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Adaptive transmission techniques, such as adaptive modulation and coding, adaptive power control, adaptive transmitter antenna diversity, etc., usually require accurate channel estimation and feedback of channel state information (CSI). For fast vehicle speeds, reliable adaptive transmission also requires long range prediction (LRP) of future CSI since the channel conditions are rapidly time-variant. In this paper, we propose to use past channel observations of one carrier to predict future CSI and perform adaptive modulation without feedback for another correlated carrier. We develop the minimum mean-square-error (MMSE) long range channel prediction algorithm that utilizes the time and frequency domain correlation function of the Rayleigh fading channel. An adaptive MMSE prediction method is also proposed. A statistical model of the prediction error that depends on the frequency and time correlation is developed and is used in the design of reliable adaptive modulation methods. We use a standard stationary fading channel model (Jakes model) and a novel physical channel model to test our algorithm. Significant gains relative to non-adaptive techniques are demonstrated for sufficiently correlated channels and realistic prediction range.