by exploiting the filter charactertics when pilot subcarriers are used for channel estimation. Kalman filters operate optimally in the steady-state condition. Therefore by avoiding the convergence period of the Kalman gain, the proposed scheme is able to perform better than the conventional method. Also, driving noise variance of the channel is difficult to obtain practical situations and accurate knowledge is important for the proper operation of the Kalman filter. Therefore, we extend our scheme to operate in the absence of the knowledge of driving noise variance by utilizing received Signal-to-Noise Ratio (SNR). Simulation results show considerable estimator performance gain can be obtained compared to the conventional Kalman filter.