Prediction-based Adaptation (PRADA) Algorithm for Modulation and Coding

TL;DR

This paper introduces PRADA, an adaptive modulation and coding algorithm that reduces feedback frequency by predicting channel states with FSMC, improving throughput while minimizing feedback.

Contribution

It presents a novel AMC algorithm using FSMC-based channel prediction to reduce feedback and enhance throughput, integrating FER into switching decisions.

Findings

Significant throughput improvement over fixed schemes

Reduced CSI feedback frequency

Effective channel prediction with FSMC

Abstract

In this paper, we propose a novel adaptive modulation and coding (AMC) algorithm dedicated to reduce the feedback frequency of the channel state information (CSI). There have been already plenty of works on AMC so as to exploit the bandwidth more efficiently with the CSI feedback to the transmitter. However, in some occasions, frequent CSI feedback is not favorable in these systems. This work considers finite-state Markov chain (FSMC) based channel prediction to alleviate the feedback while maximizing the overall throughput. We derive the close-form of the frame error rate (FER) based on channel prediction using limited CSI feedback. In addition, instead of switching settings according to the CSI, we also provide means to combine both CSI and FER as the switching parameter. Numerical results illustrate that the average throughput of the proposed algorithm has significant performance improvement over fixed modulation and coding while the CSI feedback being largely reduced.