On using a priori channel statistics for cyclic prefix optimization in OFDM

Abstract

Current communication schemes rely on instantaneous adaptation, which increases the system complexity and is memoryless. We propose a scheme for adapting system parameters based on a priori knowledge of the channel statistics to reduce feedback requirements. As a vital parameter in OFDM system, cyclic prefix (CP) length depends mainly on two channel characteristics: the channel gains and the root mean square (RMS) delay spread. Previous research assume given RMS delay spread and utilize power delay profile (PDP) for CP optimization. Since PDP indeed acts as a priori statistics of channel gains, we compare its effectiveness against a feedback scheme based on the instantaneous channel. We further simplify CP optimization by employing the prior statistics of the RMS delay spread. We derive a closed form approximation scheme for computation simplicity assuming RMS delay spread follows a lognormal distribution. We consider ergodic capacity and measure the capacity loss w.r.t. an instantaneous scheme. The observed 1% – 4% capacity loss through simulation validates our proposed scheme. We also present an online method to learn the prior statistics of RMS delay spread from feedback whenever available.