Power Allocation Policies for Convolutional and Turbo Coded Systems over Fading Channels

In this paper we study adaptive power allocation (PA) policies for improving the performance of convolutional and turbo codes on fading channels. The transmitter has an average power constraint. The fading process can he continuous (e.g. Rayleigh distribution). Perfect channel state information at the transmitter (CSIT) and the receiver (CSIR) are assumed. For convolutional codes, we consider block (slow) fading and fast fading environments separately and propdse new PA policies that reduce the BER. We do a comparative study of the proposed PA policies with commonly used policies e.g., water filling, (truncated) ChaMel inversion and an optimal policy proposed by Hayes for an uncoded system. For all the cases we study, we show that the prw posed policies substantially outperform the commonly used policies. Among the previously known policies only Hayes' policy gives performance improvement over constant PA. We show that inter leaving with PA can improve the performance of coded systems on block fading channels significantly. We also make the important observation that the improvements in BER obtained with PA increase with SNR, which is in sharp contrast to the negligible gain in channel capacity obtained with PA [6]. Since direct optimization for turbo codes is difficult, we use the policies derived for convolutional codes on the constituent convolutional codes of turbo codes and show that significant performance improvements can be obtained. Index Terms -Power allocation, block fading, fast fading, inter leaving, convolutional codes, turbo codes.