Realizing the potential of adaptive transmission techniques through long range prediction for rapidly time-variant fading mobile radio channels

In rapidly time-varying mobile radio channels, the transmitter and receiver are not usually optimized for current channel conditions, and thus fail to exploit the full potential of the fading channel. To overcome this limitation, several adaptive transmission techniques, such as adaptive modulation and coding, adaptive power control, and adaptive transmitter diversity, etc. were recently proposed to satisfy the tremendous growth in demand for wireless communication capacity. To realize these methods in practice, prediction of the fading channel coefficients several tens-to-hundreds of symbols ahead is essential. In this thesis, we study a novel adaptive long range fading channel prediction algorithm (LRP) and investigate its utilization with adaptive transmission techniques.; Our proposed channel prediction algorithm computes the linear Minimum Mean Squared Error (MMSE) estimates of future fading coefficients based on past observations. This algorithm can forecast fading signals far into the future due to its significant memory span achieved by using sufficiently low sampling rate given a fixed model order. The prediction method is further enhanced by an adaptive tracking method that increases accuracy, reduces the effect of noise and maintains the robustness of long range prediction as the physical channel parameters vary.; In addition to validating our method on standard stationary Jakes model, a method of images was utilized to create a novel physical channel model where fading is viewed as a deterministic process formed by the addition of several scattered components. The amplitude, frequency and phase of each component slowly vary as the vehicle moves through an interference pattern. Our novel physical model allows to test the proposed LRP algorithm and identify typical and challenging situations encountered in practice. Actual field measurements provided by Ericsson, Inc. were also used to validate the performance of the prediction method and the insights of the novel physical model. The effect of non-stationarity on the performance of LRP was quantified.; One of goal of the long range channel prediction method is to enable adaptive transmission techniques. We investigate the application of the LRP in Selective Transmitter Diversity (STD) scheme for flat fading channel. Also, we extended our channel prediction for flat fading to the multipath fading case. Three approaches to power prediction were suggested and theoretical MMSE analysis was given. We also studied the combined STD and RAKE scheme for fast fading DS/CDMA fading channels. Simulation results show that significant performance gain can be achieved using predicted channel state information (CSI) relative to the delayed CSI. Joint adaptive variable rate Multilevel Quadrature Amplitude Modulation (MQAM) and channel prediction was also addressed in this thesis. We analyze the statistical behavior of the errors generated by our previously proposed long range prediction algorithm. The modulation level selection rule based on the predicted CSI and the statistical model of prediction error is studied. Both numerical and simulation results show that our prediction technique makes adaptive modulation feasible for the realistic fading mobile radio channels. Moreover, a simple adaptive power control method, truncated channel inversion (TCI), was also studied in conjunction with our LRP algorithm.; In summary, we study a novel adaptive long range channel prediction algorithm and demonstrate that this channel prediction algorithm provides an enabling technique to realize the potential of adaptive transmission methods for rapidly time-variant fading mobile radio channels.