Link Adaptation Schemes Based on Parametric Estimation of SNR Distribution Over Nakagami-$m$ Fading Channels

Abstract

Link adaptation (LA) schemes maximize average spectral efficiency (ASE) using accurate channel state information (CSI) and knowledge of signal to noise ratio (SNR) distribution. In practical systems, partial CSI is available at the transmitter through feedback of modulation and coding scheme index, often known as channel quality indicator (CQI). However, parameters of SNR distribution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\Theta }$ </tex-math></inline-formula> ) are not known or only assumed to be known. We propose two LA schemes, for execution at the receiver and transmitter, respectively, each without prior knowledge about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\Theta }$ </tex-math></inline-formula> . In the receiver centric schemes, parametric estimation of SNR distribution is performed using unquantized SNR samples. In the transmitter centric scheme, we estimate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\Theta }$ </tex-math></inline-formula> from CQI samples using an iterative joint quantization-estimation algorithm. These parametric estimates form long-term SNR distribution, which is used to compute SNR switching thresholds. Using these thresholds, rate and power adaptation decisions are made based on instantaneous SNR. We have derived the maximum-likelihood estimator and Cramer–Rao lower bound for the proposed estimator using CQI feedback for Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channels and demonstrate that near optimal ASE can be achieved while using the proposed schemes as in an ideal scenario, where perfect knowledge about CSI and SNR distribution are available <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> .