Adaptive Transmission Methods Research Articles

Data-Aided Noise Reduction for Long-Range Fading Prediction in Adaptive Modulation Systems

The long-range prediction (LRP) of fading signals enables adaptive transmission methods for rapidly varying mobile radio channels encountered in vehicular communications, but its performance is severely degraded by the additive noise and interference. A data-aided noise reduction (DANR) method is proposed to enhance the accuracy of fading prediction and to improve the spectral efficiency of adaptive modulation systems enabled by the LRP. The DANR includes an adaptive pilot transmission mechanism, robust noise reduction (NR), and decision-directed channel estimation. Due to improved prediction accuracy and low pilot rates, the DANR results in higher spectral efficiency than previously proposed NR techniques, which rely on oversampled pilots. It is also demonstrated that DANR-aided LRP increases the coding gain of adaptive trellis-coded modulation (ATCM). Finally, for low-to-medium signal-to-noise ratio (SNR) values, we show that LRP-enabled adaptive modulation performs better for realistic reflector configurations than for the conventional Jakes model (JM) data set.

Fading Channel Prediction for Mobile Radio Adaptive Transmission Systems

Adaptive transmission methods can potentially aid the achievement of high data rates required for mobile radio multimedia services. To realize this potential, the transmitter needs accurate channel state information (CSI) for the upcoming transmission frame. In most mobile radio systems, the CSI is estimated at the receiver and fed back to the transmitter. However, unless the mobile speed is very low, the estimated CSI cannot be used directly to select the parameters of adaptive transmission systems, since it quickly becomes outdated due to the rapid channel variation caused by multipath fading. To enable adaptive transmission for mobile radio systems, prediction of future fading channel samples is required. Several fundamental issues arise in the design and testing of fading prediction algorithms for adaptive transmission systems. These include complexity, robustness, choice of an appropriate channel model for algorithm validation, channel estimation and noise reduction required for reliable prediction, and design and analysis of adaptive transmission methods aided by fading prediction algorithms. We use these criteria in the review of recent advances in the area of fading channel prediction. We also demonstrate that reliable fading prediction makes adaptive transmission feasible in diverse wireless communication systems.

Adaptive OFDM System with Optimal RCPC Code-Rate for Each Subcarrier

Adaptive transmission methods improve the performance of wireless communication system by adjusting parameters like modulation, code-rate, and power depending on the channel state adaptively. In this letter, we consider the adaptive code-rate OFDM system in which code-rate of each subcarrier is adapted optimally. RCPC code is used to obtain different code-rate for each subcarrier. Performance analysis shows that 3--6 dB SNR gain or up to 30--50% data rate increase is achieved at bit error rate 10-6.

Long-range prediction of fading signals

It was previously proposed to adapt several transmission methods, including modulation, power control, channel coding, and antenna diversity to rapidly time variant fading channel conditions. Prediction of the channel coefficients several tens-to-hundreds of symbols ahead is essential to realize these methods in practice. We describe a novel adaptive long-range fading channel prediction algorithm (LRP) and its utilization with adaptive transmission methods. The LRP is validated for standard stationary fading models and tested with measured data and with data produced by our novel realistic physical channel model. Both numerical and simulation results show that long-range prediction makes adaptive transmission techniques feasible for mobile radio channels.